Remote Sensing

21 pages, 20217 KiB

Open AccessArticle

A Method to Obtain Orange Crop Geometry Information Using a Mobile Terrestrial Laser Scanner and 3D Modeling

by André F. Colaço^1,*,†

, Rodrigo G. Trevisan¹

, José P. Molin¹

, Joan R. Rosell-Polo²

and Alexandre Escolà²

¹ Biosystems Engineering Department, Luiz de Queiroz College of Agriculture, University of São Paulo, 13418-900 Piracicaba, Brazil

² Department of Agricultural and Forest Engineering, Research Group on AgroICT & Precision Agriculture, Agrotecnio Centre, School of Agrifood and Forestry Science and Engineering, University of Lleida, 25198 Lleida, Spain

^† Present address: CSIRO, Waite Campus, Locked Bag 2, Glen Osmond, SA 5064, Australia.

Remote Sens. 2017, 9(8), 763; https://doi.org/10.3390/rs9080763 - 25 Jul 2017

Cited by 71 | Viewed by 8664

Abstract

LiDAR (Light Detection and Ranging) technology has been used to obtain geometrical attributes of tree crops in small field plots, sometimes using manual steps in data processing. The objective of this study was to develop a method for estimating canopy volume and height [...] Read more.

LiDAR (Light Detection and Ranging) technology has been used to obtain geometrical attributes of tree crops in small field plots, sometimes using manual steps in data processing. The objective of this study was to develop a method for estimating canopy volume and height based on a mobile terrestrial laser scanner suited for large commercial orange groves. A 2D LiDAR sensor and a GNSS (Global Navigation Satellite System) receiver were mounted on a vehicle for data acquisition. A georeferenced point cloud representing the laser beam impacts on the crop was created and later classified into transversal sections along the row or into individual trees. The convex-hull and the alpha-shape reconstruction algorithms were used to reproduce the shape of the tree crowns. Maps of canopy volume and height were generated for a 25 ha orange grove. The different options of data processing resulted in different values of canopy volume. The alpha-shape algorithm was considered a good option to represent individual trees whereas the convex-hull was better when representing transversal sections of the row. Nevertheless, the canopy volume and height maps produced by those two methods were similar. The proposed system is useful for site-specific management in orange groves. Full article

(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)

▼ Show Figures

Graphical abstract

13 pages, 9532 KiB

Open AccessArticle

A Simple Normalized Difference Approach to Burnt Area Mapping Using Multi-Polarisation C-Band SAR

by Jeanine Engelbrecht^1,2,*

, Andre Theron^1,2

, Lufuno Vhengani¹ and Jaco Kemp²

¹ CSIR Meraka Institute, Pretoria 0001, South Africa

² Department of Geography & Environmental Studies, Stellenbosch University, Stellenbosch 7600, South Africa

Remote Sens. 2017, 9(8), 764; https://doi.org/10.3390/rs9080764 - 31 Jul 2017

Cited by 60 | Viewed by 8315

Abstract

In fire-prone ecosystems, periodic fires are vital for ecosystem functioning. Fire managers seek to promote the optimal fire regime by managing fire season and frequency requiring detailed information on the extent and date of previous burns. This paper investigates a Normalised Difference α-Angle [...] Read more.

In fire-prone ecosystems, periodic fires are vital for ecosystem functioning. Fire managers seek to promote the optimal fire regime by managing fire season and frequency requiring detailed information on the extent and date of previous burns. This paper investigates a Normalised Difference α-Angle (NDαI) approach to burn-scar mapping using C-band data. Polarimetric decompositions are used to derive α-angles from pre-burn and post-burn scenes and NDαI is calculated to identify decreases in vegetation between the scenes. The technique was tested in an area affected by a wildfire in January 2016 in the Western Cape, South Africa. The quad-pol H-A-α decomposition was applied to RADARSAT-2 data and the dual-pol H-α decomposition was applied to Sentinel-1A data. The NDαI results were compared to a burn scar extracted from Sentinel-2A data. High overall accuracies of 97.4% (Kappa = 0.72) and 94.8% (Kappa = 0.57) were obtained for RADARSAT-2 and Sentinel-1A, respectively. However, large omission errors were found and correlated strongly with areas of high local incidence angle for both datasets. The combined use of data from different orbits will likely reduce these errors. Furthermore, commission errors were observed, most notably on Sentinel-1A results. These errors may be due to the inability of the dual-pol H-α decomposition to effectively distinguish between scattering mechanisms. Despite these errors, the results revealed that burnt areas could be extracted and were in good agreement with the results from Sentinel-2A. Therefore, the approach can be considered in areas where persistent cloud cover or smoke prevents the extraction of burnt area information using conventional multispectral approaches. Full article

(This article belongs to the Special Issue Remote Sensing of Wildfire)

▼ Show Figures

Graphical abstract

23 pages, 8560 KiB

Open AccessArticle

Correcting InSAR Topographically Correlated Tropospheric Delays Using a Power Law Model Based on ERA-Interim Reanalysis

by Bangyan Zhu^1,2,*

, Jiancheng Li² and Wei Tang³

¹ SAR/InSAR Engineering Applications Laboratory (SEAL), Nanjing Institute of Surveying, Mapping & Geotechnical Investigation, Co., Ltd., Nanjing 210019, China

² School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China

³ College of Geoscience and Surveying Engineering, China University of Mining & Technology, Beijing 100083, China

Remote Sens. 2017, 9(8), 765; https://doi.org/10.3390/rs9080765 - 26 Jul 2017

Cited by 14 | Viewed by 5184

Abstract

Tropospheric delay caused by spatiotemporal variations in pressure, temperature, and humidity in the lower troposphere remains one of the major challenges in Interferometric Synthetic Aperture Radar (InSAR) deformation monitoring applications. Acquiring an acceptable level of accuracy (millimeter-level) for small amplitude surface displacement is [...] Read more.

Tropospheric delay caused by spatiotemporal variations in pressure, temperature, and humidity in the lower troposphere remains one of the major challenges in Interferometric Synthetic Aperture Radar (InSAR) deformation monitoring applications. Acquiring an acceptable level of accuracy (millimeter-level) for small amplitude surface displacement is difficult without proper delay estimation. Tropospheric delay can be estimated from the InSAR phase itself using the spatiotemporal relationship between the phase and topography, but separating the deformation signal from the tropospheric delay is difficult when the deformation is topographically related. Approaches using external data such as ground GPS networks, space-borne spectrometers, and meteorological observations have been exploited with mixed success in the past. These methods are plagued, however, by low spatiotemporal resolution, unfavorable weather conditions or limited coverage. A phase-based power law method recently proposed by Bekaert et al. estimates the tropospheric delay by assuming the phase and topography following a power law relationship. This method can account for the spatial variation of the atmospheric properties and can be applied to interferograms containing topographically correlated deformation. However, the parameter estimates of this method are characterized by two limitations: one is that the power law coefficients are estimated using the sounding data, which are not always available in a study region; the other is that the scaled factor between band-filtered topography and phase is inverted by least squares regression, which is not outlier-resistant. To address these problems, we develop and test a power law model based on ERA-Interim (PLE). Our version estimates the power law coefficients by using ERA-Interim (ERA-I) reanalysis. A robust estimation technique was introduced in the PLE method to estimate the scaled factor, which is insensitive to outliers. We applied the PLE method to ENVISAT ASAR images collected over Southern California, US, and Tianshan, China. We compared tropospheric corrections estimated from using our PLE method with the corrections estimated using the linear method and ERA-I method. Accuracy was evaluated by using delay measurements from the Medium Resolution Imaging Spectrometer (MERIS) onboard the ENVISAT satellite. The PLE method consistently delivered greater standard deviation (STD) reduction after tropospheric corrections than both the linear method and ERA-I method and agreed well with the MERIS measurements. Full article

(This article belongs to the Section Atmospheric Remote Sensing)

▼ Show Figures

Graphical abstract

24 pages, 11859 KiB

Open AccessArticle

Wall-to-Wall Tree Type Mapping from Countrywide Airborne Remote Sensing Surveys

by Lars T. Waser^1,*

, Christian Ginzler¹ and Nataliia Rehush²

¹ Department of Landscape Dynamics, Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Zuercherstrasse 111, Birmensdorf 8903, Switzerland

² Department of Forest Resources and Management, Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Zuercherstrasse 111, Birmensdorf 8903, Switzerland

Remote Sens. 2017, 9(8), 766; https://doi.org/10.3390/rs9080766 - 27 Jul 2017

Cited by 57 | Viewed by 8028

Abstract

Although wall-to-wall, accurate, and up-to-date forest composition maps at the stand level are a fundamental input for many applications, ranging from global environmental issues to local forest management planning, countrywide mapping approaches on the tree type level remain rare. This paper presents and [...] Read more.

Although wall-to-wall, accurate, and up-to-date forest composition maps at the stand level are a fundamental input for many applications, ranging from global environmental issues to local forest management planning, countrywide mapping approaches on the tree type level remain rare. This paper presents and validates an innovative remote sensing based approach for a countrywide mapping of broadleaved and coniferous trees in Switzerland with a spatial resolution of 3 m. The classification approach incorporates a random forest classifier, explanatory variables from multispectral aerial imagery and a Digital Terrain Model (DTM) from Airborne Laser Scanning (ALS) data, digitized training polygons and independent validation data from the National Forest Inventory (NFI). The methodological workflow was optimized for an area of 41,285 km² that is characterized by temperate forests within a complex topography. Whereas high model overall accuracies (0.99) and kappa (0.98) were achieved, the comparison of the tree type map with independent NFI data revealed significant deviations that are related to underestimations of broadleaved trees (median of −3.17%). Constraints of the tree type mapping approach are mostly related to the acquisition date and time of the imagery and the topographic (negative) effects on the prediction. A comparison with the most recent High Resolution Layers (HRL) forest 2012 from the European Environmental Agency revealed that the tree type map is superior regarding spatial resolution, level of detail and accuracy. The high-quality map achieved with the approach presented here is of great value for optimizing forest management and planning activities and is also an important information source for applications outside the forestry sector. Full article

(This article belongs to the Section Forest Remote Sensing)

▼ Show Figures

Graphical abstract

22 pages, 19092 KiB

Open AccessEditor’s ChoiceArticle

Gauging the Severity of the 2012 Midwestern U.S. Drought for Agriculture

by Xiang Zhang^1,2

, Chehan Wei³, Renee Obringer⁴, Deren Li^1,5, Nengcheng Chen^1,5,*

and Dev Niyogi^2,4,*

¹ State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan 430079, China

² Department of Agronomy-Crops, Soil, Environmental Science, Purdue University, West Lafayette, IN 47907, USA

³ Lyles School of Civil Engineering, Purdue University, West Lafayette, IN 47907, USA

⁴ Department of Earth, Atmospheric, and Planetary Sciences, Purdue University, West Lafayette, IN 47907, USA

⁵ Collaborative Innovation Center of Geospatial Technology, Wuhan 430079, China

Remote Sens. 2017, 9(8), 767; https://doi.org/10.3390/rs9080767 - 26 Jul 2017

Cited by 9 | Viewed by 6750

Abstract

Different drought indices often provide different diagnoses of drought severity, making it difficult to determine the best way to evaluate these different drought monitoring results. Additionally, the ability of a newly proposed drought index, the Process-based Accumulated Drought Index (PADI) has not yet [...] Read more.

Different drought indices often provide different diagnoses of drought severity, making it difficult to determine the best way to evaluate these different drought monitoring results. Additionally, the ability of a newly proposed drought index, the Process-based Accumulated Drought Index (PADI) has not yet been tested in United States. In this study, we quantified the severity of 2012 drought which affected the agricultural output for much of the Midwestern US. We used several popular drought indices, including the Standardized Precipitation Index and Standardized Precipitation Evapotranspiration Index with multiple time scales, Palmer Drought Severity Index, Palmer Z-index, VegDRI, and PADI by comparing the spatial distribution, temporal evolution, and crop impacts produced by each of these indices with the United States Drought Monitor. Results suggested this drought incubated around June 2011 and ended in May 2013. While different drought indices depicted drought severity variously. SPI outperformed SPEI and has decent correlation with yield loss especially at a 6 months scale and in the middle growth season, while VegDRI and PADI demonstrated the highest correlation especially in late growth season, indicating they are complementary and should be used together. These results are valuable for comparing and understanding the different performances of drought indices in the Midwestern US. Full article

(This article belongs to the Special Issue Remote Sensing of Land-Atmosphere Interactions)

▼ Show Figures

Graphical abstract

21 pages, 23688 KiB

Open AccessArticle

Performance Evaluation for China’s Planned CO₂-IPDA

by Ge Han¹

, Xin Ma^2,*, Ailin Liang³

, Tianhao Zhang³

, Yannan Zhao⁴, Miao Zhang⁵ and Wei Gong^3,*

¹ International School of Software, Wuhan University, Wuhan 430079, China

² Electronic Information School, Wuhan University, Wuhan 430079, China

³ State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China

⁴ Key Laboratory of Earthquake Geodesy, Institute of Seismology, China Earthquake Administration, Wuhan 430071, China

⁵ School of Environmental Science and Tourism, Nanyang Normal University, Wolong Road 1638, Nan Yang 473061, China

Remote Sens. 2017, 9(8), 768; https://doi.org/10.3390/rs9080768 - 26 Jul 2017

Cited by 43 | Viewed by 6097

Abstract

Active remote sensing of atmospheric XCO₂ has several advantages over existing passive remote sensors, including global coverage, a smaller footprint, improved penetration of aerosols, and night observation capabilities. China is planning to launch a multi-functional atmospheric observation satellite equipped with a CO [...] Read more.

Active remote sensing of atmospheric XCO₂ has several advantages over existing passive remote sensors, including global coverage, a smaller footprint, improved penetration of aerosols, and night observation capabilities. China is planning to launch a multi-functional atmospheric observation satellite equipped with a CO₂-IPDA (integrated path differential absorption Lidar) to measure columnar concentrations of atmospheric CO₂ globally. As space and power are limited on the satellite, compromises have been made to accommodate other passive sensors. In this study, we evaluated the sensitivity of the system’s retrieval accuracy and precision to some critical parameters to determine whether the current configuration is adequate to obtain the desired results and whether any further compromises are possible. We then mapped the distribution of random errors across China and surrounding regions using pseudo-observations to explore the performance of the planned CO₂-IPDA over these regions. We found that random errors of less than 0.3% can be expected for most regions of our study area, which will allow the provision of valuable data that will help researchers gain a deeper insight into carbon cycle processes and accurately estimate carbon uptake and emissions. However, in the areas where major anthropogenic carbon sources are located, and in coastal seas, random errors as high as 0.5% are predicted. This is predominantly due to the high concentrations of aerosols, which cause serious attenuation of returned signals. Novel retrieving methods must, therefore, be developed in the future to suppress interference from low surface reflectance and high aerosol loading. Full article

(This article belongs to the Special Issue Remote Sensing of Greenhouse Gases)

▼ Show Figures

Graphical abstract

9 pages, 1136 KiB

Open AccessFeature PaperLetter

On the Objectivity of the Objective Function—Problems with Unsupervised Segmentation Evaluation Based on Global Score and a Possible Remedy

by Sebastian Böck^*, Markus Immitzer

and Clement Atzberger

Institute of Surveying, Remote Sensing and Land Information (IVFL), University of Natural Resources and Life Sciences, Vienna (BOKU), Peter Jordan Strasse 82, 1190 Vienna, Austria

Remote Sens. 2017, 9(8), 769; https://doi.org/10.3390/rs9080769 - 27 Jul 2017

Cited by 37 | Viewed by 5281

Abstract

Image segmentation is a crucial stage at the very beginning of many geographic object-based image analysis (GEOBIA) workflows. While segmentation quality is generally deemed of great importance, selecting adequate tuning parameters for a segmentation algorithm can be tedious and subjective. Procedures to automatically [...] Read more.

Image segmentation is a crucial stage at the very beginning of many geographic object-based image analysis (GEOBIA) workflows. While segmentation quality is generally deemed of great importance, selecting adequate tuning parameters for a segmentation algorithm can be tedious and subjective. Procedures to automatically choose parameters of a segmentation algorithm are meant to make the process objective and reproducible. One of those approaches, and perhaps the most frequently used unsupervised parameter optimization method in the context of GEOBIA is called the objective function, also known as Global Score. Unfortunately, the method exhibits a hitherto widely neglected, yet severe source of instability, which makes quality rankings inconsistent. We demonstrate the issue in detail and propose a modification of the Global Score to mitigate the problem. This hopefully serves as a starting point to spark further development of the popular approach. Full article

▼ Show Figures

Graphical abstract

16 pages, 3246 KiB

Open AccessArticle

Diurnal Cycle Relationships between Passive Fluorescence, PRI and NPQ of Vegetation in a Controlled Stress Experiment

by Luis Alonso^*

, Shari Van Wittenberghe, Julia Amorós-López, Joan Vila-Francés, Luis Gómez-Chova

and Jose Moreno

Image Processing Laboratory (IPL), University of Valencia, C/Catedrático José Beltrán, 2, Paterna, 46980 Valencia, Spain

Remote Sens. 2017, 9(8), 770; https://doi.org/10.3390/rs9080770 - 28 Jul 2017

Cited by 69 | Viewed by 9414

Abstract

In order to estimate vegetation photosynthesis from remote sensing observations; some critical parameters need to be quantified. From all absorbed light; the plant needs to release any excess that is not used for photosynthesis; by non-photochemical quenching; by fluorescence emission and unregulated thermal [...] Read more.

In order to estimate vegetation photosynthesis from remote sensing observations; some critical parameters need to be quantified. From all absorbed light; the plant needs to release any excess that is not used for photosynthesis; by non-photochemical quenching; by fluorescence emission and unregulated thermal dissipation. Non-photochemical quenching (NPQ) processes are controlled photoprotective mechanisms which; once activated; strongly control the dynamics of photochemical efficiency. With illumination conditions increasing and decreasing during a diurnal cycle; photoprotection mechanisms needs to change accordingly. The goal of this work is to quantify dynamic NPQ; measured from active fluorescence measurements; based on passive proximal sensing leaf measurements. During a 22-day controlled light and water stress experiment on a tobacco (Nicotiana tabacum L.) leaf we measured the diurnal dynamics of passive fluorescence (Chl F); the Photochemical Reflectance Index (PRI); the Absorbed Photosynthetically Active Radiation (APAR) and leaf temperature in combination with the actively retrieved non-photochemical quenching (NPQ) parameter. Based on a bi-linear combination of diurnal APAR and PRI (plane fit model) we succeeded to estimate NPQ with a RMSE of 0.08. The simple plane fit model estimation represents well the diurnal NPQ dynamics; except for the high light stress phase; when additional reversible photoinhibition processes took place. The present works presents a way of determining NPQ from passive remote sensing measurements; as a necessary step towards estimating photosynthetic rate. Full article

(This article belongs to the Special Issue Remote Sensing of Vegetation Fluorescence and Photosynthetic Efficiency)

▼ Show Figures

Graphical abstract

16 pages, 5868 KiB

Open AccessArticle

Supervised Classification of Power Lines from Airborne LiDAR Data in Urban Areas

by Yanjun Wang¹

, Qi Chen^2,*

, Lin Liu^3,4,*

, Dunyong Zheng¹, Chaokui Li¹ and Kai Li¹

¹ National-Local Joint Engineering Laboratory of Geo-Spatial Information Technology, Hunan University of Science and Technology, No. 1 Taoyuan Road, Xiangtan 411201, China

² Department of Geography, University of Hawaii at Mānoa, 2424 Maile Way, Honolulu, HI 96822, USA

³ Department of Geography, University of Cincinnati, Braunstein Hall, 400E, Cincinnati, OH 45221, USA

⁴ School of Geography and Planning, Sun Yat-Sen University, 135 Xingangxi Road, Guangzhou 510275, China

Remote Sens. 2017, 9(8), 771; https://doi.org/10.3390/rs9080771 - 28 Jul 2017

Cited by 76 | Viewed by 10461

Abstract

Automatic extraction of power lines using airborne LiDAR (Light Detection and Ranging) data has been one of the most important topics for electric power management. However, this is very challenging over complex urban areas, where power lines are in close proximity to buildings [...] Read more.

Automatic extraction of power lines using airborne LiDAR (Light Detection and Ranging) data has been one of the most important topics for electric power management. However, this is very challenging over complex urban areas, where power lines are in close proximity to buildings and trees. In this paper, we presented a new, semi-automated and versatile framework that consists of four steps: (i) power line candidate point filtering, (ii) local neighborhood selection, (iii) spatial structural feature extraction, and (iv) SVM classification. We introduced the power line corridor direction for candidate point filtering and multi-scale slant cylindrical neighborhood for spatial structural features extraction. In a detailed evaluation involving seven scales and four types for local neighborhood selection, 26 structural features, and two datasets, we demonstrated that the use of multi-scale slant cylindrical neighborhood for individual 3D points significantly improved the power line classification. The experiments indicated that precision, recall and quality rate of power line classification is more than 98%, 98% and 97%, respectively. Additionally, we showed that our approach can reduce the whole processing time while achieving high accuracy. Full article

▼ Show Figures

Graphical abstract

27 pages, 5112 KiB

Open AccessArticle

Continental Shelf-Scale Passive Acoustic Detection and Characterization of Diesel-Electric Ships Using a Coherent Hydrophone Array

by Wei Huang¹, Delin Wang¹, Heriberto Garcia¹, Olav Rune Godø² and Purnima Ratilal^1,*

¹ Department of Electrical and Computer Engineering, Northeastern University, 360 Huntington Ave, Boston, MA 02115, USA

² Institute of Marine Research, Post Office Box 1870, Nordnes, N-5817 Bergen, Norway

Remote Sens. 2017, 9(8), 772; https://doi.org/10.3390/rs9080772 - 28 Jul 2017

Cited by 22 | Viewed by 7671

Abstract

The passive ocean acoustic waveguide remote sensing (POAWRS) technique is employed to detect and characterize the underwater sound radiated from three scientific research and fishing vessels received at long ranges on a large-aperture densely-sampled horizontal coherent hydrophone array. The sounds radiated from the [...] Read more.

The passive ocean acoustic waveguide remote sensing (POAWRS) technique is employed to detect and characterize the underwater sound radiated from three scientific research and fishing vessels received at long ranges on a large-aperture densely-sampled horizontal coherent hydrophone array. The sounds radiated from the research vessel (RV) Delaware II in the Gulf of Maine, and the RV Johan Hjort and the fishing vessel (FV) Artus in the Norwegian Sea are found to be dominated by distinct narrowband tonals and cyclostationary signals in the 150 Hz to 2000 Hz frequency range. The source levels of these signals are estimated by correcting the received pressure levels for transmission losses modeled using a calibrated parabolic equation-based acoustic propagation model for random range-dependent ocean waveguides. The probability of the detection region for the most prominent signal radiated by each ship is estimated and shown to extend over areas spanning roughly 200 km in diameter when employing a coherent hydrophone array. The current standard procedure for quantifying ship-radiated sound source levels via one-third octave bandwidth intensity averaging smoothes over the prominent tonals radiated by a ship that can stand 10 to 30 dB above the local broadband level, which may lead to inaccurate or incorrect assessments of the impact of ship-radiated sound. Full article

(This article belongs to the Section Ocean Remote Sensing)

▼ Show Figures

Graphical abstract

20 pages, 5056 KiB

Open AccessArticle

Improving Super-Resolution Mapping by Combining Multiple Realizations Obtained Using the Indicator-Geostatistics Based Method

by Zhongkui Shi¹

, Peijun Li^1,2,*

, Huiran Jin³, Yugang Tian⁴, Yan Chen⁵ and Xianfeng Zhang¹

¹ Institute of Remote Sensing and GIS, School of Earth and Space Sciences, Peking University, Beijing 100871, China

² Shandong Provincial Key Laboratory of Depositional Mineralization & Sedimentary Minerals, Shandong University of Science and Technology, Qingdao 266590, Shandong, China

³ Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA

⁴ Faculty of Information Engineering, China University of Geosciences, Wuhan 430074, China

⁵ Migu Digital Media Co. Ltd., Hangzhou 310030, China

Remote Sens. 2017, 9(8), 773; https://doi.org/10.3390/rs9080773 - 28 Jul 2017

Cited by 10 | Viewed by 5069

Abstract

Indicator-geostatistics based super-resolution mapping (IGSRM) is a popular super-resolution mapping (SRM) method. Unlike most existing SRM methods that produce only one SRM result each, IGSRM generates multiple equally plausible super-resolution realizations (i.e., SRM results). However, multiple super-resolution realizations are not desirable in many [...] Read more.

Indicator-geostatistics based super-resolution mapping (IGSRM) is a popular super-resolution mapping (SRM) method. Unlike most existing SRM methods that produce only one SRM result each, IGSRM generates multiple equally plausible super-resolution realizations (i.e., SRM results). However, multiple super-resolution realizations are not desirable in many applications, where only one SRM result is usually required. These super-resolution realizations may have different strengths and weaknesses. This paper proposes a novel two-step combination method of generating a single SRM result from multiple super-resolution realizations obtained by IGSRM. In the first step of the method, a constrained majority rule is proposed to combine multiple super-resolution realizations generated by IGSRM into a single SRM result under the class proportion constraint. In the second step, partial pixel swapping is proposed to further improve the SRM result obtained in the previous step. The proposed combination method was evaluated for two study areas. The proposed method was quantitatively compared with IGSRM and Multiple SRM (M-SRM), an existing multiple SRM result combination method, in terms of thematic accuracy and geometric accuracy. Experimental results show that the proposed method produces SRM results that are better than those of IGSRM and M-SRM. For example, in the first example, the overall accuracy of the proposed method is 7.43–10.96% higher than that of the IGSRM method for different scale factors, and 1.09–3.44% higher than that of the M-SRM, while, in the second example, the improvement in overall accuracy is 2.42–4.92%, and 0.08–0.90%, respectively. The proposed method provides a general framework for combining multiple results from different SRM methods. Full article

▼ Show Figures

Graphical abstract

19 pages, 2837 KiB

Open AccessArticle

Impact Analysis of Climate Change on Snow over a Complex Mountainous Region Using Weather Research and Forecast Model (WRF) Simulation and Moderate Resolution Imaging Spectroradiometer Data (MODIS)-Terra Fractional Snow Cover Products

by Xiaoduo Pan^1,2,3, Xin Li^1,4,*, Guodong Cheng^1,5, Rensheng Chen¹ and Kuolin Hsu³

¹ Key Laboratory of Remote Sensing of Gansu Province, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China

² Heihe Remote Sensing Experimental Research Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China

³ The Henry Samueli School of Engineering, University of California, Irvine, CA 92697, USA

⁴ CAS Center for Excellence in Tibetan Plateau Earth Sciences, Beijing 100101, China

⁵ Institute of Urban Development, Shanghai Normal University, Shanghai, 200234, China

Remote Sens. 2017, 9(8), 774; https://doi.org/10.3390/rs9080774 - 29 Jul 2017

Cited by 10 | Viewed by 6689

Abstract

Climate change has a complex effect on snow at the regional scale. The change in snow patterns under climate change remains unknown for certain regions. Here, we used high spatiotemporal resolution snow-related variables simulated by a weather research and forecast model (WRF) including [...] Read more.

Climate change has a complex effect on snow at the regional scale. The change in snow patterns under climate change remains unknown for certain regions. Here, we used high spatiotemporal resolution snow-related variables simulated by a weather research and forecast model (WRF) including snowfall, snow water equivalent and snow depth along with fractional snow cover (FSC) data extracted from Moderate Resolution Imaging Spectroradiometer Data (MODIS)-Terra to evaluate the effects of climate change on snow over the Heihe River Basin (HRB), a typical inland river basin in arid northwestern China from 2000 to 2013. We utilized Empirical Orthogonal Function (EOF) analysis and Mann-Kendall/Theil-Sen trend analysis to evaluate the results. The results are as follows: (1) FSC, snow water equivalent, and snow depth across the entire HRB region decreased, especially at elevations over 4500 m; however, snowfall increased at mid-altitude ranges in the upstream area of the HRB. (2) Total snowfall also increased in the upstream area of the HRB; however, the number of snowfall days decreased. Therefore, the number of extreme snow events in the upstream area of the HRB may have increased. (3) Snowfall over the downstream area of the HRB decreased. Thus, ground stations, WRF simulations and remote sensing products can be used to effectively explore the effect of climate change on snow at the watershed scale. Full article

(This article belongs to the Special Issue Snow Remote Sensing)

▼ Show Figures

Graphical abstract

22 pages, 845 KiB

Open AccessArticle

Hybrid Spectral Unmixing: Using Artificial Neural Networks for Linear/Non-Linear Switching

by Asmau M. Ahmed¹, Olga Duran¹, Yahya Zweiri^1,2,*

and Mike Smith³

¹ Faculty of Science, Engineering and Computing, Kingston University London, London SW15 3DW, UK

² Robotics Institute, Khalifa University of Science and Technology, P.O. Box 127788, Abu Dhabi 127788, UAE

³ Royal Geographical Society (with IBG), 1 Kensington Gore, London SW7 2AR, UK

Remote Sens. 2017, 9(8), 775; https://doi.org/10.3390/rs9080775 - 29 Jul 2017

Cited by 33 | Viewed by 6448

Abstract

Spectral unmixing is a key process in identifying spectral signature of materials and quantifying their spatial distribution over an image. The linear model is expected to provide acceptable results when two assumptions are satisfied: (1) The mixing process should occur at macroscopic level [...] Read more.

Spectral unmixing is a key process in identifying spectral signature of materials and quantifying their spatial distribution over an image. The linear model is expected to provide acceptable results when two assumptions are satisfied: (1) The mixing process should occur at macroscopic level and (2) Photons must interact with single material before reaching the sensor. However, these assumptions do not always hold and more complex nonlinear models are required. This study proposes a new hybrid method for switching between linear and nonlinear spectral unmixing of hyperspectral data based on artificial neural networks. The neural networks was trained with parameters within a window of the pixel under consideration. These parameters are computed to represent the diversity of the neighboring pixels and are based on the Spectral Angular Distance, Covariance and a non linearity parameter. The endmembers were extracted using Vertex Component Analysis while the abundances were estimated using the method identified by the neural networks (Vertex Component Analysis, Fully Constraint Least Square Method, Polynomial Post Nonlinear Mixing Model or Generalized Bilinear Model). Results show that the hybrid method performs better than each of the individual techniques with high overall accuracy, while the abundance estimation error is significantly lower than that obtained using the individual methods. Experiments on both synthetic dataset and real hyperspectral images demonstrated that the proposed hybrid switch method is efficient for solving spectral unmixing of hyperspectral images as compared to individual algorithms. Full article

▼ Show Figures

Graphical abstract

17 pages, 2209 KiB

Open AccessArticle

An Improved Spectrum Model for Sea Surface Radar Backscattering at L-Band

by Yanlei Du^1,2,3

, Xiaofeng Yang^1,2,*

, Kun-Shan Chen²

, Wentao Ma² and Ziwei Li²

¹ The Key Laboratory for Earth Observation of Hainan Province, Sanya 572029, China

² State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China

³ University of Chinese Academy of Sciences, Beijing 100049, China

Remote Sens. 2017, 9(8), 776; https://doi.org/10.3390/rs9080776 - 29 Jul 2017

Cited by 42 | Viewed by 8202

Abstract

L-band active microwave remote sensing is one of the most important technical methods of ocean environmental monitoring and dynamic parameter retrieval. Recently, a unique negative upwind-crosswind (NUC) asymmetry of L-band ocean backscatter over a low wind speed range was observed. To study the [...] Read more.

L-band active microwave remote sensing is one of the most important technical methods of ocean environmental monitoring and dynamic parameter retrieval. Recently, a unique negative upwind-crosswind (NUC) asymmetry of L-band ocean backscatter over a low wind speed range was observed. To study the directional features of L-band ocean surface backscattering, a new directional spectrum model is proposed and built into the advanced integral equation method (AIEM). This spectrum combines Apel’s omnidirectional spectrum and an improved empirical angular spreading function (ASF). The coefficients in the ASF were determined by the fitting of radar observations so that it provides a better description of wave directionality, especially over wavenumber ranges from short-gravity waves to capillary waves. Based on the improved spectrum and the AIEM scattering model, L-band NUC asymmetry at low wind speeds and positive upwind-crosswind (PUC) asymmetry at higher wind speeds are simulated successfully. The model outputs are validated against Aquarius/SAC-D observations under different incidence angles, azimuth angles and wind speed conditions. Full article

(This article belongs to the Special Issue Ocean Radar)

▼ Show Figures

Graphical abstract

17 pages, 3574 KiB

Open AccessArticle

A New Urban Index for Expressing Inner-City Patterns Based on MODIS LST and EVI Regulated DMSP/OLS NTL

by Yangxiaoyue Liu^1,2, Yaping Yang^1,3,*, Wenlong Jing^4,5,6

, Ling Yao^1,3

, Xiafang Yue^1,3 and Xiaodan Zhao^1,3

¹ State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

² University of Chinese Academy of Sciences, Beijing 100049, China

³ Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China

⁴ Guangzhou Institute of Geography, Guangzhou 510070, China

⁵ Key Laboratory of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangzhou 510070, China

⁶ Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangzhou 510070, China

Remote Sens. 2017, 9(8), 777; https://doi.org/10.3390/rs9080777 - 29 Jul 2017

Cited by 24 | Viewed by 7448

Abstract

With the rapid pace of urban expansion, comprehensively understanding urban spatial patterns, built environments, green-spaces distributions, demographic distributions, and economic activities becomes more meaningful. Night Time Lights (NTL) images acquired through the Operational Linescan System of the US Defense Meteorological Satellite Program (DMSP/OLS [...] Read more.

With the rapid pace of urban expansion, comprehensively understanding urban spatial patterns, built environments, green-spaces distributions, demographic distributions, and economic activities becomes more meaningful. Night Time Lights (NTL) images acquired through the Operational Linescan System of the US Defense Meteorological Satellite Program (DMSP/OLS NTL) have long been utilized to monitor urban areas and their expansion characteristics since this system detects variation in NTL emissions. However, the pixel saturation phenomenon leads to a serious limitation in mapping luminance variations in urban zones with nighttime illumination levels that approach or exceed the pixel saturation limits of OLS sensors. Consequently, we propose an NTL-based city index that utilizes the Moderate-resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST) and Enhanced Vegetation Index (EVI) images to regulate and compensate for desaturation on NTL images acquired from corresponding urban areas. The regulated results achieve good performance in differentiating central business districts (CBDs), airports, and urban green spaces. Consequently, these derived imageries could effectively convey the structural details of urban cores. In addition, compared with the Vegetation Adjusted NTL Urban Index (VANUI), LST-and-EVI-regulated-NTL-city index (LERNCI) reveals superior capability in delineating the spatial structures of selected metropolis areas across the world, especially in the large cities of developing countries. The currently available results indicate that LERNCI corresponds better to city spatial patterns. Moreover, LERNCI displays a remarkably better “goodness-of-fit” correspondence with both the Version 1 Nighttime Visible Infrared Imaging Radiometer Suite Day/Night Band Composite (NPP/VIIRS DNB) data and the WorldPop population-density data compared with the VANUI imageries. Thus, LERNCI can act as a helpful indicator for differentiating and classifying regional economic activities, population aggregations, and energy-consumption and city-expansion patterns. LERNCI can also serve as a valuable auxiliary reference for decision-making processes that concern subjects such as urban planning and easing the central functions of metropolis. Full article

(This article belongs to the Special Issue Recent Advances in Remote Sensing with Nighttime Lights)

▼ Show Figures

Graphical abstract

20 pages, 2854 KiB

Open AccessArticle

Synergistic Use of Remote Sensing and Modeling to Assess an Anomalously High Chlorophyll-a Event during Summer 2015 in the South Central Red Sea

by Wenzhao Li¹, Hesham El-Askary^2,3,4,*

, K. P. ManiKandan⁵, Mohamed A. Qurban^5,6, Michael J. Garay⁷

and Olga V. Kalashnikova⁷

¹ Computational and Data Sciences Graduate Program, Schmid College of Science and Technology, Chapman University, Orange, CA 92866, USA

² Center of Excellence in Earth Systems Modeling & Observations, Chapman University, Orange, CA 92866, USA

³ Schmid College of Science and Technology, Chapman University, Orange, CA 92866, USA

⁴ Department of Environmental Sciences, Faculty of Science, Alexandria University, Moharem Bek, Alexandria 21522, Egypt

⁵ Center for Environment and Water, The Research Institute, King Fahd University of Petroleum and Minerals (KFUPM), Dhahran 31261, Saudi Arabia

⁶ Geosciences Department, the college of Petroleum Engineering & Geosciences, King Fahd University of Petroleum and Minerals (KFUPM), Dhahran 31261, Saudi Arabia

⁷ Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA

Remote Sens. 2017, 9(8), 778; https://doi.org/10.3390/rs9080778 - 29 Jul 2017

Cited by 21 | Viewed by 8541

Abstract

An anomalously high chlorophyll-a (Chl-a) event (>2 mg/m³) during June 2015 in the South Central Red Sea (17.5° to 22°N, 37° to 42°E) was observed using Moderate Resolution Imaging Spectroradiometer (MODIS) data from the Terra and Aqua satellite [...] Read more.

An anomalously high chlorophyll-a (Chl-a) event (>2 mg/m³) during June 2015 in the South Central Red Sea (17.5° to 22°N, 37° to 42°E) was observed using Moderate Resolution Imaging Spectroradiometer (MODIS) data from the Terra and Aqua satellite platforms. This differs from the low Chl-a values (<0.5 mg/m³) usually encountered over the same region during summertime. To assess this anomaly and possible causes, we used a wide range of oceanographical and meteorological datasets, including Chl-a concentrations, sea surface temperature (SST), sea surface height (SSH), mixed layer depth (MLD), ocean current velocity and aerosol optical depth (AOD) obtained from different sensors and models. Findings confirmed this anomalous behavior in the spatial domain using Hovmöller data analysis techniques, while a time series analysis addressed monthly and daily variability. Our analysis suggests that a combination of factors controlling nutrient supply contributed to the anomalous phytoplankton growth. These factors include horizontal transfer of upwelling water through eddy circulation and possible mineral fertilization from atmospheric dust deposition. Coral reefs might have provided extra nutrient supply, yet this is out of the scope of our analysis. We thought that dust deposition from a coastal dust jet event in late June, coinciding with the phytoplankton blooms in the area under investigation, might have also contributed as shown by our AOD findings. However, a lag cross correlation showed a two- month lag between strong dust outbreak and the high Chl-a anomaly. The high Chl-a concentration at the edge of the eddy emphasizes the importance of horizontal advection in fertilizing oligotrophic (nutrient poor) Red Sea waters. Full article

(This article belongs to the Section Ocean Remote Sensing)

▼ Show Figures

Graphical abstract

20 pages, 3345 KiB

Open AccessArticle

Estimating Daily Reference Evapotranspiration in a Semi-Arid Region Using Remote Sensing Data

by Peshawa M. Najmaddin^1,2,*, Mick J. Whelan¹

and Heiko Balzter^1,3

¹ Centre for Landscape and Climate Research, School of Geography, Geology and the Environment, University of Leicester, Leicester LE1 7RH, UK

² Department of Soil and Water Science, Faculty of Agricultural Sciences, University of Sulaimani, Iraq-Kurdistan Region-Sulaimani-Bekrajo 46011, Iraq

³ National Centre for Earth Observation, University of Leicester, Leicester LE1 7RH, UK

Remote Sens. 2017, 9(8), 779; https://doi.org/10.3390/rs9080779 - 29 Jul 2017

Cited by 36 | Viewed by 8369

Abstract

Estimating daily evapotranspiration is challenging when ground observation data are not available or scarce. Remote sensing can be used to estimate the meteorological data necessary for calculating reference evapotranspiration ETₒ. Here, we assessed the accuracy of daily ETₒ estimates derived from remote [...] Read more.

Estimating daily evapotranspiration is challenging when ground observation data are not available or scarce. Remote sensing can be used to estimate the meteorological data necessary for calculating reference evapotranspiration ETₒ. Here, we assessed the accuracy of daily ETₒ estimates derived from remote sensing (ETₒ_-RS) compared with those derived from four ground-based stations (ETₒ_-G) in Kurdistan (Iraq) over the period 2010–2014. Near surface air temperature, relative humidity and cloud cover fraction were derived from the Atmospheric Infrared Sounder/Advanced Microwave Sounding Unit (AIRS/AMSU), and wind speed at 10 m height from MERRA (Modern-Era Retrospective Analysis for Research and Application). Four methods were used to estimate ETₒ: Hargreaves–Samani (HS), Jensen–Haise (JH), McGuinness–Bordne (MB) and the FAO Penman Monteith equation (PM). ETₒ_-G (PM) was adopted as the main benchmark. HS underestimated ETₒby 2%–3% (R² = 0.86 to 0.90; RMSE = 0.95 to 1.2 mm day⁻¹ at different stations). JH and MB overestimated ETₒby 8% to 40% (R²= 0.85 to 0.92; RMSE from 1.18 to 2.18 mm day⁻¹). The annual average values of ETₒ estimated using RS data and ground-based data were similar to one another reflecting low bias in daily estimates. They ranged between 1153 and 1893 mm year⁻¹ for ETₒ_-G and between 1176 and 1859 mm year⁻¹ for ETₒ_-RS for the different stations. Our results suggest that ETₒ_-RS(HS)can yield accurate and unbiased ETₒestimates for semi-arid regions which can be usefully employed in water resources management. Full article

(This article belongs to the Special Issue Remote Sensing of Arid/Semiarid Lands)

▼ Show Figures

Graphical abstract

16 pages, 2811 KiB

Open AccessArticle

A Robust Inversion Algorithm for Surface Leaf and Soil Temperatures Using the Vegetation Clumping Index

by Zunjian Bian^1,2

, Biao Cao^1,†, Hua Li^1,†, Yongming Du^1,*, Lisheng Song³, Wenjie Fan⁴, Qing Xiao^1,† and Qinhuo Liu^1,*

¹ State Key Laboratory of Remote Sensing, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 10010, China

² College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China

³ Chongqing Key Laboratory of Karst Environment School of Geographical Sciences, Southwest University, Chongqing 400712, China

⁴ Institute of RS and GIS, Peking University, Beijing 100871, China

^† These authors contributed equally to this work.

Remote Sens. 2017, 9(8), 780; https://doi.org/10.3390/rs9080780 - 30 Jul 2017

Cited by 11 | Viewed by 5676

Abstract

The inversion of land surface component temperatures is an essential source of information for mapping heat fluxes and the angular normalization of thermal infrared (TIR) observations. Leaf and soil temperatures can be retrieved using multiple-view-angle TIR observations. In a satellite-scale pixel, the clumping [...] Read more.

The inversion of land surface component temperatures is an essential source of information for mapping heat fluxes and the angular normalization of thermal infrared (TIR) observations. Leaf and soil temperatures can be retrieved using multiple-view-angle TIR observations. In a satellite-scale pixel, the clumping effect of vegetation is usually present, but it is not completely considered during the inversion process. Therefore, we introduced a simple inversion procedure that uses gap frequency with a clumping index (GCI) for leaf and soil temperatures over both crop and forest canopies. Simulated datasets corresponding to turbid vegetation, regularly planted crops and randomly distributed forest were generated using a radiosity model and were used to test the proposed inversion algorithm. The results indicated that the GCI algorithm performed well for both crop and forest canopies, with root mean squared errors of less than 1.0 °C against simulated values. The proposed inversion algorithm was also validated using measured datasets over orchard, maize and wheat canopies. Similar results were achieved, demonstrating that using the clumping index can improve inversion results. In all evaluations, we recommend using the GCI algorithm as a foundation for future satellite-based applications due to its straightforward form and robust performance for both crop and forest canopies using the vegetation clumping index. Full article

(This article belongs to the Special Issue Remote Sensing for Land Surface Temperature (LST) Estimation, Generation, and Analysis)

▼ Show Figures

Graphical abstract

17 pages, 5515 KiB

Open AccessArticle

Reconstructing Satellite-Based Monthly Precipitation over Northeast China Using Machine Learning Algorithms

by Wenlong Jing^1,2,3

, Pengyan Zhang^4,*, Hao Jiang^1,2,3

and Xiaodan Zhao⁵

¹ Guangzhou Institute of Geography, Guangzhou 510070, China

² Key Laboratory of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangzhou 510070, China

³ Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangzhou 510070, China

⁴ College of Environment and Planning, Henan University, Kaifeng 475004, China

⁵ State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

Remote Sens. 2017, 9(8), 781; https://doi.org/10.3390/rs9080781 - 30 Jul 2017

Cited by 19 | Viewed by 5307

Abstract

Attaining accurate precipitation data is critical to understanding land surface processes and global climate change. The development of satellite sensors and remote sensing technology has resulted in multi-source precipitation datasets that provide reliable estimates of precipitation over un-gauged areas. However, gaps exist over [...] Read more.

Attaining accurate precipitation data is critical to understanding land surface processes and global climate change. The development of satellite sensors and remote sensing technology has resulted in multi-source precipitation datasets that provide reliable estimates of precipitation over un-gauged areas. However, gaps exist over high latitude areas due to the limited spatial extent of several satellite-based precipitation products. In this study, we propose an approach for the reconstruction of the Tropical Rainfall Measuring Mission (TRMM) 3B43 monthly precipitation data over Northeast China based on the interaction between precipitation and surface environment. Two machine learning algorithms, support vector machine (SVM) and random forests (RF), are implemented to detect possible relationships between precipitation and normalized difference vegetation index (NDVI), land surface temperature (LST), and digital elevation model (DEM). The relationships between precipitation and geographical location variations based on longitude and latitude are also considered in the reconstruction model. The reconstruction of monthly precipitation in the study area is conducted in two spatial resolutions (25 km and 1 km). The validation is performed using in-situ observations from eight meteorological stations within the study area. The results show that the RF algorithm is robust and not sensitive to the choice of parameters, while the training accuracy of the SVM algorithm has relatively large fluctuations depending on the parameter settings and month. The precipitation data reconstructed with RF show strong correlation with in situ observations at each station and are more accurate than that obtained using the SVM algorithm. In general, the accuracy of the estimated precipitation at 1 km resolution is slightly lower than that of data at 25 km resolution. The estimation errors are positively related to the average precipitation. Full article

(This article belongs to the Special Issue Remote Sensing Precipitation Measurement, Validation, and Applications)

▼ Show Figures

Figure 1

16 pages, 4265 KiB

Open AccessArticle

Discriminative Feature Metric Learning in the Affinity Propagation Model for Band Selection in Hyperspectral Images

by Chen Yang^1,2,*

, Yulei Tan¹

, Lorenzo Bruzzone³, Laijun Lu¹ and Renchu Guan^4,*

¹ College of Earth Sciences, Jilin University, Changchun 130061, China

² Lab of Moon and Deepspace Exploration, National Astronomical Observatories, Chinese Academy of Sciences, Beijing 100012, China

³ Department of Information Engineering and Computer Science, University of Trento, 38050 Trento, Italy

⁴ College of Computer Science and Technology, Jilin University, Changchun 130012, China

Remote Sens. 2017, 9(8), 782; https://doi.org/10.3390/rs9080782 - 30 Jul 2017

Cited by 30 | Viewed by 5480

Abstract

Traditional supervised band selection (BS) methods mainly consider reducing the spectral redundancy to improve hyperspectral imagery (HSI) classification with class labels and pairwise constraints. A key observation is that pixels spatially close to each other in HSI have probably the same signature, while [...] Read more.

Traditional supervised band selection (BS) methods mainly consider reducing the spectral redundancy to improve hyperspectral imagery (HSI) classification with class labels and pairwise constraints. A key observation is that pixels spatially close to each other in HSI have probably the same signature, while pixels further away from each other in the space have a high probability of belonging to different classes. In this paper, we propose a novel discriminative feature metric-based affinity propagation (DFM-AP) technique where the spectral and the spatial relationships among pixels are constructed by a new type of discriminative constraint. This discriminative constraint involves chunklet and discriminative information, which are introduced into the BS process. The chunklet information allows for grouping of spectrally-close and spatially-close pixels together without requiring explicit knowledge of their class labels, while discriminative information provides important separability information. A discriminative feature metric (DFM) is proposed with the discriminative constraints modeled in terms of an optimal criterion for identifying an efficient distance metric learning method, which involves discriminative component analysis (DCA). Following this, the representative subset of bands can be identified by means of an exemplar-based clustering algorithm, which is also known as the process of affinity propagation. Experimental results show that the proposed approach yields a better performance in comparison with several representative class label and pairwise constraint-based BS algorithms. The proposed DFM-AP improves the classification performance with discriminative constraints by selecting highly discriminative bands with low redundancy. Full article

▼ Show Figures

Graphical abstract

24 pages, 10149 KiB

Open AccessArticle

A Novel Spaceborne Sliding Spotlight Range Sweep Synthetic Aperture Radar: System and Imaging

by Yan Wang¹, Jingwen Li², Jian Yang^1,* and Bing Sun²

¹ Department of Electronic Engineering, Tsinghua University, Beijing 100084, China

² School of Electronic Information Engineering, Beihang University, Beijing 100191, China

Remote Sens. 2017, 9(8), 783; https://doi.org/10.3390/rs9080783 - 31 Jul 2017

Cited by 8 | Viewed by 5051

Abstract

In this paper, a new Spaceborne Sliding Spotlight Range Sweep Synthetic Aperture Radar (SSS-RSSAR) is proposed to generate a high-resolution image of a Region of Interest (ROI) tilted with respect to the satellite track. Comparing to the traditional Spaceborne Sliding Spotlight Synthetic Aperture [...] Read more.

In this paper, a new Spaceborne Sliding Spotlight Range Sweep Synthetic Aperture Radar (SSS-RSSAR) is proposed to generate a high-resolution image of a Region of Interest (ROI) tilted with respect to the satellite track. Comparing to the traditional Spaceborne Sliding Spotlight Synthetic Aperture Radar (SSS-SAR), the SSS-RSSAR is superior in contributing to less data amount, lighter computational load and hence higher observation efficiency. Unlike the Spaceborne Stripmap Range Sweep Synthetic Aperture Radar (SS-RSSAR) proposed in a previous paper, the SSS-RSSAR not only continuously sweeps the beam in range for the ROI tracking, but also in azimuth to enlarge the synthetic aperture for an improved azimuth resolution. Two aspects of the SSS-RSSAR are focused: system and imaging. For the system part, a Continuous Varying Pulse Interval (CVPI) technique is proposed to avoid the transmission blockage problem by non-uniformly adjusting the pulse intervals based on the geometry. For the imaging part, a Modified Polar Format Algorithm (MPFA) is proposed to accommodate the original polar format algorithm to the echo received with the CVPI technique. Moreover, an integrate system parameter design flow for the SSS-RSSAR is also suggested. The presented approach is evaluated by exploiting the point target simulations. Full article

▼ Show Figures

Figure 1

20 pages, 16518 KiB

Open AccessArticle

Agricultural Expansion and Intensification in the Foothills of Mount Kenya: A Landscape Perspective

by Sandra Eckert^1,*

, Boniface Kiteme², Evanson Njuguna² and Julie Gwendolin Zaehringer¹

¹ Centre for Development and Environment, University of Bern, Hallerstrasse 10, 3012 Bern, Switzerland

² Centre for Training and Integrated Research in ASAL Development, 10400 Nanyuki, Kenya

Remote Sens. 2017, 9(8), 784; https://doi.org/10.3390/rs9080784 - 31 Jul 2017

Cited by 31 | Viewed by 10134

Abstract

This study spatially assesses, quantifies, and visualizes the agricultural expansion and land use intensification in the northwestern foothills of Mount Kenya over the last 30 years: processes triggered by population growth, and, more recently, by large-scale commercial investments. We made use of Google [...] Read more.

This study spatially assesses, quantifies, and visualizes the agricultural expansion and land use intensification in the northwestern foothills of Mount Kenya over the last 30 years: processes triggered by population growth, and, more recently, by large-scale commercial investments. We made use of Google Earth Engine to access the USGS Landsat data archive and to generate cloud-free seasonal composites. These enabled us to accurately differentiate between rainfed and irrigated cropland, which was important for assessing agricultural intensification. We developed three land cover and land use classifications using the random forest classifier, and assessed land cover and land use change by creating cross-tabulation matrices for the intervals from 1987 to 2002, 2002 to 2016, and 1987 to 2016 and calculating the net change. We then applied a landscape mosaic approach to each classification to identify landscape types categorized by land use intensity. We discuss the impacts of landscape changes on natural habitats, biodiversity, and water. Kappa accuracies for the three classifications lay between 78.3% and 82.1%. Our study confirms that rainfed and irrigated cropland expanded at the expense of natural habitats, including protected areas. Agricultural expansion took place mainly in the 1980s and 1990s, whereas agricultural intensification largely happened after 2000. Since then, not only large-scale producers, but also many smallholders have begun to practice irrigated farming. The spatial pattern of agricultural expansion and intensification in the study area is defined by water availability. Agricultural intensification and the expansion of horticulture agribusinesses increase pressure on water. Furthermore, the observed changes have heightened pressure on pasture and idle land due to the decrease in natural and agropastoral landscapes. Conflicts between pastoralists, smallholder farmers, large-scale ranches, and wildlife might further increase, particularly during the dry seasons and in years of extreme drought. Full article

(This article belongs to the Special Issue Monitoring Agricultural Land-Use Change and Land-Use Intensity)

▼ Show Figures

Graphical abstract

12 pages, 6335 KiB

Open AccessArticle

Autonomous Collection of Forest Field Reference—The Outlook and a First Step with UAV Laser Scanning

by Anttoni Jaakkola¹, Juha Hyyppä¹, Xiaowei Yu¹, Antero Kukko¹

, Harri Kaartinen¹

, Xinlian Liang¹, Hannu Hyyppä² and Yunsheng Wang^1,*

¹ Finnish Geospatial Research Institute, National Land Survey, Geodeetinrinne 2, FI-02430 Masala, Finland

² School of Engineering, Aalto University, P.O. Box 14100, FI-0076 Aalto, Finland

Remote Sens. 2017, 9(8), 785; https://doi.org/10.3390/rs9080785 - 31 Jul 2017

Cited by 97 | Viewed by 10122

Abstract

A compact solution for the accurate and automated collection of field data in forests has long been anticipated, and tremendous efforts have been made by applying various remote sensing technologies. The employment of advanced techniques, such as the smartphone-based relascope, terrestrial and mobile [...] Read more.

A compact solution for the accurate and automated collection of field data in forests has long been anticipated, and tremendous efforts have been made by applying various remote sensing technologies. The employment of advanced techniques, such as the smartphone-based relascope, terrestrial and mobile photogrammetry, and laser scanning, have led to steady progress, thus steering their applications to a practical stage. However, all recent strategies require human operation for data acquisition, either to place the instrument on site (e.g., terrestrial laser scanning, TLS) or to carry the instrument by an operator (e.g., personal laser scanning, PLS), which remained laborious and expensive. In this paper, a new concept of autonomous forest field investigation is proposed, which includes data collection above and inside the forest canopy by integrating an unmanned aircraft vehicle (UAV) with autonomous driving. As a first step towards realizing this concept, the feasibility of automated tree-level field measurements from a mini-UAV laser scanning system is evaluated. A “low-cost” Velodyne Puck LITE laser scanner is applied for the test. It is revealed that, with the above canopy flight data, the detection rate was 100% for isolated and dominant trees. The accuracy of direct measurements on the diameter at breast height (DBH) from the point cloud is between 5.5 and 6.8 cm due to the system and the methodological error propagation. The estimation of DBH from point cloud metrics, on the other hand, showed an accuracy of 2.6 cm, which is comparable to the accuracies obtained with terrestrial surveys using mobile laser scanning (MLS), TLS or photogrammetric point clouds. The estimation of basal area, stem volume and biomass of individual trees could be obtained with less than 20% RMSE, which is adequate for field reference measurements at tree level. Such results indicate that the concept of UAV laser scanning-based automated tree-level field reference collection can be feasible, even though the whole topic requires further research. Full article

(This article belongs to the Section Forest Remote Sensing)

▼ Show Figures

Graphical abstract

13 pages, 1723 KiB

Open AccessArticle

Transferability of Economy Estimation Based on DMSP/OLS Night-Time Light

by Kun Qi¹, Yi’na Hu², Chengqi Cheng¹ and Bo Chen^1,*

¹ College of Engineering, Peking University, Beijing 100871, China

² College of Urban and Environmental Sciences, Peking University, Beijing 100871, China

Remote Sens. 2017, 9(8), 786; https://doi.org/10.3390/rs9080786 - 31 Jul 2017

Cited by 18 | Viewed by 4870

Abstract

Despite the fact that economic data are of great significance in the assessment of human socioeconomic development, the application of this data has been hindered partly due to the unreliable and inefficient economic censuses conducted in developing countries. The night-time light (NTL) imagery [...] Read more.

Despite the fact that economic data are of great significance in the assessment of human socioeconomic development, the application of this data has been hindered partly due to the unreliable and inefficient economic censuses conducted in developing countries. The night-time light (NTL) imagery from the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP/OLS) provides one of the most important ways to evaluate an economy with low cost and high efficiency. However, little research has addressed the transferability of the estimation across years. Based on the entire DN series from 0 to 63 of NTL data and GDP data in 31 provinces of mainland China from 2000 to 2012, this paper aims to study the transferability of economy estimation across years, with four linear and non-linear data mining methods, including the Multiple Linear Regression (MLR), Local Weighted Regression (LWR), Partial Least Squares Regression (PLSR), and Support Vector Machine Regression (SVMR). We firstly built up the GDP estimation model based on the NTL data in each year with each method respectively, then applied each model to the other 12 years for the evaluation of the time series transferability. Results revealed that the performances of models differ greatly across years and methods: PLSR (mean of ) and SVMR (mean of ) are superior to MLR (mean of ) and LWR (mean of ) for model calibration; only PLSR (mean of , mean of ) holds a strong transferability among different years; the frequency of three DN sections of (0–1), (4–16), and (57–63) are especially important for economy estimation. Such results are expected to provide a more comprehensive understanding of the NTL, which can be used for economy estimation across years. Full article

(This article belongs to the Special Issue Recent Advances in Remote Sensing with Nighttime Lights)

▼ Show Figures

Graphical abstract

17 pages, 20024 KiB

Open AccessCase Report

A-DInSAR Monitoring of Landslide and Subsidence Activity: A Case of Urban Damage in Arcos de la Frontera, Spain

by Guadalupe Bru^1,*, Pablo J. González², Rosa M. Mateos^3,4,5, Francisco J. Roldán³, Gerardo Herrera^3,4,5

, Marta Béjar-Pizarro^3,4,5

and José Fernández¹

¹ Institute of Geosciences (CSIC, UCM), Fac. de Ciencias Matemáticas, Plaza de Ciencias, 3, Madrid 28040,Spain

² Centre for the Observation and Modelling of Earthquakes, Volcanoes and Tectonics (COMET) andDepartment of Earth, Ocean and Ecological Sciences, University of Liverpool, Liverpool L69 3BX, UK

³ Geoscience Research Department, Geological Survey of Spain, Rios Rosas, 23, Madrid 28003, Spain

⁴ Geoscience Research Department, Geohazards InSAR Laboratory and Modeling Group (InSARlab),Geological Survey of Spain, Alenza 1, Madrid 28003, Spain

⁵ EuroGeoSurveys: Earth Observation and Geohazards Expert Group (EOEG), 36-38, Rue Joseph II, Brussels 1000, Belgium

Remote Sens. 2017, 9(8), 787; https://doi.org/10.3390/rs9080787 - 31 Jul 2017

Cited by 31 | Viewed by 8385

Abstract

Terrain surface displacements at a site can be induced by more than one geological process. In this work, we use advanced differential interferometry SAR (A-DInSAR) to measure ground deformation in Arcos de la Frontera (SW Spain), where severe damages related to landslide activity [...] Read more.

Terrain surface displacements at a site can be induced by more than one geological process. In this work, we use advanced differential interferometry SAR (A-DInSAR) to measure ground deformation in Arcos de la Frontera (SW Spain), where severe damages related to landslide activity and subsidence have occurred in recent years. The damages are concentrated in two residential neighborhoods constructed between 2001 and 2006. One of the neighborhoods, called La Verbena, is located at the head of an active retrogressive landslide that has an extension of around 0.17 × 10⁶ m² and developed in weathered clayey soils. Landslide motion has caused building deterioration since they were constructed. After a heavy rainfall period in winter 2009–2010, the movement was accelerated, worsening the situation. The other neighborhood, Pueblos Blancos, was built over a poorly compacted artificial filling undergoing a spatially variable consolidation process which has also led to severe damage to buildings. For both cases, a short set of C-band data from the “ENVISAT 2010+” project has been used to monitor surface displacement for the period spanning April 2011–January 2012. In this work we characterize the mechanism of both ground deformation processes using in situ and remote sensing techniques along with a detailed geological interpretation and urban damage distribution. Full article

(This article belongs to the Special Issue Remote Sensing of Landslides)

▼ Show Figures

Graphical abstract

7 pages, 210 KiB

Open AccessEditorial

Preface: Land Surface Processes and Interactions—From HCMM to Sentinel Missions and Beyond

by Zhongbo Su^1,*, Zoltán Vekerdy^1,2

and Yijian Zeng¹

¹ Department of Water Resources, Faculty of Geo-Information Science and Earth Observations (ITC), University of Twente, Hengelosestraat 99, 7514 AE Enschede, The Netherlands

² Department of Water Management, Faculty of Agricultural and Environmental Sciences, Szent István University, Páter Károly u. 1., 2100 Gödöllő, Hungary

Remote Sens. 2017, 9(8), 788; https://doi.org/10.3390/rs9080788 - 31 Jul 2017

Viewed by 4223

Abstract

The scientific understanding of the energy and water fluxes between land and atmosphere primarily predicates our capacity to describe, model, and predict the highly complex Earth system, which is formed by mutually interlinked components (land, atmosphere, and ocean) [...] Full article

(This article belongs to the Special Issue Land Surface Processes and Interactions—From HCMM to Sentinel Missions and beyond)

18 pages, 7372 KiB

Open AccessArticle

Downscaling Land Surface Temperature in an Arid Area by Using Multiple Remote Sensing Indices with Random Forest Regression

by Yingbao Yang, Chen Cao, Xin Pan^*, Xiaolong Li and Xi Zhu

School of Earth Science and Engineering, Hohai University, 8 Buddha City West Road, Nanjing 210098, China

Remote Sens. 2017, 9(8), 789; https://doi.org/10.3390/rs9080789 - 31 Jul 2017

Cited by 125 | Viewed by 9979

Abstract

Many downscaling algorithms have been proposed to address the issue of coarse-resolution land surface temperature (LST) derived from available satellite-borne sensors. However, few studies have focused on improving LST downscaling in arid regions (especially in deserts) because of inaccurate remote sensing LST products. [...] Read more.

Many downscaling algorithms have been proposed to address the issue of coarse-resolution land surface temperature (LST) derived from available satellite-borne sensors. However, few studies have focused on improving LST downscaling in arid regions (especially in deserts) because of inaccurate remote sensing LST products. In this study, LST was downscaled by a random forest model between LST and multiple remote sensing indices (such as soil-adjusted vegetation index, normalized multi-band drought index, modified normalized difference water index, and normalized difference building index) in an arid region with an oasis–desert ecotone. The proposed downscaling approach, which involves the selection of remote sensing indices, was evaluated using LST derived from the MODIS LST product of Zhangye City in Heihe Basin. The spatial resolution of MODIS LST was downscaled from 1 km to 500 m. Results of visual and quantitative analyses show that the distribution of downscaled LST matched that of the oasis and desert ecosystem. The lowest (approximately 22 °C) and highest temperatures (higher than 37 °C) were detected in the middle oasis and desert regions, respectively. Furthermore, the proposed approach achieves relatively satisfactory downscaling results, with coefficient of determination and root mean square error of 0.84 and 2.42 °C, respectively. The proposed approach shows higher accuracy and minimization of the MODIS LST in the desert region compared with other methods. Optimal availability occurs in the vegetated region during summer and autumn. In addition, the approach is also efficient and reliable for LST downscaling of Landsat images. Future tasks include reliable LST downscaling in challenging regions. Full article

(This article belongs to the Special Issue Remote Sensing for Land Surface Temperature (LST) Estimation, Generation, and Analysis)

▼ Show Figures

Graphical abstract

23 pages, 6165 KiB

Open AccessArticle

Local Geometric Structure Feature for Dimensionality Reduction of Hyperspectral Imagery

by Fulin Luo^1,2

, Hong Huang^2,*

, Yule Duan², Jiamin Liu² and Yinghua Liao³

¹ State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China

² Key Laboratory of Optoelectronic Technology and Systems of the Education Ministry of China, Chongqing University, Chongqing 400044, China

³ Department of Mechanical Engineering, Sichuan University of Science and Engineering, Zigong 643000, China

Remote Sens. 2017, 9(8), 790; https://doi.org/10.3390/rs9080790 - 1 Aug 2017

Cited by 151 | Viewed by 6802

Abstract

Marginal Fisher analysis (MFA) exploits the margin criterion to compact the intraclass data and separate the interclass data, and it is very useful to analyze the high-dimensional data. However, MFA just considers the structure relationships of neighbor points, and it cannot effectively represent [...] Read more.

Marginal Fisher analysis (MFA) exploits the margin criterion to compact the intraclass data and separate the interclass data, and it is very useful to analyze the high-dimensional data. However, MFA just considers the structure relationships of neighbor points, and it cannot effectively represent the intrinsic structure of hyperspectral imagery (HSI) that possesses many homogenous areas. In this paper, we propose a new dimensionality reduction (DR) method, termed local geometric structure Fisher analysis (LGSFA), for HSI classification. Firstly, LGSFA uses the intraclass neighbor points of each point to compute its reconstruction point. Then, an intrinsic graph and a penalty graph are constructed to reveal the intraclass and interclass properties of hyperspectral data. Finally, the neighbor points and corresponding intraclass reconstruction points are used to enhance the intraclass-manifold compactness and the interclass-manifold separability. LGSFA can effectively reveal the intrinsic manifold structure and obtain the discriminating features of HSI data for classification. Experiments on the Salinas, Indian Pines, and Urban data sets show that the proposed LGSFA algorithm achieves the best classification results than other state-of-the-art methods. Full article

▼ Show Figures

Figure 1

17 pages, 2106 KiB

Open AccessArticle

Mapping and Assessment of PM₁₀ and O₃ Removal by Woody Vegetation at Urban and Regional Level

by Lina Fusaro¹, Federica Marando^1,*

, Alessandro Sebastiani¹, Giulia Capotorti¹, Carlo Blasi¹, Riccardo Copiz¹, Luca Congedo²

, Michele Munafò²

, Luisella Ciancarella³ and Fausto Manes¹

¹ Department of Environmental Biology, Sapienza University of Rome, 00185 Rome, Italy

² ISPRA Italian National Institute for Environmental Protection and Research, 00144 Rome, Italy

³ ENEA—Italian National Agency for New Technologies, Energy and Sustainable Economic Development—Atmospheric Pollution Laboratory, 40129 Bologna, Italy

Remote Sens. 2017, 9(8), 791; https://doi.org/10.3390/rs9080791 - 1 Aug 2017

Cited by 44 | Viewed by 7591

Abstract

This study is the follow up of the URBAN-MAES pilot implemented in the framework of the EnRoute project. The study aims at mapping and assessing the process of particulate matter (PM₁₀) and tropospheric ozone (O₃) removal by various forest [...] Read more.

This study is the follow up of the URBAN-MAES pilot implemented in the framework of the EnRoute project. The study aims at mapping and assessing the process of particulate matter (PM₁₀) and tropospheric ozone (O₃) removal by various forest and shrub ecosystems. Different policy levels and environmental contexts were considered, namely the Metropolitan city of Rome and, at a wider level, the Latium region. The approach involves characterization of the main land cover and ecosystems using Sentinel-2 images, enabling a detailed assessment of Ecosystem Service (ES), and monetary valuation based on externality values. The results showed spatial variations in the pattern of PM₁₀ and O₃ removal inside the Municipality and in the more rural Latium hinterland, reflecting the spatial dynamics of the two pollutants. Evergreen species displayed higher PM₁₀ removal efficiency, whereas deciduous species showed higher O₃ absorption in both rural and urban areas. The overall pollution removal accounted for 5123 and 19,074 Mg of PM₁₀ and O₃, respectively, with a relative monetary benefit of 161 and 149 Million Euro for PM₁₀ and O₃, respectively. Our results provide spatially explicit evidence that may assist policymakers in land-oriented decisions towards improving Green Infrastructure and maximizing ES provision at different governance levels. Full article

(This article belongs to the Special Issue Remote Sensing Applications to Human Health)

▼ Show Figures

Graphical abstract

22 pages, 17589 KiB

Open AccessArticle

Sea State Observation through a Three-Antenna Hybrid XT/AT InSAR Configuration: A Preliminary Study Based on the InSAeS4 Airborne System

by Antonio Natale¹, Giuseppe Jackson^1,2, Carmen Esposito¹, Gianfranco Fornaro¹, Riccardo Lanari^1,*

and Stefano Perna^1,2

¹ Istituto per il Rilevamento Elettromagnetico dell’Ambiente (IREA)—Consiglio Nazionale delle Ricerche (CNR), 80124 Napoli, Italy

² Dipartimento di Ingegneria (DI), Università degli Studi di Napoli “Parthenope”, 80143 Napoli, Italy

Remote Sens. 2017, 9(8), 792; https://doi.org/10.3390/rs9080792 - 1 Aug 2017

Cited by 7 | Viewed by 5001

Abstract

In this work, we investigate the sea surface monitoring capabilities of a Synthetic Aperture Radar (SAR) system equipped with a three-antenna hybrid Across Track (XT)/Along Track (AT) inteferometric configuration. To do this, we focus on the X-Band airborne InSAeS4 SAR system. Moreover, we [...] Read more.

In this work, we investigate the sea surface monitoring capabilities of a Synthetic Aperture Radar (SAR) system equipped with a three-antenna hybrid Across Track (XT)/Along Track (AT) inteferometric configuration. To do this, we focus on the X-Band airborne InSAeS4 SAR system. Moreover, we propose a simple but effective methodology that allows simultaneous retrieval of the sea surface height and velocity by means of a straightforward, easy-to-implement, linear inversion procedure, which is very general and can be implemented with any system equipped with a three-antenna hybrid XT/AT Interferometric SAR (InSAR) configuration. In our case, we present an experiment carried out in January 2013 in South Italy over the coastline stretch of the Campania region including the Volturno River outlet. In this regard, we highlight that in situ measurements of the retrieved sea surface height and velocity at the time of the airborne mission are unfortunately not available. Notwithstanding, the obtained results show some interesting evidence that the estimated quantities are physically sound. This, on the one side, provides a preliminary validation of the effectiveness of the overall presented methodology and, on the other side, highlights the potentialities of the three-antenna hybrid XT/AT InSAR configuration of the InSAeS4 system for sea state monitoring. Full article

(This article belongs to the Special Issue Radar Systems for the Societal Challenges)

▼ Show Figures

Graphical abstract

22 pages, 6911 KiB

Open AccessEditor’s ChoiceArticle

Application of Abundance Map Reference Data for Spectral Unmixing

by McKay D. Williams^*, John P. Kerekes

and Jan Van Aardt

Chester F. Carlson Center for Imaging Science, Rochester Institute of Technology, Rochester, NY 14623, USA

Remote Sens. 2017, 9(8), 793; https://doi.org/10.3390/rs9080793 - 1 Aug 2017

Cited by 8 | Viewed by 5883

Abstract

Reference data (“ground truth”) maps have traditionally been used to assess the accuracy of classification algorithms. These maps typically classify pixels or areas of imagery as belonging to a finite number of ground cover classes, but do not include sub-pixel abundance estimates; therefore, [...] Read more.

Reference data (“ground truth”) maps have traditionally been used to assess the accuracy of classification algorithms. These maps typically classify pixels or areas of imagery as belonging to a finite number of ground cover classes, but do not include sub-pixel abundance estimates; therefore, they are not sufficiently detailed to directly assess the performance of spectral unmixing algorithms. Our research aims to efficiently generate, validate, and apply abundance map reference data (AMRD) to airborne remote sensing scenes. Scene-wide AMRD for this study were generated using the remotely sensed reference data (RSRD) technique, which spatially aggregates classification or unmixing results from fine scale imagery (e.g., 1-m GSD) to co-located coarse scale imagery (e.g., 10-m GSD or larger). Validation of the accuracy of these methods was previously performed for generic 10 m × 10 m coarse scale imagery, resulting in AMRD with known accuracy. The purpose of this paper was to apply this previously validated AMRD to specific examples of airborne coarse scale imagery. Application of AMRD involved three main parts: (1) spatial alignment of coarse and fine scale imagery; (2) aggregation of fine scale abundances to produce coarse scale imagery specific AMRD; and (3) demonstration of comparisons between coarse scale unmixing abundances and AMRD. Spatial alignment was performed using our new scene-wide spectral comparison (SWSC) algorithm, which aligned imagery with accuracy approaching the distance of a single fine scale pixel. We compared simple rectangular aggregation to coarse sensor point-spread function (PSF) aggregation, and found that PSF returned lower error, but that rectangular aggregation more accurately estimated true AMRD at ground level. We demonstrated various metrics for comparing unmixing results to AMRD, including several new techniques which adjust for known error in the reference data itself. These metrics indicated that fully constrained linear unmixing of AVIRIS imagery across all three scenes returned an average error of 10.83% per class and pixel. Our reference data research has demonstrated a viable methodology to efficiently generate, validate, and apply AMRD to specific examples of airborne remote sensing imagery, thereby enabling direct quantitative assessment of spectral unmixing performance. Full article

▼ Show Figures

Graphical abstract

17 pages, 7135 KiB

Open AccessArticle

Canopy-Level Photochemical Reflectance Index from Hyperspectral Remote Sensing and Leaf-Level Non-Photochemical Quenching as Early Indicators of Water Stress in Maize

by Shuren Chou^1,2, Jing M. Chen^1,2,3,*, Hua Yu^1,2, Bin Chen^1,2,3, Xiuying Zhang^1,2, Holly Croft³, Shoaib Khalid^2,4, Meng Li⁵

and Qin Shi⁶

¹ Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, International Institute for Earth System Science, Nanjing University, Nanjing 210023, China

² School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing 210023, China

³ Department of Geography and Program in Planning, University of Toronto, Toronto, ON M5S 3G3, Canada

⁴ Department of Geography, Government College University, Faisalabad 38000, Pakistan

⁵ School of Applied Meteorology, Nanjing University of Information Science & Technology, Nanjing 210044, China

⁶ Institute of Botany, Jiangsu Province and Chinese Academy of Sciences, Nanjing 210014, China

Remote Sens. 2017, 9(8), 794; https://doi.org/10.3390/rs9080794 - 2 Aug 2017

Cited by 34 | Viewed by 8121

Abstract

In this study, we evaluated the effectiveness of photochemical reflectance index (PRI) and non-photochemical quenching (NPQ) for assessing water stress in maize for the purpose of developing remote sensing techniques for monitoring water deficits in crops. Leaf-level chlorophyll fluorescence and canopy-level PRI were [...] Read more.

In this study, we evaluated the effectiveness of photochemical reflectance index (PRI) and non-photochemical quenching (NPQ) for assessing water stress in maize for the purpose of developing remote sensing techniques for monitoring water deficits in crops. Leaf-level chlorophyll fluorescence and canopy-level PRI were measured concurrently over a maize field with five different irrigation treatments, ranging from 20% to 90% of the field capacity (FC). Significant correlations were found between leaf-level NPQ (NPQ_leaf) and the ratio of chlorophyll to carotenoid content (Chl/Car) (R² = 0.71, p < 0.01) and between NPQ_leaf and the actual photochemical efficiency of photosystem II (ΔF/F_m′) (R² = 0.81, p < 0.005). At the early growing stage, both canopy-level PRI and NPQ_leaf are good indicators of water stress (R² = 0.65 and p < 0.05; R² = 0.63 and p < 0.05, respectively). For assessment of extreme water stress on plant growth, a relationship is also established between the quantum yield of photochemistry in PSII (ΦP) and the quantum yield of fluorescence (ΦF) as determined from photochemical quenching (PQ) and non-photochemical quenching (NPQ_leaf) of excitation energy at different water stress levels. These results would be helpful in monitoring soil water stress on crops at large scales using remote sensing techniques. Full article

(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)

▼ Show Figures

Graphical abstract

15 pages, 6767 KiB

Open AccessArticle

Refocusing of Moving Targets in SAR Images via Parametric Sparse Representation

by Yichang Chen^1,2

, Gang Li^2,*, Qun Zhang^1,3 and Jinping Sun⁴

¹ Institute of Information and Navigation, Air Force Engineering University, Xi’an 710077, China

² Department of Electronic Engineering, Tsinghua University, Beijing 100084, China

³ Key Laboratory for Information Science of Electromagnetic Wave (Ministry of Education), Fudan University, Shanghai 200433, China

⁴ School of Electronic and Information Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100191, China

Remote Sens. 2017, 9(8), 795; https://doi.org/10.3390/rs9080795 - 2 Aug 2017

Cited by 34 | Viewed by 6426

Abstract

In this paper, a parametric sparse representation (PSR) method is proposed for refocusing of moving targets in synthetic aperture radar (SAR) images. In regular SAR images, moving targets are defocused due to unknown motion parameters. Refocusing of moving targets requires accurate phase compensation [...] Read more.

In this paper, a parametric sparse representation (PSR) method is proposed for refocusing of moving targets in synthetic aperture radar (SAR) images. In regular SAR images, moving targets are defocused due to unknown motion parameters. Refocusing of moving targets requires accurate phase compensation of echo data. In the proposed method, the region of interest (ROI) data containing the moving targets are extracted from the complex SAR image and represented in a sparse fashion through a parametric transform, which is related to the phase compensation parameter. By updating the reflectivities of moving target scatterers and the parametric transform in an iterative fashion, the phase compensation parameter can be accurately estimated and the SAR images of moving targets can be refocused well. The proposed method directly operates on small-size defocused ROI data, which helps to reduce the computational burden and suppress the clutter. Compared to other existing ROI-based methods, the proposed method can suppress asymmetric side-lobes and improve the image quality. Both simulated data and real SAR data collected by GF-3 satellite are used to validate the effectiveness of the proposed method. Full article

(This article belongs to the Special Issue Ocean Remote Sensing with Synthetic Aperture Radar)

▼ Show Figures

Graphical abstract

26 pages, 12177 KiB

Open AccessArticle

Comparison of the Selected State-Of-The-Art 3D Indoor Scanning and Point Cloud Generation Methods

by Ville V. Lehtola^1,2,*,†

, Harri Kaartinen^1,†

, Andreas Nüchter³, Risto Kaijaluoto¹, Antero Kukko¹

, Paula Litkey¹, Eija Honkavaara¹

, Tomi Rosnell¹

, Matti T. Vaaja²

, Juho-Pekka Virtanen², Matti Kurkela², Aimad El Issaoui¹, Lingli Zhu¹, Anttoni Jaakkola¹ and Juha Hyyppä¹

¹ Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute FGI, Geodeetinrinne 2, FI-02430 Masala, Finland

² Institute of Measuring and Modeling for the Built Environment, Aalto University, P.O. box 15800, 00076 Aalto, Finland

³ Informatics VII—Robotics and Telematics, Julius Maximilians University Würzburg, 97074 Würzburg, Germany

^† These authors contributed equally to this work.

Remote Sens. 2017, 9(8), 796; https://doi.org/10.3390/rs9080796 - 2 Aug 2017

Cited by 186 | Viewed by 17917

Abstract

Accurate three-dimensional (3D) data from indoor spaces are of high importance for various applications in construction, indoor navigation and real estate management. Mobile scanning techniques are offering an efficient way to produce point clouds, but with a lower accuracy than the traditional terrestrial [...] Read more.

Accurate three-dimensional (3D) data from indoor spaces are of high importance for various applications in construction, indoor navigation and real estate management. Mobile scanning techniques are offering an efficient way to produce point clouds, but with a lower accuracy than the traditional terrestrial laser scanning (TLS). In this paper, we first tackle the problem of how the quality of a point cloud should be rigorously evaluated. Previous evaluations typically operate on some point cloud subset, using a manually-given length scale, which would perhaps describe the ranging precision or the properties of the environment. Instead, the metrics that we propose perform the quality evaluation to the full point cloud and over all of the length scales, revealing the method precision along with some possible problems related to the point clouds, such as outliers, over-completeness and misregistration. The proposed methods are used to evaluate the end product point clouds of some of the latest methods. In detail, point clouds are obtained from five commercial indoor mapping systems, Matterport, NavVis, Zebedee, Stencil and Leica Pegasus: Backpack, and three research prototypes, Aalto VILMA , FGI Slammer and the Würzburg backpack. These are compared against survey-grade TLS point clouds captured from three distinct test sites that each have different properties. Based on the presented experimental findings, we discuss the properties of the proposed metrics and the strengths and weaknesses of the above mapping systems and then suggest directions for future research. Full article

▼ Show Figures

Graphical abstract

20 pages, 19345 KiB

Open AccessArticle

The Uncertainty of Nighttime Light Data in Estimating Carbon Dioxide Emissions in China: A Comparison between DMSP-OLS and NPP-VIIRS

by Xiwen Zhang^1,2, Jiansheng Wu^1,2,*, Jian Peng² and Qiwen Cao³

¹ Key Laboratory for Urban Habitat Environmental Science and Technology, Shenzhen Graduate School, Peking University, Shenzhen 518055, China

² Key Laboratory for Earth Surface Processes, Ministry of Education, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China

³ School of Architecture, Tsinghua University, Beijing 100871, China

Remote Sens. 2017, 9(8), 797; https://doi.org/10.3390/rs9080797 - 2 Aug 2017

Cited by 57 | Viewed by 8688

Abstract

Nighttime light data can characterize urbanization, economic development, population density, energy consumption and other human activities. Additionally, carbon dioxide (CO₂) emissions are closely related to the scope and intensity of human activities. In this study, we assess the utility of nighttime [...] Read more.

Nighttime light data can characterize urbanization, economic development, population density, energy consumption and other human activities. Additionally, carbon dioxide (CO₂) emissions are closely related to the scope and intensity of human activities. In this study, we assess the utility of nighttime light data as a powerful tool to reflect CO₂ emissions from energy consumption, analyze the uncertainty associated with different nighttime light data for modeling CO₂ emissions, and provide guidance and a reference for modeling CO₂ emissions based on nighttime light data. In this paper, Mainland China was taken as a case study, and nighttime light datasets (the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) nighttime light data and the Suomi National Polar-Orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) nighttime light data) as well as a global gridded CO₂ emissions dataset (PKU-CO₂) were used to perform simple regressions at provincial, prefectural and 0.1° × 0.1° grid levels, respectively. The analyses are aimed at exploring the accuracy and uncertainty of DMSP-OLS and NPP-VIIRS nighttime light data in modeling CO₂ emissions at different spatial scales. The improvement of nighttime light index and the potential factors influencing the effects of modeling CO₂ emissions based on nighttime light datasets were also explored. The results show that DMSP-OLS is superior to NPP-VIIRS in modeling CO₂ emissions at all spatial scales, and the bigger the scale, the more evident the advantages of DMSP-OLS. When modeling CO₂ emissions with nighttime light datasets, not only the total amount of lights within a given statistical unit but also the agglomeration degree of lights should be taken into account. Furthermore, the geographical location and socio-economic conditions at the study site, such as gross regional product per capita (GRP per capita), population, and urbanization were shown to have an impact on the regression effect of the nighttime lights-CO₂ emissions model. The regression effect was found to be better at higher latitude and longitude areas with higher GRP per capita and higher urbanization, while population showed little effect on the regression effect of the nighttime lights - CO₂ emissions model. The limitation of this study is that the thresholds of potential factors are unclear and the quantitative guidance is insufficient. Full article

(This article belongs to the Special Issue Recent Advances in Remote Sensing with Nighttime Lights)

▼ Show Figures

Graphical abstract

18 pages, 19495 KiB

Open AccessEditor’s ChoiceArticle

Ongoing Conflict Makes Yemen Dark: From the Perspective of Nighttime Light

by Wei Jiang^1,2

, Guojin He^1,3,4,*, Tengfei Long^1,3,4,* and Huichan Liu^1,3,4

¹ Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China

² University of Chinese Academy of Sciences, Beijing 100049, China

³ Key Laboratory of Earth Observation Hainan Province, Hainan 572029, China

⁴ Sanya Institute of Remote Sensing, Hainan 572029, China

Remote Sens. 2017, 9(8), 798; https://doi.org/10.3390/rs9080798 - 3 Aug 2017

Cited by 59 | Viewed by 16228

Abstract

The Yemen conflict has caused a severe humanitarian crisis. This study aims to evaluate the Yemen crisis by making use of time series nighttime light images from the Suomi National Polar-Orbiting Partnership Visible Infrared Imaging Radiometer Suite sensor (NPP-VIIRS). We develop a process [...] Read more.

The Yemen conflict has caused a severe humanitarian crisis. This study aims to evaluate the Yemen crisis by making use of time series nighttime light images from the Suomi National Polar-Orbiting Partnership Visible Infrared Imaging Radiometer Suite sensor (NPP-VIIRS). We develop a process flow to correct NPP-VIIRS nighttime light from April 2012 to March 2017 by employing the Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) stable nighttime light image. The time series analyses at national scales show that there is a sharp decline in the study period from February 2015 to June 2015 and that the total nighttime light (TNL) of Yemen decreased by 71.60% in response to the decline period. The nighttime light in all provinces also showed the same decline period, which indicates that the Saudi-led airstrikes caused widespread and severe humanitarian crisis in Yemen. Spatial pattern analysis shows that the areas of declining nighttime light are mainly concentrated in Sana’a, Dhamar, Ibb, Ta’izz, ’Adan, Shabwah and Hadramawt. According to the validation with high-resolution images, the decline in nighttime light in Western cities is caused by the damage of urban infrastructure, including airports and construction; moreover, the reason for the decline in nighttime light in eastern cities is the decrease in oil exploration. Using nighttime light remote sensing imagery, our findings suggest that war made Yemen dark and provide support for international humanitarian assistance organizations. Full article

(This article belongs to the Special Issue Recent Advances in Remote Sensing with Nighttime Lights)

▼ Show Figures

Graphical abstract

20 pages, 5700 KiB

Open AccessArticle

Ocean Oil Spill Classification with RADARSAT-2 SAR Based on an Optimized Wavelet Neural Network

by Dongmei Song^1,2,*

, Yaxiong Ding^1,3, Xiaofeng Li⁴

, Biao Zhang⁵

and Mingyu Xu^1,3

¹ School of Geosciences, China University of Petroleum, Qingdao 266580, China

² Laboratory for Marine Mineral Resources, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266071, China

³ Graduate School, China University of Petroleum, Qingdao 266580, China

⁴ GST at National Oceanic and Atmospheric Administration (NOAA)/NESDIS, College Park, MD 20740-3818, USA

⁵ School of Marine Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China

Remote Sens. 2017, 9(8), 799; https://doi.org/10.3390/rs9080799 - 3 Aug 2017

Cited by 54 | Viewed by 9009

Abstract

Oil spill accidents from ship or oil platform cause damage to marine and coastal environment and ecosystems. To monitor such spill events from space, fully polarimetric (Pol-SAR) synthetic aperture radar (SAR) has been greatly used in improving oil spill observation. Aiming to promote [...] Read more.

Oil spill accidents from ship or oil platform cause damage to marine and coastal environment and ecosystems. To monitor such spill events from space, fully polarimetric (Pol-SAR) synthetic aperture radar (SAR) has been greatly used in improving oil spill observation. Aiming to promote ocean oil spill classification accuracy, we developed a new oil spill identification method by combining multiple fully polarimetric SAR features data with an optimized wavelet neural network classifier (WNN). Two sets of RADARSAT-2 fully polarimetric SAR data are applied to test the validity of the developed method. The experimental results show that: (1) the convergence ability of optimized WNN can be enhanced, improving overall classification accuracy of ocean oil spill, in comparison to the classification results based on a common un-optimized WNN classifier; and (2) the joint use of the multiple fully Pol-SAR features as the inputs of the classifier can achieve better classification result than that only with single fully Pol-SAR feature. The developed method can improve classification accuracy by 4.96% and 7.75%, compared with the classification results with un-optimized WNN and only with one single fully polarimetric SAR feature. The classification overall accuracy based on the proposed approach can reach 97.67%. Experimental results have proven that the proposed approach is effective and applicable to classify the ocean oil spill. Full article

(This article belongs to the Special Issue Radar Remote Sensing of Oceans and Coastal Areas)

▼ Show Figures

Graphical abstract

15 pages, 3272 KiB

Open AccessArticle

How Do Aerosol Properties Affect the Temporal Variation of MODIS AOD Bias in Eastern China?

by Minghui Tao¹, Zifeng Wang^1,*, Jinhua Tao^1,*, Liangfu Chen¹, Jun Wang²

, Can Hou¹, Lunche Wang³

, Xiaoguang Xu²

and Hao Zhu¹

¹ State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China

² Department of Chemical and Biochemical Engineering, Center of Global and Regional Environmental Research, University of Iowa, Iowa City, IA 52242, USA

³ Laboratory of Critical Zone Evolution, School of Earth Sciences, China University of Geosciences, Wuhan 430074, China

Remote Sens. 2017, 9(8), 800; https://doi.org/10.3390/rs9080800 - 3 Aug 2017

Cited by 27 | Viewed by 5354

Abstract

The rapid changes of aerosol sources in eastern China during recent decades could bring considerable uncertainties for satellite retrieval algorithms that assume little spatiotemporal variation in aerosol single scattering properties (such as single scattering albedo (SSA) and the size distribution for fine-mode and [...] Read more.

The rapid changes of aerosol sources in eastern China during recent decades could bring considerable uncertainties for satellite retrieval algorithms that assume little spatiotemporal variation in aerosol single scattering properties (such as single scattering albedo (SSA) and the size distribution for fine-mode and coarse mode aerosols) in East Asia. Here, using ground-based observations in six AERONET sites, we characterize typical aerosol optical properties (including their spatiotemporal variation) in eastern China, and evaluate their impacts on Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 aerosol retrieval bias. Both the SSA and fine-mode particle sizes increase from northern to southern China in winter, reflecting the effect of relative humidity on particle size. The SSA is ~0.95 in summer regardless of the AEROENT stations in eastern China, but decreases to 0.85 in polluted winter in northern China. The dominance of larger and highly scattering fine-mode particles in summer also leads to the weakest phase function in the backscattering direction. By focusing on the analysis of high aerosol optical depth (AOD) (>0.4) conditions, we find that the overestimation of the AOD in Dark Target (DT) retrieval is prevalent throughout the whole year, with the bias decreasing from northern China, characterized by a mixture of fine and coarse (dust) particles, to southern China, which is dominated by fine particles. In contrast, Deep Blue (DB) retrieval tends to overestimate the AOD only in fall and winter, and underestimates it in spring and summer. While the retrievals from both the DT and DB algorithms show a reasonable estimation of the fine-mode fraction of AOD, the retrieval bias cannot be attributed to the bias in the prescribed SSA alone, and is more due to the bias in the prescribed scattering phase function (or aerosol size distribution) in both algorithms. In addition, a large yearly change in aerosol single scattering properties leads to correspondingly obvious variations in the time series of MODIS AOD bias. Our results reveal that the aerosol single scattering properties in the MODIS algorithm are insufficient to describe a large variation of aerosol properties in eastern China (especially change of particle size), and can be further improved by using newer AERONET data. Full article

(This article belongs to the Special Issue Remote Sensing of Atmospheric Pollution)

▼ Show Figures

Graphical abstract

19 pages, 2932 KiB

Open AccessArticle

Regional-Scale High Spatial Resolution Mapping of Aboveground Net Primary Productivity (ANPP) from Field Survey and Landsat Data: A Case Study for the Country of Wales

by Emma J. Tebbs^1,2,*

, Clare S. Rowland¹

, Simon M. Smart¹, Lindsay C. Maskell¹ and Lisa R. Norton¹

¹ Centre for Ecology and Hydrology, Lancaster Environment Centre, Library Avenue, Bailrigg, Lancaster LA1 4AP, UK

² Department of Geography, King’s College London, Strand Campus, London WC2R 2LS, UK

Remote Sens. 2017, 9(8), 801; https://doi.org/10.3390/rs9080801 - 4 Aug 2017

Cited by 12 | Viewed by 6832

Abstract

This paper presents an alternative approach for high spatial resolution vegetation productivity mapping at a regional scale, using a combination of Normalised Difference Vegetation Index (NDVI) imagery and widely distributed ground-based Above-ground Net Primary Production (ANPP) estimates. Our method searches through all available [...] Read more.

This paper presents an alternative approach for high spatial resolution vegetation productivity mapping at a regional scale, using a combination of Normalised Difference Vegetation Index (NDVI) imagery and widely distributed ground-based Above-ground Net Primary Production (ANPP) estimates. Our method searches through all available single-date NDVI imagery to identify the images which give the best NDVI–ANPP relationship. The derived relationships are then used to predict ANPP values outside of field survey plots. This approach enables the use of the high spatial resolution (30 m) Landsat 8 sensor, despite its low revisit frequency that is further reduced by cloud cover. This is one of few studies to investigate the NDVI–ANPP relationship across a wide range of temperate habitats and strong relationships were observed (R² = 0.706), which increased when only grasslands were considered (R² = 0.833). The strongest NDVI–ANPP relationships occurred during the spring “green-up” period. A reserved subset of 20% of ground-based ANPP estimates was used for validation and results showed that our method was able to estimate ANPP with a RMSE of 15–21%. This work is important because we demonstrate a general methodological framework for mapping of ANPP from local to regional scales, with the potential to be applied to any temperate ecosystems with a pronounced green up period. Our approach allows spatial extrapolation outside of field survey plots to produce a continuous surface product, useful for capturing spatial patterns and representing small-scale heterogeneity, and well-suited for modelling applications. The data requirements for implementing this approach are also discussed. Full article

(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)

▼ Show Figures

Graphical abstract

30 pages, 8136 KiB

Open AccessReview

A Scientometric Visualization Analysis for Night-Time Light Remote Sensing Research from 1991 to 2016

by Kai Hu¹

, Kunlun Qi², Qingfeng Guan^2,*, Chuanqing Wu^3,4, Jingmin Yu⁵, Yaxian Qing¹, Jie Zheng¹, Huayi Wu¹ and Xi Li¹

¹ The State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China

² Faculty of Information Engineering, China University of Geosciences (Wuhan), Wuhan 430074, China

³ Economics and Management School, Wuhan University, Wuhan 430079, China

⁴ Center for Regional Economics Research, Wuhan University, Wuhan 430079, China

⁵ Changjiang Spatial Information Technology Engineering Co., Ltd, Wuhan 430000, China

Remote Sens. 2017, 9(8), 802; https://doi.org/10.3390/rs9080802 - 4 Aug 2017

Cited by 54 | Viewed by 9144

Abstract

In this paper, we conducted a scientometric analysis based on the Night-Time Light (NTL) remote sensing related literature datasets retrieved from Science Citation Index Expanded and Social Science Citation Index in Web of Science core collection database. Using the methods of bibliometric and [...] Read more.

In this paper, we conducted a scientometric analysis based on the Night-Time Light (NTL) remote sensing related literature datasets retrieved from Science Citation Index Expanded and Social Science Citation Index in Web of Science core collection database. Using the methods of bibliometric and Social Network Analysis (SNA), we drew several conclusions: (1) NTL related studies have become a research hotspot, especially after 2011 when the second generation of NTL satellites, the Suomi National Polar-orbiting Partnership (S-NPP) Satellite with the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor was on board. In the same period, the open-access policy of the long historical dataset of the first generation satellite Defense Meteorological Satellite Program’s Operational Linescan System (DMSP/OLS) started. (2) Most related studies are conducted by authors from USA and China, and the USA takes the lead in the field. We identified the biggest research communities constructed by co-authorships and the related important authors and topics by SNA. (3) By the visualization and analysis of the topic evolution using the co-word and co-cited reference networks, we can clearly see that: the research topics change from hardware oriented studies to more real-world applications; and from the first generation of the satellite DMSP/OLS to the second generation of satellite S-NPP. Although the Day Night Band (DNB) of the S-NPP exhibits higher spatial and radiometric resolution and better calibration conditions than the first generation DMSP/OLS, the longer historical datasets in DMSP/OLS are still important in long-term and large-scale human activity analysis. (4) In line with the intuitive knowledge, the NTL remote sensing related studies display stronger connections (such as interpretive frame, context, and academic purpose) to the social sciences than the general remote sensing discipline. The citation trajectories are visualized based on the dual-maps, thus the research preferences for combining the environmental, ecological, economic, and political science disciplines are clearly exhibited. Overall, the picture of the NTL remote sensing research is presented from the scientist-level, topic-level, and discipline-level interactions. Based on these analyses, we also discuss the possible trends in the future work, such as combining NTL studies with social science research and social media data. Full article

(This article belongs to the Special Issue Recent Advances in Remote Sensing with Nighttime Lights)

▼ Show Figures

Graphical abstract

18 pages, 34609 KiB

Open AccessArticle

Effect of Label Noise on the Machine-Learned Classification of Earthquake Damage

by Jared Frank^1,2, Umaa Rebbapragada^2,*

, James Bialas³, Thomas Oommen³

and Timothy C. Havens³

¹ Department of Computer Science, Cornell University, 402 Gates Hall, Ithaca, NY 14850, USA

² Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Drive, Pasadena, CA 91109, USA

³ Michigan Technological University, 1400 Townsend Drive, Houghton, MI 49931, USA

Remote Sens. 2017, 9(8), 803; https://doi.org/10.3390/rs9080803 - 4 Aug 2017

Cited by 32 | Viewed by 7560

Abstract

Automated classification of earthquake damage in remotely-sensed imagery using machine learning techniques depends on training data, or data examples that are labeled correctly by a human expert as containing damage or not. Mislabeled training data are a major source of classifier error due [...] Read more.

Automated classification of earthquake damage in remotely-sensed imagery using machine learning techniques depends on training data, or data examples that are labeled correctly by a human expert as containing damage or not. Mislabeled training data are a major source of classifier error due to the use of imprecise digital labeling tools and crowdsourced volunteers who are not adequately trained on or invested in the task. The spatial nature of remote sensing classification leads to the consistent mislabeling of classes that occur in close proximity to rubble, which is a major byproduct of earthquake damage in urban areas. In this study, we look at how mislabeled training data, or label noise, impact the quality of rubble classifiers operating on high-resolution remotely-sensed images. We first study how label noise dependent on geospatial proximity, or geospatial label noise, compares to standard random noise. Our study shows that classifiers that are robust to random noise are more susceptible to geospatial label noise. We then compare the effects of label noise on both pixel- and object-based remote sensing classification paradigms. While object-based classifiers are known to outperform their pixel-based counterparts, this study demonstrates that they are more susceptible to geospatial label noise. We also introduce a new labeling tool to enhance precision and image coverage. This work has important implications for the Sendai framework as autonomous damage classification will ensure rapid disaster assessment and contribute to the minimization of disaster risk. Full article

(This article belongs to the Special Issue Earth Observation to Support Disaster Preparedness and Disaster Risk Management)

▼ Show Figures

Graphical abstract

19 pages, 6874 KiB

Open AccessArticle

Image Fusion-Based Land Cover Change Detection Using Multi-Temporal High-Resolution Satellite Images

by Biao Wang^1,2,*

, Jaewan Choi³, Seokeun Choi³, Soungki Lee³

, Penghai Wu¹

and Yan Gao¹

¹ School of Resources and Environmental Engineering, Anhui University, Hefei 230601, Anhui, China

² Anhui Key Laboratory of Smart City and Geographical Condition Monitoring, Hefei 230031, Anhui, China

³ School of Civil Engineering, Chungbuk National University, Cheongju 361763, Chungbuk, Korea

Remote Sens. 2017, 9(8), 804; https://doi.org/10.3390/rs9080804 - 5 Aug 2017

Cited by 34 | Viewed by 8721

Abstract

Change detection is usually treated as a problem of explicitly detecting land cover transitions in satellite images obtained at different times, and helps with emergency response and government management. This study presents an unsupervised change detection method based on the image fusion of [...] Read more.

Change detection is usually treated as a problem of explicitly detecting land cover transitions in satellite images obtained at different times, and helps with emergency response and government management. This study presents an unsupervised change detection method based on the image fusion of multi-temporal images. The main objective of this study is to improve the accuracy of unsupervised change detection from high-resolution multi-temporal images. Our method effectively reduces change detection errors, since spatial displacement and spectral differences between multi-temporal images are evaluated. To this end, a total of four cross-fused images are generated with multi-temporal images, and the iteratively reweighted multivariate alteration detection (IR-MAD) method—a measure for the spectral distortion of change information—is applied to the fused images. In this experiment, the land cover change maps were extracted using multi-temporal IKONOS-2, WorldView-3, and GF-1 satellite images. The effectiveness of the proposed method compared with other unsupervised change detection methods is demonstrated through experimentation. The proposed method achieved an overall accuracy of 80.51% and 97.87% for cases 1 and 2, respectively. Moreover, the proposed method performed better when differentiating the water area from the vegetation area compared to the existing change detection methods. Although the water area beneath moderate and sparse vegetation canopy was captured, vegetation cover and paved regions of the water body were the main sources of omission error, and commission errors occurred primarily in pixels of mixed land use and along the water body edge. Nevertheless, the proposed method, in conjunction with high-resolution satellite imagery, offers a robust and flexible approach to land cover change mapping that requires no ancillary data for rapid implementation. Full article

▼ Show Figures

Graphical abstract

13 pages, 2388 KiB

Open AccessTechnical Note

LiDAR and Orthophoto Synergy to optimize Object-Based Landscape Change: Analysis of an Active Landslide

by Martijn Kamps^*, Willem Bouten

and Arie, C. Seijmonsbergen

Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam, 1090 GE Amsterdam, The Netherlands

Remote Sens. 2017, 9(8), 805; https://doi.org/10.3390/rs9080805 - 5 Aug 2017

Cited by 17 | Viewed by 9471

Abstract

Active landslides have three major effects on landscapes: (1) land cover change, (2) topographical change, and (3) above ground biomass change. Data derived from multi-temporal Light Detection and Ranging technology (LiDAR) are used in combination with multi-temporal orthophotos to quantify these changes between [...] Read more.

Active landslides have three major effects on landscapes: (1) land cover change, (2) topographical change, and (3) above ground biomass change. Data derived from multi-temporal Light Detection and Ranging technology (LiDAR) are used in combination with multi-temporal orthophotos to quantify these changes between 2006 and 2012, caused by an active deep-seated landslide near the village of Doren in Austria. Land-cover is classified by applying membership-based classification and contextual improvements based on the synergy of orthophotos and LiDAR-based elevation data. Topographical change is calculated by differencing of LiDAR derived digital terrain models. The above ground biomass is quantified by applying a local-maximum algorithm for tree top detection, in combination with allometric equations. The land cover classification accuracies were improved from 65% (using only LiDAR) and 76% (using only orthophotos) to 90% (using data synergy) for 2006. A similar increase from respectively 64% and 75% to 91% was established for 2012. The increased accuracies demonstrate the effectiveness of using data synergy of LiDAR and orthophotos using object-based image analysis to quantify landscape changes, caused by an active landslide. The method has great potential to be transferred to larger areas for use in landscape change analyses. Full article

(This article belongs to the Special Issue Remote Sensing of Landslides)

▼ Show Figures

Graphical abstract

17 pages, 32407 KiB

Open AccessArticle

Automatic Cloud and Shadow Detection in Optical Satellite Imagery Without Using Thermal Bands—Application to Suomi NPP VIIRS Images over Fennoscandia

by Eija Parmes^*, Yrjö Rauste, Matthieu Molinier

, Kaj Andersson^† and Lauri Seitsonen

¹ VTT Technical Research Centre of Finland Ltd., Remote Sensing Team, PL 1000, FI-02044 VTT, Finland

^† Current address: Independent Consultant, Helsinki, Finland.

Remote Sens. 2017, 9(8), 806; https://doi.org/10.3390/rs9080806 - 5 Aug 2017

Cited by 22 | Viewed by 10494

Abstract

In land monitoring applications, clouds and shadows are considered noise that should be removed as automatically and quickly as possible, before further analysis. This paper presents a method to detect clouds and shadows in Suomi NPP satellite’s VIIRS (Visible Infrared Imaging Radiometer Suite) [...] Read more.

In land monitoring applications, clouds and shadows are considered noise that should be removed as automatically and quickly as possible, before further analysis. This paper presents a method to detect clouds and shadows in Suomi NPP satellite’s VIIRS (Visible Infrared Imaging Radiometer Suite) satellite images. The proposed cloud and shadow detection method has two distinct features when compared to many other methods. First, the method does not use the thermal bands and can thus be applied to other sensors which do not contain thermal channels, such as Sentinel-2 data. Secondly, the method uses the ratio between blue and green reflectance to detect shadows. Seven hundred and forty-seven VIIRS images over Fennoscandia from August 2014 to April 2016 were processed to train and develop the method. Twenty four points from every tenth of the images were used in accuracy assessment. These 1752 points were interpreted visually to cloud, cloud shadow and clear classes, then compared to the output of the cloud and shadow detection. The comparison on VIIRS images showed 94.2% correct detection rates and 11.1% false alarms for clouds, and respectively 36.1% and 82.7% for shadows. The results on cloud detection were similar to state-of-the-art methods. Shadows showed correctly on the northern edge of the clouds, but many shadows were wrongly assigned to other classes in some cases (e.g., to water class on lake and forest boundary, or with shadows over cloud). This may be due to the low spatial resolution of VIIRS images, where shadows are only a few pixels wide and contain lots of mixed pixels. Full article

(This article belongs to the Special Issue Atmospheric Correction of Remote Sensing Data)

▼ Show Figures

Graphical abstract

22 pages, 6692 KiB

Open AccessArticle

Automated Quantification of Surface Water Inundation in Wetlands Using Optical Satellite Imagery

by Ben DeVries^1,*

, Chengquan Huang¹, Megan W. Lang², John W. Jones³, Wenli Huang¹

, Irena F. Creed⁴

and Mark L. Carroll^5,6

¹ Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA

² U.S. Fish and Wildlife Service, National Wetland Inventory, Falls Church, VA 22041 USA

³ U.S. Geological Survey, Eastern Geographic Science Center, Reston, VA 20192-000, USA

⁴ Department of Biology, University of Western Ontario, London, ON N6A 3K7, Canada

⁵ Biospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA

⁶ Science Systems and Applications Inc., Lanham, MD 20706, USA

Remote Sens. 2017, 9(8), 807; https://doi.org/10.3390/rs9080807 - 7 Aug 2017

Cited by 93 | Viewed by 12650

Abstract

We present a fully automated and scalable algorithm for quantifying surface water inundation in wetlands. Requiring no external training data, our algorithm estimates sub-pixel water fraction (SWF) over large areas and long time periods using Landsat data. We tested our SWF algorithm over [...] Read more.

We present a fully automated and scalable algorithm for quantifying surface water inundation in wetlands. Requiring no external training data, our algorithm estimates sub-pixel water fraction (SWF) over large areas and long time periods using Landsat data. We tested our SWF algorithm over three wetland sites across North America, including the Prairie Pothole Region, the Delmarva Peninsula and the Everglades, representing a gradient of inundation and vegetation conditions. We estimated SWF at 30-m resolution with accuracies ranging from a normalized root-mean-square-error of 0.11 to 0.19 when compared with various high-resolution ground and airborne datasets. SWF estimates were more sensitive to subtle inundated features compared to previously published surface water datasets, accurately depicting water bodies, large heterogeneously inundated surfaces, narrow water courses and canopy-covered water features. Despite this enhanced sensitivity, several sources of errors affected SWF estimates, including emergent or floating vegetation and forest canopies, shadows from topographic features, urban structures and unmasked clouds. The automated algorithm described in this article allows for the production of high temporal resolution wetland inundation data products to support a broad range of applications. Full article

(This article belongs to the Special Issue Remote Sensing for Flood Mapping and Monitoring of Flood Dynamics)

▼ Show Figures

Graphical abstract

13 pages, 2602 KiB

Open AccessArticle

Modeling the Effect of the Spatial Pattern of Airborne Lidar Returns on the Prediction and the Uncertainty of Timber Merchantable Volume

by Sarah Yoga^1,*, Jean Bégin¹

, Benoît St-Onge² and Martin Riopel¹

¹ Department of Wood and Forest Sciences, Université Laval, Quebec City, QC G1V 0A6, Canada

² Department of Geography, Université du Québec à Montréal, Montréal, QC H3C 3P8, Canada

Remote Sens. 2017, 9(8), 808; https://doi.org/10.3390/rs9080808 - 6 Aug 2017

Cited by 5 | Viewed by 4081

Abstract

Lidar data are regularly used to characterize forest structures. In this study, we determine the effects of three lidar attributes (density, spacing, scanning angle) on the accuracy and the uncertainty of timber merchantable volume estimates of balsam fir stands (Abies balsamea (L.) [...] Read more.

Lidar data are regularly used to characterize forest structures. In this study, we determine the effects of three lidar attributes (density, spacing, scanning angle) on the accuracy and the uncertainty of timber merchantable volume estimates of balsam fir stands (Abies balsamea (L.) Mill.) in eastern Canada. We used lidar point clouds to compute predictor variables of the merchantable volume in a nonlinear model. The best model included the mean height of first returns, the proportion of first returns below 2 m and the canopy surface roughness index. Our analysis shows a high correlation between lidar and field data of 119 plots (pseudo-R² = 0.91), however, residuals were heteroscedastic. More precise parameter estimates were obtained by adding to the model a variance function of variables describing the mean height of returns and the skewness of the area distribution of triangulated lidar returns. The residual standard deviation was better estimated (3.7 m³ ha⁻¹ multiplied by the variance function versus 28.0 m³ ha⁻¹). We found no effect of density on the predictions (p-value = 0.74). This suggests that the height and the spatial pattern of returns, rather than the density, should be considered to better assess the uncertainty of merchantable volume estimates. Full article

(This article belongs to the Section Forest Remote Sensing)

▼ Show Figures

Figure 1

19 pages, 4563 KiB

Open AccessArticle

Towards an Operational Use of Geophysics for Archaeology in Henan (China): Methodological Approach and Results in Kaifeng

by Nicola Masini^1,2,*

, Luigi Capozzoli³

, Panpan Chen^4,*, Fulong Chen^2,5, Gerardo Romano⁶

, Peng Lu⁴, Panpan Tang², Maria Sileo¹, Qifeng Ge⁷ and Rosa Lasaponara³

¹ Institute for Archaeological and Architectural Heritage, National Research Council C.da Santa Loja, 85050 Tito Scalo (PZ), Italy

² Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, No. 9 Dengzhuang South Road, Haidian District, Beijing 100094, China

³ Institute of Methodologies for Environmental Analysis, National Research Council C.da Santa Loja, 85050 Tito Scalo (PZ), Italy

⁴ Institute of Geography, Henan Academy of Sciences, Zhengzhou 450052, China

⁵ International Centre on Space Technologies for Natural and Cultural Heritage under the Auspices of UNESCO, No. 9 Dengzhuang South Road, Haidian District, Beijing 100094, China

⁶ University of Bari, Department of Geoscience and Geoenvironment 70125 Bari, Italy

⁷ Kaifeng Institute of Archaeology CASS, Henan 475000, China

Remote Sens. 2017, 9(8), 809; https://doi.org/10.3390/rs9080809 - 6 Aug 2017

Cited by 53 | Viewed by 7019

Abstract

One of the major issues in buried archeological sites especially if characterized by intense human activity, complex structures, and several constructive phases, is: to what depth conduct the excavation? The answer depends on a number of factors, among these one of the most [...] Read more.

One of the major issues in buried archeological sites especially if characterized by intense human activity, complex structures, and several constructive phases, is: to what depth conduct the excavation? The answer depends on a number of factors, among these one of the most important is the a priori and reliable knowledge of what the subsoil can preserve. To this end, geophysics (if used in strong synergy with archaeological research) can help in the planning of time, depth, and modes of excavation also when the physical characteristics of the remains and their matrix are not ideal for archaeo-geophysical applications. This is the case of a great part of the archaeological sites in Henan, the cradle of the most important cultures in China and the seat of several capitals for more than two millennia. There, the high depth of buried remains covered by alluvial deposits and the building materials, mainly made by rammed earth, did not favor the use of geophysics. In this paper, we present and discuss the GPR and ERT prospection we conducted in Kaifeng (Henan, China), nearby a gate of the city walls dated to the Northern Song Dynasty. The integration of GPR and ERT provided useful information for the identification and characterization of archaeological remains buried at different depths. Actually, each geophysical technique, GPR frequency (used for the data acquisition) as well as each way to analyze and visualize the results (from radargrams to time slice) only provided partial information of little use if alone. The integration of the diverse techniques, data processing and visualization enabled us to optimize the penetration capability, the resolution for the detection of archaeological features and their interpretation. Finally, the results obtained from the GPR and ERT surveys were correlated with archaeological stratigraphy, available nearby the investigated area. This enabled us to further improve the interpretation of results from GPR and ERT survey and also to date the anthropogenic layers from Qing to Yuan Dynasty. Full article

(This article belongs to the Special Issue Radar Systems for the Societal Challenges)

▼ Show Figures

Graphical abstract

18 pages, 5435 KiB

Open AccessArticle

Precise Orbit Determination of BeiDou Satellites with Contributions from Chinese National Continuous Operating Reference Stations

by Ming Chen^1,2,4, Yang Liu^3,4,*, Jiming Guo^1,*

, Weiwei Song^3,4, Peng Zhang^2,4, Junli Wu^2,4 and Di Zhang¹

² National Geomatics Center of China, Beijing 100830, China

³ GNSS Research Center, Wuhan University, Wuhan 430079, China

⁴ Key Laboratory of Navigation & Location Based Service, National Administration of Survey, Mapping and Geoinformation, Beijing 100830, China

¹ School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China

Remote Sens. 2017, 9(8), 810; https://doi.org/10.3390/rs9080810 - 6 Aug 2017

Cited by 10 | Viewed by 7492

Abstract

The precise orbit determination (POD) for BeiDou satellites is usually limited by the insufficient quantity and poor distribution of ground tracking stations. To cope with this problem, this study used the GPS and BeiDou joint POD method based on Chinese national continuous operating [...] Read more.

The precise orbit determination (POD) for BeiDou satellites is usually limited by the insufficient quantity and poor distribution of ground tracking stations. To cope with this problem, this study used the GPS and BeiDou joint POD method based on Chinese national continuous operating reference stations (CNCORS) and IGS/MGEX stations. The results show that the 3D RMS of the differences of overlapping arcs is better than 22 cm for geostationary orbit (GEO) satellites and better than 10 cm for inclined geosynchronous orbit (IGSO) and medium earth orbit (MEO) satellites. The radial RMS is better than 2 cm for all three types of BeiDou satellites. The results of satellite laser ranging (SLR) residuals show that the RMS of the IGSO and MEO satellites is better than 5 cm, whereas the GEO satellite has a systematic bias. This study investigates the contributions of CNCORS to the POD of BeiDou satellites. The results show that after the incorporation of CNCORS, the precision of overlapping arcs of the GEO, IGSO, and MEO satellites is improved by 15.5%, 57.5%, and 5.3%, respectively. In accordance with the improvement in the precision of overlapping arcs, the accuracy of the IGSO and MEO satellites assessed by the SLR is improved by 30.1% and 4.8%, respectively. The computation results and analysis demonstrate that the inclusion of CNCORS yields the biggest contribution in the improvement of orbit accuracy for IGSO satellites, when compared to GEO satellites, while the orbit improvement for MEO satellites is the lowest due to their global coverage. Full article

(This article belongs to the Special Issue Multi-Constellation Global Navigation Satellite Systems: Methods and Applications)

▼ Show Figures

Graphical abstract

19 pages, 4516 KiB

Open AccessArticle

Radiometric Cross-Calibration of GF-4 PMS Sensor Based on Assimilation of Landsat-8 OLI Images

by Yepei Chen¹, Kaimin Sun^1,*

, Deren Li¹, Ting Bai¹ and Chengquan Huang²

¹ State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China

² Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA

Remote Sens. 2017, 9(8), 811; https://doi.org/10.3390/rs9080811 - 9 Aug 2017

Cited by 34 | Viewed by 5114

Abstract

Earth observation data obtained from remote sensors must undergo radiometric calibration before use in quantitative applications. However, the large view angles of the panchromatic multispectral sensor (PMS) aboard the GF-4 satellite pose challenges for cross-calibration due to the effects of atmospheric radiation transfer [...] Read more.

Earth observation data obtained from remote sensors must undergo radiometric calibration before use in quantitative applications. However, the large view angles of the panchromatic multispectral sensor (PMS) aboard the GF-4 satellite pose challenges for cross-calibration due to the effects of atmospheric radiation transfer and the bidirectional reflectance distribution function (BRDF). To address this problem, this paper introduces a novel cross-calibration method based on data assimilation considering cross-calibration as an optimal approximation problem. The GF-4 PMS was cross-calibrated with the well-calibrated Landsat-8 Operational Land Imager (OLI) as the reference sensor. In order to correct unequal bidirectional reflection effects, an adjustment factor for the BRDF was established, making complex models unnecessary. The proposed method employed the Shuffled Complex Evolution-University of Arizona (SCE-UA) algorithm to find the optimal calibration coefficients and BRDF adjustment factor through an iterative process. The validation results revealed a surface reflectance error of <5% for the new cross-calibration coefficients. The accuracy of calibration coefficients were significantly improved when compared to the officially published coefficients as well as those derived using conventional methods. The uncertainty produced by the proposed method was less than 7%, meeting the demands for future quantitative applications and research. This method is also applicable to other sensors with large view angles. Full article

▼ Show Figures

Graphical abstract

19 pages, 9313 KiB

Open AccessArticle

Novel Decomposition Scheme for Characterizing Urban Air Quality with MODIS

by Prakhar Misra^*, Aya Fujikawa and Wataru Takeuchi

¹ Department of Civil Engineering, Institute of Industrial Science, The University of Tokyo, Meguro, Tokyo 153-8505, Japan

^† Current address: Institute of Industrial Science, The University of Tokyo, Tokyo 153-8505, Japan

Remote Sens. 2017, 9(8), 812; https://doi.org/10.3390/rs9080812 - 7 Aug 2017

Cited by 17 | Viewed by 5637

Abstract

Urban air pollution is one of the most widespread global sustainability problems. Previous research has studied growth or fall of particulate matter (PM) levels using on-ground monitoring stations in urban regions. However, studying this worldwide is difficult because most cities do not have [...] Read more.

Urban air pollution is one of the most widespread global sustainability problems. Previous research has studied growth or fall of particulate matter (PM) levels using on-ground monitoring stations in urban regions. However, studying this worldwide is difficult because most cities do not have sufficient infrastructure to monitor air quality. Thus, satellite data is increasingly being employed to solve this limitation. In this paper, we use 16 years (2001–2016) of aerosol optical depth (AOD) and Angstrom exponent (

α

) datasets, retrieved from MODIS (Moderate Resolution Imaging Spectroradiometer) sensors on the National Aeronautics and Space Administration’s (NASA) Terra satellite to study air quality over 60 locations globally. We propose a novel technique, called AirRGB decomposition, to characterize urban air quality by decomposing AOD and

α

retrievals into ‘components’ of three distinct scenarios. In the AirRGB decomposition method, using AOD and

α

dataset three scenarios were investigated: ‘R’—high

α

and high AOD, ‘G’—high

α

and low AOD, and ‘B’—low

α

and low AOD values. These scenarios were mapped and quantified over a triangular red, green and blue color scale. This visualization easily segregates regions having a high concentration of industrial aerosol from only natural aerosols. Our analysis indicates that a sharp divide exists between North American and European cities and Asian cities in terms of baseline pollution and slopes of R and G trends. We found that while pollution in cities in China has started to decrease (e.g., since 2011 for Beijing), it continues to increase in South Asia and Southeast Asia. e.g., R offset of Beijing and New Delhi was 54.98 and 50.43 respectively but R slope was −0.04 and 0.08 respectively. High offset (≥45) and slope (≥0.025) of B for New York, Tokyo, Sydney and Sao Paolo shows that they have clean air, which is still getting better. Full article

(This article belongs to the Section Atmospheric Remote Sensing)

▼ Show Figures

Graphical abstract

21 pages, 28699 KiB

Open AccessArticle

On-Board GNSS/IMU Assisted Feature Extraction and Matching for Oblique UAV Images

by San Jiang

and Wanshou Jiang^*

State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430072, China

Remote Sens. 2017, 9(8), 813; https://doi.org/10.3390/rs9080813 - 7 Aug 2017

Cited by 47 | Viewed by 7965

Abstract

Feature extraction and matching is a crucial task in the fields of computer vision and photogrammetry. Even though wide researches have been reported, some issues are still existing for oblique images. This paper exploits the use of on-board GNSS/IMU (Global Navigation Satellite System/Inertial [...] Read more.

Feature extraction and matching is a crucial task in the fields of computer vision and photogrammetry. Even though wide researches have been reported, some issues are still existing for oblique images. This paper exploits the use of on-board GNSS/IMU (Global Navigation Satellite System/Inertial Measurement Unit) data to achieve efficient and reliable feature extraction and matching for oblique unmanned aerial vehicle (UAV) images. Firstly, rough POS (Positioning and Orientation System) is calculated for each image with cooperation of on-board GNSS/IMU data and camera installation angles, which enables image rectification and footprint calculation. Secondly, two robust strategies, including the geometric rectification and tile strategy, are considered to address the issues caused by perspective deformations and to relieve the side-effects of image down-sampling. According to the results of individual performance evaluation, four combinations of these two strategies are designed and comprehensively compared in BA (Bundle Adjustment) experiments by using a real oblique UAV dataset. The results reported in this paper demonstrate that the solution with the tiling strategy is superior to the other solutions in terms of efficiency, completeness and accuracy. For feature extraction and matching of oblique UAV images, it is proposed to combine the tiling strategy with existing workflows to achieve an efficient and reliable solution. Full article

(This article belongs to the Special Issue Multi-Constellation Global Navigation Satellite Systems: Methods and Applications)

▼ Show Figures

Graphical abstract

25 pages, 5516 KiB

Open AccessArticle

Atmospheric Correction of Multi-Spectral Littoral Images Using a PHOTONS/AERONET-Based Regional Aerosol Model

by Driss Bru¹, Bertrand Lubac^1,*

, Cassandra Normandin¹, Arthur Robinet¹, Michel Leconte¹, Olivier Hagolle², Nadège Martiny³ and Cédric Jamet⁴

¹ Univ. Bordeaux, UMR 5805, EPOC, Environnements et Paléoenvironnements Océaniques et Continentaux, 33615 Pessac, France

² CNES, CNRS, IRD, Univ. Toulouse, UMR 5126, CESBIO, Centre d’Etudes Spatiales de la BIOsphère, 31401 Toulouse, France

³ Univ. Bourgogne, UMR 6282, Biogéosciences, 21000 Dijon, France

⁴ Univ. Littoral Cote d’Opale, Univ. Lille, CNRS, UMR 8187, LOG, Laboratoire d’Océanologie et de Géosciences, 62930 Wimereux, France

Remote Sens. 2017, 9(8), 814; https://doi.org/10.3390/rs9080814 - 8 Aug 2017

Cited by 10 | Viewed by 6133

Abstract

Spatial resolution is the main instrumental requirement for the multi-spectral optical space missions that address the scientific issues of marine coastal systems. This spatial resolution should be at least decametric. Aquatic color data processing associated with these environments requires specific atmospheric corrections (AC) [...] Read more.

Spatial resolution is the main instrumental requirement for the multi-spectral optical space missions that address the scientific issues of marine coastal systems. This spatial resolution should be at least decametric. Aquatic color data processing associated with these environments requires specific atmospheric corrections (AC) suitable for the spectral characteristics of high spatial resolution sensors (HRS) as well as the high range of atmospheric and marine optical properties. The objective of the present study is to develop and demonstrate the potential of a ground-based AC approach adaptable to any HRS for regional monitoring and security of littoral systems. The in Situ-based Atmospheric CORrection (SACOR) algorithm is based on simulations provided by a Successive Order of Scattering code (SOS), which is constrained by a simple regional aerosol particle model (RAM). This RAM is defined from the mixture of a standard tropospheric and maritime aerosol type. The RAM is derived from the following two processes. The first process involved the analysis of a 6-year data set composed of aerosol optical and microphysical properties acquired through the ground-based PHOTONS/AERONET network located at Arcachon (France). The second process was related to aerosol climatology using the NOAA hybrid single-particle Lagrangian integrated trajectory (HYSPLIT) model. Results show that aerosols have a bimodal particle size distribution regardless of the season and are mainly represented by a mixed coastal continental type. Furthermore, the results indicate that aerosols originate from both the Atlantic Ocean (53.6%) and Continental Europe (46.4%). Based on these results, absorbing biomass burning, urban-industrial and desert dust particles have not been considered although they represent on average 19% of the occurrences. This represents the main current limitation of the RAM. An assessment of the performances of SACOR is then performed by inter-comparing the water-leaving reflectance (

ρ_{w}

) retrievals with three different AC methods (ACOLITE, MACCS and 6SV using three different standard aerosol types) using match-ups (N = 8) composed of Landsat-8/Operational Land Imager (OLI) scenes and field radiometric measurements. Results indicate consistency with the SWIR-based ACOLITE method, which shows the best performance, except in the green channel where SACOR matches well with the in-situ data (relative error of 7%). In conclusion, the study demonstrates the high potential of the SACOR approach for the retrieval of

ρ_{w}

. In the future, the method could be improved by using an adaptive aerosol model, which may select the most relevant local aerosol model following the origin of the atmospheric air mass, and could be applied to the latest HRS (Sentinel-2/MSI, SPOT6-7, Pleiades 1A-1B). Full article

(This article belongs to the Special Issue Atmospheric Correction of Remote Sensing Data)

▼ Show Figures

Graphical abstract

23 pages, 8746 KiB

Open AccessArticle

Validation of Automatically Generated Global and Regional Cropland Data Sets: The Case of Tanzania

by Juan Carlos Laso Bayas^1,*

, Linda See¹

, Christoph Perger¹

, Christina Justice², Catherine Nakalembe², Jan Dempewolf² and Steffen Fritz¹

¹ Ecosystems Services and Management Program, International Institute for Applied Systems Analysis (IIASA), Laxenburg A-2361, Austria

² Department of Geographical Sciences, University of Maryland, College Park, Maryland, MD 20742, USA

Remote Sens. 2017, 9(8), 815; https://doi.org/10.3390/rs9080815 - 9 Aug 2017

Cited by 15 | Viewed by 5076

Abstract

There is a need to validate existing global cropland maps since they are used for different purposes including agricultural monitoring and assessment. In this paper we validate three recent global products (ESA-CCI, GlobeLand30, FROM-GC) and one regional product (Tanzania Land Cover 2010 Scheme [...] Read more.

There is a need to validate existing global cropland maps since they are used for different purposes including agricultural monitoring and assessment. In this paper we validate three recent global products (ESA-CCI, GlobeLand30, FROM-GC) and one regional product (Tanzania Land Cover 2010 Scheme II) using a validation data set that was collected by students through the Geo-Wiki tool. The ultimate aim was to understand the usefulness of these products for agricultural monitoring. Data were collected wall-to-wall for Kilosa district and for a sample across Tanzania. The results show that the amount of and spatial extent of cropland in the different products differs considerably from 8% to 42% for Tanzania, with similar values for Kilosa district. The agreement of the validation data with the four different products varied between 36% and 54% and highlighted that cropland is overestimated by the ESA-CCI and underestimated by FROM-GC. The validation data were also analyzed for consistency between the student interpreters and also compared with a sample interpreted by five experts for quality assurance. Regarding consistency between the students, there was more than 80% agreement if one difference in cropland category was considered (e.g., between low and medium cropland) while most of the confusion with the experts was also within one category difference. In addition to the validation of current cropland products, the data set collected by the students also has potential value as a training set for improving future cropland products. Full article

(This article belongs to the Special Issue Validation on Global Land Cover Datasets)

▼ Show Figures

Figure 1

13 pages, 4294 KiB

Open AccessArticle

Mapping Aboveground Carbon in Oil Palm Plantations Using LiDAR: A Comparison of Tree-Centric versus Area-Based Approaches

by Matheus H. Nunes¹, Robert M. Ewers², Edgar C. Turner³ and David A. Coomes^1,*

¹ Forest Ecology and Conservation Group, Department of Plant Sciences, University of Cambridge, Downing Street, Cambridge CB2 3EA, UK

² Silwood Park Campus, Imperial College London, Buckhusrt Road, Ascot SL5 7PY, UK

³ Department of Zoology, University of Cambridge, Downing Street, Cambridge CB2 3EJ, UK

Remote Sens. 2017, 9(8), 816; https://doi.org/10.3390/rs9080816 - 9 Aug 2017

Cited by 23 | Viewed by 8415

Abstract

Southeast Asia is the epicentre of world palm oil production. Plantations in Malaysia have increased 150% in area within the last decade, mostly at the expense of tropical forests. Maps of the aboveground carbon density (ACD) of vegetation generated by remote sensing technologies, [...] Read more.

Southeast Asia is the epicentre of world palm oil production. Plantations in Malaysia have increased 150% in area within the last decade, mostly at the expense of tropical forests. Maps of the aboveground carbon density (ACD) of vegetation generated by remote sensing technologies, such as airborne LiDAR, are vital for quantifying the effects of land use change for greenhouse gas emissions, and many papers have developed methods for mapping forests. However, nobody has yet mapped oil palm ACD from LiDAR. The development of carbon prediction models would open doors to remote monitoring of plantations as part of efforts to make the industry more environmentally sustainable. This paper compares the performance of tree-centric and area-based approaches to mapping ACD in oil palm plantations. We find that an area-based approach gave more accurate estimates of carbon density than tree-centric methods and that the most accurate estimation model includes LiDAR measurements of top-of-canopy height and canopy cover. We show that tree crown segmentation is sensitive to crown density, resulting in less accurate tree density and ACD predictions, but argue that tree-centric approach can nevertheless be useful for monitoring purposes, providing a method to detect, extract and count oil palm trees automatically from images. Full article

(This article belongs to the Section Forest Remote Sensing)

▼ Show Figures

Figure 1

21 pages, 12094 KiB

Open AccessArticle

Estimation of Satellite-Based SO₄²⁻ and NH₄⁺ Composition of Ambient Fine Particulate Matter over China Using Chemical Transport Model

by Yidan Si^1,2, Shenshen Li^1,*, Liangfu Chen^1,*, Chao Yu¹ and Wende Zhu³

¹ State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China

² University of the Chinese Academy of Sciences, Beijing 100049, China

³ School of Computer and Information Engineering, Henan University, Kaifeng 475004, China

Remote Sens. 2017, 9(8), 817; https://doi.org/10.3390/rs9080817 - 9 Aug 2017

Cited by 11 | Viewed by 5953

Abstract

Epidemiologic and health impact studies have examined the chemical composition of ambient PM_2.5 in China but have been constrained by the paucity of long-term ground measurements. Using the GEOS-Chem chemical transport model and satellite-derived PM_2.5 data, sulfate and ammonium levels were [...] Read more.

Epidemiologic and health impact studies have examined the chemical composition of ambient PM_2.5 in China but have been constrained by the paucity of long-term ground measurements. Using the GEOS-Chem chemical transport model and satellite-derived PM_2.5 data, sulfate and ammonium levels were estimated over China from 2004 to 2014. A comparison of the satellite-estimated dataset with model simulations based on ground measurements obtained from the literature indicated our results are more accurate. Using satellite-derived PM_2.5 data with a spatial resolution of 0.1 × 0.1°, we further presented finer satellite-estimated sulfate and ammonium concentrations in anthropogenic polluted regions, including the NCP (the North China Plain), the SCB (the Sichuan Basin) and the PRD (the Pearl River Delta). Linear regression results obtained on a national scale yielded an r value of 0.62, NMB of −35.9%, NME of 48.2%, ARB_50% of 53.68% for sulfate and an r value of 0.63, slope of 0.67, and intercept of 5.14 for ammonium. In typical regions, the satellite-derived dataset was significantly robust. Based on the satellite-derived dataset, the spatial-temporal variation of 11-year annual average satellite-derived SO₄²⁻ and NH₄⁺ concentrations and time series of monthly average concentrations were also investigated. On a national scale, both exhibited a downward trend each year between 2004 and 2014 (SO₄²⁻: −0.61%; NH₄⁺: −0.21%), large values were mainly concentrated in the NCP and SCB. For regions captured at a finer resolution, the inter-annual variation trends presented a positive trend over the periods 2004–2007 and 2008–2011, followed by a negative trend over the period 2012–2014, and sulfate concentrations varied appreciably. Moreover, the seasonal distributions of the 11-year satellite-derived dataset over China were presented. The distribution of both sulfate and ammonium concentrations exhibited seasonal characteristics, with the seasonal concentrations ranking as follows: winter > summer > autumn > spring. High concentrations of these species were concentrated in the NCP and SCB, originating from coal-fired power plants and agricultural activities, respectively. Efforts to reduce sulfur dioxide (SO₂) emissions have yielded remarkable results since the government has adopted stricter control measures in recent years. Moreover, ammonia emissions should be controlled while reducing the concentration of sulfur, nitrogen and particulate matter. This study provides an assessment of the population’s exposure to certain chemical components. Full article

(This article belongs to the Special Issue Aerosol Remote Sensing)

▼ Show Figures

Graphical abstract

5 pages, 743 KiB

Open AccessEditorial

Water Optics and Water Colour Remote Sensing

by Yunlin Zhang^1,*

, Claudia Giardino²

and Linhai Li³

¹ Taihu Laboratory for Lake Ecosystem Research, State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China

² Institute for Electromagnetic Sensing of the Environment, National Research Council of Italy, via Bassini 15, 20133 Milan, Italy

³ Marine Physical Laboratory, Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA 92093, USA

Remote Sens. 2017, 9(8), 818; https://doi.org/10.3390/rs9080818 - 9 Aug 2017

Cited by 22 | Viewed by 6503

Abstract

The editorial paper aims to highlight the main topics investigated in the Special Issue (SI) “Water Optics and Water Colour Remote Sensing”. The outcomes of the 21 papers published in the SI are presented, along with a bibliometric analysis in the same field, [...] Read more.

The editorial paper aims to highlight the main topics investigated in the Special Issue (SI) “Water Optics and Water Colour Remote Sensing”. The outcomes of the 21 papers published in the SI are presented, along with a bibliometric analysis in the same field, namely, water optics and water colour remote sensing. This editorial summarises how the research articles of the SI approach the study of bio-optical properties of aquatic systems, the development of remote sensing algorithms, and the application of time-series satellite data for assessing long-term and temporal-spatial dynamics in inland, coastal, and oceanic waters. The SI shows the progress with a focus on: (1) bio-optical properties (three papers); (2) atmospheric correction and data uncertainties (five papers); (3) remote sensing estimation of chlorophyll-a (Chl-a) (eight papers); (4) remote sensing estimation of suspended matter and chromophoric dissolved organic matter (CDOM) (four papers); and (5) water quality and water ecology remote sensing (four papers). Overall, the SI presents a variety of applications at the global scale (with case studies in Europe, Asia, South and North America, and the Antarctic), achieved with different remote sensing instruments, such as hyperspectral field and airborne sensors, ocean colour radiometry, geostationary platforms, and the multispectral Landsat and Sentinel-2 satellites. The bibliometric analysis, carried out to include research articles published from 1900 to 2016, indicates that “chlorophyll-a”, “ocean colour”, “phytoplankton”, “SeaWiFS” (Sea-Viewing Wide Field-of-View Sensor), and “chromophoric dissolved organic matter” were the five most frequently used keywords in the field. The SI contents, along with the bibliometric analysis, clearly suggest that remote sensing of Chl-a is one of the topmost investigated subjects in the field. Full article

(This article belongs to the Special Issue Water Optics and Water Colour Remote Sensing)

▼ Show Figures

Figure 1

17 pages, 8491 KiB

Open AccessArticle

A Modified Dual-Baseline PolInSAR Method for Forest Height Estimation

by Qinghua Xie^1,2, Jianjun Zhu^1,*, Changcheng Wang^1,*

, Haiqiang Fu¹, Juan M. Lopez-Sanchez²

and J. David Ballester-Berman²

¹ School of Geosciences and Info-Physics, Central South University, Changsha 410083, China

² Institute for Computing Research (IUII), University of Alicante, E-03080 Alicante, Spain

Remote Sens. 2017, 9(8), 819; https://doi.org/10.3390/rs9080819 - 9 Aug 2017

Cited by 38 | Viewed by 6155

Abstract

This paper investigates the potentials and limitations of a simple dual-baseline PolInSAR (DBPI) method for forest height inversion. This DBPI method follows the classical three-stage inversion method’s idea used in single baseline PolInSAR (SBPI) inversion, but it avoids the assumption of the smallest [...] Read more.

This paper investigates the potentials and limitations of a simple dual-baseline PolInSAR (DBPI) method for forest height inversion. This DBPI method follows the classical three-stage inversion method’s idea used in single baseline PolInSAR (SBPI) inversion, but it avoids the assumption of the smallest ground-to-volume amplitude ratio (GVR) by employing an additional baseline to constrain the inversion procedure. In this paper, we present for the first time an assessment of such a method on real PolInSAR data over boreal forest. Additionally, we propose an improvement on the original DBPI method by incorporating the sloped random volume over ground (S-RVoG) model in order to reduce the range terrain slope effect. Therefore, a digital elevation model (DEM) is needed to provide the slope information in the proposed method. Three scenes of P-band airborne PolInSAR data acquired by E-SAR and light detection and ranging (LIDAR) data available in the BioSAR2008 campaign are employed for testing purposes. The performance of the SBPI, DBPI, and modified DBPI methods is compared. The results show that the DBPI method extracts forest heights with an average root mean square error (RMSE) of 4.72 m against LIDAR heights for trees of 18 m height on average. It presents a significant improvement of forest height accuracy over the SBPI method (with a stand-level mean improvement of 42.86%). Concerning the modified DBPI method, it consistently improves the accuracy of forest height inversion over sloped areas. This improvement reaches a stand-level mean of 21.72% improvement (with a mean RMSE of 4.63 m) for slopes greater than 10°. Full article

(This article belongs to the Special Issue Recent Advances in Polarimetric SAR Interferometry)

▼ Show Figures

Graphical abstract

26 pages, 4862 KiB

Open AccessArticle

Soil Moisture Data Assimilation in a Hydrological Model: A Case Study in Belgium Using Large-Scale Satellite Data

by Pierre Baguis^* and Emmanuel Roulin

Royal Meteorological Institute of Belgium, Avenue Circulaire 3, B-1180 Brussels, Belgium

Remote Sens. 2017, 9(8), 820; https://doi.org/10.3390/rs9080820 - 10 Aug 2017

Cited by 22 | Viewed by 7295

Abstract

In the present study, we focus on the assimilation of satellite observations for Surface Soil Moisture (SSM) in a hydrological model. The satellite data are produced in the framework of the EUMETSAT project H-SAF and are based on measurements with the Advanced radar [...] Read more.

In the present study, we focus on the assimilation of satellite observations for Surface Soil Moisture (SSM) in a hydrological model. The satellite data are produced in the framework of the EUMETSAT project H-SAF and are based on measurements with the Advanced radar Scatterometer (ASCAT), embarked on the Meteorological Operational satellites (MetOp). The product generated with these measurements has a horizontal resolution of 25 km and represents the upper few centimeters of soil. Our approach is based on the Ensemble Kalman Filter technique (EnKF), where observation and model uncertainties are taken into account, implemented in a conceptual hydrological model. The analysis is carried out in the Demer catchment of the Scheldt River Basin in Belgium, for the period from June 2013–May 2016. In this context, two methodological advances are being proposed. First, the generation of stochastic terms, necessary for the EnKF, of bounded variables like SSM is addressed with the aid of specially-designed probability distributions, so that the bounds are never exceeded. Second, bias due to the assimilation procedure itself is removed using a post-processing technique. Subsequently, the impact of SSM assimilation on the simulated streamflow is estimated using a series of statistical measures based on the ensemble average. The differences from the control simulation are then assessed using a two-dimensional bootstrap sampling on the ensemble generated by the assimilation procedure. Our analysis shows that data assimilation combined with bias correction can improve the streamflow estimations or, at a minimum, produce results statistically indistinguishable from the control run of the hydrological model. Full article

(This article belongs to the Special Issue Retrieval, Validation and Application of Satellite Soil Moisture Data)

▼ Show Figures

Graphical abstract

23 pages, 6906 KiB

Open AccessArticle

Estimation of Fugacity of Carbon Dioxide in the East Sea Using In Situ Measurements and Geostationary Ocean Color Imager Satellite Data

by Eunna Jang¹, Jungho Im^1,*

, Geun-Ha Park² and Young-Gyu Park³

¹ School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, Korea

² East Sea Research Institute, Korea Institute of Ocean Science and Technology, Uljin 36315, Korea

³ Korea Institute of Ocean Science and Technology, Ansan 15627, Korea

Remote Sens. 2017, 9(8), 821; https://doi.org/10.3390/rs9080821 - 10 Aug 2017

Cited by 26 | Viewed by 6359

Abstract

The ocean is closely related to global warming and on-going climate change by regulating amounts of carbon dioxide through its interaction with the atmosphere. The monitoring of ocean carbon dioxide is important for a better understanding of the role of the ocean as [...] Read more.

The ocean is closely related to global warming and on-going climate change by regulating amounts of carbon dioxide through its interaction with the atmosphere. The monitoring of ocean carbon dioxide is important for a better understanding of the role of the ocean as a carbon sink, and regional and global carbon cycles. This study estimated the fugacity of carbon dioxide (ƒCO₂) over the East Sea located between Korea and Japan. In situ measurements, satellite data and products from the Geostationary Ocean Color Imager (GOCI) and the Hybrid Coordinate Ocean Model (HYCOM) reanalysis data were used through stepwise multi-variate nonlinear regression (MNR) and two machine learning approaches (i.e., support vector regression (SVR) and random forest (RF)). We used five ocean parameters—colored dissolved organic matter (CDOM; <0.3 m⁻¹), chlorophyll-a concentration (Chl-a; <21 mg/m³), mixed layer depth (MLD; <160 m), sea surface salinity (SSS; 32–35), and sea surface temperature (SST; 8–28 °C)—and four band reflectance (Rrs) data (400 nm–565 nm) and their ratios as input parameters to estimate surface seawater ƒCO₂ (270–430 μatm). Results show that RF generally performed better than stepwise MNR and SVR. The root mean square error (RMSE) of validation results by RF was 5.49 μatm (1.7%), while those of stepwise MNR and SVR were 10.59 μatm (3.2%) and 6.82 μatm (2.1%), respectively. Ocean parameters (i.e., sea surface salinity (SSS), sea surface temperature (SST), and mixed layer depth (MLD)) appeared to contribute more than the individual bands or band ratios from the satellite data. Spatial and seasonal distributions of monthly ƒCO₂ produced from the RF model and sea-air CO₂ flux were also examined. Full article

(This article belongs to the Section Ocean Remote Sensing)

▼ Show Figures

Graphical abstract

16 pages, 8643 KiB

Open AccessArticle

Evaluation of Satellite-Altimetry-Derived Pycnocline Depth Products in the South China Sea

by Yingying Chen^1,2, Kai Yu^1,3

, Changming Dong^1,4, Zhigang He⁵, Yunwei Yan³ and Dongxiao Wang^2,*

¹ School of Marine Science, Nanjing University of Information Science and Technology, Nanjing 210044, China

² State Key Laboratory of Tropical Oceanography, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou 510000, China

³ State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, State Oceanic Administration, Hangzhou 310000, China

⁴ Department of Atmospheric and Oceanic Sciences, University of California, Los Angeles, CA 90095, USA

⁵ College of Ocean and Earth Science, Xiamen University, Xiamen 361000, China

Remote Sens. 2017, 9(8), 822; https://doi.org/10.3390/rs9080822 - 12 Aug 2017

Cited by 4 | Viewed by 7767

Abstract

The climatological monthly gridded World Ocean Atlas 2013 temperature and salinity data and satellite altimeter sea level anomaly data are used to build two altimeter-derived high-resolution real-time upper layer thickness products based on a highly simplified two-layer ocean model of the South China [...] Read more.

The climatological monthly gridded World Ocean Atlas 2013 temperature and salinity data and satellite altimeter sea level anomaly data are used to build two altimeter-derived high-resolution real-time upper layer thickness products based on a highly simplified two-layer ocean model of the South China Sea. One product uses the proportional relationship between the sea level anomaly and upper layer thickness anomaly. The other one adds a modified component (

η_{M}^{'}

) to account for the barotropic and thermodynamic processes that are neglected in the former product. The upper layer thickness, in this work, represents the depth of the main pycnocline, which is defined as the thickness from the sea surface to the 25 kg/m³ isopycnal depth. The mean upper layer thickness in the semi-closed South China Sea is ~120 m and the mean reduced gravity is ~0.073 m/s², which is about one order of magnitude larger than the value obtained in the open deep ocean. The long-term temperature observations from three moored buoys, the conductivity-temperature-depth profiles from three joint cruises, and the Argo measurements from 2006 to 2015 are used to compare and evaluate these two upper layer thickness products. It shows that adding the

η_{M}^{'}

component is necessary to simulate the upper layer thickness in some situations, especially in summer and fall in the northern South China Sea. Full article

(This article belongs to the Special Issue Satellite Altimetry for Earth Sciences)

▼ Show Figures

Graphical abstract

13 pages, 17909 KiB

Open AccessArticle

Low-Altitude Aerial Methane Concentration Mapping

by Bara J. Emran, Dwayne D. Tannant

and Homayoun Najjaran^*

School of Engineering, The University of British Columbia, Kelowna V1V1V7, BC, Canada

Remote Sens. 2017, 9(8), 823; https://doi.org/10.3390/rs9080823 - 10 Aug 2017

Cited by 64 | Viewed by 13985

Abstract

Detection of leaks of fugitive greenhouse gases (GHGs) from landfills and natural gas infrastructure is critical for not only their safe operation but also for protecting the environment. Current inspection practices involve moving a methane detector within the target area by a person [...] Read more.

Detection of leaks of fugitive greenhouse gases (GHGs) from landfills and natural gas infrastructure is critical for not only their safe operation but also for protecting the environment. Current inspection practices involve moving a methane detector within the target area by a person or vehicle. This procedure is dangerous, time consuming, labor intensive and above all unavailable when access to the desired area is limited. Remote sensing by an unmanned aerial vehicle (UAV) equipped with a methane detector is a cost-effective and fast method for methane detection and monitoring, especially for vast and remote areas. This paper describes the integration of an off-the-shelf laser-based methane detector into a multi-rotor UAV and demonstrates its efficacy in generating an aerial methane concentration map of a landfill. The UAV flies a preset flight path measuring methane concentrations in a vertical air column between the UAV and the ground surface. Measurements were taken at 10 Hz giving a typical distance between measurements of 0.2 m when flying at 2 m/s. The UAV was set to fly at 25 to 30 m above the ground. We conclude that besides its utility in landfill monitoring, the proposed method is ready for other environmental applications as well as the inspection of natural gas infrastructure that can release methane with much higher concentrations. Full article

(This article belongs to the Special Issue Remote Sensing of Greenhouse Gases)

▼ Show Figures

Graphical abstract

19 pages, 5624 KiB

Open AccessArticle

Automatic Power Line Inspection Using UAV Images

by Yong Zhang¹

, Xiuxiao Yuan^1,2,*, Wenzhuo Li¹ and Shiyu Chen¹

¹ School of Remote Sensing and Information Engineering, Wuhan University, 129 Luoyu Road, Wuhan 430079, China

² Collaborative Innovation Center of Geospatial Technology, Wuhan 430079, China

Remote Sens. 2017, 9(8), 824; https://doi.org/10.3390/rs9080824 - 10 Aug 2017

Cited by 156 | Viewed by 13296

Abstract

Power line inspection ensures the safe operation of a power transmission grid. Using unmanned aerial vehicle (UAV) images of power line corridors is an effective way to carry out these vital inspections. In this paper, we propose an automatic inspection method for power [...] Read more.

Power line inspection ensures the safe operation of a power transmission grid. Using unmanned aerial vehicle (UAV) images of power line corridors is an effective way to carry out these vital inspections. In this paper, we propose an automatic inspection method for power lines using UAV images. This method, known as the power line automatic measurement method based on epipolar constraints (PLAMEC), acquires the spatial position of the power lines. Then, the semi patch matching based on epipolar constraints (SPMEC) dense matching method is applied to automatically extract dense point clouds within the power line corridor. Obstacles can then be automatically detected by calculating the spatial distance between a power line and the point cloud representing the ground. Experimental results show that the PLAMEC automatically measures power lines effectively with a measurement accuracy consistent with that of manual stereo measurements. The height root mean square (RMS) error of the point cloud was 0.233 m, and the RMS error of the power line was 0.205 m. In addition, we verified the detected obstacles in the field and measured the distance between the canopy and power line using a laser range finder. The results show that the difference of these two distances was within ±0.5 m. Full article

▼ Show Figures

Graphical abstract

13 pages, 8350 KiB

Open AccessArticle

Semi-Analytical Retrieval of the Diffuse Attenuation Coefficient in Large and Shallow Lakes from GOCI, a High Temporal-Resolution Satellite

by Changchun Huang^1,2,3,4

and Ling Yao^5,*

¹ School of Geography Science, Nanjing Normal University, Nanjing 210023, China

² State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210023, China

³ Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, Nanjing 210023, China

⁴ Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing Normal University, Nanjing 210023, China

⁵ State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

Remote Sens. 2017, 9(8), 825; https://doi.org/10.3390/rs9080825 - 11 Aug 2017

Cited by 10 | Viewed by 4013

Abstract

Monitoring the dynamic characteristics of the diffuse attenuation coefficient (K_d(490)) on the basis of the high temporal-resolution satellite data is critical for regulating the ecological environment of lake. By measuring the in-situ K_d(490) and the remote-sensing reflectance, a [...] Read more.

Monitoring the dynamic characteristics of the diffuse attenuation coefficient (K_d(490)) on the basis of the high temporal-resolution satellite data is critical for regulating the ecological environment of lake. By measuring the in-situ K_d(490) and the remote-sensing reflectance, a semi-analytical algorithm for K_d(490) was developed to determine the short-term variation of K_d(490). From 2006 to 2014, the data about 412 samples (among which 60 were used as match-up points, 282 for calibrating dataset and the remaining 70 for validating dataset) were gathered from nine expeditions to calibrate and validate the aforesaid semi-analytical algorithm. The root mean square percentage error (RMSP) and the mean absolute relative error (MAPE) of validation datasets were respectively 27.44% and 22.60 ± 15.57%, while that of the match-up datasets were respectively 34.29% and 27.57 ± 20.56%. These percentages indicate that the semi-analytical algorithm and Geostationary Ocean Color Imager (GOCI) data are applicable to obtain the short-term variation of K_d(490) in the turbid shallow inland waters. The short-term GOCI-observed K_d(490) shows a significant seasonal and spatial variation and a similar distribution to the matching Moderate Resolution Imaging Spectroradiometer (MODIS) which derived K_d(490). A comparative analysis on wind (observed by buoys) and GOCI-derived K_d(490) suggests that wind is a primary driving factor of K_d(490) variation, but the lacustrine morphometry affects the wind force that is contributing to K_d(490) variation. Full article

▼ Show Figures

Graphical abstract

22 pages, 22252 KiB

Open AccessArticle

LiDAR Validation of a Video-Derived Beachface Topography on a Tidal Flat

by David Didier^1,*, Pascal Bernatchez¹, Emmanuel Augereau², Charles Caulet², Dany Dumont³

, Eliott Bismuth¹, Louis Cormier¹, France Floc’h² and Christophe Delacourt²

¹ Québec-Océan, Centre d’études nordiques, Chaire de Recherche en Géoscience Côtière, Université du Québec à Rimouski, Rimouski, QC G5L 3A1, Canada

² Laboratoire Géosciences Océan UMR 6538, Institut Universitaire Européen de la mer, Université de Bretagne Occidentale, Brest, 29280 Plouzané, France

³ Institut des Sciences de la mer de Rimouski, Université du Québec à Rimouski, Québec-Océan, Physique des Océans—Laboratoire de Rimouski, Rimouski, QC, G5L 3A1, Canada

Remote Sens. 2017, 9(8), 826; https://doi.org/10.3390/rs9080826 - 11 Aug 2017

Cited by 16 | Viewed by 5401

Abstract

Increasingly used shore-based video stations enable a high spatiotemporal frequency analysis of shoreline migration. Shoreline detection techniques combined with hydrodynamic conditions enable the creation of digital elevation models (DEMs). However, shoreline elevations are often estimated based on nearshore process empirical equations leading to [...] Read more.

Increasingly used shore-based video stations enable a high spatiotemporal frequency analysis of shoreline migration. Shoreline detection techniques combined with hydrodynamic conditions enable the creation of digital elevation models (DEMs). However, shoreline elevations are often estimated based on nearshore process empirical equations leading to uncertainties in video-based topography. To achieve high DEM correspondence between both techniques, we assessed video-derived DEMs against LiDAR surveys during low energy conditions. A newly installed video system on a tidal flat in the St. Lawrence Estuary, Atlantic Canada, served as a test case. Shorelines were automatically detected from time-averaged (TIMEX) images using color ratios in low energy conditions synchronously with mobile terrestrial LiDAR during two different surveys. Hydrodynamic (waves and tides) data were recorded in-situ, and established two different cases of water elevation models as a basis for shoreline elevations. DEMs were created and tested against LiDAR. Statistical analysis of shoreline elevations and migrations were made, and morphological variability was assessed between both surveys. Results indicate that the best shoreline elevation model includes both the significant wave height and the mean water level. Low energy conditions and in-situ hydrodynamic measurements made it possible to produce video-derived DEMs virtually as accurate as a LiDAR product, and therefore make an effective tool for coastal managers. Full article

▼ Show Figures

Graphical abstract

19 pages, 13211 KiB

Open AccessFeature PaperArticle

Determinants of Aboveground Biomass across an Afromontane Landscape Mosaic in Kenya

by Hari Adhikari^1,*

, Janne Heiskanen¹

, Mika Siljander¹

, Eduardo Maeda²

, Vuokko Heikinheimo¹

and Petri K. E. Pellikka¹

¹ Department of Geosciences and Geography, University of Helsinki, P.O. Box 68, FI-00014 Helsinki, Finland

² Fisheries and Environmental Management Group, Department of Environmental Sciences, University of Helsinki, P.O. Box 65, FI-00014 Helsinki, Finland

Remote Sens. 2017, 9(8), 827; https://doi.org/10.3390/rs9080827 - 11 Aug 2017

Cited by 25 | Viewed by 8152

Abstract

Afromontane tropical forests maintain high biodiversity and provide valuable ecosystem services, such as carbon sequestration. The spatial distribution of aboveground biomass (AGB) in forest-agriculture landscape mosaics is highly variable and controlled both by physical and human factors. In this study, the objectives were [...] Read more.

Afromontane tropical forests maintain high biodiversity and provide valuable ecosystem services, such as carbon sequestration. The spatial distribution of aboveground biomass (AGB) in forest-agriculture landscape mosaics is highly variable and controlled both by physical and human factors. In this study, the objectives were (1) to generate a map of AGB for the Taita Hills, in Kenya, based on field measurements and airborne laser scanning (ALS), and (2) to examine determinants of AGB using geospatial data and statistical modelling. The study area is located in the northernmost part of the Eastern Arc Mountains, with an elevation range of approximately 600–2200 m. The field measurements were carried out in 215 plots in 2013–2015 and ALS flights conducted in 2014–2015. Multiple linear regression was used for predicting AGB at a 30 m × 30 m resolution based on canopy cover and the 25th percentile height derived from ALS returns (R² = 0.88, RMSE = 52.9 Mg ha⁻¹). Boosted regression trees (BRT) were used for examining the relationship between AGB and explanatory variables at a 250 m × 250 m resolution. According to the results, AGB patterns were controlled mainly by mean annual precipitation (MAP), the distribution of croplands and slope, which explained together 69.8% of the AGB variation. The highest AGB densities have been retained in the semi-natural vegetation in the higher elevations receiving more rainfall and in the steep slope, which is less suitable for agriculture. AGB was also relatively high in the eastern slopes as indicated by the strong interaction between slope and aspect. Furthermore, plantation forests, topographic position and the density of buildings had a minor influence on AGB. The findings demonstrate the utility of ALS-based AGB maps and BRT for describing AGB distributions across Afromontane landscapes, which is important for making sustainable land management decisions in the region. Full article

(This article belongs to the Special Issue Biomass Remote Sensing in Forest Landscapes)

▼ Show Figures

Graphical abstract

15 pages, 6664 KiB

Open AccessArticle

Adaptive Estimation of Crop Water Stress in Nectarine and Peach Orchards Using High-Resolution Imagery from an Unmanned Aerial Vehicle (UAV)

by Suyoung Park^1,*, Dongryeol Ryu¹

, Sigfredo Fuentes²

, Hoam Chung³

, Esther Hernández-Montes⁴ and Mark O’Connell⁵

¹ Department of Infrastructure Engineering, The University of Melbourne, Parkville, VIC 3010, Australia

² School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, The University of Melbourne, Parkville, VIC 3010, Australia

³ Department of Mechanical and Aerospace Engineering, Monash University, Clayton, VIC 3168, Australia

⁴ Research Group on Plant Biology under Mediterranean Conditions, Department of Biology, UIB-Universitat de les Illes Balears, Carretera de Valldemossa Km 7.5, 07122 Palma de Mallorca, Spain

⁵ Department of Economic Development, Jobs, Transport and Resources, Tatura, VIC 3616, Australia

Remote Sens. 2017, 9(8), 828; https://doi.org/10.3390/rs9080828 - 11 Aug 2017

Cited by 131 | Viewed by 10955

Abstract

The capability to monitor water status from crops on a regular basis can enhance productivity and water use efficiency. In this paper, high-resolution thermal imagery acquired by an unmanned aerial vehicle (UAV) was used to map plant water stress and its spatial variability, [...] Read more.

The capability to monitor water status from crops on a regular basis can enhance productivity and water use efficiency. In this paper, high-resolution thermal imagery acquired by an unmanned aerial vehicle (UAV) was used to map plant water stress and its spatial variability, including sectors under full irrigation and deficit irrigation over nectarine and peach orchards at 6.12 cm ground sample distance. The study site was classified into sub-regions based on crop properties, such as cultivars and tree training systems. In order to enhance the accuracy of the mapping, edge extraction and filtering were conducted prior to the probability modelling employed to obtain crop-property-specific (‘adaptive’ hereafter) lower and higher temperature references (T_wet and T_dry respectively). Direct measurements of stem water potential (SWP, ψ_stem) and stomatal conductance (g_s) were collected concurrently with UAV remote sensing and used to validate the thermal index as crop biophysical parameters. The adaptive crop water stress index (CWSI) presented a better agreement with both ψ_stem and g_s with determination coefficients (R²) of 0.72 and 0.82, respectively, while the conventional CWSI applied by a single set of hot and cold references resulted in biased estimates with R² of 0.27 and 0.34, respectively. Using a small number of ground-based measurements of SWP, CWSI was converted to a high-resolution SWP map to visualize spatial distribution of the water status at field scale. The results have important implications for the optimal management of irrigation for crops. Full article

▼ Show Figures

Graphical abstract

20 pages, 11349 KiB

Open AccessArticle

An Improved Vegetation Adjusted Nighttime Light Urban Index and Its Application in Quantifying Spatiotemporal Dynamics of Carbon Emissions in China

by Xing Meng¹, Ji Han^2,*

and Cheng Huang¹

¹ School of Geographical Sciences, East China Normal University, Dongchuan Road 500, Shanghai 200241, China

² Shanghai Key Laboratory for Urban Ecological Processes and Eco-Restoration, School of Ecological and Environmental Sciences, East China Normal University, Dongchuan Road 500, Shanghai 200241, China

Remote Sens. 2017, 9(8), 829; https://doi.org/10.3390/rs9080829 - 11 Aug 2017

Cited by 50 | Viewed by 8503

Abstract

Nighttime Light (NTL) has been widely used as a proxy of many socio-environmental issues. However, the limited range of sensor radiance of NTL prevents its further application and estimation accuracy. To improve the performance, we developed an improved Vegetation Adjusted Nighttime light Urban [...] Read more.

Nighttime Light (NTL) has been widely used as a proxy of many socio-environmental issues. However, the limited range of sensor radiance of NTL prevents its further application and estimation accuracy. To improve the performance, we developed an improved Vegetation Adjusted Nighttime light Urban Index (VANUI) through fusing multi-year NTL with population density, the Normalized Difference Vegetation Index and water body data and applied it to fine-scaled carbon emission analysis in China. The results proved that our proposed index could reflect more spatial variation of human activities. It is also prominent in reducing the carbon modeling error at the inter-city level and distinguishing the emission heterogeneity at the intra-city level. Between 1995 and 2013, CO₂ emissions increased significantly in China, but were distributed unevenly in space with high density emissions mainly located in metropolitan areas and provincial capitals. In addition to Beijing-Tianjin-Hebei, the Yangzi River Delta and the Pearl River Delta, the Shandong Peninsula has become a new emission hotspot that needs special attention in carbon mitigation. The improved VANUI and its application to the carbon emission issue not only broadened our understanding of the spatiotemporal dynamics of fine-scaled CO₂ emission, but also provided implications for low-carbon and sustainable development plans. Full article

(This article belongs to the Special Issue Recent Advances in Remote Sensing with Nighttime Lights)

▼ Show Figures

Graphical abstract

16 pages, 10906 KiB

Open AccessArticle

An Adaptive Offset Tracking Method with SAR Images for Landslide Displacement Monitoring

by Jiehua Cai¹, Changcheng Wang^1,2,*

, Xiaokang Mao³ and Qijie Wang¹

¹ School of Geosciences and Info-Physics, Central South University, Changsha 410083, China

² Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring, Ministry of Education, Central South University, Changsha 410083, China

³ China Railway Siyuan Survey and Design Group Company, Wuhan 430063, China

Remote Sens. 2017, 9(8), 830; https://doi.org/10.3390/rs9080830 - 11 Aug 2017

Cited by 40 | Viewed by 9632

Abstract

With the development of high-resolution Synthetic Aperture Radar (SAR) systems, researchers are increasingly paying attention to the application of SAR offset tracking methods in ground deformation estimation. The traditional normalized cross correlation (NCC) tracking method is based on regular matching windows. For areas [...] Read more.

With the development of high-resolution Synthetic Aperture Radar (SAR) systems, researchers are increasingly paying attention to the application of SAR offset tracking methods in ground deformation estimation. The traditional normalized cross correlation (NCC) tracking method is based on regular matching windows. For areas with different moving characteristics, especially the landslide boundary areas, the NCC method will produce incorrect results. This is because in landslide boundary areas, the pixels of the regular matching window include two or more types of moving characteristics: some pixels with large displacement, and others with small or no displacement. These two kinds of pixels are uncorrelated, which result in inaccurate estimations. This paper proposes a new offset tracking method with SAR images based on the adaptive matching window to improve the accuracy of landslide displacement estimation. The proposed method generates an adaptive matching window that only contains pixels with similar moving characteristics. Three SAR images acquired by the Jet Propulsion Laboratory’s Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) system are selected to estimate the surface deformation of the Slumgullion landslide located in the southwestern Colorado, USA. The results show that the proposed method has higher accuracy than the traditional NCC method, especially in landslide boundary areas. Furthermore, it can obtain more detailed displacement information in landslide boundary areas. Full article

(This article belongs to the Special Issue Radar Interferometry for Geohazards)

▼ Show Figures

Graphical abstract

27 pages, 30658 KiB

Open AccessEditor’s ChoiceArticle

Identifying Droughts Affecting Agriculture in Africa Based on Remote Sensing Time Series between 2000–2016: Rainfall Anomalies and Vegetation Condition in the Context of ENSO

by Karina Winkler^1,2,3,*, Ursula Gessner² and Volker Hochschild³

¹ Company of Remote Sensing and Environmental Research (SLU), Kohlsteiner Str. 5, 81243 Munich, Germany

² German Remote Sensing Data Center (DFD), German Aerospace Center (DLR), 82234Wessling, Germany

³ Institute of Geography, University of Tuebingen, Ruemelinstr. 19-23, 72070 Tuebingen, Germany

Remote Sens. 2017, 9(8), 831; https://doi.org/10.3390/rs9080831 - 11 Aug 2017

Cited by 102 | Viewed by 15493

Abstract

Droughts are amongst the most destructive natural disasters in the world. In large regions of Africa, where water is a limiting factor and people strongly rely on rain-fed agriculture, droughts have frequently led to crop failure, food shortages and even humanitarian crises. In [...] Read more.

Droughts are amongst the most destructive natural disasters in the world. In large regions of Africa, where water is a limiting factor and people strongly rely on rain-fed agriculture, droughts have frequently led to crop failure, food shortages and even humanitarian crises. In eastern and southern Africa, major drought episodes have been linked to El Niño-Southern Oscillation (ENSO) events. In this context and with limited in-situ data available, remote sensing provides valuable opportunities for continent-wide assessment of droughts with high spatial and temporal resolutions. This study aimed to monitor agriculturally relevant droughts over Africa between 2000–2016 with a specific focus on growing seasons using remote sensing-based drought indices. Special attention was paid to the observation of drought dynamics during major ENSO episodes to illuminate the connection between ENSO and droughts in eastern and southern Africa. We utilized Tropical Rainfall Measuring Mission (TRMM)-based Standardized Precipitation Index (SPI) with

0 . 25^{\circ}

resolution and Moderate-resolution Imaging Spectroradiometer (MODIS)-derived Vegetation Condition Index (VCI) with 500 m resolution as indices for analysing the spatio-temporal patterns of droughts. We combined the drought indices with information on the timing of site-specific growing seasons derived from MODIS-based multi-annual average of Normalized Difference Vegetation Index (NDVI). We proved the applicability of SPI-3 and VCI as indices for a comprehensive continental-scale monitoring of agriculturally relevant droughts. The years 2009 and 2011 could be revealed as major drought years in eastern Africa, whereas southern Africa was affected by severe droughts in 2003 and 2015/2016. Drought episodes occurred over large parts of southern Africa during strong El Niño events. We observed a mixed drought pattern in eastern Africa, where areas with two growing seasons were frequently affected by droughts during La Niña and zones of unimodal rainfall regimes showed droughts during the onset of El Niño. During La Niña 2010/2011, large parts of cropland areas in Somalia (88%), Sudan (64%) and South Sudan (51%) were affected by severe to extreme droughts during the growing seasons. However, no universal El Niño- or La Niña-related response pattern of droughts could be deduced for the observation period of 16 years. In this regard, we discussed multi-year atmospheric fluctuations and characteristics of ENSO variants as further influences on the interconnection between ENSO and droughts. By utilizing remote sensing-based drought indices focussed on agricultural zones and periods, this study attempts to contribute to a better understanding of spatio-temporal patterns of droughts affecting agriculture in Africa, which can be essential for implementing strategies of drought hazard mitigation. Full article

(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)

▼ Show Figures

Graphical abstract

18 pages, 11085 KiB

Open AccessArticle

Mapping Annual Riparian Water Use Based on the Single-Satellite-Scene Approach

by Kul Khand^1,*, Saleh Taghvaeian¹

and Leila Hassan-Esfahani²

¹ Department of Biosystems & Agricultural Engineering, Oklahoma State University, Stillwater, OK 74078, USA

² Utah Water Research Laboratory, Utah State University, Logan, UT 84321, USA

Remote Sens. 2017, 9(8), 832; https://doi.org/10.3390/rs9080832 - 12 Aug 2017

Cited by 10 | Viewed by 4718

Abstract

The accurate estimation of water use by groundwater-dependent riparian vegetation is of great importance to sustainable water resource management in arid/semi-arid regions. Remote sensing methods can be effective in this regard, as they capture the inherent spatial variability in riparian ecosystems. The single-satellite-scene [...] Read more.

The accurate estimation of water use by groundwater-dependent riparian vegetation is of great importance to sustainable water resource management in arid/semi-arid regions. Remote sensing methods can be effective in this regard, as they capture the inherent spatial variability in riparian ecosystems. The single-satellite-scene (SSS) method uses a derivation of the Normalized Difference Vegetation Index (NDVI) from a single space-borne image during the peak growing season and minimal ground-based meteorological data to estimate the annual riparian water use on a distributed basis. This method was applied to a riparian ecosystem dominated by tamarisk along a section of the lower Colorado River in southern California. The results were compared against the estimates of a previously validated remotely sensed energy balance model for the year 2008 at two different spatial scales. A pixel-wide comparison showed good correlation (R² = 0.86), with a mean residual error of less than 104 mm∙year⁻¹ (18%). This error reduced to less than 95 mm∙year⁻¹ (15%) when larger areas were used in comparisons. In addition, the accuracy improved significantly when areas with no and low vegetation cover were excluded from the analysis. The SSS method was then applied to estimate the riparian water use for a 23-year period (1988–2010). The average annual water use over this period was 748 mm∙year⁻¹ for the entire study area, with large spatial variability depending on vegetation density. Comparisons with two independent water use estimates showed significant differences. The MODIS evapotranspiration product (MOD16) was 82% smaller, and the crop-coefficient approach employed by the US Bureau of Reclamation was 96% larger, than that from the SSS method on average. Full article

(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)

▼ Show Figures

Graphical abstract

16 pages, 13271 KiB

Open AccessArticle

A Study of Landfast Ice with Sentinel-1 Repeat-Pass Interferometry over the Baltic Sea

by Marjan Marbouti^1,*, Jaan Praks², Oleg Antropov²

, Eero Rinne³ and Matti Leppäranta¹

¹ Department of Physics, University of Helsinki, 00100 Helsinki, Finland

² Department of Electronics and Nanoengineering, School of Electrical Engineering, Aalto University, P.O. Box 11000, FI-00076 AALTO, 02150 Espoo, Finland

³ Finnish Meteorological Institute, Marine Research, Erik Palméninaukio 1, 00560, Helsinki, Finland

Remote Sens. 2017, 9(8), 833; https://doi.org/10.3390/rs9080833 - 12 Aug 2017

Cited by 27 | Viewed by 7853

Abstract

Mapping of fast ice displacement and investigating sea ice rheological behavior is a major open topic in coastal ice engineering and sea ice modeling. This study presents first results on Sentinel-1 repeat-pass space borne synthetic aperture radar interferometry (InSAR) in the Gulf of [...] Read more.

Mapping of fast ice displacement and investigating sea ice rheological behavior is a major open topic in coastal ice engineering and sea ice modeling. This study presents first results on Sentinel-1 repeat-pass space borne synthetic aperture radar interferometry (InSAR) in the Gulf of Bothnia over the fast ice areas. An InSAR pair acquired in February 2015 with a temporal baseline of 12 days has been studied here in detail. According to our results, the surface of landfast ice in the study area was stable enough to preserve coherence over the 12-day baseline, while previous InSAR studies over the fast ice used much shorter temporal baselines. The advantage of longer temporal baseline is in separating the fast ice from drift ice and detecting long term trends in deformation maps. The interferogram showed displacement of fast ice on the order of 40 cm in the study area. Parts of the displacements were attributed to forces caused by sea level tilt, currents, and thermal expansion, but the main factor of the displacement seemed to be due to compression of the drift ice driven by southwest winds with high speed. Further interferometric phase and the coherence measurements over the fast ice are needed in the future for understanding sea ice mechanism and establishing sustainability of the presented InSAR approach for monitoring dynamics of the landfast ice with Sentinel-1 data. Full article

(This article belongs to the Section Ocean Remote Sensing)

▼ Show Figures

Graphical abstract

10 pages, 5358 KiB

Open AccessArticle

Removal of Thin Cirrus Scattering Effects in Landsat 8 OLI Images Using the Cirrus Detecting Channel

by Bo-Cai Gao^1,* and Rong-Rong Li²

¹ Remote Sensing Division, Code 7232, Naval Research Lab, Washington, DC 20375, USA

² Remote Sensing Division, Code 7234, Naval Research Lab, Washington, DC 20375, USA

Remote Sens. 2017, 9(8), 834; https://doi.org/10.3390/rs9080834 - 12 Aug 2017

Cited by 37 | Viewed by 10235

Abstract

Thin cirrus clouds frequently contaminate images acquired with either Landsat 7 ETM+ or the earlier generation of Landsat series satellite instruments. The situation has changed since the launch of the Landsat 8 Operational Land Imager (OLI) into space in 2013. OLI implemented a [...] Read more.

Thin cirrus clouds frequently contaminate images acquired with either Landsat 7 ETM+ or the earlier generation of Landsat series satellite instruments. The situation has changed since the launch of the Landsat 8 Operational Land Imager (OLI) into space in 2013. OLI implemented a cirrus detecting channel (Band 9) centered within a strong atmospheric water vapor absorption band near 1.375 μm with a width of 30 nm. The specifications for this channel were the same as those specified for the NASA Moderate Resolution Imaging Spectroradiometer (MODIS) in the early 1990s. The OLI Band 9 has been proven to be very effective in detecting and masking thin cirrus-contaminated pixels at the high spatial resolution of 30 m. However, this channel has not yet been routinely used for the correction of thin cirrus effects in other OLI band images. In this article, we describe an empirical technique for the removal of thin cirrus scattering effects in OLI visible near infrared (IR) and shortwave IR (SWIR) spectral regions. We present results from applications of the technique to three OLI data sets. We also discuss issues associated with parallax anomalies in OLI data. Full article

(This article belongs to the Special Issue Atmospheric Correction of Remote Sensing Data)

▼ Show Figures

Graphical abstract

21 pages, 6510 KiB

Open AccessArticle

Challenges in Methane Column Retrievals from AVIRIS-NG Imagery over Spectrally Cluttered Surfaces: A Sensitivity Analysis

by Minwei Zhang¹, Ira Leifer^2,*

and Chuanmin Hu¹

¹ College of Marine Science, University of South Florida, 140 7th Ave. South, St. Petersburg, FL 33701, USA

² Bubbleology Research International, 1642 Elm Ave, Solvang, CA 93463, USA

Remote Sens. 2017, 9(8), 835; https://doi.org/10.3390/rs9080835 - 12 Aug 2017

Cited by 7 | Viewed by 6815

Abstract

A comparison between efforts to detect methane anomalies by a simple band ratio approach from the Airborne Visual Infrared Imaging Spectrometer-Classic (AVIRIS-C) data for the Kern Front oil field, Central California, and the Coal Oil Point marine hydrocarbon seep field, offshore southern California, [...] Read more.

A comparison between efforts to detect methane anomalies by a simple band ratio approach from the Airborne Visual Infrared Imaging Spectrometer-Classic (AVIRIS-C) data for the Kern Front oil field, Central California, and the Coal Oil Point marine hydrocarbon seep field, offshore southern California, was conducted. The detection succeeded for the marine source and failed for the terrestrial source, despite these sources being of comparable strength. Scene differences were investigated in higher spectral and spatial resolution collected by the AVIRIS-C successor instrument, AVIRIS Next Generation (AVIRIS-NG), by a sensitivity study. Sensitivity to factors including water vapor, aerosol, planetary boundary layer (PBL) structure, illumination and viewing angle, and surface albedo clutter were explored. The study used the residual radiance method, with sensitivity derived from MODTRAN (MODerate resolution atmospheric correction TRANsmission) simulations of column methane (XCH₄). Simulations used the spectral specifications and geometries of AVIRIS-NG and were based on a uniform or an in situ vertical CH₄ profile, which was measured concurrent with the AVIRIS-NG data. Small but significant sensitivity was found for PBL structure and water vapor; however, highly non-linear, extremely strong sensitivity was found for surface albedo error. For example, a 10% decrease in the surface albedo corresponded to a 300% XCH₄ increase over background XCH₄ to compensate for the total signal, less so for stronger plumes. This strong non-linear sensitivity resulted from the high percentage of surface-reflected radiance in the airborne at-sensor total radiance. Coarse spectral resolution and feedback from interferents like water vapor underlay this sensitivity. Imaging spectrometry like AVIRIS and the Hyperspectral InfraRed Imager (HyspIRI) candidate satellite mission, have the advantages of contextual spatial information and greater at-sensor total radiance. However, they also face challenges due to their relatively broad spectral resolution compared to trace gas specific orbital sensors, e.g., the Greenhouse gases Observing SATellite (GOSAT), which is especially applicable to trace gas retrievals over scenes with high spectral albedo variability. Results of the sensitivity analysis are applicable for the residual radiance method and CH₄ profiles used in the analysis, but they illustrate potential significant challenges in CH₄ retrievals using other approaches. Full article

(This article belongs to the Special Issue Remote Sensing of Greenhouse Gases)

▼ Show Figures

Figure 1

30 pages, 14090 KiB

Open AccessArticle

Estimating Subpixel Surface Heat Fluxes through Applying Temperature-Sharpening Methods to MODIS Data

by Xiaojun Li^1,2

, Xiaozhou Xin^1,*

, Jingjun Jiao^1,2, Zhiqing Peng^1,2, Hailong Zhang¹

, Shanshan Shao³ and Qinhuo Liu^1,4

¹ State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China

² University of Chinese Academy of Sciences, Beijing 100049, China

³ College of Educational Science, Anhui Normal University, Wuhu 241000, China

⁴ Joint Center for Global Change Studies (JCGCS), Beijing 100875, China

Remote Sens. 2017, 9(8), 836; https://doi.org/10.3390/rs9080836 - 12 Aug 2017

Cited by 17 | Viewed by 5330

Abstract

Using high-resolution satellite data to perform routine (i.e., daily to weekly) monitoring of surface evapotranspiration, evapotranspiration (ET) (or LE, i.e., latent heat flux) has not been feasible because of the low frequency of satellite coverage over regions of interest (i.e., approximately every two [...] Read more.

Using high-resolution satellite data to perform routine (i.e., daily to weekly) monitoring of surface evapotranspiration, evapotranspiration (ET) (or LE, i.e., latent heat flux) has not been feasible because of the low frequency of satellite coverage over regions of interest (i.e., approximately every two weeks). Cloud cover further reduces the number of useable observations, and the utility of these data for routine ET or LE monitoring is limited. Moderate-resolution satellite imagery is available multiple times per day; however, the spatial resolution of these data is too coarse to enable the estimation of ET from individual agricultural fields or variations in ET or LE. The objective of this study is to combine high-resolution satellite data collected in the visible and near-infrared (VNIR) bands with data from the MODIS thermal-infrared (TIR) bands to estimate subpixel surface LE. Two temperature-sharpening methods, the disaggregation procedure for radiometric surface temperature (DisTrad) and the geographically-weighted regression (GWR)-based downscaling algorithm, were used to obtain accurate subpixel land surface temperature (LST) within the Zhangye oasis in China, where the surface is heterogeneous. The downscaled LSTs were validated using observations collected during the HiWATER-MUSOEXE (Multi-Scale Observation Experiment on Evapotranspiration) project. In addition, a remote sensing-based energy balance model was used to compare subpixel MODIS LST-based turbulent heat fluxes estimates with those obtained using the two LST downscaling approaches. The footprint validation results showed that the direct use of the MODIS LST approach does not consider LST heterogeneity at all, leading to significant errors (i.e., the root mean square error is 73.15 W·m⁻²) in LE, whereas the errors in the LE estimates obtained using DisTrad and GWR were 45.84 W·m⁻² and 47.38 W·m⁻², respectively. Furthermore, additional analysis showed that the ability of DisTrad and GWR to capture subpixel LST variations depends on the value of Shannon’s diversity index (SHDI) and the surface type within the flux contribution source area. Full article

(This article belongs to the Section Remote Sensing in Geology, Geomorphology and Hydrology)

▼ Show Figures

Figure 1

16 pages, 4438 KiB

Open AccessArticle

National BDS Augmentation Service System (NBASS) of China: Progress and Assessment

by Chuang Shi^1,2, Fu Zheng¹, Yidong Lou^1,*, Shengfeng Gu¹, Weixing Zhang¹, Xiaolei Dai¹, Xianjie Li¹, Hailin Guo¹ and Xiaopeng Gong¹

¹ GNSS Research Center, Wuhan University, 129 Luoyu Road, Wuhan 430079, China

² School of Electronic and Information Engineering, Beihang University, 37 Xueyuan Road, Beijing 100083, China

Remote Sens. 2017, 9(8), 837; https://doi.org/10.3390/rs9080837 - 12 Aug 2017

Cited by 27 | Viewed by 6238

Abstract

Abstract: In this contribution, the processing strategies of real-time BeiDou System (BDS) precise orbits, clocks, and ionospheric corrections in the National BDS Augmentation Service System (NBASS) are briefly introduced. The Root Mean Square (RMS) of BDS predicted orbits are better than 10 cm [...] Read more.

Abstract: In this contribution, the processing strategies of real-time BeiDou System (BDS) precise orbits, clocks, and ionospheric corrections in the National BDS Augmentation Service System (NBASS) are briefly introduced. The Root Mean Square (RMS) of BDS predicted orbits are better than 10 cm in radial and cross-track components, and the accuracy of the BDS real-time clock is better than 0.5 ns for Inclined Geosynchronous Orbit (IGSO) and Mid Earth Orbit (MEO) satellites. The accuracy of BDS Geostationary Earth Orbit (GEO) orbits and clocks are worse than the IGSO and MEO satellites due to its poor geometry conditions. The real-time ionospheric correction is evaluated by cross-validation, and the average accuracy in the vertical direction is about 4 TECU. With these real-time corrections, the overall single and dual-frequency kinematic precise point positioning (PPP) performance in China are evaluated in terms of positioning accuracy at the 95% confidence level and convergence time. The BDS PPP positioning accuracy shows significant regional characteristics due to the geometry distribution of BDS satellites and the accuracy of ionospheric model in different regions. The BDS dual-frequency PPP positioning accuracy in high-latitude and western fringe region is about 0.5 m and 1.0 m in the horizontal and vertical component, respectively, while the horizontal accuracy is better than 0.2 m and the vertical accuracy is better than 0.3 m in the midlands. The convergence time of the BDS PPP is much longer than the GPS PPP and it needs more than 60 min to achieve the accuracy better than 10 cm in both horizontal and vertical directions for dual-frequency PPP. Similar with dual-frequency PPP, the positioning accuracy of the BDS single-frequency PPP in the fringe region is worse than other regions. The positioning in the midlands can achieve 0.5 m in horizontal component and 1.0 m in the vertical component. In addition, when GPS and BDS are combined, the positioning performance of both single-frequency and dual-frequency PPP can be greatly improved. Full article

(This article belongs to the Special Issue Multi-Constellation Global Navigation Satellite Systems: Methods and Applications)

▼ Show Figures

Graphical abstract

22 pages, 5437 KiB

Open AccessArticle

Evaluating Sentinel-2 and Landsat-8 Data to Map Sucessional Forest Stages in a Subtropical Forest in Southern Brazil

by Camile Sothe^1,*

, Cláudia Maria de Almeida¹

, Veraldo Liesenberg² and Marcos Benedito Schimalski²

¹ National Institute for Space Research (INPE), Remote Sensing Department (DSR), 12227-010 São José dos Campos-SP, Brazil

² Santa Catarina State University (UDESC), Department of Forest Engineering (DEF), 88520-000 Lages-SC, Brazil

Remote Sens. 2017, 9(8), 838; https://doi.org/10.3390/rs9080838 - 13 Aug 2017

Cited by 107 | Viewed by 15895

Abstract

Studies designed to discriminate different successional forest stages play a strategic role in forest management, forest policy and environmental conservation in tropical environments. The discrimination of different successional forest stages is still a challenge due to the spectral similarity among the concerned classes. [...] Read more.

Studies designed to discriminate different successional forest stages play a strategic role in forest management, forest policy and environmental conservation in tropical environments. The discrimination of different successional forest stages is still a challenge due to the spectral similarity among the concerned classes. Considering this, the objective of this paper was to investigate the performance of Sentinel-2 and Landsat-8 data for discriminating different successional forest stages of a patch located in a subtropical portion of the Atlantic Rain Forest in Southern Brazil with the aid of two machine learning algorithms and relying on the use of spectral reflectance data selected over two seasons and attributes thereof derived. Random Forest (RF) and Support Vector Machine (SVM) were used as classifiers with different subsets of predictor variables (multitemporal spectral reflectance, textural metrics and vegetation indices). All the experiments reached satisfactory results, with Kappa indices varying between 0.9, with Landsat-8 spectral reflectance alone and the SVM algorithm, and 0.98, with Sentinel-2 spectral reflectance alone also associated with the SVM algorithm. The Landsat-8 data had a significant increase in accuracy with the inclusion of other predictor variables in the classification process besides the pure spectral reflectance bands. The classification methods SVM and RF had similar performances in general. As to the RF method, the texture mean of the red-edge and SWIR bands were considered the most important ranked attributes for the classification of Sentinel-2 data, while attributes resulting from multitemporal bands, textural metrics of SWIR bands and vegetation indices were the most important ones in the Landsat-8 data classification. Full article

(This article belongs to the Special Issue Multisource Remote Sensing Data Fusion and Applications in Vegetation Monitoring)

▼ Show Figures

Graphical abstract

24 pages, 19673 KiB

Open AccessFeature PaperArticle

Multiscale Remote Sensing to Map the Spatial Distribution and Extent of Cropland in the Sudanian Savanna of West Africa

by Gerald Forkuor^1,*, Christopher Conrad²

, Michael Thiel², Benewinde J-B. Zoungrana¹ and Jérôme E. Tondoh¹

¹ West African Science Service Center on Climate Change and Adapted Land Use, Avenue Mouammar Kadhafi, 06 BP 9507 Ouagadougou, Burkina Faso

² Remote Sensing Unit, University of Würzburg, Oswald-Külpe-Weg 86, 97074 Würzburg, Germany

Remote Sens. 2017, 9(8), 839; https://doi.org/10.3390/rs9080839 - 14 Aug 2017

Cited by 32 | Viewed by 7300

Abstract

Food security is the topmost priority on the global agenda. Accurate agricultural statistics (i.e., cropped area) are essential for decision making and developing appropriate programs to achieve food security. However, derivation of these essential agricultural statistics, especially in developing countries, is fraught with [...] Read more.

Food security is the topmost priority on the global agenda. Accurate agricultural statistics (i.e., cropped area) are essential for decision making and developing appropriate programs to achieve food security. However, derivation of these essential agricultural statistics, especially in developing countries, is fraught with many challenges including financial, logistical and human capacity limitations. This study investigated the use of fractional cover approaches in mapping cropland area in the heterogeneous landscape of West Africa. Discrete cropland areas identified from multi-temporal Landsat data were upscaled to MODIS resolution using random forest regression. Producer’s accuracy and user’s accuracy of the cropland class in the Landsat scale analysis averaged 95% and 94%, respectively, indicating good separability between crop and non-crop land. Validation of the fractional cropland cover map at MODIS resolution (MODIS_FCM) revealed an overall mean absolute error of 19%. Comparison of MODIS_FCM with the MODIS land cover product (e.g., MODIS_LCP) demonstrate the suitability of the proposed approach to cropped area estimation in smallholder dominant heterogeneous landscapes over existing global solutions. Comparison with official government statistics (i.e., cropped area) revealed variable levels of agreement and partly enormous disagreements, which clearly indicate the need to integrate remote sensing approaches and ground based surveys conducted by agricultural ministries in improving cropped area estimation. The recent availability of a wide range of open access remote sensing data is expected to expedite this integration and contribute missing information urgently required for regional assessments of food security in West Africa and beyond. Full article

(This article belongs to the Special Issue Monitoring Agricultural Land-Use Change and Land-Use Intensity)

▼ Show Figures

Graphical abstract

18 pages, 9026 KiB

Open AccessArticle

Topic Modelling for Object-Based Unsupervised Classification of VHR Panchromatic Satellite Images Based on Multiscale Image Segmentation

by Li Shen^1,2,*

, Linmei Wu^1,2, Yanshuai Dai^1,2, Wenfan Qiao^1,2 and Ying Wang^1,2

¹ State-Province Joint Engineering Laboratory of Spatial Information Technology for High-Speed Railway Safety, Southwest Jiaotong University, Chengdu 611756, China

² Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China

Remote Sens. 2017, 9(8), 840; https://doi.org/10.3390/rs9080840 - 14 Aug 2017

Cited by 9 | Viewed by 6823

Abstract

Image segmentation is a key prerequisite for object-based classification. However, it is often difficult, or even impossible, to determine a unique optimal segmentation scale due to the fact that various geo-objects, and even an identical geo-object, present at multiple scales in very high [...] Read more.

Image segmentation is a key prerequisite for object-based classification. However, it is often difficult, or even impossible, to determine a unique optimal segmentation scale due to the fact that various geo-objects, and even an identical geo-object, present at multiple scales in very high resolution (VHR) satellite images. To address this problem, this paper presents a novel unsupervised object-based classification for VHR panchromatic satellite images using multiple segmentations via the latent Dirichlet allocation (LDA) model. Firstly, multiple segmentation maps of the original satellite image are produced by means of a common multiscale segmentation technique. Then, the LDA model is utilized to learn the grayscale histogram distribution for each geo-object and the mixture distribution of geo-objects within each segment. Thirdly, the histogram distribution of each segment is compared with that of each geo-object using the Kullback-Leibler (KL) divergence measure, which is weighted with a constraint specified by the mixture distribution of geo-objects. Each segment is allocated a geo-object category label with the minimum KL divergence. Finally, the final classification map is achieved by integrating the multiple classification results at different scales. Extensive experimental evaluations are designed to compare the performance of our method with those of some state-of-the-art methods for three different types of images. The experimental results over three different types of VHR panchromatic satellite images demonstrate the proposed method is able to achieve scale-adaptive classification results, and improve the ability to differentiate the geo-objects with spectral overlap, such as water and grass, and water and shadow, in terms of both spatial consistency and semantic consistency. Full article

(This article belongs to the Special Issue Learning to Understand Remote Sensing Images)

▼ Show Figures

Graphical abstract

18 pages, 3545 KiB

Open AccessArticle

A Probabilistic Weighted Archetypal Analysis Method with Earth Mover’s Distance for Endmember Extraction from Hyperspectral Imagery

by Weiwei Sun^1,2,*, Dianfa Zhang¹, Yan Xu³, Long Tian³, Gang Yang¹ and Weiyue Li^4,*

¹ Department of Geography and Spatial Information Techniques, Ningbo University, Ningbo 315211, China

² State Key Lab of Information Engineering on Survey, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China

³ Department of Electrical and Computer Engineering, Mississippi State University, Starkville, MS 39762, USA

⁴ Institute of Urban Studies, Shanghai Normal University, Shanghai 200234, China

Remote Sens. 2017, 9(8), 841; https://doi.org/10.3390/rs9080841 - 14 Aug 2017

Cited by 7 | Viewed by 5342

Abstract

A Probabilistic Weighted Archetypal Analysis method with Earth Mover’s Distance (PWAA-EMD) is proposed to extract endmembers from hyperspectral imagery (HSI). The PWAA-EMD first utilizes the EMD dissimilarity matrix to weight the coefficient matrix in the regular Archetypal Analysis (AA). The EMD metric considers [...] Read more.

A Probabilistic Weighted Archetypal Analysis method with Earth Mover’s Distance (PWAA-EMD) is proposed to extract endmembers from hyperspectral imagery (HSI). The PWAA-EMD first utilizes the EMD dissimilarity matrix to weight the coefficient matrix in the regular Archetypal Analysis (AA). The EMD metric considers manifold structures of spectral signatures in the HSI data and could better quantify the dissimilarity features among pairwise pixels. Second, the PWAA-EMD adopts the Bayesian framework and formulates the improved AA into a probabilistic inference problem by maximizing a joint posterior density. Third, the optimization problem is solved by the iterative multiplicative update scheme, with a careful initialization from the two-stage algorithm and the proper endmembers are finally obtained. The synthetic and real Cuprite Hyperspectral datasets are utilized to verify the performance of PWAA-EMD and five popular methods are implemented to make comparisons. The results show that PWAA-EMD surpasses all the five methods in the average results of spectral angle distance (SAD) and root-mean-square-error (RMSE). Especially, the PWAA-EMD obtains more accurate estimation than AA in almost all the classes of endmembers including two similar ones. Therefore, the PWAA-EMD could be an alternative choice for endmember extraction on the hyperspectral data. Full article

▼ Show Figures

Figure 1

15 pages, 6364 KiB

Open AccessArticle

Long-Term Water Storage Changes of Lake Volta from GRACE and Satellite Altimetry and Connections with Regional Climate

by Shengnan Ni^1,2,*

, Jianli Chen³, Clark R. Wilson^3,4 and Xiaogong Hu¹

¹ Shanghai Astronomical Observatory, Chinese Academy of Sciences, Shanghai 200030, China

² University of Chinese Academy of Sciences, Beijing 100049, China

³ Center for Space Research, University of Texas at Austin, Austin, TX 78759, USA

⁴ Department of Geological Sciences, Jackson School of Geosciences, University of Texas at Austin, Austin, TX 78712, USA

Remote Sens. 2017, 9(8), 842; https://doi.org/10.3390/rs9080842 - 14 Aug 2017

Cited by 29 | Viewed by 9366

Abstract

Satellite gravity data from the Gravity Recovery and Climate Experiment (GRACE) provides a quantitative measure of terrestrial water storage (TWS) change at different temporal and spatial scales. In this study, we investigate the ability of GRACE to quantitatively monitor long-term hydrological characteristics over [...] Read more.

Satellite gravity data from the Gravity Recovery and Climate Experiment (GRACE) provides a quantitative measure of terrestrial water storage (TWS) change at different temporal and spatial scales. In this study, we investigate the ability of GRACE to quantitatively monitor long-term hydrological characteristics over the Lake Volta region. Principal component analysis (PCA) is employed to study temporal and spatial variability of long-term TWS changes. Long-term Lake Volta water storage change appears to be the dominant long-term TWS change signal in the Volta basin. GRACE-derived TWS changes and precipitation variations compiled by the Global Precipitation Climatology Centre (GPCC) are related both temporally and spatially, but spatial leakage attenuates the magnitude of GRACE estimates, especially at small regional scales. Using constrained forward modeling, we successfully remove leakage error in GRACE estimates. After this leakage correction, GRACE-derived Lake Volta water storage changes agree remarkably well with independent estimates from satellite altimetry at interannual and longer time scales. This demonstrates the value of GRACE estimates to monitor and quantify water storage changes in lakes, especially in relatively small regions with complicated topography. Full article

(This article belongs to the Special Issue Remote Sensing of Climate Change and Water Resources)

▼ Show Figures

Graphical abstract

24 pages, 3181 KiB

Open AccessArticle

An Accuracy Assessment of Derived Digital Elevation Models from Terrestrial Laser Scanning in a Sub-Tropical Forested Environment

by Jasmine Muir^1,2,3,*

, Nicholas Goodwin^1,2, John Armston^1,2,4

, Stuart Phinn¹

and Peter Scarth^1,2

¹ Joint Remote Sensing Research Program, School of Earth and Environmental Sciences, University of Queensland, St. Lucia, Brisbane 4067, Queensland, Australia

² Department of Science, Information Technology and Innovation (DSITI) Remote Sensing Centre, Ecosciences Precinct, Brisbane 4001, Queensland, Australia

³ School of Science and Technology, University of New England, Armidale 2350, New South Wales, Australia

⁴ Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA

Remote Sens. 2017, 9(8), 843; https://doi.org/10.3390/rs9080843 - 14 Aug 2017

Cited by 13 | Viewed by 5784

Abstract

Forest structure attributes produced from terrestrial laser scanning (TLS) rely on normalisation of the point cloud values from sensor coordinates to height above ground. One method to do this is through the derivation of an accurate and repeatable digital elevation model (DEM) from [...] Read more.

Forest structure attributes produced from terrestrial laser scanning (TLS) rely on normalisation of the point cloud values from sensor coordinates to height above ground. One method to do this is through the derivation of an accurate and repeatable digital elevation model (DEM) from the TLS point cloud that is used to adjust the height. The primary aim of this paper was to test a number of TLS scan configurations, filtering options and output DEM grid resolutions (from 0.02 m to 1.0 m) to define a best practice method for DEM generation in sub-tropical forest environments. The generated DEMs were compared to both total station (TS) spot heights and a 1-m DEM generated from airborne laser scanning (ALS) to assess accuracy. The comparison to TS spot heights found that a DEM produced using the minimum elevation (minimum Z value) from a point cloud derived from a single scan had mean errors >1 m for DEM grid resolutions <0.2 m at a 25-m plot radius. At a 1-m grid resolution, the mean error was 0.19 m. The addition of a filtering approach that combined a median filter with a progressive morphological filter and a global percentile filter was able to reduce mean error of the 0.02-m grid resolution DEM to 0.31 m at a 25-m plot radius using all returns. Using multiple scan positions to derive the DEM reduced the mean error for all DEM methods. Our results suggest that a simple minimum Z filtering DEM method using a single scan at the grid resolution of 1 m can produce mean errors <0.2 m, but for a small grid resolution, such as 0.02 m, a more complex filtering approach and multiple scan positions are required to reduced mean errors. The additional validation data provided by the 1-m ALS DEM showed that when using the combined filtering method on a point cloud derived from a single scan at the plot centre, errors between 0.1 and 0.5 m occurred in the TLS DEM for all tested grid resolutions at a plot radius of 25 m. These findings present a protocol for DEM production from TLS data at a range of grid resolutions and provide an overview of factors affecting DEMs produced from single and multiple TLS scan positions. Full article

(This article belongs to the Section Forest Remote Sensing)

▼ Show Figures

Graphical abstract

21 pages, 11236 KiB

Open AccessArticle

Extension of a Fast GLRT Algorithm to 5D SAR Tomography of Urban Areas

by Alessandra Budillon^*

, Angel Caroline Johnsy and Gilda Schirinzi

Dipartimento di Ingegneria, Università degli Studi di Napoli “Parthenope”, Naples 80143, Italy

Remote Sens. 2017, 9(8), 844; https://doi.org/10.3390/rs9080844 - 14 Aug 2017

Cited by 35 | Viewed by 6008

Abstract

This paper analyzes a method for Synthetic Aperture Radar (SAR) Tomographic (TomoSAR) imaging, allowing the detection of multiple scatterers that can exhibit time deformation and thermal dilation by using a CFAR (Constant False Alarm Rate) approach. In the last decade, several methods for [...] Read more.

This paper analyzes a method for Synthetic Aperture Radar (SAR) Tomographic (TomoSAR) imaging, allowing the detection of multiple scatterers that can exhibit time deformation and thermal dilation by using a CFAR (Constant False Alarm Rate) approach. In the last decade, several methods for TomoSAR have been proposed. The objective of this paper is to present the results obtained on high resolution tomographic SAR data of urban areas, by using a statistical test for detecting multiple scatterers that takes into account phase variations due to possible deformations and/or thermal dilation. The test can be evaluated in terms of probability of detection (P_D) and probability of false alarm (P_FA), and is based on an approximation of a Generalized Likelihood Ratio Test (GLRT), denoted as Fast-Sup-GLRT. It was already applied and validated by the authors in the 3D case, while here it is extended and experimented in the 5D case. Numerical experiments on simulated and on StripMap TerraSAR-X (TSX) data have been carried out. The presented results show that the adopted method allows the detection of a large number of scatterers and the estimation of their position with a good accuracy, and that the consideration of the thermal dilation and surface deformation helps in recovering more single and double scatterers, with respect to the case in which these contributions are not taken into account. Moreover, the capability of method to provide reliable estimates of the deformations in urban structure suggests its use in structure stress monitoring. Full article

▼ Show Figures

Graphical abstract

17 pages, 11999 KiB

Open AccessArticle

Assimilation of Sentinel-1 Derived Sea Surface Winds for Typhoon Forecasting

by Yi Yu^1,*, Xiaofeng Yang^2,3, Weimin Zhang¹

, Boheng Duan¹, Xiaoqun Cao¹ and Hongze Leng¹

¹ School of Computer Science, National University of Defense Technology, Changsha 410073, China

² State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China

³ The Key Laboratory for Earth Observation of Hainan Province, Sanya 572029, China

Remote Sens. 2017, 9(8), 845; https://doi.org/10.3390/rs9080845 - 14 Aug 2017

Cited by 13 | Viewed by 5331

Abstract

High-resolution synthetic aperture radar (SAR) wind observations provide fine structural information for tropical cycles and could be assimilated into numerical weather prediction (NWP) models. However, in the conventional method assimilating the u and v components for SAR wind observations (SAR_uv), the wind direction [...] Read more.

High-resolution synthetic aperture radar (SAR) wind observations provide fine structural information for tropical cycles and could be assimilated into numerical weather prediction (NWP) models. However, in the conventional method assimilating the u and v components for SAR wind observations (SAR_uv), the wind direction is not a state vector and its observational error is not considered during the assimilation calculation. In this paper, an improved method for wind observation directly assimilates the SAR wind observations in the form of speed and direction (SAR_sd). This method was implemented to assimilate the sea surface wind retrieved from Sentinel-1 synthetic aperture radar (SAR) in the basic three-dimensional variational system for the Weather Research and Forecasting Model (WRF 3DVAR). Furthermore, a new quality control scheme for wind observations is also presented. Typhoon Lionrock in August 2016 is chosen as a case study to investigate and compare both assimilation methods. The experimental results show that the SAR wind observations can increase the number of the effective observations in the area of a typhoon and have a positive impact on the assimilation analysis. The numerical forecast results for this case show better results for the SAR_sd method than for the SAR_uv method. The SAR_sd method looks very promising for winds assimilation under typhoon conditions, but more cases need to be considered to draw final conclusions. Full article

(This article belongs to the Special Issue Ocean Remote Sensing with Synthetic Aperture Radar)

▼ Show Figures

Graphical abstract

17 pages, 5576 KiB

Open AccessArticle

A Novel Method of Change Detection in Bi-Temporal PolSAR Data Using a Joint-Classification Classifier Based on a Similarity Measure

by Jinqi Zhao^1,2

, Jie Yang^1,*, Zhong Lu²

, Pingxiang Li¹, Wensong Liu¹

and Le Yang¹

¹ State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China

² Huffington Department of Earth Sciences, Southern Methodist University, Dallas, TX 75275, USA

Remote Sens. 2017, 9(8), 846; https://doi.org/10.3390/rs9080846 - 15 Aug 2017

Cited by 17 | Viewed by 6018

Abstract

Accurate and timely change detection of the Earth’s surface features is extremely important for understanding the relationships and interactions between people and natural phenomena. Owing to the all-weather response capability, polarimetric synthetic aperture radar (PolSAR) has become a key tool for change detection. [...] Read more.

Accurate and timely change detection of the Earth’s surface features is extremely important for understanding the relationships and interactions between people and natural phenomena. Owing to the all-weather response capability, polarimetric synthetic aperture radar (PolSAR) has become a key tool for change detection. Change detection includes both unsupervised and supervised methods. Unsupervised change detection is simple and effective, but cannot detect the type of land cover change. Supervised change detection can detect the type of land cover change, but is easily affected and depended by the human interventions. To solve these problems, a novel method of change detection using a joint-classification classifier (JCC) based on a similarity measure is introduced. The similarity measure is obtained by a test statistic and the Kittler and Illingworth (TSKI) minimum-error thresholding algorithm, which is used to automatically control the JCC. The efficiency of the proposed method is demonstrated by the use of bi-temporal PolSAR images acquired by RADARSAT-2 over Wuhan, China. The experimental results show that the proposed method can identify the different types of land cover change and can reduce both the false detection rate and false alarm rate in the change detection. Full article

▼ Show Figures

Graphical abstract

23 pages, 2342 KiB

Open AccessReview

Stochastic Bias Correction and Uncertainty Estimation of Satellite-Retrieved Soil Moisture Products

by Ju Hyoung Lee^1,2,*, Chuanfeng Zhao³

and Yann Kerr²

¹ Agricultural and Life Science Research Institute, Seoul National University, Seoul 08826, Korea

² CESBIO, 13 Avenue du Colonel Roche, UMR 5126, 31401 Toulouse, France

³ College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China

Remote Sens. 2017, 9(8), 847; https://doi.org/10.3390/rs9080847 - 15 Aug 2017

Cited by 14 | Viewed by 5431

Abstract

To apply satellite-retrieved soil moisture to a short-range weather prediction, we review a stochastic approach for reducing foot print scale biases and estimating its uncertainties. First, we discuss a challenge of representativeness errors. Before describing retrieval errors in more detail, we clarify a [...] Read more.

To apply satellite-retrieved soil moisture to a short-range weather prediction, we review a stochastic approach for reducing foot print scale biases and estimating its uncertainties. First, we discuss a challenge of representativeness errors. Before describing retrieval errors in more detail, we clarify a conceptual difference between error and uncertainty in basic metrological terms of the International Organization for Standardization (ISO), and briefly summarize how current retrieval algorithms deal with a challenge of land surface heterogeneity. As compared to relative approaches such as Triple Collocation, or cumulative distribution function (CDF) matching that aim for climatology stationary errors at time-scale of years, we address a stochastic approach for reducing instantaneous retrieval errors at time-scale of several hours to days. The stochastic approach has a potential as a global scheme to resolve systematic errors introducing from instrumental measurements, geo-physical parameters, and surface heterogeneity across the globe, because it does not rely on the ground measurements or reference data to be compared with. Full article

(This article belongs to the Section Remote Sensing in Geology, Geomorphology and Hydrology)

▼ Show Figures

Figure 1

22 pages, 9400 KiB

Open AccessArticle

Pre-Trained AlexNet Architecture with Pyramid Pooling and Supervision for High Spatial Resolution Remote Sensing Image Scene Classification

by Xiaobing Han^1,2, Yanfei Zhong^1,2,*

, Liqin Cao^3,* and Liangpei Zhang^1,2

¹ State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430079, China

² Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan 430079, China

³ School of Printing and Packaging, Wuhan University, Wuhan 430079, China

Remote Sens. 2017, 9(8), 848; https://doi.org/10.3390/rs9080848 - 16 Aug 2017

Cited by 297 | Viewed by 40389

Abstract

The rapid development of high spatial resolution (HSR) remote sensing imagery techniques not only provide a considerable amount of datasets for scene classification tasks but also request an appropriate scene classification choice when facing with finite labeled samples. AlexNet, as a relatively simple [...] Read more.

The rapid development of high spatial resolution (HSR) remote sensing imagery techniques not only provide a considerable amount of datasets for scene classification tasks but also request an appropriate scene classification choice when facing with finite labeled samples. AlexNet, as a relatively simple convolutional neural network (CNN) architecture, has obtained great success in scene classification tasks and has been proven to be an excellent foundational hierarchical and automatic scene classification technique. However, current HSR remote sensing imagery scene classification datasets always have the characteristics of small quantities and simple categories, where the limited annotated labeling samples easily cause non-convergence. For HSR remote sensing imagery, multi-scale information of the same scenes can represent the scene semantics to a certain extent but lacks an efficient fusion expression manner. Meanwhile, the current pre-trained AlexNet architecture lacks a kind of appropriate supervision for enhancing the performance of this model, which easily causes overfitting. In this paper, an improved pre-trained AlexNet architecture named pre-trained AlexNet-SPP-SS has been proposed, which incorporates the scale pooling—spatial pyramid pooling (SPP) and side supervision (SS) to improve the above two situations. Extensive experimental results conducted on the UC Merced dataset and the Google Image dataset of SIRI-WHU have demonstrated that the proposed pre-trained AlexNet-SPP-SS model is superior to the original AlexNet architecture as well as the traditional scene classification methods. Full article

(This article belongs to the Special Issue Remote Sensing Big Data: Theory, Methods and Applications)

▼ Show Figures

Figure 1

1 pages, 143 KiB

Open AccessCorrection

Correction: Yao, P. et al. Rebuilding Long Time Series Global Soil Moisture Products Using the Neural Network Adopted the Microwave Vegetation Index. Remote Sens. 2017, 9, 35

by Panpan Yao^1,2

, Jiancheng Shi^2,3,*

, Tianjie Zhao^2,3, Hui Lu^3,4 and Amen Al-Yaari⁵

¹ Graduate School of University of Chinese Academy of Sciences, Beijing 100049, China

² State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China

³ The Joint Center for Global Change Studies, Beijing 100875, China

⁴ Ministry of Education Key Laboratory for Earth System Modeling, and Department of Earth System Science, Tsinghua University, Beijing 100084, China

⁵ INRA, UMR1391 ISPA, 33140 Villenave d’Ornon, France

Remote Sens. 2017, 9(8), 849; https://doi.org/10.3390/rs9080849 - 16 Aug 2017

Cited by 4 | Viewed by 3778

Abstract

After publication of the research paper [1], the authors wish to make the following correction to this paper. In the fourth line from the bottom in abstract, due to a typing error, “RMSE = 0.84 m3/m3” should be replaced with “RMSE = 0.084 [...] Read more.

After publication of the research paper [1], the authors wish to make the following correction to this paper. In the fourth line from the bottom in abstract, due to a typing error, “RMSE = 0.84 m3/m3” should be replaced with “RMSE = 0.084 m3/m3”.[...] Full article

18 pages, 3292 KiB

Open AccessLetter

What is the Direction of Land Change? A New Approach to Land-Change Analysis

by Mingde You^1,2

, Anthony M. Filippi^1,2,*, İnci Güneralp^1,2 and Burak Güneralp^1,2

¹ Department of Geography, 3147 TAMU, Texas A & M University, College Station, TX 77843, USA

² Center for Geospatial Science, Applications and Technology (GEOSAT), Texas A & M University, College Station, TX 77843, USA

Remote Sens. 2017, 9(8), 850; https://doi.org/10.3390/rs9080850 - 16 Aug 2017

Cited by 4 | Viewed by 6328

Abstract

Accurate characterization of the direction of land change is a neglected aspect of land dynamics. Knowledge on direction of historical land change can be useful information when understanding relative influence of different land-change drivers is of interest. In this study, we present a [...] Read more.

Accurate characterization of the direction of land change is a neglected aspect of land dynamics. Knowledge on direction of historical land change can be useful information when understanding relative influence of different land-change drivers is of interest. In this study, we present a novel perspective on land-change analysis by focusing on directionality of change. To this end, we employed Maximum Cross-Correlation (MCC) approach to estimate the directional change in land cover in a dynamic river floodplain environment using Landsat 5 Thematic Mapper (TM) images. This approach has previously been used for detecting and measuring fluid and ice motions but not to study directional changes in land cover. We applied the MCC approach on land-cover class membership layers derived from fuzzy remote-sensing image classification. We tested the sensitivity of the resulting displacement vectors to three user-defined parameters—template size, search window size, and a threshold parameter to determine valid (non-noisy) displacement vectors—that directly affect the generation of change, or displacement, vectors; this has not previously been thoroughly investigated in any application domain. The results demonstrate that it is possible to quantitatively measure the rate of directional change in land cover in this floodplain environment using this particular approach. Sensitivity analyses indicate that template size and MCC threshold parameter are more influential on the displacement vectors than search window size. The results vary by land-cover class, suggesting that spatial configuration of land-cover classes should be taken into consideration in the implementation of the method. Full article

▼ Show Figures

Graphical abstract

24 pages, 34337 KiB

Open AccessArticle

Reflectance Intensity Assisted Automatic and Accurate Extrinsic Calibration of 3D LiDAR and Panoramic Camera Using a Printed Chessboard

by Weimin Wang^1,*

, Ken Sakurada¹ and Nobuo Kawaguchi^1,2

¹ Graduate School of Engineering, Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8601, Japan

² Institute of Innovation for Future Society, Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8601, Japan

Remote Sens. 2017, 9(8), 851; https://doi.org/10.3390/rs9080851 - 16 Aug 2017

Cited by 87 | Viewed by 15081

Abstract

This paper presents a novel method for fully automatic and convenient extrinsic calibration of a 3D LiDAR and a panoramic camera with a normally printed chessboard. The proposed method is based on the 3D corner estimation of the chessboard from the sparse point [...] Read more.

This paper presents a novel method for fully automatic and convenient extrinsic calibration of a 3D LiDAR and a panoramic camera with a normally printed chessboard. The proposed method is based on the 3D corner estimation of the chessboard from the sparse point cloud generated by one frame scan of the LiDAR. To estimate the corners, we formulate a full-scale model of the chessboard and fit it to the segmented 3D points of the chessboard. The model is fitted by optimizing the cost function under constraints of correlation between the reflectance intensity of laser and the color of the chessboard’s patterns. Powell’s method is introduced for resolving the discontinuity problem in optimization. The corners of the fitted model are considered as the 3D corners of the chessboard. Once the corners of the chessboard in the 3D point cloud are estimated, the extrinsic calibration of the two sensors is converted to a 3D-2D matching problem. The corresponding 3D-2D points are used to calculate the absolute pose of the two sensors with Unified Perspective-n-Point (UPnP). Further, the calculated parameters are regarded as initial values and are refined using the Levenberg-Marquardt method. The performance of the proposed corner detection method from the 3D point cloud is evaluated using simulations. The results of experiments, conducted on a Velodyne HDL-32e LiDAR and a Ladybug3 camera under the proposed re-projection error metric, qualitatively and quantitatively demonstrate the accuracy and stability of the final extrinsic calibration parameters. Full article

▼ Show Figures

Graphical abstract

10 pages, 3732 KiB

Open AccessArticle

Influence of Droughts on Mid-Tropospheric CO₂

by Xun Jiang^1,*, Angela Kao¹, Abigail Corbett¹

, Edward Olsen², Thomas Pagano², Albert Zhai^3,4, Sally Newman³, Liming Li⁵ and Yuk Yung³

¹ Department of Earth & Atmospheric Sciences, University of Houston, Houston, TX 77004, USA

² Science Division, Jet Propulsion Laboratory, California Institute of Technology, 1200 E California Blvd, Pasadena, CA 91125, USA

³ Division of Geological and Planetary Sciences, California Institute of Technology, 1200 E California Blvd, Pasadena, CA 91125, USA

⁴ La Cañada High School, La Cañada Flintridge, Los Angeles, CA 91011, USA

⁵ Department of Physics, University of Houston, Houston, TX 77004, USA

Remote Sens. 2017, 9(8), 852; https://doi.org/10.3390/rs9080852 - 17 Aug 2017

Cited by 4 | Viewed by 5147

Abstract

Using CO₂ data from the Atmospheric Infrared Sounder (AIRS), it is found for the first time that the mid-tropospheric CO₂ concentration is ~1 part per million by volume higher during dry years than wet years over the southwestern USA from June [...] Read more.

Using CO₂ data from the Atmospheric Infrared Sounder (AIRS), it is found for the first time that the mid-tropospheric CO₂ concentration is ~1 part per million by volume higher during dry years than wet years over the southwestern USA from June to September. The mid-tropospheric CO₂ differences between dry and wet years are related to circulation and CO₂ surface fluxes. During drought conditions, vertical pressure velocity from NCEP2 suggests that there is more rising air over most regions, which can help bring high surface concentrations of CO₂ to the mid-troposphere. In addition to the circulation, there is more CO₂ emitted from the biosphere to the atmosphere during droughts in some regions, which can contribute to higher concentrations of CO₂ in the atmosphere. Results obtained from this study demonstrate the significant impact of droughts on atmospheric CO₂ and therefore on a feedback cycle contributing to greenhouse gas warming. It can also help us better understand atmospheric CO₂, which plays a critical role in our climate system. Full article

(This article belongs to the Special Issue Remote Sensing of Greenhouse Gases)

▼ Show Figures

Graphical abstract

15 pages, 3615 KiB

Open AccessArticle

Specular Reflection Effects Elimination in Terrestrial Laser Scanning Intensity Data Using Phong Model

by Kai Tan^1,2,* and Xiaojun Cheng¹

¹ College of Surveying and Geo-Informatics, Tongji University, No. 1239, Siping Road, Shanghai 200092, China

² State Key Laboratory of Estuarine and Coastal Research, East China Normal University, No. 3663, North Zhongshan Road, Shanghai 200062, China

Remote Sens. 2017, 9(8), 853; https://doi.org/10.3390/rs9080853 - 17 Aug 2017

Cited by 38 | Viewed by 15290

Abstract

The intensity value recorded by terrestrial laser scanning (TLS) systems is significantly influenced by the incidence angle. The incidence angle effect is an object property, which is mainly related to target scattering properties, surface structures, and even some instrumental effects. Most existing models [...] Read more.

The intensity value recorded by terrestrial laser scanning (TLS) systems is significantly influenced by the incidence angle. The incidence angle effect is an object property, which is mainly related to target scattering properties, surface structures, and even some instrumental effects. Most existing models focus on diffuse reflections of rough surfaces and ignore specular reflections, despite that both reflections simultaneously exist in all natural surfaces. Due to the coincidence of the emitter and receiver in TLS, specular reflections can be ignored at large incidence angles. On the contrary, at small incidence angles, TLS detectors can receive a portion of specular reflections. The received specular reflections can trigger highlight phenomenon (hot-spot effects) in the intensity data of the scanned targets, particularly those with a relatively smooth or highly-reflective surface. In this study, a new method that takes diffuse and specular reflections, as well as the instrumental effects into consideration, is proposed to eliminate the specular reflection effects in TLS intensity data. Diffuse reflections and instrumental effects are modeled by a polynomial based on Lambertian reference targets, whereas specular reflections are modeled by the Phong model. The proposed method is tested and validated on different targets scanned by the Faro Focus^3D 120 terrestrial scanner. Results imply that the coefficient of variation of the intensity data from a homogeneous surface is reduced by approximately 38% when specular reflections are considered. Compared with existing methods, the proposed method exhibits good feasibility and high accuracy in eliminating the specular reflection effects for intensity image interpretation and 3D point cloud representation by intensity. Full article

▼ Show Figures

Graphical abstract

21 pages, 12254 KiB

Open AccessArticle

Technical Evaluation of Sentinel-1 IW Mode Cross-Pol Radar Backscattering from the Ocean Surface in Moderate Wind Condition

by Lanqing Huang¹, Bin Liu^1,*

, Xiaofeng Li²

, Zenghui Zhang¹ and Wenxian Yu¹

¹ Shanghai Key Laboratory of Intelligent Sensing and Recognition, Shanghai Jiao Tong University, Shanghai 200240, China

² Global Science and Technology, National Oceanic and Atmospheric Administration (NOAA)/NOAA Satellite and Information Service, College Park, MD 20740, USA

Remote Sens. 2017, 9(8), 854; https://doi.org/10.3390/rs9080854 - 17 Aug 2017

Cited by 28 | Viewed by 7945

Abstract

The Sentinel-1 synthetic aperture radar (SAR) allows sufficient resources for cross-pol wind speed retrievals over the ocean. In this paper, we present technical evaluation on wind retrieval from both Sentinel-1A and Sentinel-1B IW cross-pol images. Algorithms are based on the existing theoretical and [...] Read more.

The Sentinel-1 synthetic aperture radar (SAR) allows sufficient resources for cross-pol wind speed retrievals over the ocean. In this paper, we present technical evaluation on wind retrieval from both Sentinel-1A and Sentinel-1B IW cross-pol images. Algorithms are based on the existing theoretical and empirical ones derived from the RADARSAT-2 cross-pol data. First, to better understand the Sentinel-1 observed normalized radar cross section (NRCS) values under various environmental conditions, we constructed a dataset that integrates SAR images with wind field information from scatterometer measurements. There are 11,883 matchup data in the experimental dataset. We then calculated the systemic noise floor of Sentinel-1 IW mode, and presented its unique noise characteristics among different sub-bands. Based on the calculated NESZ measurements, the noise is removed for all matchup data. Empirical relationships among the noise free NRCS

σ_{VH}^{0}

, wind speed, wind direction, and radar incidence angle are analyzed for each sub-band, and a piecewise model is proposed. We showed that a larger correlation coefficient, r, is achieved by including both wind direction and incidence terms in the model. Validation against scatterometer measurements showed the suitability of the proposed model. Full article

(This article belongs to the Special Issue Ocean Remote Sensing with Synthetic Aperture Radar)

▼ Show Figures

Graphical abstract

17 pages, 6956 KiB

Open AccessArticle

Multi-Year Mapping of Maize and Sunflower in Hetao Irrigation District of China with High Spatial and Temporal Resolution Vegetation Index Series

by Bing Yu and Songhao Shang^*

State Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, China

Remote Sens. 2017, 9(8), 855; https://doi.org/10.3390/rs9080855 - 18 Aug 2017

Cited by 47 | Viewed by 8564

Abstract

Crop identification in large irrigation districts is important for crop yield estimation, hydrological simulation, and agricultural water management. Remote sensing provides an opportunity to visualize crops in the regional scale. However, the use of coarse resolution remote sensing images for crop identification usually [...] Read more.

Crop identification in large irrigation districts is important for crop yield estimation, hydrological simulation, and agricultural water management. Remote sensing provides an opportunity to visualize crops in the regional scale. However, the use of coarse resolution remote sensing images for crop identification usually causes great errors due to the presence of mixed pixels in regions with complex planting structure of crops. Therefore, it is preferable to use remote sensing data with high spatial and temporal resolutions in crop identification. This study aimed to map multi-year distributions of major crops (maize and sunflower) in Hetao Irrigation District, the third largest irrigation district in China, using HJ-1A/1B CCD images with high spatial and temporal resolutions. The Normalized Difference Vegetation Index (NDVI) series obtained from HJ-1A/1B CCD images was fitted with an asymmetric logistic curve to find the NDVI characteristics and phenological metrics for both maize and sunflower. Nine combinations of NDVI characteristics and phenological metrics were compared to obtain the optimal classifier to map maize and sunflower from 2009 to 2015. Results showed that the classification ellipse with the NDVI characteristic of the left inflection point in the NDVI curve and the phenological metric from the left inflection point to the peak point normalized, with mean values of corresponding grassland indexes achieving the minimum mean relative error of 10.82% for maize and 4.38% for sunflower. The corresponding Kappa coefficient was 0.62. These results indicated that the vegetation and phenology-based classifier using HJ-1A/1B data could effectively identify multi-year distribution of maize and sunflower in the study region. It was found that maize was mainly distributed in the middle part of the irrigation district (Hangjinhouqi and Linhe), while sunflower mainly in the east part (Wuyuan). The planting sites of sunflower had been gradually expanded from Wuyuan to the north part of Hangjinhouqi and Linhe. These results were in agreement with the local economic policy. Results also revealed the increasing trends of both maize and sunflower planting areas during the study period. Full article

(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)

▼ Show Figures

Graphical abstract

23 pages, 41986 KiB

Open AccessArticle

A Hierarchical Extension of General Four-Component Scattering Power Decomposition

by Sinong Quan^1,*

, Deliang Xiang¹

, Boli Xiong¹, Canbin Hu² and Gangyao Kuang¹

¹ College of Electronic Science, National University of Defense Technology, Changsha 410073, China

² Research Academy of NBC Defense, Beijing 102205, China

Remote Sens. 2017, 9(8), 856; https://doi.org/10.3390/rs9080856 - 18 Aug 2017

Cited by 20 | Viewed by 4231

Abstract

The overestimation of volume scattering (OVS) is an intrinsic drawback in model-based polarimetric synthetic aperture radar (PolSAR) target decomposition. It severely impacts the accuracy measurement of scattering power and leads to scattering mechanism ambiguity. In this paper, a hierarchical extended general four-component scattering [...] Read more.

The overestimation of volume scattering (OVS) is an intrinsic drawback in model-based polarimetric synthetic aperture radar (PolSAR) target decomposition. It severely impacts the accuracy measurement of scattering power and leads to scattering mechanism ambiguity. In this paper, a hierarchical extended general four-component scattering power decomposition method (G4U) is presented. The conventional G4U is first proposed by Singh et al. and it has advantages in full use of information and volume scattering characterization. However, the OVS still exists in the G4U and it causes a scattering mechanism ambiguity in some oriented urban areas. In the proposed method, matrix rotations by the orientation angle and the helix angle are applied. Afterwards, the transformed coherency matrix is applied to the four-component decomposition scheme with two refined models. Moreover, the branch condition applied in the G4U is substituted by the ratio of correlation coefficient (RCC), which is used as a criterion for hierarchically implementing the decomposition. The performance of this approach is demonstrated and evaluated with the Airborne Synthetic Aperture Radar (AIRSAR), Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR), Radarsat-2, and the Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) fully polarimetric data over different test sites. Comparison studies are carried out and demonstrated that the proposed method exhibits promising improvements in the OVS and scattering mechanism characterization. Full article

(This article belongs to the Section Remote Sensing Image Processing)

▼ Show Figures

Graphical abstract

20 pages, 5235 KiB

Open AccessArticle

A Robust Algorithm for Estimating Surface Fractional Vegetation Cover from Landsat Data

by Linqing Yang¹

, Kun Jia^1,*

, Shunlin Liang^1,2

, Xiangqin Wei³

, Yunjun Yao¹

and Xiaotong Zhang¹

¹ State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

² Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA

³ Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China

Remote Sens. 2017, 9(8), 857; https://doi.org/10.3390/rs9080857 - 19 Aug 2017

Cited by 47 | Viewed by 6778

Abstract

Fractional vegetation cover (FVC) is an essential land surface parameter for Earth surface process simulations and global change studies. The currently existing FVC products are mostly obtained from low or medium resolution remotely sensed data, while many applications require the fine spatial resolution [...] Read more.

Fractional vegetation cover (FVC) is an essential land surface parameter for Earth surface process simulations and global change studies. The currently existing FVC products are mostly obtained from low or medium resolution remotely sensed data, while many applications require the fine spatial resolution FVC product. The availability of well-calibrated coverage of Landsat imagery over large areas offers an opportunity for the production of FVC at fine spatial resolution. Therefore, the objective of this study is to develop a general and reliable land surface FVC estimation algorithm for Landsat surface reflectance data under various land surface conditions. Two machine learning methods multivariate adaptive regression splines (MARS) model and back-propagation neural networks (BPNNs) were trained using samples from PROSPECT leaf optical properties model and the scattering by arbitrarily inclined leaves (SAIL) model simulations, which included Landsat reflectance and corresponding FVC values, and evaluated to choose the method which had better performance. Thereafter, the MARS model, which had better performance in the independent validation, was evaluated using ground FVC measurements from two case study areas. The direct validation of the FVC estimated using the proposed algorithm (Heihe: R² = 0.8825, RMSE = 0.097; Chengde using Landsat 7 ETM+: R² = 0.8571, RMSE = 0.078, Chengde using Landsat 8 OLI: R² = 0.8598, RMSE = 0.078) showed the proposed method had good performance. Spatial-temporal assessment of the estimated FVC from Landsat 7 ETM+ and Landsat 8 OLI data confirmed the robustness and consistency of the proposed method. All these results indicated that the proposed algorithm could obtain satisfactory accuracy and had the potential for the production of high-quality FVC estimates from Landsat surface reflectance data. Full article

(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)

▼ Show Figures

Graphical abstract

17 pages, 3859 KiB

Open AccessArticle

Deriving Hourly PM2.5 Concentrations from Himawari-8 AODs over Beijing–Tianjin–Hebei in China

by Wei Wang¹

, Feiyue Mao^1,2,3,*, Lin Du^4,*, Zengxin Pan¹, Wei Gong^1,3 and Shenghui Fang²

¹ State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan 430079, China

² School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China

³ Collaborative Innovation Center for Geospatial Technology, Wuhan 430079, China

⁴ Faculty of Information Engineering, China University of Geosciences, Wuhan 430074, China

Remote Sens. 2017, 9(8), 858; https://doi.org/10.3390/rs9080858 - 19 Aug 2017

Cited by 116 | Viewed by 12483

Abstract

Monitoring fine particulate matter with diameters of less than 2.5 μm (PM2.5) is a critical endeavor in the Beijing–Tianjin–Hebei (BTH) region, which is one of the most polluted areas in China. Polar orbit satellites are limited by observation frequency, which is insufficient for [...] Read more.

Monitoring fine particulate matter with diameters of less than 2.5 μm (PM2.5) is a critical endeavor in the Beijing–Tianjin–Hebei (BTH) region, which is one of the most polluted areas in China. Polar orbit satellites are limited by observation frequency, which is insufficient for understanding PM2.5 evolution. As a geostationary satellite, Himawari-8 can obtain hourly optical depths (AODs) and overcome the estimated PM2.5 concentrations with low time resolution. In this study, the evaluation of Himawari-8 AODs by comparing with Aerosol Robotic Network (AERONET) measurements showed Himawari-8 retrievals (Level 3) with a mild underestimate of about −0.06 and approximately 57% of AODs falling within the expected error established by the Moderate-resolution Imaging Spectroradiometer (MODIS) (±(0.05 + 0.15AOD)). Furthermore, the improved linear mixed-effect model was proposed to derive the surface hourly PM2.5 from Himawari-8 AODs from July 2015 to March 2017. The estimated hourly PM2.5 concentrations agreed well with the surface PM2.5 measurements with high R² (0.86) and low RMSE (24.5 μg/m³). The average estimated PM2.5 in the BTH region during the study time range was about 55 μg/m³. The estimated hourly PM2.5 concentrations ranged extensively from 35.2 ± 26.9 μg/m³ (1600 local time) to 65.5 ± 54.6 μg/m³ (1100 local time) at different hours. Full article

(This article belongs to the Special Issue Remote Sensing of Atmospheric Pollution)

▼ Show Figures

Graphical abstract

20 pages, 1615 KiB

Open AccessArticle

Wave Height Estimation from Shadowing Based on the Acquired X-Band Marine Radar Images in Coastal Area

by Yanbo Wei, Zhizhong Lu^*

, Gen Pian and Hong Liu

College of Automation, Harbin Engineering University, No. 145 Nantong Street, Harbin 150001, China

Remote Sens. 2017, 9(8), 859; https://doi.org/10.3390/rs9080859 - 21 Aug 2017

Cited by 23 | Viewed by 5465

Abstract

In this paper, the retrieving significant wave height from X-band marine radar images based on shadow statistics is investigated, since the retrieving accuracy can not be seriously affected by environmental factors and the method has the advantage of without any external reference to [...] Read more.

In this paper, the retrieving significant wave height from X-band marine radar images based on shadow statistics is investigated, since the retrieving accuracy can not be seriously affected by environmental factors and the method has the advantage of without any external reference to calibrate. However, the accuracy of the significant wave height estimated from the radar image acquired at the near-shore area is not ideal. To solve this problem, the effect of water depth is considered in the theoretical derivation of estimated wave height based on the sea surface slope. And then, an improved retrieving algorithm which is suitable for both in deep water area and shallow water area is developed. In addition, the radar data are sparsely processed in advance in order to achieve high quality edge image for the requirement of shadow statistic algorithm, since the high resolution radar images will lead to angle-blurred for the image edge detection and time-consuming in the estimation of sea surface slope. The data acquired from Pingtan Test Base in Fujian Province were used to verify the effectiveness of the proposed algorithm. The experimental results demonstrate that the improved method which takes into account the water depth is more efficient and effective and has better performance for retrieving significant wave height in the shallow water area, compared to the in situ buoy data as the ground truth and that of the existing shadow statistic method. Full article

(This article belongs to the Section Ocean Remote Sensing)

▼ Show Figures

Graphical abstract

14 pages, 6002 KiB

Open AccessArticle

Contextual Region-Based Convolutional Neural Network with Multilayer Fusion for SAR Ship Detection

by Miao Kang, Kefeng Ji^*, Xiangguang Leng

and Zhao Lin

School of Electronic Science and Engineering, National University of Defense Technology, Sanyi Avenue, Changsha 410073, China

Remote Sens. 2017, 9(8), 860; https://doi.org/10.3390/rs9080860 - 20 Aug 2017

Cited by 335 | Viewed by 14166

Abstract

Synthetic aperture radar (SAR) ship detection has been playing an increasingly essential role in marine monitoring in recent years. The lack of detailed information about ships in wide swath SAR imagery poses difficulty for traditional methods in exploring effective features for ship discrimination. [...] Read more.

Synthetic aperture radar (SAR) ship detection has been playing an increasingly essential role in marine monitoring in recent years. The lack of detailed information about ships in wide swath SAR imagery poses difficulty for traditional methods in exploring effective features for ship discrimination. Being capable of feature representation, deep neural networks have achieved dramatic progress in object detection recently. However, most of them suffer from the missing detection of small-sized targets, which means that few of them are able to be employed directly in SAR ship detection tasks. This paper discloses an elaborately designed deep hierarchical network, namely a contextual region-based convolutional neural network with multilayer fusion, for SAR ship detection, which is composed of a region proposal network (RPN) with high network resolution and an object detection network with contextual features. Instead of using low-resolution feature maps from a single layer for proposal generation in a RPN, the proposed method employs an intermediate layer combined with a downscaled shallow layer and an up-sampled deep layer to produce region proposals. In the object detection network, the region proposals are projected onto multiple layers with region of interest (ROI) pooling to extract the corresponding ROI features and contextual features around the ROI. After normalization and rescaling, they are subsequently concatenated into an integrated feature vector for final outputs. The proposed framework fuses the deep semantic and shallow high-resolution features, improving the detection performance for small-sized ships. The additional contextual features provide complementary information for classification and help to rule out false alarms. Experiments based on the Sentinel-1 dataset, which contains twenty-seven SAR images with 7986 labeled ships, verify that the proposed method achieves an excellent performance in SAR ship detection. Full article

(This article belongs to the Special Issue Ocean Remote Sensing with Synthetic Aperture Radar)

▼ Show Figures

Figure 1

22 pages, 11020 KiB

Open AccessArticle

Effects of Small-Scale Gold Mining Tailings on the Underwater Light Field in the Tapajós River Basin, Brazilian Amazon

by Felipe De Lucia Lobo^1,2,*

, Maycira Costa^1,*, Evlyn Márcia Leão De Moraes Novo^2,*

and Kevin Telmer^1,3,*

¹ Spectral Lab, Department of Geography, University of Victoria, 3800 Finnerty Road, Victoria, BC V8P 5C2, Canada

² Remote Sensing Division, National Institute for Space Research (INPE), Av. dos Astronautas 1758, Jardim da Granja 12227-010, São José dos Campos, Brazil

³ Artisanal Gold Council, 101-732 Cormorant St., Victoria, BC V8W 4A5, Canada

Remote Sens. 2017, 9(8), 861; https://doi.org/10.3390/rs9080861 - 21 Aug 2017

Cited by 18 | Viewed by 8920

Abstract

Artisanal and Small-scale Gold Mining (ASGM) within the Amazon region has created several environmental impacts, such as mercury contamination and changes in water quality due to increased siltation. This paper describes the effects of water siltation on the underwater light environment of rivers [...] Read more.

Artisanal and Small-scale Gold Mining (ASGM) within the Amazon region has created several environmental impacts, such as mercury contamination and changes in water quality due to increased siltation. This paper describes the effects of water siltation on the underwater light environment of rivers under different levels of gold mining activities in the Tapajós River Basin. Furthermore, it investigates possible impacts on the phytoplankton community. Two field campaigns were conducted in the Tapajós River Basin, during high water level and during low water level seasons, to measure Inherent and Apparent Optical Properties (IOPs, AOPs), including scattering (b) and absorption (a) coefficients and biogeochemical data (sediment content, pigments, and phytoplankton quantification). The biogeochemical data was separated into five classes according to the concentration of total suspended solids (TSS) ranging from 1.8 mg·L⁻¹ to 113.6 mg·L⁻¹. The in-water light environment varied among those classes due to a wide range of concentrations of inorganic TSS originated from different levels of mining activities. For tributaries with low or no influence of mining tailings (TSS up to 6.8 mg·L⁻¹), waters are relatively more absorbent with b:a ratio of 0.8 at 440 nm and b₆₆₀ magnitude of 2.1 m⁻¹. With increased TSS loadings from mining operations (TSS over 100 mg·L⁻¹), the scattering process prevails over absorption (b:a ratio of 10.0 at 440 nm), and b₆₆₀ increases to 20.8 m⁻¹. Non-impacted tributaries presented a critical depth for phytoplankton productivity of up to 6.0 m with available light evenly distributed throughout the spectra. Whereas for greatly impacted waters, attenuation of light was faster, reducing the critical depth to about 1.7 m, with most of the available light comprising of red wavelengths. Overall, a dominance of diatoms was observed for the upstream rivers, whereas cyanobacteria prevailed in the low section of the Tapajós River. The results suggest that the spatial and temporal distribution of phytoplankton in the Tapajós River Basin is not only a function of light availability, but rather depends on the interplay of factors, including flood pulse, water velocity, nutrient availability, and seasonal variation of incoming irradiance. Ongoing research indicates that the effects of mining tailings on the aquatic environment, described here, are occurring in several rivers within the Amazon River Basin. Full article

(This article belongs to the Special Issue Remote Sensing of Water Quality)

▼ Show Figures

Graphical abstract

25 pages, 11338 KiB

Open AccessArticle

Mapping Regional Urban Extent Using NPP-VIIRS DNB and MODIS NDVI Data

by Run Wang^1,2, Bo Wan^1,2,*, Qinghua Guo³, Maosheng Hu^1,2 and Shunping Zhou^1,2

¹ Faculty of Information Engineering, China University of Geosciences, No. 388 Lumo Road, Wuhan 430074, China

² National Engineering Research Center of Geographic Information System, Wuhan 430074, China

³ State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China

Remote Sens. 2017, 9(8), 862; https://doi.org/10.3390/rs9080862 - 21 Aug 2017

Cited by 43 | Viewed by 9198

Abstract

The accurate and timely monitoring of regional urban extent is helpful for analyzing urban sprawl and studying environmental issues related to urbanization. This paper proposes a classification scheme for large-scale urban extent mapping by combining the Day/Night Band of the Visible Infrared Imaging [...] Read more.

The accurate and timely monitoring of regional urban extent is helpful for analyzing urban sprawl and studying environmental issues related to urbanization. This paper proposes a classification scheme for large-scale urban extent mapping by combining the Day/Night Band of the Visible Infrared Imaging Radiometer Suite on the Suomi National Polar-orbiting Partnership Satellite (NPP-VIIRS DNB) and the Normalized Difference Vegetation Index from the Moderate Resolution Imaging Spectroradiometer products (MODIS NDVI). A Back Propagation (BP) neural network based one-class classification method, the Present-Unlabeled Learning (PUL) algorithm, is employed to classify images into urban and non-urban areas. Experiments are conducted in mainland China (excluding surrounding islands) to detect urban areas in 2012. Results show that the proposed model can successfully map urban area with a kappa of 0.842 on the pixel level. Most of the urban areas are identified with a producer’s accuracy of 79.63%, and only 10.42% the generated urban areas are misclassified with a user’s accuracy of 89.58%. At the city level, among 647 cities, only four county-level cities are omitted. To evaluate the effectiveness of the proposed scheme, three contrastive analyses are conducted: (1) comparing the urban map obtained in this paper with that generated by the Defense Meteorological Satellite Program/Operational Linescan System Nighttime Light Data (DMSP/OLS NLD) and MODIS NDVI and with that extracted from MCD12Q1 in MODIS products; (2) comparing the performance of the integration of NPP-VIIRS DNB and MODIS NDVI with single input data; and (3) comparing the classification method used in this paper (PUL) with a linear method (Large-scale Impervious Surface Index (LISI)). According to our analyses, the proposed classification scheme shows great potential to map regional urban extents in an effective and efficient manner. Full article

(This article belongs to the Special Issue Recent Advances in Remote Sensing with Nighttime Lights)

▼ Show Figures

Graphical abstract

14 pages, 19550 KiB

Open AccessTechnical Note

A Dynamic Landsat Derived Normalized Difference Vegetation Index (NDVI) Product for the Conterminous United States

by Nathaniel P. Robinson^1,2,*

, Brady W. Allred^1,2, Matthew O. Jones^1,2, Alvaro Moreno², John S. Kimball^1,2, David E. Naugle¹, Tyler A. Erickson³ and Andrew D. Richardson^4,5

¹ W.A. Franke College of Forestry and Conservation, University of Montana, Missoula, MT 59812, USA

² Numerical Terradynamic Simulation Group, University of Montana, Missoula, MT 59812, USA

³ Google, Inc., Mountain View, CA 94043, USA

⁴ School of Informatics, Computing and Cyber Systems, Northern Arizona University, Flagstaff, AZ 86011, USA

⁵ Center for Ecosystem Science and Society, Northern Arizona University, Flagstaff, AZ 86011, USA

Remote Sens. 2017, 9(8), 863; https://doi.org/10.3390/rs9080863 - 21 Aug 2017

Cited by 199 | Viewed by 29122

Abstract

Satellite derived vegetation indices (VIs) are broadly used in ecological research, ecosystem modeling, and land surface monitoring. The Normalized Difference Vegetation Index (NDVI), perhaps the most utilized VI, has countless applications across ecology, forestry, agriculture, wildlife, biodiversity, and other disciplines. Calculating satellite derived [...] Read more.

Satellite derived vegetation indices (VIs) are broadly used in ecological research, ecosystem modeling, and land surface monitoring. The Normalized Difference Vegetation Index (NDVI), perhaps the most utilized VI, has countless applications across ecology, forestry, agriculture, wildlife, biodiversity, and other disciplines. Calculating satellite derived NDVI is not always straight-forward, however, as satellite remote sensing datasets are inherently noisy due to cloud and atmospheric contamination, data processing failures, and instrument malfunction. Readily available NDVI products that account for these complexities are generally at coarse resolution; high resolution NDVI datasets are not conveniently accessible and developing them often presents numerous technical and methodological challenges. We address this deficiency by producing a Landsat derived, high resolution (30 m), long-term (30+ years) NDVI dataset for the conterminous United States. We use Google Earth Engine, a planetary-scale cloud-based geospatial analysis platform, for processing the Landsat data and distributing the final dataset. We use a climatology driven approach to fill missing data and validate the dataset with established remote sensing products at multiple scales. We provide access to the composites through a simple web application, allowing users to customize key parameters appropriate for their application, question, and region of interest. Full article

(This article belongs to the Collection Google Earth Engine Applications)

▼ Show Figures

Graphical abstract

12 pages, 5098 KiB

Open AccessArticle

Erosion Associated with Seismically-Induced Landslides in the Middle Longmen Shan Region, Eastern Tibetan Plateau, China

by Zhikun Ren^1,2,*, Zhuqi Zhang² and Jinhui Yin²

¹ Key Laboratory of Active Tectonics and Volcano, Institute of Geology, China Earthquake Administration, Beijing 100029, China

² State Key Laboratory of Earthquake Dynamics, Institute of Geology, China Earthquake Administration, Beijing 100029, China

Remote Sens. 2017, 9(8), 864; https://doi.org/10.3390/rs9080864 - 21 Aug 2017

Cited by 11 | Viewed by 6009

Abstract

The 2008 Wenchuan earthquake and associated co-seismic landslide was the most recent expression of the rapid deformation and erosion occurring in the eastern Tibetan Plateau. The erosion associated with co-seismic landslides balances the long-term tectonic uplift in the topographic evolution of the region; [...] Read more.

The 2008 Wenchuan earthquake and associated co-seismic landslide was the most recent expression of the rapid deformation and erosion occurring in the eastern Tibetan Plateau. The erosion associated with co-seismic landslides balances the long-term tectonic uplift in the topographic evolution of the region; however, the quantitative relationship between earthquakes, uplift, and erosion is still unknown. In order to quantitatively distinguish the seismically-induced erosion in the total erosion, here, we quantify the Wenchuan earthquake-induced erosion using the digital elevation model (DEM) differential method and previously-reported landslide volumes. Our results show that the seismically-induced erosion is comparable with the pre-earthquake short-term erosion. The seismically-induced erosion rate contributes ~50% of the total erosion rate, which suggests that the local topographic evolution of the middle Longmen Shan region may be closely related to tectonic events, such as the 2008 Wenchuan earthquake. We propose that seismically-induced erosion is a very important component of the total erosion, particularly in active orogenic regions. Our results demonstrate that the remote sensing technique of differential DEM provides a powerful tool for evaluating the volume of co-seismic landslides produced in intermountain regions by strong earthquakes. Full article

(This article belongs to the Special Issue Remote Sensing of Landslides)

▼ Show Figures

Graphical abstract

14 pages, 14199 KiB

Open AccessArticle

Azimuth Ambiguities Removal in Littoral Zones Based on Multi-Temporal SAR Images

by Xiangguang Leng

, Kefeng Ji^*, Shilin Zhou and Huanxin Zou

School of Electronic Science and Engineering, National University of Defense Technology, Sanyi Avenue, Changsha 410073, China

Remote Sens. 2017, 9(8), 866; https://doi.org/10.3390/rs9080866 - 22 Aug 2017

Cited by 17 | Viewed by 8613

Abstract

Synthetic aperture radar (SAR) is one of the most important techniques for ocean monitoring. Azimuth ambiguities are a real problem in SAR images today, which can cause performance degradation in SAR ocean applications. In particular, littoral zones can be strongly affected by land-based [...] Read more.

Synthetic aperture radar (SAR) is one of the most important techniques for ocean monitoring. Azimuth ambiguities are a real problem in SAR images today, which can cause performance degradation in SAR ocean applications. In particular, littoral zones can be strongly affected by land-based sources, whereas they are usually regions of interest (ROI). Given the presence of complexity and diversity in littoral zones, azimuth ambiguities removal is a tough problem. As SAR sensors can have a repeat cycle, multi-temporal SAR images provide new insight into this problem. A method for azimuth ambiguities removal in littoral zones based on multi-temporal SAR images is proposed in this paper. The proposed processing chain includes co-registration, local correlation, binarization, masking, and restoration steps. It is designed to remove azimuth ambiguities caused by fixed land-based sources. The idea underlying the proposed method is that sea surface is dynamic, whereas azimuth ambiguities caused by land-based sources are constant. Thus, the temporal consistence of azimuth ambiguities is higher than sea clutter. It opens up the possibilities to use multi-temporal SAR data to remove azimuth ambiguities. The design of the method and the experimental procedure are based on images from the Sentinel data hub of Europe Space Agency (ESA). Both Interferometric Wide Swath (IW) and Stripmap (SM) mode images are taken into account to validate the proposed method. This paper also presents two RGB composition methods for better azimuth ambiguities visualization. Experimental results show that the proposed method can remove azimuth ambiguities in littoral zones effectively. Full article

(This article belongs to the Special Issue Ocean Remote Sensing with Synthetic Aperture Radar)

▼ Show Figures

Graphical abstract

17 pages, 5375 KiB

Open AccessArticle

The Effects of Aerosol on the Retrieval Accuracy of NO₂ Slant Column Density

by Hyunkee Hong¹, Jhoon Kim^2,3

, Ukkyo Jeong^4,5, Kyung-soo Han¹ and Hanlim Lee^1,*

¹ Division of Earth Environmental System Science Major of Spatial Information Engineering, Pukyong National University, Busan 608-737, Korea

² Department of Atmosphere Science, Yonsei University, Seoul 03722, Korea

³ Harvard Smithonian Center for Astrophysics, Cambridge, MA 02421, USA

⁴ Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD 20742, USA

⁵ Goddard Space Flight Center, NASA, Greenbelt, MD 20771, USA

Remote Sens. 2017, 9(8), 867; https://doi.org/10.3390/rs9080867 - 22 Aug 2017

Cited by 7 | Viewed by 6522

Abstract

We investigate the effects of aerosol optical depth (AOD), single scattering albedo (SSA), aerosol peak height (APH), measurement geometry (solar zenith angle (SZA) and viewing zenith angle (VZA)), relative azimuth angle, and surface reflectance on the accuracy of NO₂ slant column density [...] Read more.

We investigate the effects of aerosol optical depth (AOD), single scattering albedo (SSA), aerosol peak height (APH), measurement geometry (solar zenith angle (SZA) and viewing zenith angle (VZA)), relative azimuth angle, and surface reflectance on the accuracy of NO₂ slant column density using synthetic radiance. High AOD and APH are found to decrease NO₂ SCD retrieval accuracy. In moderately polluted (5 × 10¹⁵ molecules cm⁻² < NO₂ vertical column density (VCD) < 2 × 10¹⁶ molecules cm⁻²) and clean regions (NO₂ VCD < 5 × 10¹⁵ molecules cm⁻²), the correlation coefficient (R) between true NO₂ SCDs and those retrieved is 0.88 and 0.79, respectively, and AOD and APH are about 0.1 and is 0 km, respectively. However, when AOD and APH are about 1.0 and 4 km, respectively, the R decreases to 0.84 and 0.53 in moderately polluted and clean regions, respectively. On the other hand, in heavily polluted regions (NO₂ VCD > 2 × 10¹⁶ molecules cm⁻²), even high AOD and APH values are found to have a negligible effect on NO₂ SCD precision. In high AOD and APH conditions in clean NO₂ regions, the R between true NO₂ SCDs and those retrieved increases from 0.53 to 0.58 via co-adding four pixels spatially, showing the improvement in accuracy of NO₂ SCD retrieval. In addition, the high SZA and VZA are also found to decrease the accuracy of the NO₂ SCD retrieval. Full article

(This article belongs to the Section Atmospheric Remote Sensing)

▼ Show Figures

Graphical abstract

20 pages, 3658 KiB

Open AccessArticle

Optimal Decision Fusion for Urban Land-Use/Land-Cover Classification Based on Adaptive Differential Evolution Using Hyperspectral and LiDAR Data

by Yanfei Zhong^1,2,*

, Qiong Cao^1,2,*, Ji Zhao³

, Ailong Ma^1,2,*, Bei Zhao⁴ and Liangpei Zhang^1,2

¹ The State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China

² Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan 430079, China

³ College of Computer Science, China University of Geosciences, Wuhan 430074, China

⁴ Department of Geography and Resource Management, The Chinese University of Hong Kong, Hong Kong, China

Remote Sens. 2017, 9(8), 868; https://doi.org/10.3390/rs9080868 - 22 Aug 2017

Cited by 68 | Viewed by 6792

Abstract

Hyperspectral images and light detection and ranging (LiDAR) data have, respectively, the high spectral resolution and accurate elevation information required for urban land-use/land-cover (LULC) classification. To combine the respective advantages of hyperspectral and LiDAR data, this paper proposes an optimal decision fusion method [...] Read more.

Hyperspectral images and light detection and ranging (LiDAR) data have, respectively, the high spectral resolution and accurate elevation information required for urban land-use/land-cover (LULC) classification. To combine the respective advantages of hyperspectral and LiDAR data, this paper proposes an optimal decision fusion method based on adaptive differential evolution, namely ODF-ADE, for urban LULC classification. In the ODF-ADE framework the normalized difference vegetation index (NDVI), gray-level co-occurrence matrix (GLCM) and digital surface model (DSM) are extracted to form the feature map. The three different classifiers of the maximum likelihood classifier (MLC), support vector machine (SVM) and multinomial logistic regression (MLR) are used to classify the extracted features. To find the optimal weights for the different classification maps, weighted voting is used to obtain the classification result and the weights of each classification map are optimized by the differential evolution algorithm which uses a self-adaptive strategy to obtain the parameter adaptively. The final classification map is obtained after post-processing based on conditional random fields (CRF). The experimental results confirm that the proposed algorithm is very effective in urban LULC classification. Full article

▼ Show Figures

Graphical abstract

17 pages, 6407 KiB

Open AccessArticle

Detection of Asian Dust Storm Using MODIS Measurements

by Yong Xie¹, Wenhao Zhang^2,*

and John J. Qu³

¹ School of Geography and Remote Sensing, Nanjing University of Information Science & Technology, Nanjing 210044, China

² Institute of Remote Sensing and digital Earth, Chinese Academy of Sciences, Beijing 100101, China

³ Environmental Science and Technology Center (ESTC) and Department of Geography and GeoInformation Science (GGS), George Mason University, Fairfax, VA 22030, USA

Remote Sens. 2017, 9(8), 869; https://doi.org/10.3390/rs9080869 - 22 Aug 2017

Cited by 29 | Viewed by 7177

Abstract

Every year, a large number of aerosols are released from dust storms into the atmosphere, which may have potential impacts on the climate, environment, and air quality. Detecting dust aerosols and monitoring their movements and evolutions in a timely manner is a very [...] Read more.

Every year, a large number of aerosols are released from dust storms into the atmosphere, which may have potential impacts on the climate, environment, and air quality. Detecting dust aerosols and monitoring their movements and evolutions in a timely manner is a very significant task. Satellite remote sensing has been demonstrated as an effective means for observing dust aerosols. In this paper, an algorithm based on the multi-spectral technique for detecting dust aerosols was developed by combining measurements of moderate resolution imaging spectroradiometer (MODIS) reflective solar bands and thermal emissive bands. Data from dust events that occurred during the past several years were collected as training data for spectral and statistical analyses. According to the spectral curves of various scene types, a series of spectral bands was selected individually or jointly, and corresponding thresholds were defined for step-by-step scene classification. The multi-spectral algorithm was applied mainly to detect dust storms in Asia. The detection results were validated not only visually with MODIS true color images, but also quantitatively with products of Ozone Monitoring Instrument (OMI) and Cloud Aerosol Lidar with Orthogonal Polarization (CALIOP). The validations showed that this multi-spectral detection algorithm was suitable to monitor dust aerosols in the selected study areas. Full article

(This article belongs to the Section Atmospheric Remote Sensing)

▼ Show Figures

Graphical abstract

20 pages, 53630 KiB

Open AccessArticle

A Study of Spatial Soil Moisture Estimation Using a Multiple Linear Regression Model and MODIS Land Surface Temperature Data Corrected by Conditional Merging

by Chunggil Jung¹

, Yonggwan Lee¹

, Younghyun Cho²

and Seongjoon Kim^1,*

¹ Department of Civil, Environmental and Plant Engneering, Konkuk University, 120 Neungdong-ro, Gwangjin-gu, Seoul 05029, Korea

² Hydrometeorological Cooperation Center, Korea Water Resources Corporation, 11 Gyoyookwon-ro, Gwacheon-si, Gyeonggi-do 13841, Korea

Remote Sens. 2017, 9(8), 870; https://doi.org/10.3390/rs9080870 - 22 Aug 2017

Cited by 40 | Viewed by 9190

Abstract

This study attempts to estimate spatial soil moisture in South Korea (99,000 km²) from January 2013 to December 2015 using a multiple linear regression (MLR) model and the Terra moderate-resolution imaging spectroradiometer (MODIS) land surface temperature (LST) and normalized distribution vegetation [...] Read more.

This study attempts to estimate spatial soil moisture in South Korea (99,000 km²) from January 2013 to December 2015 using a multiple linear regression (MLR) model and the Terra moderate-resolution imaging spectroradiometer (MODIS) land surface temperature (LST) and normalized distribution vegetation index (NDVI) data. The MODIS NDVI was used to reflect vegetation variations. Observed precipitation was measured using the automatic weather stations (AWSs) of the Korea Meteorological Administration (KMA), and soil moisture data were recorded at 58 stations operated by various institutions. Prior to MLR analysis, satellite LST data were corrected by applying the conditional merging (CM) technique and observed LST data from 71 KMA stations. The coefficient of determination (R²) of the original LST and observed LST was 0.71, and the R² of corrected LST and observed LST was 0.95 for 3 selected LST stations. The R² values of all corrected LSTs were greater than 0.83 for total 71 LST stations. The regression coefficients of the MLR model were estimated seasonally considering the five-day antecedent precipitation. The p-values of all the regression coefficients were less than 0.05, and the R² values were between 0.28 and 0.67. The reason for R² values less than 0.5 is that the soil classification at each observation site was not completely accurate. Additionally, the observations at most of the soil moisture monitoring stations used in this study started in December 2014, and the soil moisture measurements did not stabilize. Notably, R² and root mean square error (RMSE) in winter were poor, as reflected by the many missing values, and uncertainty existed in observations due to freezing and mechanical errors in the soil. Thus, the prediction accuracy is low in winter due to the difficulty of establishing an appropriate regression model. Specifically, the estimated map of the soil moisture index (SMI) can be used to better understand the severity of droughts with the variability of soil moisture. Full article

(This article belongs to the Special Issue Satellite Remote Sensing for Water Resources in a Changing Climate)

▼ Show Figures

Graphical abstract

Journal Menu

Journal Browser

Remote Sens., Volume 9, Issue 8 (August 2017) – 108 articles

Further Information

Guidelines

MDPI Initiatives

Follow MDPI