Previous Issue

E-Mail Alert

Add your e-mail address to receive forthcoming issues of this journal:

Journal Browser

Journal Browser

Table of Contents

Remote Sens., Volume 9, Issue 7 (July 2017)

  • Issues are regarded as officially published after their release is announced to the table of contents alert mailing list.
  • You may sign up for e-mail alerts to receive table of contents of newly released issues.
  • PDF is the official format for papers published in both, html and pdf forms. To view the papers in pdf format, click on the "PDF Full-text" link, and use the free Adobe Readerexternal link to open them.
Cover Story It has long been assumed that South Asia's ancient Indus Civilization was riverine, but many [...] Read more.
View options order results:
result details:
Displaying articles 1-111
Export citation of selected articles as:

Research

Jump to: Other

Open AccessArticle Mapping Typical Urban LULC from Landsat Imagery without Training Samples or Self-Defined Parameters
Remote Sens. 2017, 9(7), 700; doi:10.3390/rs9070700
Received: 14 June 2017 / Revised: 26 June 2017 / Accepted: 28 June 2017 / Published: 7 July 2017
PDF Full-text (22656 KB) | HTML Full-text | XML Full-text
Abstract
Land use/land cover (LULC) change is one of the most important indicators in understanding the interactions between humans and the environment. Traditionally, when LULC maps are produced yearly, most existing remote-sensing methods have to collect ground reference data annually, as the classifiers have
[...] Read more.
Land use/land cover (LULC) change is one of the most important indicators in understanding the interactions between humans and the environment. Traditionally, when LULC maps are produced yearly, most existing remote-sensing methods have to collect ground reference data annually, as the classifiers have to be trained individually in each corresponding year. This study presented a novel strategy to map LULC classes without training samples or assigning parameters. First of all, several novel indices were carefully selected from the index pool, which were able to highlight certain LULC very well. Following this, a common unsupervised classifier was employed to extract the LULC from the associated index image without assigning thresholds. Finally, a supervised classification was implemented with samples automatically collected from the unsupervised classification outputs. Results illustrated that the proposed method could achieve satisfactory performance, reaching similar accuracies to traditional approaches. Findings of this study demonstrate that the proposed strategy is a simple and effective alternative to mapping urban LULC. With the proposed strategy, the budget and time required for remote-sensing data processing could be reduced dramatically. Full article
Figures

Open AccessArticle Optimal Seamline Detection for Orthoimage Mosaicking by Combining Deep Convolutional Neural Network and Graph Cuts
Remote Sens. 2017, 9(7), 701; doi:10.3390/rs9070701
Received: 21 April 2017 / Revised: 13 June 2017 / Accepted: 5 July 2017 / Published: 7 July 2017
PDF Full-text (20866 KB) | HTML Full-text | XML Full-text
Abstract
When mosaicking orthoimages, especially in urban areas with various obvious ground objects like buildings, roads, cars or trees, the detection of optimal seamlines is one of the key technologies for creating seamless and pleasant image mosaics. In this paper, we propose a new
[...] Read more.
When mosaicking orthoimages, especially in urban areas with various obvious ground objects like buildings, roads, cars or trees, the detection of optimal seamlines is one of the key technologies for creating seamless and pleasant image mosaics. In this paper, we propose a new approach to detect optimal seamlines for orthoimage mosaicking with the use of deep convolutional neural network (CNN) and graph cuts. Deep CNNs have been widely used in many fields of computer vision and photogrammetry in recent years, and graph cuts is one of the most widely used energy optimization frameworks. We first propose a deep CNN for land cover semantic segmentation in overlap regions between two adjacent images. Then, the energy cost of each pixel in the overlap regions is defined based on the classification probabilities of belonging to each of the specified classes. To find the optimal seamlines globally, we fuse the CNN-classified energy costs of all pixels into the graph cuts energy minimization framework. The main advantage of our proposed method is that the pixel similarity energy costs between two images are defined using the classification results of the CNN based semantic segmentation instead of using the image informations of color, gradient or texture as traditional methods do. Another advantage of our proposed method is that the semantic informations are fully used to guide the process of optimal seamline detection, which is more reasonable than only using the hand designed features defined to represent the image differences. Finally, the experimental results on several groups of challenging orthoimages show that the proposed method is capable of finding high-quality seamlines among urban and non-urban orthoimages, and outperforms the state-of-the-art algorithms and the commercial software based on the visual comparison, statistical evaluation and quantitative evaluation based on the structural similarity (SSIM) index. Full article
Figures

Figure 1

Open AccessArticle Estimating Mangrove Canopy Height and Above-Ground Biomass in the Everglades National Park with Airborne LiDAR and TanDEM-X Data
Remote Sens. 2017, 9(7), 702; doi:10.3390/rs9070702
Received: 1 June 2017 / Revised: 29 June 2017 / Accepted: 4 July 2017 / Published: 7 July 2017
PDF Full-text (10278 KB) | HTML Full-text | XML Full-text
Abstract
Mangrove forests are important natural ecosystems due to their ability to capture and store large amounts of carbon. Forest structural parameters, such as canopy height and above-ground biomass (AGB), provide a good measure for monitoring temporal changes in carbon content. The protected coastal
[...] Read more.
Mangrove forests are important natural ecosystems due to their ability to capture and store large amounts of carbon. Forest structural parameters, such as canopy height and above-ground biomass (AGB), provide a good measure for monitoring temporal changes in carbon content. The protected coastal mangrove forest of the Everglades National Park (ENP) provides an ideal location for studying these processes, as harmful human activities are minimal. We estimated mangrove canopy height and AGB in the ENP using Airborne LiDAR/Laser (ALS) and TanDEM-X (TDX) datasets acquired between 2011 and 2013. Analysis of both datasets revealed that mangrove canopy height can reach up to ~25 m and AGB can reach up to ~250 Mg•ha−1. In general, mangroves ranging from 9 m to 12 m in stature dominate the forest canopy. The comparison of ALS and TDX canopy height observations yielded an R2 = 0.85 and Root Mean Square Error (RMSE) = 1.96 m. Compared to a previous study based on data acquired during 2000–2004, our analysis shows an increase in mangrove stature and AGB, suggesting that ENP mangrove forests are continuing to accumulate biomass. Our results suggest that ENP mangrove forests have managed to recover from natural disturbances, such as Hurricane Wilma. Full article
(This article belongs to the Section Forest Remote Sensing)
Figures

Figure 1

Open AccessArticle Multi-Channel Deconvolution for Forward-Looking Phase Array Radar Imaging
Remote Sens. 2017, 9(7), 703; doi:10.3390/rs9070703
Received: 14 February 2017 / Revised: 28 June 2017 / Accepted: 5 July 2017 / Published: 7 July 2017
PDF Full-text (1653 KB) | HTML Full-text | XML Full-text
Abstract
The cross-range resolution of forward-looking phase array radar (PAR) is limited by the effective antenna beamwidth since the azimuth echo is the convolution of antenna pattern and targets’ backscattering coefficients. Therefore, deconvolution algorithms are proposed to improve the imaging resolution under the limited
[...] Read more.
The cross-range resolution of forward-looking phase array radar (PAR) is limited by the effective antenna beamwidth since the azimuth echo is the convolution of antenna pattern and targets’ backscattering coefficients. Therefore, deconvolution algorithms are proposed to improve the imaging resolution under the limited antenna beamwidth. However, as a typical inverse problem, deconvolution is essentially a highly ill-posed problem which is sensitive to noise and cannot ensure a reliable and robust estimation. In this paper, multi-channel deconvolution is proposed for improving the performance of deconvolution, which intends to considerably alleviate the ill-posed problem of single-channel deconvolution. To depict the performance improvement obtained by multi-channel more effectively, evaluation parameters are generalized to characterize the angular spectrum of antenna pattern or singular value distribution of observation matrix, which are conducted to compare different deconvolution systems. Here we present two multi-channel deconvolution algorithms which improve upon the traditional deconvolution algorithms via combining with multi-channel technique. Extensive simulations and experimental results based on real data are presented to verify the effectiveness of the proposed imaging methods. Full article
Figures

Open AccessArticle MMASTER: Improved ASTER DEMs for Elevation Change Monitoring
Remote Sens. 2017, 9(7), 704; doi:10.3390/rs9070704
Received: 6 June 2017 / Revised: 4 July 2017 / Accepted: 4 July 2017 / Published: 8 July 2017
PDF Full-text (25530 KB) | HTML Full-text | XML Full-text
Abstract
The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) system on board the Terra (EOS AM-1) satellite has been a source of stereoscopic images covering the whole globe at 15-m resolution with consistent quality for over 16 years. The potential of these data
[...] Read more.
The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) system on board the Terra (EOS AM-1) satellite has been a source of stereoscopic images covering the whole globe at 15-m resolution with consistent quality for over 16 years. The potential of these data in terms of geomorphological analysis and change detection in three dimensions is unrivaled and should be exploited more. Due to uncorrected errors in the image geometry due to sensor motion (“jitter”), however, the quality of the DEMs and orthoimages currently available is often insufficient for a number of applications, including surface change detection. We have therefore developed a series of algorithms packaged under the name MicMac ASTER (MMASTER). It is composed of a tool to compute Rational Polynomial Coefficient (RPC) models from the ASTER metadata, a method that improves the quality of the matching by identifying and correcting jitter-induced cross-track parallax errors and a correction for along-track jitter when computing differences between DEMs (either with another MMASTER DEM or with another data source). Our method outputs more precise DEMs with less unmatched areas and reduced overall noise compared to NASA’s standard AST14DMO product. The algorithms were implemented in the open source photogrammetric library and software suite MicMac. Here, we briefly examine the potential of MMASTER-produced DEMs to investigate a variety of geomorphological changes, including river erosion, seismic deformation, changes in biomass, volcanic deformation and glacier mass balance. Full article
(This article belongs to the Section Remote Sensing in Geology, Geomorphology and Hydrology)
Figures

Open AccessArticle High Resolution Orthomosaics of African Coral Reefs: A Tool for Wide-Scale Benthic Monitoring
Remote Sens. 2017, 9(7), 705; doi:10.3390/rs9070705
Received: 27 March 2017 / Revised: 23 June 2017 / Accepted: 4 July 2017 / Published: 8 July 2017
PDF Full-text (10846 KB) | HTML Full-text | XML Full-text
Abstract
Coral reefs play a key role in coastal protection and habitat provision. They are also well known for their recreational value. Attempts to protect these ecosystems have not successfully stopped large-scale degradation. Significant efforts have been made by government and research organizations to
[...] Read more.
Coral reefs play a key role in coastal protection and habitat provision. They are also well known for their recreational value. Attempts to protect these ecosystems have not successfully stopped large-scale degradation. Significant efforts have been made by government and research organizations to ensure that coral reefs are monitored systematically to gain a deeper understanding of the causes, the effects and the extent of threats affecting coral reefs. However, further research is needed to fully understand the importance that sampling design has on coral reef characterization and assessment. This study examines the effect that sampling design has on the estimation of seascape metrics when coupling semi-autonomous underwater vehicles, structure-from-motion photogrammetry techniques and high resolution (0.4 cm) underwater imagery. For this purpose, we use FRAGSTATS v4 to estimate key seascape metrics that enable quantification of the area, density, edge, shape, contagion, interspersion and diversity of sessile organisms for a range of sampling scales (0.5 m × 0.5 m, 2 m × 2 m, 5 m × 5 m, 7 m × 7 m), quadrat densities (from 1–100 quadrats) and sampling strategies (nested vs. random) within a 1655 m2 case study area in Ponta do Ouro Partial Marine Reserve (Mozambique). Results show that the benthic community is rather disaggregated within a rocky matrix; the embedded patches frequently have a small size and a regular shape; and the population is highly represented by soft corals. The genus Acropora is the more frequent and shows bigger colonies in the group of hard corals. Each of the seascape metrics has specific requirements of the sampling scale and quadrat density for robust estimation. Overall, the majority of the metrics were accurately identified by sampling scales equal to or coarser than 5 m × 5 m and quadrat densities equal to or larger than 30. The study indicates that special attention needs to be dedicated to the design of coral reef monitoring programmes, with decisions being based on the seascape metrics and statistics being determined. The results presented here are representative of the eastern South Africa coral reefs and are expected to be transferable to coral reefs with similar characteristics. The work presented here is limited to one study site and further research is required to confirm the findings. Full article
(This article belongs to the Section Ocean Remote Sensing)
Figures

Figure 1

Open AccessArticle Dry Season Evapotranspiration Dynamics over Human-Impacted Landscapes in the Southern Amazon Using the Landsat-Based METRIC Model
Remote Sens. 2017, 9(7), 706; doi:10.3390/rs9070706
Received: 24 May 2017 / Revised: 26 June 2017 / Accepted: 5 July 2017 / Published: 9 July 2017
PDF Full-text (4923 KB) | HTML Full-text | XML Full-text
Abstract
Although seasonal and temporal variations in evapotranspiration (ET) in Amazonia have been studied based upon flux-tower data and coarse resolution satellite-based models, ET dynamics over human-impacted landscapes are highly uncertain in this region. In this study, we estimate ET rates from critical land
[...] Read more.
Although seasonal and temporal variations in evapotranspiration (ET) in Amazonia have been studied based upon flux-tower data and coarse resolution satellite-based models, ET dynamics over human-impacted landscapes are highly uncertain in this region. In this study, we estimate ET rates from critical land cover types over highly fragmented landscapes in the southern Amazon and characterize the ET dynamics during the dry season using the METRIC (Mapping Evapotranspiration at high Resolution with Internalized Calibration) model. METRIC, a Landsat-based ET model, that generates spatially continuous ET estimates at a 30 m spatial resolution widely used for agricultural applications, was adapted to the southern Amazon by using the NDVI indexed reference ET fraction (ETrF) approach. Compared to flux tower-based ET rates, this approach showed an improved performance on the forest ET estimation over the standard METRIC approach, with R2 = 0.73 from R2 = 0.70 and RMSE reduced from 0.77 mm/day to 0.35 mm/day. We used this approach integrated into the METRIC procedure to estimate ET rates from primary, regenerated, and degraded forests and pasture in Acre, Rondônia, and Mato Grosso, all located in the southern Amazon, during the dry season in 2009. The lowest ET rates occurred in Mato Grosso, the driest region. Acre and Rondônia, both located in the southwestern Amazon, had similar ET rates for all land cover types. Dry season ET rates between primary forest and regenerated forest were similar (p > 0.05) in all sites, ranging between 2.5 and 3.4 mm/day for both forest cover types in the three sites. ET rates from degraded forest in Mato Grosso were significantly lower (p < 0.05) compared to the other forest cover types, with a value of 2.03 mm/day on average. Pasture showed the lowest ET rates during the dry season at all study sites, with the dry season average ET varying from 1.7 mm/day in Mato Grosso to 2.8 mm/day in Acre. Full article
Figures

Open AccessArticle Estimation of Forest Aboveground Biomass in Changbai Mountain Region Using ICESat/GLAS and Landsat/TM Data
Remote Sens. 2017, 9(7), 707; doi:10.3390/rs9070707
Received: 20 June 2017 / Revised: 4 July 2017 / Accepted: 4 July 2017 / Published: 9 July 2017
PDF Full-text (17845 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Mapping the magnitude and spatial distribution of forest aboveground biomass (AGB, in Mg·ha−1) is crucial to improve our understanding of the terrestrial carbon cycle. Landsat/TM (Thematic Mapper) and ICESat/GLAS (Ice, Cloud, and land Elevation Satellite, Geoscience Laser Altimeter System) data were
[...] Read more.
Mapping the magnitude and spatial distribution of forest aboveground biomass (AGB, in Mg·ha−1) is crucial to improve our understanding of the terrestrial carbon cycle. Landsat/TM (Thematic Mapper) and ICESat/GLAS (Ice, Cloud, and land Elevation Satellite, Geoscience Laser Altimeter System) data were integrated to estimate the AGB in the Changbai Mountain area. Firstly, four forest types were delineated according to TM data classification. Secondly, different models for prediction of the AGB at the GLAS footprint level were developed from GLAS waveform metrics and the AGB was derived from field observations using multiple stepwise regression. Lastly, GLAS-derived AGB, in combination with vegetation indices, leaf area index (LAI), canopy closure, and digital elevation model (DEM), were used to drive a data fusion model based on the random forest approach for extrapolating the GLAS footprint AGB to a continuous AGB map. The classification result showed that the Changbai Mountain region was characterized as forest-rich in altitudinal vegetation zones. The contribution of remote sensing variables in modeling the AGB was evaluated. Vegetation index metrics account for large amount of contribution in AGB ranges <150 Mg·ha−1, while canopy closure has the largest contribution in AGB ranges ≥150 Mg·ha−1. Our study revealed that spatial information from two sensors and DEM could be combined to estimate the AGB with an R2 of 0.72 and an RMSE of 25.24 Mg·ha−1 in validation at stand level (size varied from ~0.3 ha to ~3 ha). Full article
(This article belongs to the Section Forest Remote Sensing)
Figures

Figure 1

Open AccessArticle Estimation of Winter Wheat Above-Ground Biomass Using Unmanned Aerial Vehicle-Based Snapshot Hyperspectral Sensor and Crop Height Improved Models
Remote Sens. 2017, 9(7), 708; doi:10.3390/rs9070708
Received: 12 May 2017 / Revised: 5 July 2017 / Accepted: 6 July 2017 / Published: 10 July 2017
PDF Full-text (6532 KB) | HTML Full-text | XML Full-text
Abstract
Correct estimation of above-ground biomass (AGB) is necessary for accurate crop growth monitoring and yield prediction. We estimated AGB based on images obtained with a snapshot hyperspectral sensor (UHD 185 firefly, Cubert GmbH, Ulm, Baden-Württemberg, Germany) mounted on an unmanned aerial vehicle (UAV).
[...] Read more.
Correct estimation of above-ground biomass (AGB) is necessary for accurate crop growth monitoring and yield prediction. We estimated AGB based on images obtained with a snapshot hyperspectral sensor (UHD 185 firefly, Cubert GmbH, Ulm, Baden-Württemberg, Germany) mounted on an unmanned aerial vehicle (UAV). The UHD 185 images were used to calculate the crop height and hyperspectral reflectance of winter wheat canopies from hyperspectral and panchromatic images. We constructed several single-parameter models for AGB estimation based on spectral parameters, such as specific bands, spectral indices (e.g., Ratio Vegetation Index (RVI), NDVI, Greenness Index (GI) and Wide Dynamic Range VI (WDRVI)) and crop height and several models combined with spectral parameters and crop height. Comparison with experimental results indicated that incorporating crop height into the models improved the accuracy of AGB estimations (the average AGB is 6.45 t/ha). The estimation accuracy of single-parameter models was low (crop height only: R2 = 0.50, RMSE = 1.62 t/ha, MAE = 1.24 t/ha; R670 only: R2 = 0.54, RMSE = 1.55 t/ha, MAE = 1.23 t/ha; NDVI only: R2 = 0.37, RMSE = 1.81 t/ha, MAE = 1.47 t/ha; partial least squares regression R2 = 0.53, RMSE = 1.69, MAE = 1.20), but accuracy increased when crop height and spectral parameters were combined (partial least squares regression modeling: R2 = 0.78, RMSE = 1.08 t/ha, MAE = 0.83 t/ha; verification: R2 = 0.74, RMSE = 1.20 t/ha, MAE = 0.96 t/ha). Our results suggest that crop height determined from the new UAV-based snapshot hyperspectral sensor can improve AGB estimation and is advantageous for mapping applications. This new method can be used to guide agricultural management. Full article
(This article belongs to the Special Issue Earth Observations for Precision Farming in China (EO4PFiC))
Figures

Open AccessArticle Learning-Based Sub-Pixel Change Detection Using Coarse Resolution Satellite Imagery
Remote Sens. 2017, 9(7), 709; doi:10.3390/rs9070709
Received: 11 May 2017 / Revised: 16 June 2017 / Accepted: 5 July 2017 / Published: 10 July 2017
PDF Full-text (2182 KB) | HTML Full-text | XML Full-text
Abstract
Moderate Resolution Imaging Spectroradiometer (MODIS) data are effective and efficient for monitoring urban dynamics such as urban cover change and thermal anomalies, but the spatial resolution provided by MODIS data is 500 m (for most of its shorter spectral bands), which results in
[...] Read more.
Moderate Resolution Imaging Spectroradiometer (MODIS) data are effective and efficient for monitoring urban dynamics such as urban cover change and thermal anomalies, but the spatial resolution provided by MODIS data is 500 m (for most of its shorter spectral bands), which results in difficulty in detecting subtle spatial variations within a coarse pixel—especially for a fast-growing city. Given that the historical land use/cover products and satellite data at finer resolution are valuable to reflect the urban dynamics with more spatial details, finer spatial resolution images, as well as land cover products at previous times, are exploited in this study to improve the change detection capability of coarse resolution satellite data. The proposed approach involves two main steps. First, pairs of coarse and finer resolution satellite data at previous times are learned and then applied to generate synthetic satellite data with finer spatial resolution from coarse resolution satellite data. Second, a land cover map was produced at a finer spatial resolution and adjusted with the obtained synthetic satellite data and prior land cover maps. The approach was tested for generating finer resolution synthetic Landsat images using MODIS data from the Guangzhou study area. The finer resolution Landsat-like data were then applied to detect land cover changes with more spatial details. Test results show that the change detection accuracy using the proposed approach with the synthetic Landsat data is much better than the results using the original MODIS data or conventional spatial and temporal fusion-based approaches. The proposed approach is beneficial for detecting subtle urban land cover changes with more spatial details when multitemporal coarse satellite data are available. Full article
(This article belongs to the Special Issue Learning to Understand Remote Sensing Images)
Figures

Open AccessArticle Improved Model for Depth Bias Correction in Airborne LiDAR Bathymetry Systems
Remote Sens. 2017, 9(7), 710; doi:10.3390/rs9070710
Received: 24 May 2017 / Revised: 5 July 2017 / Accepted: 6 July 2017 / Published: 10 July 2017
PDF Full-text (2059 KB) | HTML Full-text | XML Full-text
Abstract
Airborne LiDAR bathymetry (ALB) is efficient and cost effective in obtaining shallow water topography, but often produces a low-accuracy sounding solution due to the effects of ALB measurements and ocean hydrological parameters. In bathymetry estimates, peak shifting of the green bottom return caused
[...] Read more.
Airborne LiDAR bathymetry (ALB) is efficient and cost effective in obtaining shallow water topography, but often produces a low-accuracy sounding solution due to the effects of ALB measurements and ocean hydrological parameters. In bathymetry estimates, peak shifting of the green bottom return caused by pulse stretching induces depth bias, which is the largest error source in ALB depth measurements. The traditional depth bias model is often applied to reduce the depth bias, but it is insufficient when used with various ALB system parameters and ocean environments. Therefore, an accurate model that considers all of the influencing factors must be established. In this study, an improved depth bias model is developed through stepwise regression in consideration of the water depth, laser beam scanning angle, sensor height, and suspended sediment concentration. The proposed improved model and a traditional one are used in an experiment. The results show that the systematic deviation of depth bias corrected by the traditional and improved models is reduced significantly. Standard deviations of 0.086 and 0.055 m are obtained with the traditional and improved models, respectively. The accuracy of the ALB-derived depth corrected by the improved model is better than that corrected by the traditional model. Full article
(This article belongs to the Section Ocean Remote Sensing)
Figures

Open AccessFeature PaperArticle An Empirical Algorithm for Wave Retrieval from Co-Polarization X-Band SAR Imagery
Remote Sens. 2017, 9(7), 711; doi:10.3390/rs9070711
Received: 17 April 2017 / Revised: 28 June 2017 / Accepted: 6 July 2017 / Published: 11 July 2017
PDF Full-text (5775 KB) | HTML Full-text | XML Full-text
Abstract
In this study, we proposed an empirical algorithm for significant wave height (SWH) retrieval from TerraSAR-X/TanDEM (TS-X/TD-X) X-band synthetic aperture radar (SAR) co-polarization (vertical-vertical (VV) and horizontal-horizontal (HH)) images. As the existing empirical algorithm at X-band, i.e., XWAVE, is applied for wave retrieval
[...] Read more.
In this study, we proposed an empirical algorithm for significant wave height (SWH) retrieval from TerraSAR-X/TanDEM (TS-X/TD-X) X-band synthetic aperture radar (SAR) co-polarization (vertical-vertical (VV) and horizontal-horizontal (HH)) images. As the existing empirical algorithm at X-band, i.e., XWAVE, is applied for wave retrieval from HH-polarization TS-X/TD-X image, polarization ratio (PR) has to be used for inverting wind speed, which is treated as an input in XWAVE. Wind speed encounters saturation in tropical cyclone. In our work, wind speed is replaced by normalized radar cross section (NRCS) to avoiding using SAR-derived wind speed, which does not work in high winds, and the empirical algorithm can be conveniently implemented without converting NRCS in HH-polarization to NRCS in VV-polarization by using X-band PR. A total of 120 TS-X/TD-X images, 60 in VV-polarization and 60 in HH-polarization, with homogenous wave patterns, and the coincide significant wave height data from European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis field at a 0.125° grid were collected as a dataset for tuning the algorithm. The range of SWH is from 0 to 7 m. We then applied the algorithm to 24 VV and 21 HH additional SAR images to extract SWH at locations of 30 National Oceanic and Atmospheric Administration (NOAA) National Data Buoy Center (NDBC) buoys. It is found that the algorithm performs well with a SWH stander deviation (STD) of about 0.5 m for both VV and HH polarization TS-X/TD-X images. For large wave validation (SWH 6–7 m), we applied the empirical algorithm to a tropical cyclone Sandy TD-X image acquired in 2012, and obtained good result with a SWH STD of 0.3 m. We concluded that the proposed empirical algorithm works for wave retrieval from TS-X/TD-X image in co-polarization without external sea surface wind information. Full article
(This article belongs to the Special Issue Ocean Remote Sensing with Synthetic Aperture Radar)
Figures

Open AccessArticle Assessment of Land Use-Cover Changes and Successional Stages of Vegetation in the Natural Protected Area Altas Cumbres, Northeastern Mexico, Using Landsat Satellite Imagery
Remote Sens. 2017, 9(7), 712; doi:10.3390/rs9070712
Received: 20 May 2017 / Revised: 30 June 2017 / Accepted: 8 July 2017 / Published: 11 July 2017
PDF Full-text (15812 KB) | HTML Full-text | XML Full-text
Abstract
Loss of vegetation cover is a major factor that endangers biodiversity. Therefore, the use of geographic information systems and the analysis of satellite images are important for monitoring these changes in Natural Protected Areas (NPAs). In northeastern Mexico, the Natural Protected Area Altas
[...] Read more.
Loss of vegetation cover is a major factor that endangers biodiversity. Therefore, the use of geographic information systems and the analysis of satellite images are important for monitoring these changes in Natural Protected Areas (NPAs). In northeastern Mexico, the Natural Protected Area Altas Cumbres (NPAAC) represents a relevant floristic and faunistic patch on which the impact of loss of vegetation cover has not been assessed. This work aimed to analyze changes of land use and coverage (LULCC) over the last 42 years on the interior and around the exterior of the area, and also to propose the time of succession for the most important types of vegetation. For the analysis, LANDSAT satellite images from 1973, 1986, 2000, 2005 and 2015 were used, they were classified in seven categories through a segmentation and maximum likelihood analysis. A cross-tabulation analysis was performed to determine the succession gradient. Towards the interior of the area, a significant reduction of tropical vegetation and, to a lesser extent, temperate forests was found, as well as an increase in scrub cover from 1973 to 2015. In addition, urban and vegetation-free areas, as well as modified vegetation, increased to the exterior. Towards the interior of the NPA, the processes of perturbation and recovery were mostly not linear, while in the exterior adjacent area, the presence of secondary vegetation with distinct definite time of succession was evident. The analysis carried out is the first contribution that evaluates LULCC in this important NPA of northeastern Mexico. Results suggest the need to evaluate the effects of these modifications on species. Full article
(This article belongs to the Section Forest Remote Sensing)
Figures

Open AccessArticle Intercalibration and Gaussian Process Modeling of Nighttime Lights Imagery for Measuring Urbanization Trends in Africa 2000–2013
Remote Sens. 2017, 9(7), 713; doi:10.3390/rs9070713
Received: 7 February 2017 / Revised: 3 May 2017 / Accepted: 5 July 2017 / Published: 11 July 2017
PDF Full-text (11573 KB) | HTML Full-text | XML Full-text
Abstract
Sub-Saharan Africa currently has the world’s highest urban population growth rate of any continent at roughly 4.2% annually. A better understanding of the spatiotemporal dynamics of urbanization across the continent is important to a range of fields including public health, economics, and environmental
[...] Read more.
Sub-Saharan Africa currently has the world’s highest urban population growth rate of any continent at roughly 4.2% annually. A better understanding of the spatiotemporal dynamics of urbanization across the continent is important to a range of fields including public health, economics, and environmental sciences. Nighttime lights imagery (NTL), maintained by the National Oceanic and Atmospheric Administration, offers a unique vantage point for studying trends in urbanization. A well-documented deficiency of this dataset is the lack of intra- and inter-annual calibration between satellites, which makes the imagery unsuitable for temporal analysis in their raw format. Here we have generated an ‘intercalibrated’ time series of annual NTL images for Africa (2000–2013) by building on the widely used invariant region and quadratic regression method (IRQR). Gaussian process methods (GP) were used to identify NTL latent functions independent from the temporal noise signals in the annual datasets. The corrected time series was used to explore the positive association of NTL with Gross Domestic Product (GDP) and urban population (UP). Additionally, the proportion of change in ‘lit area’ occurring in urban areas was measured by defining urban agglomerations as contiguously lit pixels of >250 km2, with all other pixels being rural. For validation, the IRQR and GP time series were compared as predictors of the invariant region dataset. Root mean square error values for the GP smoothed dataset were substantially lower. Correlation of NTL with GDP and UP using GP smoothing showed significant increases in R2 over the IRQR method on both continental and national scales. Urban growth results suggested that the majority of growth in lit pixels between 2000 and 2013 occurred in rural areas. With this study, we demonstrated the effectiveness of GP to improve conventional intercalibration, used NTL to describe temporal patterns of urbanization in Africa, and detected NTL responses to environmental and humanitarian crises. The smoothed datasets are freely available for further use. Full article
(This article belongs to the Special Issue Recent Advances in Remote Sensing with Nighttime Lights)
Figures

Open AccessArticle First Assessment of Sentinel-1A Data for Surface Soil Moisture Estimations Using a Coupled Water Cloud Model and Advanced Integral Equation Model over the Tibetan Plateau
Remote Sens. 2017, 9(7), 714; doi:10.3390/rs9070714
Received: 14 May 2017 / Revised: 20 June 2017 / Accepted: 6 July 2017 / Published: 12 July 2017
PDF Full-text (7640 KB) | HTML Full-text | XML Full-text
Abstract
The spatiotemporal distribution of soil moisture over the Tibetan Plateau is important for understanding the regional water cycle and climate change. In this paper, the surface soil moisture in the northeastern Tibetan Plateau is estimated from time-series VV-polarized Sentinel-1A observations by coupling the
[...] Read more.
The spatiotemporal distribution of soil moisture over the Tibetan Plateau is important for understanding the regional water cycle and climate change. In this paper, the surface soil moisture in the northeastern Tibetan Plateau is estimated from time-series VV-polarized Sentinel-1A observations by coupling the water cloud model (WCM) and the advanced integral equation model (AIEM). The vegetation indicator in the WCM is represented by the leaf area index (LAI), which is smoothed and interpolated from Terra Moderate Resolution Imaging Spectroradiometer (MODIS) LAI eight-day products. The AIEM requires accurate roughness parameters, which are parameterized by the effective roughness parameters. The first halves of the Sentinel-1A observations from October 2014 to May 2016 are adopted for the model calibration. The calibration results show that the backscattering coefficient (σ°) simulated from the coupled model are consistent with those of the Sentinel-1A with integrated Pearson’s correlation coefficients R of 0.80 and 0.92 for the ascending and descending data, respectively. The variability of soil moisture is correctly modeled by the coupled model. Based on the calibrated model, the soil moisture is retrieved using a look-up table method. The results show that the trends of the in situ soil moisture are effectively captured by the retrieved soil moisture with an integrated R of 0.60 and 0.82 for the ascending and descending data, respectively. The integrated bias, mean absolute error, and root mean square error are 0.006, 0.048, and 0.073 m3/m3 for the ascending data, and are 0.012, 0.026, and 0.055 m3/m3 for the descending data, respectively. Discussions of the effective roughness parameters and uncertainties in the LAI demonstrate the importance of accurate parameterizations of the surface roughness parameters and vegetation for the soil moisture retrieval. These results demonstrate the capability and reliability of Sentinel-1A data for estimating the soil moisture over the Tibetan Plateau. It is expected that our results can contribute to developing operational methods for soil moisture retrieval using the Sentinel-1A and Sentinel-1B satellites. Full article
(This article belongs to the Special Issue Retrieval, Validation and Application of Satellite Soil Moisture Data)
Figures

Open AccessArticle Assessing the Value of UAV Photogrammetry for Characterizing Terrain in Complex Peatlands
Remote Sens. 2017, 9(7), 715; doi:10.3390/rs9070715
Received: 26 April 2017 / Revised: 13 June 2017 / Accepted: 6 July 2017 / Published: 12 July 2017
PDF Full-text (7600 KB) | HTML Full-text | XML Full-text
Abstract
Microtopographic variability in peatlands has a strong influence on greenhouse gas fluxes, but we lack the ability to characterize terrain in these environments efficiently over large areas. To address this, we assessed the capacity of photogrammetric data acquired from an unmanned aerial vehicle
[...] Read more.
Microtopographic variability in peatlands has a strong influence on greenhouse gas fluxes, but we lack the ability to characterize terrain in these environments efficiently over large areas. To address this, we assessed the capacity of photogrammetric data acquired from an unmanned aerial vehicle (UAV or drone) to reproduce ground elevations measured in the field. In particular, we set out to evaluate the role of (i) vegetation/surface complexity and (ii) supplementary LiDAR data on results. We compared remote-sensing observations to reference measurements acquired with survey grade GPS equipment at 678 sample points, distributed across a 61-hectare treed bog in northwestern Alberta, Canada. UAV photogrammetric data were found to capture elevation with accuracies, by root mean squares error, ranging from 14–42 cm, depending on the state of vegetation/surface complexity. We judge the technology to perform well under all but the most-complex conditions, where ground visibility is hindered by thick vegetation. Supplementary LiDAR data did not improve results significantly, nor did it perform well as a stand-alone technology at the low densities typically available to researchers. Full article
(This article belongs to the Section Forest Remote Sensing)
Figures

Open AccessArticle Stem Measurements and Taper Modeling Using Photogrammetric Point Clouds
Remote Sens. 2017, 9(7), 716; doi:10.3390/rs9070716
Received: 3 May 2017 / Revised: 7 July 2017 / Accepted: 9 July 2017 / Published: 12 July 2017
PDF Full-text (4417 KB) | HTML Full-text | XML Full-text
Abstract
The estimation of tree biomass and the products that can be obtained from a tree stem have focused forest research for more than two centuries. Traditionally, measurements of the entire tree bole were expensive or inaccurate, even when sophisticated remote sensing techniques were
[...] Read more.
The estimation of tree biomass and the products that can be obtained from a tree stem have focused forest research for more than two centuries. Traditionally, measurements of the entire tree bole were expensive or inaccurate, even when sophisticated remote sensing techniques were used. We propose a fast and accurate procedure for measuring diameters along the merchantable portion of the stem at any given height. The procedure uses unreferenced photos captured with a consumer grade camera. A photogrammetric point cloud (PPC) is produced from the acquired images using structure from motion, which is a computer vision range imaging technique. A set of 18 loblolly pines (Pinus taeda Lindl.) from east Louisiana, USA, were photographed, subsequently cut, and the diameter measured every meter. The same diameters were measured on the point cloud with AutoCAD Civil3D. The ground point cloud reconstruction provided useful information for at most 13 m along the stem. The PPC measurements are biased, overestimating real diameters by 17.2 mm, but with a reduced standard deviation (8.2%). A linear equation with parameters of the error at a diameter at breast height (d1.3) and the error of photogrammetric rendering reduced the bias to 1.4 mm. The usability of the PPC measurements in taper modeling was assessed with four models: Max and Burkhart [1], Baldwin and Feduccia [2], Lenhart et al. [3], and Kozak [4]. The evaluation revealed that the data fit well with all the models (R2 ≥ 0.97), with the Kozak and the Baldwin and Feduccia performing the best. The results support the replacement of taper with PPC, as faster, and more accurate and precise product estimations are expected. Full article
(This article belongs to the Section Forest Remote Sensing)
Figures

Open AccessArticle Monitoring of Subsidence along Jingjin Inter-City Railway with High-Resolution TerraSAR-X MT-InSAR Analysis
Remote Sens. 2017, 9(7), 717; doi:10.3390/rs9070717
Received: 9 May 2017 / Revised: 10 July 2017 / Accepted: 10 July 2017 / Published: 12 July 2017
PDF Full-text (5258 KB) | HTML Full-text | XML Full-text
Abstract
Synthetic Aperture Radar Interferometry (InSAR), widely applied for the monitoring of land subsidence, has the advantage of high accuracy and wide coverage. High-resolution SAR data offers a chance to reveal impressive details of large-scale man-made linear features (LMLFs) with Multi-temporal InSAR (MT-InSAR) analysis.
[...] Read more.
Synthetic Aperture Radar Interferometry (InSAR), widely applied for the monitoring of land subsidence, has the advantage of high accuracy and wide coverage. High-resolution SAR data offers a chance to reveal impressive details of large-scale man-made linear features (LMLFs) with Multi-temporal InSAR (MT-InSAR) analysis. Despite these advantages, research validating high-resolution MT-InSAR results along high-speed railways with high spatial and temporal density leveling data is limited. This paper explored the monitoring ability of high-resolution MT-InSAR in an experiment along Jingjin Inter-City Railway, located in Tianjin, China. Validation between these MT-InSAR results and a high spatial/temporal density leveling measurement was conducted. A total of 37 TSX images spanning half a year were processed for MT-InSAR analysis. The distance between two consecutive leveling points is 60 m along Jingjin Inter-City railway and the time interval of the study was about one month. The Root Mean Square Error (RMSE) index of average subsidence rate comparison between MT-InSAR results and leveling data was 3.28 mm/yr, with 34 points, and that of the displacement comparison was 2.90 mm with 464 valid observations. The experimental results along Jingjin Inter-City railway showed a high correlation between these two distinct measurements. These analyses show that millimeter accuracy can be achieved with MT-InSAR analysis when monitoring subsidence along a high-speed railway. We discuss the possible reason for the subsiding center, and the characteristics of both leveling and MT-InSAR results. We propose further planning for the monitoring of subsidence over LMLFs. Full article
(This article belongs to the Section Remote Sensing in Geology, Geomorphology and Hydrology)
Figures

Open AccessArticle A Method to Improve High-Resolution Sea Ice Drift Retrievals in the Presence of Deformation Zones
Remote Sens. 2017, 9(7), 718; doi:10.3390/rs9070718
Received: 5 May 2017 / Revised: 6 July 2017 / Accepted: 9 July 2017 / Published: 12 July 2017
PDF Full-text (7350 KB) | HTML Full-text | XML Full-text
Abstract
Retrievals of sea ice drift from synthetic aperture radar (SAR) images at high spatial resolution are valuable for understanding kinematic behavior and deformation processes of the ice at different spatial scales. Ice deformation causes temporal changes in patterns observed in sequences of SAR
[...] Read more.
Retrievals of sea ice drift from synthetic aperture radar (SAR) images at high spatial resolution are valuable for understanding kinematic behavior and deformation processes of the ice at different spatial scales. Ice deformation causes temporal changes in patterns observed in sequences of SAR images; which makes it difficult to retrieve ice displacement with algorithms based on correlation and feature identification techniques. Here, we propose two extensions to a pattern matching algorithm, with the objective to improve the reliability of the retrieved sea ice drift field at spatial resolutions of a few hundred meters. Firstly, we extended a reliability assessment proposed in an earlier study, which is based on analyzing texture and correlation parameters of SAR image pairs, with the aim to reject unreliable pattern matches. The second step is specifically adapted to the presence of deformation features to avoid the erasing of discontinuities in the drift field. We suggest an adapted detection scheme that identifies linear deformation features (LDFs) in the drift vector field, and detects and replaces outliers after considering the presence of such LDFs in their neighborhood. We validate the improvement of our pattern matching algorithm by comparing the automatically retrieved drift to manually derived reference data for three SAR scenes acquired over different sea ice covered regions. Full article
(This article belongs to the Section Ocean Remote Sensing)
Figures

Open AccessArticle Modelling Shadow Using 3D Tree Models in High Spatial and Temporal Resolution
Remote Sens. 2017, 9(7), 719; doi:10.3390/rs9070719
Received: 24 May 2017 / Revised: 29 June 2017 / Accepted: 9 July 2017 / Published: 13 July 2017
PDF Full-text (1699 KB) | HTML Full-text | XML Full-text
Abstract
Information about the availability of solar irradiance for crops is of high importance for improving management practices of agricultural ecosystems such as agroforestry systems (AFS). Hence, the development of a high-resolution model that allows for the quantification of tree shading on a diurnal
[...] Read more.
Information about the availability of solar irradiance for crops is of high importance for improving management practices of agricultural ecosystems such as agroforestry systems (AFS). Hence, the development of a high-resolution model that allows for the quantification of tree shading on a diurnal and annual time scale is highly demanded to generate realistic estimations of the shading dynamics in a given AFS. We describe an approach using 3D data derived from a terrestrial laser scanner and the steps undertaken to develop a vector-based model that quantifies and visualizes the shadow cast by single trees at daily, monthly, seasonal or annual levels with the input of cylinder-based tree models. It is able to compute the shadow of given tree models in time intervals of 10 min. To simulate seasonal growth and shedding of leaves, ellipsoids as replacement for leaves can be added to the tips of the tree model’s branches. The shadow model is flexible in its input of location (latitude, longitude), tree architecture and temporal resolution. Due to the possibility to feed this model with factual climate data such as cloud covers, it represents the first 3D tree model that enables the user to retrospectively analyze the shadow regime below a given tree, and to quantify shadow-related developments in AFS. Full article
Figures

Open AccessArticle Assessment of GPM and TRMM Precipitation Products over Singapore
Remote Sens. 2017, 9(7), 720; doi:10.3390/rs9070720
Received: 2 May 2017 / Revised: 30 June 2017 / Accepted: 10 July 2017 / Published: 13 July 2017
PDF Full-text (2179 KB) | HTML Full-text | XML Full-text
Abstract
The evaluation of satellite precipitation products (SPPs) at regional and local scales is essential in improving satellite-based algorithms and sensors, as well as in providing valuable guidance when choosing alternative precipitation data for the local community. The Tropical Rainfall Measuring Mission (TRMM) has
[...] Read more.
The evaluation of satellite precipitation products (SPPs) at regional and local scales is essential in improving satellite-based algorithms and sensors, as well as in providing valuable guidance when choosing alternative precipitation data for the local community. The Tropical Rainfall Measuring Mission (TRMM) has made significant contributions to the development of various SPPs since its launch in 1997. The Global Precipitation Measurement (GPM) mission launched in 2014 and is expected to continue the success of TRMM. During the transition from the TRMM era to the GPM era, it is necessary to assess GPM products and make comparisons with TRMM products in different regions to achieve a global view of the performance of GPM products. To this end, this study aims to assess the capability of the latest Integrated Multi-satellite Retrievals for GPM (IMERG) and two TRMM Multisatellite Precipitation Analysis (TMPA) products (TMPA 3B42 and TMPA 3B42RT) in estimating precipitation over Singapore that represents a typical tropical region. The evaluation was conducted at daily, monthly, seasonal and annual scales from 1 April 2014 to 31 January 2016. The capability of SPPs in detecting rainy/non-rainy days and different precipitation classes was also evaluated. The findings showed that: (1) all SPPs correlated well with measurements from gauges at the monthly scale, but moderately at the daily scale; (2) SPPs performed better in the northeast monsoon season (1 December–15 March) than in the inter-monsoon 1 (16 March–31 May), southwest monsoon (1 June–30 September) and inter-monsoon 2 (1 October–30 November) seasons; (3) IMERG had better performance in the characterization of spatial precipitation variability and precipitation detection capability compared to the TMPA products; (4) for the daily precipitation estimates, IMERG had the lowest systematic bias, followed by 3B42 and 3B42RT; and (5) most of the SPPs overestimated moderate precipitation events (1–20 mm/day), while underestimating light (0.1–1 mm/day) and heavy (>20 mm/day) precipitation events. Overall, IMERG is superior but with only slight improvement compared to the TMPA products over Singapore. This study is one of the earliest assessments of IMERG and a comparison of it with TMPA products in Singapore. Our findings were compared with existing studies conducted in other regions, and some limitations of the IMERG and TMPA products in this tropical region were identified and discussed. This study provides an added value to the understanding of the global performance of the IMERG product. Full article
Figures

Figure 1

Open AccessArticle Papaya Tree Detection with UAV Images Using a GPU-Accelerated Scale-Space Filtering Method
Remote Sens. 2017, 9(7), 721; doi:10.3390/rs9070721
Received: 17 May 2017 / Revised: 29 June 2017 / Accepted: 8 July 2017 / Published: 13 July 2017
PDF Full-text (6410 KB) | HTML Full-text | XML Full-text
Abstract
The use of unmanned aerial vehicles (UAV) can allow individual tree detection for forest inventories in a cost-effective way. The scale-space filtering (SSF) algorithm is commonly used and has the capability of detecting trees of different crown sizes. In this study, we made
[...] Read more.
The use of unmanned aerial vehicles (UAV) can allow individual tree detection for forest inventories in a cost-effective way. The scale-space filtering (SSF) algorithm is commonly used and has the capability of detecting trees of different crown sizes. In this study, we made two improvements with regard to the existing method and implementations. First, we incorporated SSF with a Lab color transformation to reduce over-detection problems associated with the original luminance image. Second, we ported four of the most time-consuming processes to the graphics processing unit (GPU) to improve computational efficiency. The proposed method was implemented using PyCUDA, which enabled access to NVIDIA’s compute unified device architecture (CUDA) through high-level scripting of the Python language. Our experiments were conducted using two images captured by the DJI Phantom 3 Professional and a most recent NVIDIA GPU GTX1080. The resulting accuracy was high, with an F-measure larger than 0.94. The speedup achieved by our parallel implementation was 44.77 and 28.54 for the first and second test image, respectively. For each 4000 × 3000 image, the total runtime was less than 1 s, which was sufficient for real-time performance and interactive application. Full article
Figures

Open AccessArticle Estimation of SOS and EOS for Midwestern US Corn and Soybean Crops
Remote Sens. 2017, 9(7), 722; doi:10.3390/rs9070722
Received: 9 June 2017 / Revised: 6 July 2017 / Accepted: 6 July 2017 / Published: 13 July 2017
PDF Full-text (7884 KB) | HTML Full-text | XML Full-text
Abstract
Understanding crop phenology is fundamental to agricultural production, management, planning, and decision-making. This study used 250 m 16-day Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) time-series data to detect crop phenology across the Midwestern United States, 2007–2015. Key crop phenology metrics,
[...] Read more.
Understanding crop phenology is fundamental to agricultural production, management, planning, and decision-making. This study used 250 m 16-day Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) time-series data to detect crop phenology across the Midwestern United States, 2007–2015. Key crop phenology metrics, start of season (SOS) and end of season (EOS), were estimated for corn and soybean. For such a large study region, we found that MODIS-estimated SOS and EOS values were highly dependent on the nature of input time-series data, analytical methods, and threshold values chosen for crop phenology detection. With the entire sequence of MODIS EVI time-series data as input, SOS values were inconsistent compared to crop emergent dates from the United States Department of Agriculture (USDA) Crop Progress Reports (CPR). However, when we removed winter EVI images from the time-series data to reduce impacts of snow cover, we obtained much more consistent SOS estimation. Various threshold values (10 to 50% of seasonal EVI amplitude) were applied to derive SOS values. For both corn’s and soybean’s SOS estimation, a threshold value of 25% generated the best overall agreement with the CPR crop emergent dates. Root-mean-square error (RMSE) values were 4.81 and 5.30 days for corn and soybean, respectively. For corn’s EOS estimation, a threshold value of 40% led to a high R2 value of 0.82 and RMSE value of 5.16 days. We further examined spatial patterns of SOS and EOS for both crops—SOS for corn displayed a clear south-north gradient; the southern portion of the Midwest US has earlier SOS and EOS dates. Full article
(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)
Figures

Open AccessArticle Evaluation of the Multi-Scale Ultra-High Resolution (MUR) Analysis of Lake Surface Temperature
Remote Sens. 2017, 9(7), 723; doi:10.3390/rs9070723
Received: 25 May 2017 / Revised: 30 June 2017 / Accepted: 7 July 2017 / Published: 13 July 2017
PDF Full-text (5576 KB) | HTML Full-text | XML Full-text
Abstract
Obtaining accurate and timely lake surface water temperature (LSWT) analyses from satellite remains difficult. Data gaps, cloud contamination, variations in atmospheric profiles of temperature and moisture, and a lack of in situ observations provide challenges for satellite-derived LSWT for climatological analysis or input
[...] Read more.
Obtaining accurate and timely lake surface water temperature (LSWT) analyses from satellite remains difficult. Data gaps, cloud contamination, variations in atmospheric profiles of temperature and moisture, and a lack of in situ observations provide challenges for satellite-derived LSWT for climatological analysis or input into geophysical models. In this study, the Multi-scale Ultra-high Resolution (MUR) analysis of LSWT is evaluated between 2007 and 2015 over a small (Lake Oneida), medium (Lake Okeechobee), and large (Lake Michigan) lake. The advantages of the MUR LSWT analyses include daily consistency, high-resolution (~1 km), near-real time production, and multi-platform data synthesis. The MUR LSWT versus in situ measurements for Lake Michigan (Lake Okeechobee) have an overall bias (MUR LSWT-in situ) of −0.20 °C (0.31 °C) and a RMSE of 0.86 °C (0.91 °C). The MUR LSWT versus in situ measurements for Lake Oneida have overall large biases (−1.74 °C) and RMSE (3.42°C) due to a lack of available satellite imagery over the lake, but performs better during the less cloudy 15 July–30 September period. The results of this study highlight the importance of calculating validation statistics on a seasonal and annual basis for evaluating satellite-derived LSWT. Full article
(This article belongs to the Special Issue Sea Surface Temperature Retrievals from Remote Sensing)
Figures

Open AccessArticle Super-Resolution Reconstruction of Remote Sensing Images Using Multiple-Point Statistics and Isometric Mapping
Remote Sens. 2017, 9(7), 724; doi:10.3390/rs9070724
Received: 13 April 2017 / Revised: 6 July 2017 / Accepted: 10 July 2017 / Published: 15 July 2017
PDF Full-text (26731 KB) | HTML Full-text | XML Full-text
Abstract
When using coarse-resolution remote sensing images, super-resolution reconstruction is widely desired, and can be realized by reproducing the intrinsic features from a set of coarse-resolution fraction data to fine-resolution remote sensing images that are consistent with the coarse fraction information. Prior models of
[...] Read more.
When using coarse-resolution remote sensing images, super-resolution reconstruction is widely desired, and can be realized by reproducing the intrinsic features from a set of coarse-resolution fraction data to fine-resolution remote sensing images that are consistent with the coarse fraction information. Prior models of spatial structures that encode the expected features at the fine (target) resolution are helpful to constrain the spatial patterns of remote sensing images to be generated at that resolution. These prior models can be used properly by multiple-point statistics (MPS), capable of extracting the intrinsic features of patterns from prior models such as training images, and copying them to the simulated regions using hard and soft conditional data, or even without any conditional data. However, because traditional MPS methods based on linear dimensionality reduction are not suitable to deal with nonlinear data, and isometric mapping (ISOMAP) can reduce the dimensionality of nonlinear data effectively, this paper presents a sequential simulation framework for generating super-resolution remote sensing images using ISOMAP and MPS. Using four different examples, it is demonstrated that the structural characteristics of super-resolution reconstruction of remote sensing images using this method, are similar to those of training images. Full article
(This article belongs to the Section Remote Sensing in Geology, Geomorphology and Hydrology)
Figures

Figure 1

Open AccessArticle High-Resolution Remote Sensing Image Retrieval Based on CNNs from a Dimensional Perspective
Remote Sens. 2017, 9(7), 725; doi:10.3390/rs9070725
Received: 26 May 2017 / Revised: 4 July 2017 / Accepted: 9 July 2017 / Published: 14 July 2017
PDF Full-text (94442 KB) | HTML Full-text | XML Full-text
Abstract
Because of recent advances in Convolutional Neural Networks (CNNs), traditional CNNs have been employed to extract thousands of codes as feature representations for image retrieval. In this paper, we propose that more powerful features for high-resolution remote sensing image representations can be learned
[...] Read more.
Because of recent advances in Convolutional Neural Networks (CNNs), traditional CNNs have been employed to extract thousands of codes as feature representations for image retrieval. In this paper, we propose that more powerful features for high-resolution remote sensing image representations can be learned using only several tens of codes; this approach can improve the retrieval accuracy and decrease the time and storage requirements. To accomplish this goal, we first investigate the learning of a series of features with different dimensions using a few tens to thousands of codes via our improved CNN frameworks. Then, a Principal Component Analysis (PCA) is introduced to compress the high-dimensional remote sensing image feature codes learned by traditional CNNs. Comprehensive comparisons are conducted to evaluate the retrieval performance based on feature codes of different dimensions learned by the improved CNNs as well as the PCA compression. To further demonstrate the powerful ability of the low-dimensional feature representation learned by the improved CNN frameworks, a Feature Weighted Map (FWM), which can perform feature visualization and provides a better understanding of the nature of Deep Convolutional Neural Networks (DCNNs) frameworks, is explored. All the CNN models are trained from scratch using a large-scale and high-resolution remote sensing image archive, which will be published and made available to the public. The experimental results show that our method outperforms state-of-the-art CNN frameworks in terms of accuracy and storage. Full article
Figures

Open AccessArticle Retrieval of Biophysical Crop Variables from Multi-Angular Canopy Spectroscopy
Remote Sens. 2017, 9(7), 726; doi:10.3390/rs9070726
Received: 13 June 2017 / Revised: 4 July 2017 / Accepted: 12 July 2017 / Published: 14 July 2017
PDF Full-text (7466 KB) | HTML Full-text | XML Full-text
Abstract
The future German Environmental Mapping and Analysis Program (EnMAP) mission, due to launch in late 2019, will deliver high resolution hyperspectral data from space and will thus contribute to a better monitoring of the dynamic surface of the earth. Exploiting the satellite’s ±30°
[...] Read more.
The future German Environmental Mapping and Analysis Program (EnMAP) mission, due to launch in late 2019, will deliver high resolution hyperspectral data from space and will thus contribute to a better monitoring of the dynamic surface of the earth. Exploiting the satellite’s ±30° across-track pointing capabilities will allow for the collection of hyperspectral time-series of homogeneous quality. Various studies have shown the possibility to retrieve geo-biophysical plant variables, like leaf area index (LAI) or leaf chlorophyll content (LCC), from narrowband observations with fixed viewing geometry by inversion of radiative transfer models (RTM). In this study we assess the capability of the well-known PROSPECT 5B + 4SAIL (Scattering by Arbitrarily Inclined Leaves) RTM to estimate these variables from off-nadir observations obtained during a field campaign with respect to EnMAP-like sun–target–sensor-geometries. A novel approach for multiple inquiries of a large look-up-table (LUT) in hierarchical steps is introduced that accounts for the varying instances of all variables of interest. Results show that anisotropic effects are strongest for early growth stages of the winter wheat canopy which influences also the retrieval of the variables. RTM inversions from off-nadir spectra lead to a decreased accuracy for the retrieval of LAI with a relative root mean squared error (rRMSE) of 18% at nadir vs. 25% (backscatter) and 24% (forward scatter) at off-nadir. For LCC estimations, however, off-nadir observations yield improvements, i.e., rRMSE (nadir) = 24% vs. rRMSE (forward scatter) = 20%. It follows that for a variable retrieval through RTM inversion, the final user will benefit from EnMAP time-series for biophysical studies regardless of the acquisition angle and will thus be able to exploit the maximum revisit capability of the mission. Full article
Figures

Open AccessArticle 100 Years of Competition between Reduction in Channel Capacity and Streamflow during Floods in the Guadalquivir River (Southern Spain)
Remote Sens. 2017, 9(7), 727; doi:10.3390/rs9070727
Received: 30 May 2017 / Revised: 8 July 2017 / Accepted: 11 July 2017 / Published: 14 July 2017
PDF Full-text (3651 KB) | HTML Full-text | XML Full-text
Abstract
Reduction in channel capacity can trigger an increase in flood hazard over time. It represents a geomorphic driver that competes against its hydrologic counterpart where streamflow decreases. We show that this situation arose in the Guadalquivir River (Southern Spain) after impoundment. We identify
[...] Read more.
Reduction in channel capacity can trigger an increase in flood hazard over time. It represents a geomorphic driver that competes against its hydrologic counterpart where streamflow decreases. We show that this situation arose in the Guadalquivir River (Southern Spain) after impoundment. We identify the physical parameters that raised flood hazard in the period 1997–2013 with respect to past years 1910–1996 and quantify their effects by accounting for temporal trends in both streamflow and channel capacity. First, we collect historical hydrological data to lengthen records of extreme flooding events since 1910. Next, inundated areas and grade lines across a 70 km stretch of up to 2 km wide floodplain are delimited from Landsat and TerraSAR-X satellite images of the most recent floods (2009–2013). Flooded areas are also computed using standard two-dimensional Saint-Venant equations. Simulated stages are verified locally and across the whole domain with collected hydrological data and satellite images, respectively. The thoughtful analysis of flooding and geomorphic dynamics over multi-decadal timescales illustrates that non-stationary channel adaptation to river impoundment decreased channel capacity and increased flood hazard. Previous to channel squeezing and pre-vegetation encroachment, river discharges as high as 1450 m3·s−1 (the year 1924) were required to inundate the same areas as the 790 m3·s−1 streamflow for recent floods (the year 2010). We conclude that future projections of one-in-a-century river floods need to include geomorphic drivers as they compete with the reduction of peak discharges under the current climate change scenario. Full article
(This article belongs to the Special Issue Remote Sensing of Climate Change and Water Resources)
Figures

Open AccessArticle Estimating Tropical Cyclone Size in the Northwestern Pacific from Geostationary Satellite Infrared Images
Remote Sens. 2017, 9(7), 728; doi:10.3390/rs9070728
Received: 27 June 2017 / Revised: 27 June 2017 / Accepted: 11 July 2017 / Published: 14 July 2017
PDF Full-text (1634 KB) | HTML Full-text | XML Full-text
Abstract
Thirty-year (1980–2009) tropical cyclone (TC) images from geostationary satellite (GOES, Meteosat, GMS, MTSAT and FY2) infrared sensors covering the Northwestern Pacific were used to build a TC size dataset based on objective models. The models are based on a correlation between the size
[...] Read more.
Thirty-year (1980–2009) tropical cyclone (TC) images from geostationary satellite (GOES, Meteosat, GMS, MTSAT and FY2) infrared sensors covering the Northwestern Pacific were used to build a TC size dataset based on objective models. The models are based on a correlation between the size of TCs, defined as the mean azimuth radius of 34 kt surface winds (R34) and the brightness temperature radial profiles derived from satellite imagery. Using satellite images between 2001 and 2009, we obtained 16,548 matchup samples and found the correlation to be positive in the TC’s inner core region (in the annulus field 64 km from the TC center) and negative in its outer region (in the annulus field 100–250 km from the TC center). Then, we performed a stepwise regression to select the dominant variables and derived the associated coefficients for the objective models. Independent validation against best track archives shows the median estimation error to be between 27 and 65 km, which are not significantly different to other satellite series data. Finally, we applied the models to 721 TCs and made 13,726 measurements of TC size. The difference of mean TC size derived from our models, and also that from the US Joint Typhoon Warning Center (JTWC) best track archives is 19 km. The developed database is valuable in the research fields of TC structure, climatology, and the initialization of forecasting models. Full article
(This article belongs to the Section Ocean Remote Sensing)
Figures

Figure 1

Open AccessArticle Satellite Observations of El Niño Impacts on Eurasian Spring Vegetation Greenness during the Period 1982–2015
Remote Sens. 2017, 9(7), 628; doi:10.3390/rs9070628
Received: 25 April 2017 / Revised: 8 June 2017 / Accepted: 14 June 2017 / Published: 22 June 2017
PDF Full-text (7799 KB) | HTML Full-text | XML Full-text
Abstract
As Earth’s most influential naturally-recurring sea and atmospheric oscillation, ENSO results in widespread changes in the climate system not only over much of the tropics and subtropics, but also in high latitudes via atmospheric teleconnections. In the present study, the linkages between springtime
[...] Read more.
As Earth’s most influential naturally-recurring sea and atmospheric oscillation, ENSO results in widespread changes in the climate system not only over much of the tropics and subtropics, but also in high latitudes via atmospheric teleconnections. In the present study, the linkages between springtime vegetation greenness over Eurasia and El Niño are investigated based on two long-term normalized difference vegetation index (NDVI) datasets from 1982 to 2015, and possible physical mechanisms for the teleconnections are explored. Results from the Empirical Orthogonal Function (EOF) and Singular Value Decomposition (SVD) analyses consistently suggest that the spatial patterns of NDVI, with “negative-positive-negative” values, have closer connections to El Niño. In particular, East Russia is identified as the key region with the strongest negative influences from Eastern Pacific (EP) El Niño on spring vegetation growth. During EP El Niño years, suppressed convection over the Bay of Bengal (BoB) may excite a Rossby wave from the BoB to the Far East. East Russia is located in the west of a large cyclone anomaly accompanied by the strong North and Northwesterly wind anomalies and the transport of cold air from Siberia. As a result, surface air temperature decreases significantly over East Russia and thus inhibits the vegetation growth during spring in the EP El Niño years. Full article
(This article belongs to the Special Issue Remote Sensing of Land-Atmosphere Interactions)
Figures

Open AccessArticle Urban Land-Cover Dynamics in Arid China Based on High-Resolution Urban Land Mapping Products
Remote Sens. 2017, 9(7), 730; doi:10.3390/rs9070730
Received: 24 May 2017 / Revised: 1 July 2017 / Accepted: 12 July 2017 / Published: 14 July 2017
PDF Full-text (5974 KB) | HTML Full-text | XML Full-text
Abstract
Rapid urbanization has occurred in northwestern China, threatening the sustainability of its fragile dryland ecosystems. A lack of precise urban land-cover information has limited our understanding on the urbanization in the dryland. Here, we examined urban land-cover changes from 2000 to 2014 in
[...] Read more.
Rapid urbanization has occurred in northwestern China, threatening the sustainability of its fragile dryland ecosystems. A lack of precise urban land-cover information has limited our understanding on the urbanization in the dryland. Here, we examined urban land-cover changes from 2000 to 2014 in 21 major cities that comprise over 50% of the developed land in arid China, using Landsat Enhanced Thematic Mapper Plus and Operational Land Imager data, and a hybrid classification method. The 15-m resolution urban land-cover products (including impervious surfaces, vegetation, bare soil, and water bodies) had an overall accuracy of 90.37%. Based on these new land use products, we found the urbanization in arid China was characterized by the dramatic expansion of impervious surface (+13.23%) and reduction of bare soil (−13.41%), while the proportions of vegetation (+0.27%) and water (−0.10%) remained stable. The observed dynamic equilibrium of vegetated ratio implies an increasing harmonization of urbanization and greening, which was particularly important for the sustainability of fragile urban ecosystems in arid regions. From an economic perspective, gross domestic product and population were significantly correlated with impervious surfaces, and oasis cities displayed a stronger ability to attract new residents than desert cities. Full article
(This article belongs to the Special Issue Remote Sensing of Urban Ecology)
Figures

Open AccessArticle Continued Reforestation and Urban Expansion in the New Century of a Tropical Island in the Caribbean
Remote Sens. 2017, 9(7), 731; doi:10.3390/rs9070731
Received: 22 April 2017 / Revised: 22 June 2017 / Accepted: 12 July 2017 / Published: 15 July 2017
PDF Full-text (3374 KB) | HTML Full-text | XML Full-text
Abstract
Accurate and timely monitoring of tropical land cover/use (LCLU) changes is urgent due to the rapid deforestation/reforestation and its impact on global land-atmosphere interaction. However, persistent cloud cover in the tropics imposes the greatest challenge and retards LCLU mapping in mountainous areas such
[...] Read more.
Accurate and timely monitoring of tropical land cover/use (LCLU) changes is urgent due to the rapid deforestation/reforestation and its impact on global land-atmosphere interaction. However, persistent cloud cover in the tropics imposes the greatest challenge and retards LCLU mapping in mountainous areas such as the tropic island of Puerto Rico, where forest transition changed from deforestation to reforestation due to the economy shift from agriculture to industry and service after the 1940s. To improve the LCLU mapping in the tropics and to evaluate the trend of forest transition of Puerto Rico in the new century, we integrated the optical Landsat images with the L-band SAR to map LC in 2010 by taking advantage of the cloud-penetrating ability of the SAR signals. The results showed that the incorporation of SAR data with the Landsat data significantly, although not substantially, enhanced the accuracy of LCLU mapping of Puerto Rico, and the Kappa statistic reached 90.5% from 88.4% without SAR data. The enhancement of mapping by SAR is important for urban and forest, as well as locations with limited optical data caused by cloud cover. We found both forests and urban lands continued expanding in the new century despite the declining population. However, the forest cover change slowed down in 2000–2010 compared to that in 1991–2000. The deforestation rate reduced by 42.1% in 2000–2010, and the reforestation was mostly located in the east and southeast of the island where Hurricane Georges landed and caused severe vegetation damage in 1998. We also found that reforestation increased, but deforestation decreased along the topography slope. Reforestation was much higher within the protected area compared to that in the surroundings in the wet and moist forest zones. Full article
(This article belongs to the Section Forest Remote Sensing)
Figures

Figure 1

Open AccessArticle New Approach for Calculating the Effective Dielectric Constant of the Moist Soil for Microwaves
Remote Sens. 2017, 9(7), 732; doi:10.3390/rs9070732
Received: 7 May 2017 / Revised: 9 July 2017 / Accepted: 11 July 2017 / Published: 15 July 2017
PDF Full-text (1046 KB)
Abstract
Microwave remote sensing techniques are used, among others, for temporally and spatially highly-resolved observations of land-surface properties, e.g., for the management of agricultural productivity and water resource, as well as to improve the performances of numerical weather prediction and climate simulations with soil
[...] Read more.
Microwave remote sensing techniques are used, among others, for temporally and spatially highly-resolved observations of land-surface properties, e.g., for the management of agricultural productivity and water resource, as well as to improve the performances of numerical weather prediction and climate simulations with soil moisture data. In this context, the effective dielectric constant of the soil is a key variable to quantify the land surface properties. We propose a new approach for the effective dielectric constant of the multiphase soil that is based on an arithmetic average of the dielectric constants of the land-surface components with damping. The results show, on average, better agreement with experimental data than previous approaches. Furthermore, the proposed new model overcomes the theoretical limitation of previous models in the incorporation of non-physical parameters to simulate measured data experimentally with satisfactory accuracy. For microwave remote sensing such as SMAP (Soil Moisture Active Passive), SMOS (Soil Moisture and Ocean Salinity) and AMSR-E (Advanced Microwave Scanning Radiometer for EOS), the physical-based model in our study showed a 23–35% RMSE (root-mean-square error) reduction compared to the most prevalent refractive mixing model [1] in the prediction of the dielectric constant for the real and imaginary part, respectively. Furthermore, in radiowave bands used in portable soil sensors such as TDR (time-domain reflectometer) and GPR (ground-penetrating radar) the new dielectric mixing model reduced RMSE by up to 53% in the prediction of the dielectric constant. We found that the permittivity over the saturation point (porosity of dry soil) has a very different and varying pattern compared to that measured in the unsaturated condition. However, in our study, this pattern was mathematically derived from the same mixing rule applied for the unsaturated condition. It is expected that the new dielectric mixing model might help to improve the accuracy of flood monitoring by satellite. Full article
(This article belongs to the Section Remote Sensing in Geology, Geomorphology and Hydrology)
Figures

Open AccessArticle Terrestrial Remote Sensing of Snowmelt in a Diverse High-Arctic Tundra Environment Using Time-Lapse Imagery
Remote Sens. 2017, 9(7), 733; doi:10.3390/rs9070733
Received: 24 April 2017 / Revised: 27 June 2017 / Accepted: 12 July 2017 / Published: 15 July 2017
PDF Full-text (2976 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Snow cover is one of the crucial factors influencing the plant distribution in harsh Arctic regions. In tundra environments, wind redistribution of snow leads to a very heterogeneous spatial distribution which influences growth conditions for plants. Therefore, relationships between snow cover and vegetation
[...] Read more.
Snow cover is one of the crucial factors influencing the plant distribution in harsh Arctic regions. In tundra environments, wind redistribution of snow leads to a very heterogeneous spatial distribution which influences growth conditions for plants. Therefore, relationships between snow cover and vegetation should be analyzed spatially. In this study, we correlate spatial data sets on tundra vegetation types with snow cover information obtained from orthorectification and classification of images collected from a time-lapse camera installed on a mountain summit. The spatial analysis was performed over an area of 0.72 km2, representing a coastal tundra environment in southern Svalbard. The three-year monitoring is supplemented by manual measurements of snow depth, which show a statistically significant relationship between snow abundance and the occurrence of some of the analyzed land cover types. The longest snow cover duration was found on “rock debris” type and the shortest on “lichen-herb-heath tundra”, resulting in melt-out time-lag of almost two weeks between this two land cover types. The snow distribution proved to be consistent over the different years with a similar melt-out pattern occurring in every analyzed season, despite changing melt-out dates related to different weather conditions. The data set of 203 high resolution processed images used in this work is available for download in the supplementary materials. Full article
(This article belongs to the Special Issue Snow Remote Sensing)
Figures

Open AccessArticle Considering Inter-Frequency Clock Bias for BDS Triple-Frequency Precise Point Positioning
Remote Sens. 2017, 9(7), 734; doi:10.3390/rs9070734
Received: 2 June 2017 / Revised: 2 July 2017 / Accepted: 12 July 2017 / Published: 15 July 2017
PDF Full-text (2055 KB) | HTML Full-text | XML Full-text
Abstract
The joint use of multi-frequency signals brings new prospects for precise positioning and has become a trend in Global Navigation Satellite System (GNSS) development. However, a new type of inter-frequency clock bias (IFCB), namely the difference between satellite clocks computed with different ionospheric-free
[...] Read more.
The joint use of multi-frequency signals brings new prospects for precise positioning and has become a trend in Global Navigation Satellite System (GNSS) development. However, a new type of inter-frequency clock bias (IFCB), namely the difference between satellite clocks computed with different ionospheric-free carrier phase combinations, was noticed. Consequently, the B1/B3 precise point positioning (PPP) cannot directly use the current B1/B2 clock products. Datasets from 35 globally distributed stations are employed to investigate the IFCB. For new generation BeiDou Navigation Satellite System (BDS) satellites, namely BDS-3 satellites, the IFCB between B1/B2a and B1/B3 satellite clocks, between B1/B2b and B1/B3 satellite clocks, between B1C/B2a and B1C/B3 satellite clocks, and between B1C/B2b and B1C/B3 satellite clocks is analyzed, and no significant IFCB variations can be observed. The IFCB between B1/B2 and B1/B3 satellite clocks for BDS-2 satellites varies with time, and the IFCB variations are generally confined to peak amplitudes of about 5 cm. The IFCB of BDS-2 satellites exhibits periodic signal, and the accuracy of prediction for IFCB, namely the root mean square (RMS) statistic of the difference between predicted and estimated IFCB values, is 1.2 cm. A triple-frequency PPP model with consideration of IFCB is developed. Compared with B1/B2-based PPP, the positioning accuracy of triple-frequency PPP with BDS-2 satellites can be improved by 12%, 25% and 10% in east, north and vertical directions, respectively. Full article
Figures

Open AccessArticle Accuracy Improvements in the Orientation of ALOS PRISM Images Using IOP Estimation and UCL Kepler Platform Model
Remote Sens. 2017, 9(7), 634; doi:10.3390/rs9070634
Received: 10 March 2017 / Revised: 9 June 2017 / Accepted: 15 June 2017 / Published: 1 July 2017
PDF Full-text (5295 KB) | HTML Full-text | XML Full-text
Abstract
This paper presents a study that was conducted to determine the orientation of ALOS (Advanced Land Observing Satellite) PRISM (Panchromatic Remote-sensing Instrument for Stereo Mapping) triplet images, considering the estimation of interior orientation parameters (IOP) of the cameras and using the collinearity equations
[...] Read more.
This paper presents a study that was conducted to determine the orientation of ALOS (Advanced Land Observing Satellite) PRISM (Panchromatic Remote-sensing Instrument for Stereo Mapping) triplet images, considering the estimation of interior orientation parameters (IOP) of the cameras and using the collinearity equations with the UCL (University College of London) Kepler platform model, which was adapted to use coordinates referenced to the Terrestrial Reference System ITRF97. The results of the experiments showed that the accuracies of 3D coordinates calculated using 3D photogrammetric intersection increased when the IOP were also estimated. The vertical accuracy was significantly better than the horizontal accuracy. The usability of the estimated IOP was tested to perform the bundle block adjustments of another neighbouring PRISM image triplet. The results in terms of 3D photogrammetric intersection were satisfactory and were close to those obtained in the IOP estimation experiment. Full article
Figures

Figure 1

Open AccessArticle Large-Scale, Multi-Temporal Remote Sensing of Palaeo-River Networks: A Case Study from Northwest India and its Implications for the Indus Civilisation
Remote Sens. 2017, 9(7), 735; doi:10.3390/rs9070735
Received: 6 June 2017 / Revised: 6 July 2017 / Accepted: 12 July 2017 / Published: 16 July 2017
PDF Full-text (5876 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Remote sensing has considerable potential to contribute to the identification and reconstruction of lost hydrological systems and networks. Remote sensing-based reconstructions of palaeo-river networks have commonly employed single or limited time-span imagery, which limits their capacity to identify features in complex and varied
[...] Read more.
Remote sensing has considerable potential to contribute to the identification and reconstruction of lost hydrological systems and networks. Remote sensing-based reconstructions of palaeo-river networks have commonly employed single or limited time-span imagery, which limits their capacity to identify features in complex and varied landscape contexts. This paper presents a seasonal multi-temporal approach to the detection of palaeo-rivers over large areas based on long-term vegetation dynamics and spectral decomposition techniques. Twenty-eight years of Landsat 5 data, a total of 1711 multi-spectral images, have been bulk processed using Google Earth Engine© Code Editor and cloud computing infrastructure. The use of multi-temporal data has allowed us to overcome seasonal cultivation patterns and long-term visibility issues related to recent crop selection, extensive irrigation and land-use patterns. The application of this approach on the Sutlej-Yamuna interfluve (northwest India), a core area for the Bronze Age Indus Civilisation, has enabled the reconstruction of an unsuspectedly complex palaeo-river network comprising more than 8000 km of palaeo-channels. It has also enabled the definition of the morphology of these relict courses, which provides insights into the environmental conditions in which they operated. These new data will contribute to a better understanding of the settlement distribution and environmental settings in which this, often considered riverine, civilisation operated. Full article
Figures

Open AccessArticle A Burned Area Mapping Algorithm for Chinese FengYun-3 MERSI Satellite Data
Remote Sens. 2017, 9(7), 736; doi:10.3390/rs9070736
Received: 12 June 2017 / Revised: 8 July 2017 / Accepted: 12 July 2017 / Published: 16 July 2017
PDF Full-text (4551 KB) | HTML Full-text | XML Full-text
Abstract
Biomass burning is a worldwide phenomenon, which emits large amounts of carbon into the atmosphere and strongly influences the environment. Burned area is an important parameter in modeling the impacts of biomass burning on the climate and ecosystem. The Medium Resolution Spectral Imager
[...] Read more.
Biomass burning is a worldwide phenomenon, which emits large amounts of carbon into the atmosphere and strongly influences the environment. Burned area is an important parameter in modeling the impacts of biomass burning on the climate and ecosystem. The Medium Resolution Spectral Imager (MERSI) onboard FengYun-3C (FY-3C) has shown great potential for burned area mapping research, but there is still a lack of relevant studies and applications. This paper describes an automated burned area mapping algorithm that was developed using daily MERSI data. The algorithm employs time-series analysis and multi-temporal 1000-m resolution data to obtain seed pixels. To identify the burned pixels automatically, region growing and Support Vector Machine) methods have been used together with 250-m resolution data. The algorithm was tested by applying it in two experimental areas, and the accuracy of the results was evaluated by comparing them to reference burned area maps, which were interpreted manually using Landsat 8 OLI data and the MODIS MCD64A1 burned area product. The results demonstrated that the proposed algorithm was able to improve the burned area mapping accuracy at the two study sites. Full article
Figures

Figure 1

Open AccessArticle Mathematical Modeling and Accuracy Testing of WorldView-2 Level-1B Stereo Pairs without Ground Control Points
Remote Sens. 2017, 9(7), 737; doi:10.3390/rs9070737
Received: 23 June 2017 / Revised: 23 June 2017 / Accepted: 13 July 2017 / Published: 17 July 2017
PDF Full-text (72641 KB) | HTML Full-text | XML Full-text
Abstract
With very high resolution satellite (VHRS) imagery of 0.5 m, WorldView-2 (WV02) satellite images have been widely used in the field of surveying and mapping. However, for the specific WV02 satellite image geometric orientation model, there is a lack of detailed research and
[...] Read more.
With very high resolution satellite (VHRS) imagery of 0.5 m, WorldView-2 (WV02) satellite images have been widely used in the field of surveying and mapping. However, for the specific WV02 satellite image geometric orientation model, there is a lack of detailed research and explanation. This paper elaborates the construction process of the WV02 satellite rigorous sensor model (RSM), which considers the velocity aberration, the optical path delay and the atmospheric refraction. We create a new physical inverse model based on a double-iterative method. Through this inverse method, we establish the virtual control grid in the object space to calculate the rational function model (RFM) coefficients. In the RFM coefficient calculation process, we apply the correcting characteristic value method (CCVM) and least squares (LS) method to compare the two experiments’ accuracies. We apply two stereo pairs of WV02 Level 1B products in Qinghai, China to verify the algorithm and test image positioning accuracy. Under the no-control conditions, the monolithic horizontal mean square error (RMSE) of the rational polynomial coefficient (RPC) is 3.8 m. This result is 13.7% higher than the original RPC positioning accuracy provided by commercial vendors. The stereo pair horizontal positioning accuracy of both the physical and RPC models is 5.0 m circular error 90% (CE90). This result is in accordance with the WV02 satellite images nominal positioning accuracy. This paper provides a new method to improve the positioning accuracy of the WV02 satellite image RPC model without GCPs. Full article
Figures

Open AccessArticle Multi-Temporal X-Band Radar Interferometry Using Corner Reflectors: Application and Validation at the Corvara Landslide (Dolomites, Italy)
Remote Sens. 2017, 9(7), 739; doi:10.3390/rs9070739
Received: 15 May 2017 / Revised: 5 July 2017 / Accepted: 12 July 2017 / Published: 18 July 2017
PDF Full-text (8806 KB) | HTML Full-text | XML Full-text
Abstract
From the wide range of methods available to landslide researchers and practitioners for monitoring ground displacements, remote sensing techniques have increased in popularity. Radar interferometry methods with their ability to record movements in the order of millimeters have been more frequently applied in
[...] Read more.
From the wide range of methods available to landslide researchers and practitioners for monitoring ground displacements, remote sensing techniques have increased in popularity. Radar interferometry methods with their ability to record movements in the order of millimeters have been more frequently applied in recent years. Multi-temporal interferometry can assist in monitoring landslides on the regional and slope scale and thereby assist in assessing related hazards and risks. Our study focuses on the Corvara landslides in the Italian Alps, a complex earthflow with spatially varying displacement patterns. We used radar imagery provided by the COSMO-SkyMed constellation and carried out a validation of the derived time-series data with differential GPS data. Movement rates were assessed using the Permanent Scatterers based Multi-Temporal Interferometry applied to 16 artificial Corner Reflectors installed on the source, track and accumulation zones of the landslide. The overall movement trends were well covered by Permanent Scatterers based Multi-Temporal Interferometry, however, fast acceleration phases and movements along the satellite track could not be assessed with adequate accuracy due to intrinsic limitations of the technique. Overall, despite the intrinsic limitations, Multi-Temporal Interferometry proved to be a promising method to monitor landslides characterized by a linear and relatively slow movement rates. Full article
(This article belongs to the Special Issue Remote Sensing of Landslides)
Figures

Figure 1

Open AccessArticle How Reliable Is Structure from Motion (SfM) over Time and between Observers? A Case Study Using Coral Reef Bommies
Remote Sens. 2017, 9(7), 740; doi:10.3390/rs9070740
Received: 19 June 2017 / Revised: 30 June 2017 / Accepted: 12 July 2017 / Published: 18 July 2017
PDF Full-text (1398 KB) | HTML Full-text | XML Full-text
Abstract
Recent efforts to monitor the health of coral reefs have highlighted the benefits of using structure from motion-based assessments, and despite increasing use of this technique in ecology and geomorphology, no study has attempted to quantify the precision of this technique over time
[...] Read more.
Recent efforts to monitor the health of coral reefs have highlighted the benefits of using structure from motion-based assessments, and despite increasing use of this technique in ecology and geomorphology, no study has attempted to quantify the precision of this technique over time and across different observers. This study determined whether 3D models of an ecologically relevant reef structure, the coral bommie, could be constructed using structure from motion and be reliably used to measure bommie volume and surface area between different observers and over time. We also determined whether the number of images used to construct a model had an impact on the final measurements. Three dimensional models were constructed of over twenty coral bommies from Heron Island, a coral cay at the southern end of the Great Barrier Reef. This study did not detect any significant observer effect, and there were no significant differences in measurements over four sampling days. The mean measurement error across all bommies and between observers was 15 ± 2% for volume measurements and 12 ± 1% for surface area measurements. There was no relationship between the number of pictures taken for a reconstruction and the measurements from that model, however, more photographs were necessary to be able to reconstruct complete coral bommies larger than 1 m3. These results suggest that structure from motion is a viable tool for ongoing monitoring of ecologically-significant coral reefs, especially to establish effects of disturbances, provided the measurement error is considered. Full article
(This article belongs to the Section Ocean Remote Sensing)
Figures

Open AccessArticle Landsat-Based Trend Analysis of Lake Dynamics across Northern Permafrost Regions
Remote Sens. 2017, 9(7), 640; doi:10.3390/rs9070640
Received: 8 May 2017 / Revised: 15 June 2017 / Accepted: 20 June 2017 / Published: 27 June 2017
PDF Full-text (7826 KB) | HTML Full-text | XML Full-text
Abstract
Lakes are a ubiquitous landscape feature in northern permafrost regions. They have a strong impact on carbon, energy and water fluxes and can be quite responsive to climate change. The monitoring of lake change in northern high latitudes, at a sufficiently accurate spatial
[...] Read more.
Lakes are a ubiquitous landscape feature in northern permafrost regions. They have a strong impact on carbon, energy and water fluxes and can be quite responsive to climate change. The monitoring of lake change in northern high latitudes, at a sufficiently accurate spatial and temporal resolution, is crucial for understanding the underlying processes driving lake change. To date, lake change studies in permafrost regions were based on a variety of different sources, image acquisition periods and single snapshots, and localized analysis, which hinders the comparison of different regions. Here, we present a methodology based on machine-learning based classification of robust trends of multi-spectral indices of Landsat data (TM, ETM+, OLI) and object-based lake detection, to analyze and compare the individual, local and regional lake dynamics of four different study sites (Alaska North Slope, Western Alaska, Central Yakutia, Kolyma Lowland) in the northern permafrost zone from 1999 to 2014. Regional patterns of lake area change on the Alaska North Slope (−0.69%), Western Alaska (−2.82%), and Kolyma Lowland (−0.51%) largely include increases due to thermokarst lake expansion, but more dominant lake area losses due to catastrophic lake drainage events. In contrast, Central Yakutia showed a remarkable increase in lake area of 48.48%, likely resulting from warmer and wetter climate conditions over the latter half of the study period. Within all study regions, variability in lake dynamics was associated with differences in permafrost characteristics, landscape position (i.e., upland vs. lowland), and surface geology. With the global availability of Landsat data and a consistent methodology for processing the input data derived from robust trends of multi-spectral indices, we demonstrate a transferability, scalability and consistency of lake change analysis within the northern permafrost region. Full article
(This article belongs to the Special Issue Remote Sensing of Arctic Tundra)
Figures

Open AccessArticle High Precision DEM Generation Algorithm Based on InSAR Multi-Look Iteration
Remote Sens. 2017, 9(7), 741; doi:10.3390/rs9070741
Received: 13 April 2017 / Revised: 8 July 2017 / Accepted: 16 July 2017 / Published: 18 July 2017
PDF Full-text (12616 KB) | HTML Full-text | XML Full-text
Abstract
Interferometric Synthetic Aperture Radar (InSAR) is one of the most sufficient technologies to provide global digital elevation modeling (DEM). It unwraps the interferometric phase to provide observations for phase-to-height conversion. However, the phase gradient has great influence on the phase unwrapping quality, thereby
[...] Read more.
Interferometric Synthetic Aperture Radar (InSAR) is one of the most sufficient technologies to provide global digital elevation modeling (DEM). It unwraps the interferometric phase to provide observations for phase-to-height conversion. However, the phase gradient has great influence on the phase unwrapping quality, thereby affecting the topographic mapping accuracy. Multi-look processing can improve the reliability of phase unwrapping by reducing the noise phase gradient. Nevertheless, it reduces the spatial resolution while increasing the height phase gradient, thus lowering the reliability of height values. In this paper, we propose a multi-look iteration algorithm to suppress the noise and maintain the reliability of height values. First, we use a large number of looks (NL) to suppress the noise phase gradient and obtain a coarse DEM. Then taking this coarse DEM as a reference, we remove the topographic phase from the interferogram with a small NL, which will reduce height phase gradient and ensure the accuracy of phase unwrapping. Finally, we obtain DEM products with high precision and fine resolution. We validate the proposed algorithm using both simulated and real data, and obtain DEM products in a greatly undulated region using TanDEM-X data. Results show that the proposed method is capable of providing DEM with resolution of 4 m and accuracy of 1.73 m. Full article
(This article belongs to the Section Remote Sensing in Geology, Geomorphology and Hydrology)
Figures

Open AccessArticle A Satellite-Derived Climatological Analysis of Urban Heat Island over Shanghai during 2000–2013
Remote Sens. 2017, 9(7), 641; doi:10.3390/rs9070641
Received: 17 April 2017 / Revised: 6 June 2017 / Accepted: 19 June 2017 / Published: 22 June 2017
PDF Full-text (8790 KB) | HTML Full-text | XML Full-text
Abstract
The urban heat island is generally conducted based on ground observations of air temperature and remotely sensing of land surface temperature (LST). Satellite remotely sensed LST has the advantages of global coverage and consistent periodicity, which overcomes the weakness of ground observations related
[...] Read more.
The urban heat island is generally conducted based on ground observations of air temperature and remotely sensing of land surface temperature (LST). Satellite remotely sensed LST has the advantages of global coverage and consistent periodicity, which overcomes the weakness of ground observations related to sparse distributions and costs. For human related studies and urban climatology, canopy layer urban heat island (CUHI) based on air temperatures is extremely important. This study has employed remote sensing methodology to produce monthly CUHI climatology maps during the period 2000–2013, revealing the spatiotemporal characteristics of daytime and nighttime CUHI during this period of rapid urbanization in Shanghai. Using stepwise linear regression, daytime and nighttime air temperatures at the four overpass times of Terra/Aqua were estimated based on time series of Terra/Aqua-MODIS LST and other auxiliary variables including enhanced vegetation index, normalized difference water index, solar zenith angle and distance to coast. The validation results indicate that the models produced an accuracy of 1.6–2.6 °C RMSE for the four overpass times of Terra/Aqua. The models based on Terra LST showed higher accuracy than those based on Aqua LST, and nighttime air temperature estimation had higher accuracy than daytime. The seasonal analysis shows daytime CUHI is strongest in summer and weakest in winter, while nighttime CUHI is weakest in summer and strongest in autumn. The annual mean daytime CUHI during 2000–2013 is 1.0 and 2.2 °C for Terra and Aqua overpass, respectively. The annual mean nighttime CUHI is about 1.0 °C for both Terra and Aqua overpass. The resultant CUHI climatology maps provide a spatiotemporal quantification of CUHI with emphasis on temperature gradients. This study has provided information of relevance to urban planners and environmental managers for assessing and monitoring urban thermal environments which are constantly being altered by natural and anthropogenic influences. Full article
Figures

Open AccessArticle The DOM Generation and Precise Radiometric Calibration of a UAV-Mounted Miniature Snapshot Hyperspectral Imager
Remote Sens. 2017, 9(7), 642; doi:10.3390/rs9070642
Received: 5 April 2017 / Revised: 1 June 2017 / Accepted: 16 June 2017 / Published: 22 June 2017
PDF Full-text (8393 KB) | HTML Full-text | XML Full-text
Abstract
Hyperspectral remote sensing is used in precision agriculture to remotely and quickly acquire crop phenotype information. This paper describes the generation of a digital orthophoto map (DOM) and radiometric calibration for images taken by a miniaturized snapshot hyperspectral camera mounted on a lightweight
[...] Read more.
Hyperspectral remote sensing is used in precision agriculture to remotely and quickly acquire crop phenotype information. This paper describes the generation of a digital orthophoto map (DOM) and radiometric calibration for images taken by a miniaturized snapshot hyperspectral camera mounted on a lightweight unmanned aerial vehicle (UAV). The snapshot camera is a relatively new type of hyperspectral sensor that can acquire an image cube with one spectral and two spatial dimensions at one exposure. The images acquired by the hyperspectral snapshot camera need to be mosaicked together to produce a DOM and radiometrically calibrated before analysis. However, the spatial resolution of hyperspectral cubes is too low to mosaic the images together. Furthermore, there are no systematic radiometric calibration methods or procedures for snapshot hyperspectral images acquired from low-altitude carrier platforms. In this study, we obtained hyperspectral imagery using a snapshot hyperspectral sensor mounted on a UAV. We quantitatively evaluated the radiometric response linearity (RRL) and radiometric response variation (RRV) and proposed a method to correct the RRV effect. We then introduced a method to interpolate position and orientation system (POS) information and generate a DOM with low spatial resolution and a digital elevation model (DEM) using a 3D mesh model built from panchromatic images with high spatial resolution. The relative horizontal geometric precision of the DOM was validated by comparison with a DOM generated from a digital RGB camera. A surface crop model (CSM) was produced from the DEM, and crop height for 48 sampling plots was extracted and compared with the corresponding field-measured crop height to verify the relative precision of the DEM. Finally, we applied two absolute radiometric calibration methods to the generated DOM and verified their accuracy via comparison with spectra measured with an ASD Field Spec Pro spectrometer (Analytical Spectral Devices, Boulder, CO, USA). The DOM had high relative horizontal accuracy, and compared with the digital camera-derived DOM, spatial differences were below 0.05 m (RMSE = 0.035). The determination coefficient for a regression between DEM-derived and field-measured crop height was 0.680. The radiometric precision was 5% for bands between 500 and 945 nm, and the reflectance curve in the infrared spectral region did not decrease as in previous research. The pixel and data sizes for the DOM corresponding to a field area of approximately 85 m × 34 m were small (0.67 m and approximately 13.1 megabytes, respectively), which is convenient for data transmission, preprocessing and analysis. The proposed method for radiometric calibration and DOM generation from hyperspectral cubes can be used to yield hyperspectral imagery products for various applications, particularly precision agriculture. Full article
(This article belongs to the Special Issue Earth Observations for Precision Farming in China (EO4PFiC))
Figures

Open AccessArticle Feasibility of GNSS-R Ice Sheet Altimetry in Greenland Using TDS-1
Remote Sens. 2017, 9(7), 742; doi:10.3390/rs9070742
Received: 19 May 2017 / Revised: 11 July 2017 / Accepted: 13 July 2017 / Published: 19 July 2017
PDF Full-text (2287 KB) | HTML Full-text | XML Full-text
Abstract
Radar altimetry provides valuable measurements to characterize the state and the evolution of the ice sheet cover of Antartica and Greenland. Global Navigation Satellite System Reflectometry (GNSS-R) has the potential to complement the dedicated radar altimeters, increasing the temporal and spatial resolution of
[...] Read more.
Radar altimetry provides valuable measurements to characterize the state and the evolution of the ice sheet cover of Antartica and Greenland. Global Navigation Satellite System Reflectometry (GNSS-R) has the potential to complement the dedicated radar altimeters, increasing the temporal and spatial resolution of the measurements. Here we perform a study of the Greenland ice sheet using data obtained by the GNSS-R instrument aboard the British TechDemoSat-1 (TDS-1) satellite mission. TDS-1 was primarily designed to provide sea state information such as sea surface roughness or wind, but not altimetric products. The data have been analyzed with altimetric methodologies, already tested in aircraft based experiments, to extract signal delay observables to be used to infer properties of the Greenland ice sheet cover. The penetration depth of the GNSS signals into ice has also been considered. The large scale topographic signal obtained is consistent with the one obtained with ICEsat GLAS sensor, with differences likely to be related to L-band signal penetration into the ice and the along-track variations in structure and morphology of the firn and ice volumes The main conclusion derived from this work is that GNSS-R also provides potentially valuable measurements of the ice sheet cover. Thus, this methodology has the potential to complement our understanding of the ice firn and its evolution. Full article
Figures

Open AccessArticle Evaluation of the U.S. Geological Survey Landsat Burned Area Essential Climate Variable across the Conterminous U.S. Using Commercial High-Resolution Imagery
Remote Sens. 2017, 9(7), 743; doi:10.3390/rs9070743 (registering DOI)
Received: 8 May 2017 / Revised: 10 July 2017 / Accepted: 17 July 2017 / Published: 20 July 2017
PDF Full-text (6528 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
The U.S. Geological Survey has produced the Landsat Burned Area Essential Climate Variable (BAECV) product for the conterminous United States (CONUS), which provides wall-to-wall annual maps of burned area at 30 m resolution (1984–2015). Validation is a critical component in the generation of
[...] Read more.
The U.S. Geological Survey has produced the Landsat Burned Area Essential Climate Variable (BAECV) product for the conterminous United States (CONUS), which provides wall-to-wall annual maps of burned area at 30 m resolution (1984–2015). Validation is a critical component in the generation of such remotely sensed products. Previous efforts to validate the BAECV relied on a reference dataset derived from Landsat, which was effective in evaluating the product across its timespan but did not allow for consideration of inaccuracies imposed by the Landsat sensor itself. In this effort, the BAECV was validated using 286 high-resolution images, collected from GeoEye-1, QuickBird-2, Worldview-2 and RapidEye satellites. A disproportionate sampling strategy was utilized to ensure enough burned area pixels were collected. Errors of omission and commission for burned area averaged 22 ± 4% and 48 ± 3%, respectively, across CONUS. Errors were lowest across the western U.S. The elevated error of commission relative to omission was largely driven by patterns in the Great Plains which saw low errors of omission (13 ± 13%) but high errors of commission (70 ± 5%) and potentially a region-growing function included in the BAECV algorithm. While the BAECV reliably detected agricultural fires in the Great Plains, it frequently mapped tilled areas or areas with low vegetation as burned. Landscape metrics were calculated for individual fire events to assess the influence of image resolution (2 m, 30 m and 500 m) on mapping fire heterogeneity. As the spatial detail of imagery increased, fire events were mapped in a patchier manner with greater patch and edge densities, and shape complexity, which can influence estimates of total greenhouse gas emissions and rates of vegetation recovery. The increasing number of satellites collecting high-resolution imagery and rapid improvements in the frequency with which imagery is being collected means greater opportunities to utilize these sources of imagery for Landsat product validation. Full article
(This article belongs to the Special Issue Quantitative Remote Sensing of Land Surface Variables)
Figures

Open AccessArticle On-Ground Retracking to Correct Distorted Waveform in Spaceborne Global Navigation Satellite System-Reflectometry
Remote Sens. 2017, 9(7), 643; doi:10.3390/rs9070643
Received: 7 May 2017 / Revised: 10 June 2017 / Accepted: 17 June 2017 / Published: 22 June 2017
PDF Full-text (1506 KB) | HTML Full-text | XML Full-text
Abstract
Spaceborne Global Navigation Satellite System-Reflectometry (GNSS-R) has been the research focus of Earth observation because of its unique advantages; however, there are still many challenges to be resolved. The reduction of the impact of the satellite motion on the GNSS-R waveform is the
[...] Read more.
Spaceborne Global Navigation Satellite System-Reflectometry (GNSS-R) has been the research focus of Earth observation because of its unique advantages; however, there are still many challenges to be resolved. The reduction of the impact of the satellite motion on the GNSS-R waveform is the one of key technologies for spaceborne GNSS-R. The proposed delay retracking methods in existing literatures require too many instrument resources and too much priori information to refresh correlation window on each coherent integration time period. This paper aims to propose an on-ground alternative in which less frequency tracking refresh on board is needed. The model of dynamic delay waveform, which is expressed as the convolution of the pure waveform and the point spread function, are described. Based on this, the new methodology, which utilizes the least squares fitting to make the residual error between the dynamic model and measured waveform minimum, is employed to reconstruct the pure waveform. The validity of proposed method is verified using UK-DMC, UK-TDS-1 and simulated data. Moreover, the performances of sea surface height and wind speed retrieval using retracked and non-retracked waveforms are compared. The results show that (1) the MSEs between aligned and retracked waveform reduce to 0.026 and 0.044 from 0.110 and 0.156 between aligned and non-retracked waveform with the TRP of 1 s and 3 s for UK-DMC data, and for UK-TDS-1 data, the MSEs decrease from 161.02 and 227.34 to 70.10 and 61.80; (2) the standard deviation of sea surface height using retracked waveform is lower 5 times than the one using non-retracked waveform; (3) the retracked waveform could lead to a better measurement performance in wind speed retrieval. Finally, the relationship between the performance of retracking and Signal-to-Noise Ratio (SNR) is analyzed. The results show that when the SNR of the waveform is lower than 3 dB, the retrieval accuracies rapidly become worse. Full article
(This article belongs to the Section Ocean Remote Sensing)
Figures

Open AccessArticle SNR (Signal-To-Noise Ratio) Impact on Water Constituent Retrieval from Simulated Images of Optically Complex Amazon Lakes
Remote Sens. 2017, 9(7), 644; doi:10.3390/rs9070644
Received: 26 February 2017 / Revised: 24 May 2017 / Accepted: 19 June 2017 / Published: 22 June 2017
PDF Full-text (9749 KB) | HTML Full-text | XML Full-text
Abstract
Uncertainties in the estimates of water constituents are among the main issues concerning the orbital remote sensing of inland waters. Those uncertainties result from sensor design, atmosphere correction, model equations, and in situ conditions (cloud cover, lake size/shape, and adjacency effects). In the
[...] Read more.
Uncertainties in the estimates of water constituents are among the main issues concerning the orbital remote sensing of inland waters. Those uncertainties result from sensor design, atmosphere correction, model equations, and in situ conditions (cloud cover, lake size/shape, and adjacency effects). In the Amazon floodplain lakes, such uncertainties are amplified due to their seasonal dynamic. Therefore, it is imperative to understand the suitability of a sensor to cope with them and assess their impact on the algorithms for the retrieval of constituents. The objective of this paper is to assess the impact of the SNR on the Chl-a and TSS algorithms in four lakes located at Mamirauá Sustainable Development Reserve (Amazonia, Brazil). Two data sets were simulated (noisy and noiseless spectra) based on in situ measurements and on sensor design (MSI/Sentinel-2, OLCI/Sentinel-3, and OLI/Landsat 8). The dataset was tested using three and four algorithms for TSS and Chl-a, respectively. The results showed that the impact of the SNR on each algorithm displayed similar patterns for both constituents. For additive and single band algorithms, the error amplitude is constant for the entire concentration range. However, for multiplicative algorithms, the error changes according to the model equation and the Rrs magnitude. Lastly, for the exponential algorithm, the retrieval amplitude is higher for a low concentration. The OLCI sensor has the best retrieval performance (error of up to 2 μg/L for Chl-a and 3 mg/L for TSS). For MSI, the error of the additive and single band algorithms for TSS and Chl-a are low (up to 5 mg/L and 1 μg/L, respectively); but for the multiplicative algorithm, the errors were above 10 μg/L. The OLI simulation resulted in errors below 3 mg/L for TSS. However, the number and position of OLI bands restrict Chl-a retrieval. Sensor and algorithm selection need a comprehensive analysis of key factors such as sensor design, in situ conditions, water brightness (Rrs), and model equations before being applied for inland water studies. Full article
(This article belongs to the Special Issue Water Optics and Water Colour Remote Sensing)
Figures

Open AccessArticle Characterizing Regional-Scale Combustion Using Satellite Retrievals of CO, NO2 and CO2
Remote Sens. 2017, 9(7), 744; doi:10.3390/rs9070744
Received: 6 June 2017 / Revised: 7 July 2017 / Accepted: 13 July 2017 / Published: 19 July 2017
PDF Full-text (3771 KB) | HTML Full-text | XML Full-text
Abstract
We present joint analyses of satellite-observed combustion products to examine bulk characteristics of combustion in megacities and fire regions. We use retrievals of CO, NO2 and CO2 from NASA/Terra Measurement of Pollution In The Troposphere, NASA/Aura Ozone Monitoring Instrument, and JAXA
[...] Read more.
We present joint analyses of satellite-observed combustion products to examine bulk characteristics of combustion in megacities and fire regions. We use retrievals of CO, NO2 and CO2 from NASA/Terra Measurement of Pollution In The Troposphere, NASA/Aura Ozone Monitoring Instrument, and JAXA Greenhouse Gases Observing Satellite to estimate atmospheric enhancements of these co-emitted species based on their spatiotemporal variability (spread, σ) within 14 regions dominated by combustion emissions. We find that patterns in σXCOXCO2 and σXCOXNO2 are able to distinguish between combustion types across the globe. These patterns show distinct groupings for biomass burning and the developing/developed status of a region that are not well represented in global emissions inventories. We show here that such multi-species analyses can provide constraints on emission inventories, and be useful in monitoring trends and understanding regional-scale combustion. Full article
(This article belongs to the Special Issue Remote Sensing of Greenhouse Gases)
Figures

Open AccessArticle Location- and Time-Specific Hydrological Simulations with Multi-Resolution Remote Sensing Data in Urban Areas
Remote Sens. 2017, 9(7), 645; doi:10.3390/rs9070645
Received: 14 April 2017 / Revised: 13 June 2017 / Accepted: 16 June 2017 / Published: 22 June 2017
PDF Full-text (24680 KB) | HTML Full-text | XML Full-text
Abstract
A major challenge in hydrologic modeling remains the mapping of vegetation dynamics in an urban landscape. The impact of vegetation on interception storage varies over time and needs to be quantified in order to enable proper management of water resources in urban areas.
[...] Read more.
A major challenge in hydrologic modeling remains the mapping of vegetation dynamics in an urban landscape. The impact of vegetation on interception storage varies over time and needs to be quantified in order to enable proper management of water resources in urban areas. However, the heterogeneity and complexity of the urban landscape makes it challenging to monitor urban vegetation. A more detailed spatial and temporal scale is needed. To characterize surface cover at a high spatial resolution, a hyperspectral APEX image (2 m) is used, while a time series of Proba-V images (daily, 100 m) allows a detailed characterization of the seasonal variation of urban greenness. For this study, we use and validate the leaf area index (LAI) maps derived from APEX and Proba-V data for a selected pixel in the Watermaelbeek catchment in Brussels (Belgium). The ground-truthing of the Proba-V pixels includes a detailed mapping of land cover characteristics and more specifically vegetation cover throughout the seasons. LAI values calculated based on the APEX image agree with the LAI values measured from the ground (n = 106, R 2 = 0.68). Further, the aggregated APEX pixels correlate with the Proba-V pixels ( R 2 = 0.79), and the Proba-V data can be used to monitor vegetation dynamics. As the seasonal LAI measurements correspond with the Proba-V dynamics, we conclude that Proba-V images allow the characterization of vegetation dynamics at a high spatial resolution in heterogeneous areas. We create a time series of LAI maps at a high resolution (2 m), which allows a location- and time-specific simulation of interception storage and thus contributes to managing water resources in urban areas. Full article
Figures

Open AccessArticle Early Detection of Plant Physiological Responses to Different Levels of Water Stress Using Reflectance Spectroscopy
Remote Sens. 2017, 9(7), 745; doi:10.3390/rs9070745
Received: 23 March 2017 / Revised: 12 July 2017 / Accepted: 13 July 2017 / Published: 19 July 2017
PDF Full-text (5491 KB) | HTML Full-text | XML Full-text
Abstract
Early detection of water stress is critical for precision farming for improving crop productivity and fruit quality. To investigate varying rootstock and irrigation interactions in an open agricultural ecosystem, different irrigation treatments were implemented in a vineyard experimental site either: (i) nonirrigated (NIR);
[...] Read more.
Early detection of water stress is critical for precision farming for improving crop productivity and fruit quality. To investigate varying rootstock and irrigation interactions in an open agricultural ecosystem, different irrigation treatments were implemented in a vineyard experimental site either: (i) nonirrigated (NIR); (ii) with full replacement of evapotranspiration (FIR); or (iii) intermediate irrigation (INT, 50% replacement of evapotranspiration). In the summers 2014 and 2015, we collected leaf reflectance factor spectra of the vineyard using field spectroscopy along with grapevine physiological parameters. To comprehensively analyze the field-collected hyperspectral data, various band combinations were used to calculate the normalized difference spectral index (NDSI) along with 26 various indices from the literature. Then, the relationship between the indices and plant physiological parameters were examined and the strongest relationships were determined. We found that newly-identified NDSIs always performed better than the indices from the literature, and stomatal conductance (Gs) was the plant physiological parameter that showed the highest correlation with NDSI(R603,R558) calculated using leaf reflectance factor spectra (R2 = 0.720). Additionally, the best NDSI(R685,R415) for non-photochemical quenching (NPQ) was determined (R2 = 0.681). Gs resulted in being a proxy of water stress. Therefore, the partial least squares regression (PLSR) method was utilized to develop a predictive model for Gs. Our results showed that the PLSR model was inferior to the NDSI in Gs estimation (R2 = 0.680). The variable importance in the projection (VIP) was then employed to investigate the most important wavelengths that were most effective in determining Gs. The VIP analysis confirmed that the yellow band improves the prediction ability of hyperspectral reflectance factor data in Gs estimation. The findings of this study demonstrate the potential of hyperspectral spectroscopy data in motoring plant stress response. Full article
Figures

Open AccessArticle Aerosol Optical Properties and Associated Direct Radiative Forcing over the Yangtze River Basin during 2001–2015
Remote Sens. 2017, 9(7), 746; doi:10.3390/rs9070746 (registering DOI)
Received: 31 May 2017 / Revised: 7 July 2017 / Accepted: 17 July 2017 / Published: 20 July 2017
PDF Full-text (6847 KB) | HTML Full-text | XML Full-text
Abstract
The spatiotemporal variation of aerosol optical depth at 550 nm (AOD550), Ångström exponent at 470–660 nm (AE470–660), water vapor content (WVC), and shortwave (SW) instantaneous aerosol direct radiative effects (IADRE) at the top-of-atmosphere (TOA) in clear skies obtained from
[...] Read more.
The spatiotemporal variation of aerosol optical depth at 550 nm (AOD550), Ångström exponent at 470–660 nm (AE470–660), water vapor content (WVC), and shortwave (SW) instantaneous aerosol direct radiative effects (IADRE) at the top-of-atmosphere (TOA) in clear skies obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Clouds and the Earth’s Radiant Energy System (CERES) are quantitatively analyzed over the Yangtze River Basin (YRB) in China during 2001–2015. The annual and seasonal frequency distributions of AE470–660 and AOD550 reveal the dominance of fine aerosol particles over YRB. The regional average AOD550 is 0.49 ± 0.31, with high value in spring (0.58 ± 0.35) and low value in winter (0.42 ± 0.29). The higher AOD550 (≥0.6) is observed in midstream and downstream regions of YRB and Sichuan Basin due to local anthropogenic emissions and long-distance transport of dust particles, while lower AOD550 (≤0.3) is in high mountains of upstream regions. The IADRE is estimated using a linear relationship between SW upward flux and coincident AOD550 from CERES and MODIS at the satellite passing time. The regional average IADRE is −35.60 ± 6.71 Wm−2, with high value (−40.71 ± 6.86 Wm−2) in summer and low value (−29.19 ± 7.04 Wm−2) in winter, suggesting a significant cooling effect at TOA. The IADRE at TOA is lower over Yangtze River Delta (YRD) (≤−30 Wm−2) and higher in midstream region of YRB, Sichuan Basin and the source area of YRB (≥−45 Wm−2). The correlation coefficient between the 15-year monthly IADRE and AOD550 values is 0.63, which confirms the consistent spatiotemporal variation patterns over most of the YRB. However, a good agreement between IADRE and AOD is not observed in YRD and the source area of YRB, which is probably due to the combined effects of aerosol and surface properties. Full article
Figures

Open AccessArticle Good Practices for Object-Based Accuracy Assessment
Remote Sens. 2017, 9(7), 646; doi:10.3390/rs9070646
Received: 3 January 2017 / Revised: 26 May 2017 / Accepted: 19 June 2017 / Published: 22 June 2017
PDF Full-text (3477 KB) | HTML Full-text | XML Full-text
Abstract
Thematic accuracy assessment of a map is a necessary condition for the comparison of research results and the appropriate use of geographic data analysis. Good practices of accuracy assessment already exist, but Geographic Object-Based Image Analysis (GEOBIA) is based on a partition of
[...] Read more.
Thematic accuracy assessment of a map is a necessary condition for the comparison of research results and the appropriate use of geographic data analysis. Good practices of accuracy assessment already exist, but Geographic Object-Based Image Analysis (GEOBIA) is based on a partition of the spatial area of interest into polygons, which leads to specific issues. In this study, additional guidelines for the validation of object-based maps are provided. These guidelines include recommendations about sampling design, response design and analysis, as well as the evaluation of structural and positional quality. Different types of GEOBIA applications are considered with their specific issues. In particular, accuracy assessment could either focus on the count of spatial entities or on the area of the map that is correctly classified. Two practical examples are given at the end of the manuscript. Full article
Figures

Open AccessArticle Poppy Crop Height and Capsule Volume Estimation from a Single UAS Flight
Remote Sens. 2017, 9(7), 647; doi:10.3390/rs9070647
Received: 8 March 2017 / Revised: 14 June 2017 / Accepted: 20 June 2017 / Published: 22 June 2017
PDF Full-text (4439 KB) | HTML Full-text | XML Full-text
Abstract
The objective of this study was to estimate poppy plant height and capsule volume with remote sensing using an Unmanned Aircraft System (UAS). Data were obtained from field measurements and UAS flights over two poppy crops at Cambridge and Cressy in Tasmania. Imagery
[...] Read more.
The objective of this study was to estimate poppy plant height and capsule volume with remote sensing using an Unmanned Aircraft System (UAS). Data were obtained from field measurements and UAS flights over two poppy crops at Cambridge and Cressy in Tasmania. Imagery acquired from the UAS was used to produce dense point clouds using structure from motion (SfM) and multi-view stereopsis (MVS) techniques. Dense point clouds were used to generate a digital surface model (DSM) and orthophoto mosaic. An RGB index was derived from the orthophoto to extract the bare ground spaces. This bare ground space mask was used to filter the points on the ground, and a digital terrain model (DTM) was interpolated from these points. Plant height values were estimated by subtracting the DSM and DTM to generate a Crop Height Model (CHM). UAS-derived plant height (PH) and field measured PH in Cambridge were strongly correlated with R2 values ranging from 0.93 to 0.97 for Transect 1 and Transect 2, respectively, while at Cressy results from a single flight provided R2 of 0.97. Therefore, the proposed method can be considered an important step towards crop surface model (CSM) generation from a single UAS flight in situations where a bare ground DTM is unavailable. High correlations were found between UAS-derived PH and poppy capsule volume (CV) at capsule formation stage (R2 0.74), with relative error of 19.62%. Results illustrate that plant height can be reliably estimated for poppy crops based on a single UAS flight and can be used to predict opium capsule volume at capsule formation stage. Full article
Figures

Open AccessArticle Expansion of Industrial Plantations Continues to Threaten Malayan Tiger Habitat
Remote Sens. 2017, 9(7), 747; doi:10.3390/rs9070747
Received: 28 May 2017 / Revised: 4 July 2017 / Accepted: 5 July 2017 / Published: 19 July 2017
PDF Full-text (3357 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Southeast Asia has some of the highest deforestation rates globally, with Malaysia being identified as a deforestation hotspot. The Malayan tiger, a critically endangered subspecies of the tiger endemic to Peninsular Malaysia, is threatened by habitat loss and fragmentation. In this study, we
[...] Read more.
Southeast Asia has some of the highest deforestation rates globally, with Malaysia being identified as a deforestation hotspot. The Malayan tiger, a critically endangered subspecies of the tiger endemic to Peninsular Malaysia, is threatened by habitat loss and fragmentation. In this study, we estimate the natural forest loss and conversion to plantations in Peninsular Malaysia and specifically in its tiger habitat between 1988 and 2012 using the Landsat data archive. We estimate a total loss of 1.35 Mha of natural forest area within Peninsular Malaysia over the entire study period, with 0.83 Mha lost within the tiger habitat. Nearly half (48%) of the natural forest loss area represents conversion to tree plantations. The annual area of new plantation establishment from natural forest conversion increased from 20 thousand ha year−1 during 1988–2000 to 34 thousand ha year−1 during 2001–2012. Large-scale industrial plantations, primarily those of oil palm, as well as recently cleared land, constitute 80% of forest converted to plantations since 1988. We conclude that industrial plantation expansion has been a persistent threat to natural forests within the Malayan tiger habitat. Expanding oil palm plantations dominate forest conversions while those for rubber are an emerging threat. Full article
Figures

Open AccessArticle Criteria Comparison for Classifying Peatland Vegetation Types Using In Situ Hyperspectral Measurements
Remote Sens. 2017, 9(7), 748; doi:10.3390/rs9070748 (registering DOI)
Received: 24 May 2017 / Revised: 26 June 2017 / Accepted: 9 July 2017 / Published: 20 July 2017
PDF Full-text (21609 KB)
Abstract
This study aims to evaluate three classes of methods to discriminate between 13 peatland vegetation types using reflectance data. These vegetation types were empirically defined according to their composition, strata and biodiversity richness. On one hand, it is assumed that the same vegetation
[...] Read more.
This study aims to evaluate three classes of methods to discriminate between 13 peatland vegetation types using reflectance data. These vegetation types were empirically defined according to their composition, strata and biodiversity richness. On one hand, it is assumed that the same vegetation type spectral signatures have similarities. Consequently, they can be compared to a reference spectral database. To catch those similarities, several similarities criteria (related to distances (Euclidean distance, Manhattan distance, Canberra distance) or spectral shapes (Spectral Angle Mapper) or probabilistic behaviour (Spectral Information Divergence)) and several mathematical transformations of spectral signatures enhancing absorption features (such as the first derivative or the second derivative, the normalized spectral signature, the continuum removal, the continuum removal derivative reflectance, the log transformation) were investigated. Furthermore, those similarity measures were applied on spectral ranges which characterize specific biophysical properties. On the other hand, we suppose that specific biophysical properties/components may help to discriminate between vegetation types applying supervised classification such as Random Forest (RF), Support Vector Machines (SVM), Regularized Logistic Regression (RLR), Partial Least Squares-Discriminant Analysis (PLS-DA). Biophysical components can be used in a local way considering vegetation spectral indices or in a global way considering spectral ranges and transformed spectral signatures, as explained above. RLR classifier applied on spectral vegetation indices (training size = 25%) was able to achieve 77.21% overall accuracy in discriminating peatland vegetation types. It was also able to discriminate between 83.95% vegetation types considering specific spectral range [[range-phrase = –]3501350 n m ], first derivative of spectral signatures and training size = 25%. Conversely, similarity criterion was able to achieve 81.70% overall accuracy using the Canberra distance computed on the full spectral range [[range-phrase = –]3502500 n m ]. The results of this study suggest that RLR classifier and similarity criteria are promising to map the different vegetation types with high ecological values despite vegetation heterogeneity and mixture. Full article
(This article belongs to the Special Issue Hyperspectral Imaging and Applications)
Figures

Open AccessArticle On the Design of Radar Corner Reflectors for Deformation Monitoring in Multi-Frequency InSAR
Remote Sens. 2017, 9(7), 648; doi:10.3390/rs9070648
Received: 19 April 2017 / Revised: 15 June 2017 / Accepted: 21 June 2017 / Published: 25 June 2017
PDF Full-text (4555 KB) | HTML Full-text | XML Full-text
Abstract
Trihedral corner reflectors are being increasingly used as point targets in deformation monitoring studies using interferometric synthetic aperture radar (InSAR) techniques. The frequency and size dependence of the corner reflector Radar Cross Section (RCS) means that no single design can perform equally in
[...] Read more.
Trihedral corner reflectors are being increasingly used as point targets in deformation monitoring studies using interferometric synthetic aperture radar (InSAR) techniques. The frequency and size dependence of the corner reflector Radar Cross Section (RCS) means that no single design can perform equally in all the possible imaging modes and radar frequencies available on the currently orbiting Synthetic Aperture Radar (SAR) satellites. Therefore, either a corner reflector design tailored to a specific data type or a compromise design for multiple data types is required. In this paper, I outline the practical and theoretical considerations that need to be made when designing appropriate radar targets, with a focus on supporting multi-frequency SAR data. These considerations are tested by performing field experiments on targets of different size using SAR images from TerraSAR-X, COSMO-SkyMed and RADARSAT-2. Phase noise behaviour in SAR images can be estimated by measuring the Signal-to-Clutter ratio (SCR) in individual SAR images. The measured SCR of a point target is dependent on its RCS performance and the influence of clutter near to the deployed target. The SCR is used as a metric to estimate the expected InSAR displacement error incurred by the design of each target and to validate these observations against theoretical expectations. I find that triangular trihedral corner reflectors as small as 1 m in dimension can achieve a displacement error magnitude of a tenth of a millimetre or less in medium-resolution X-band data. Much larger corner reflectors (2.5 m or greater) are required to achieve the same displacement error magnitude in medium-resolution C-band data. Compromise designs should aim to satisfy the requirements of the lowest SAR frequency to be used, providing that these targets will not saturate the sensor of the highest frequency to be used. Finally, accurate boresight alignment of the corner reflector can be critical to the overall target performance. Alignment accuracies better than 4° in azimuth and elevation will incur a minimal impact on the displacement error in X and C-band data. Full article
(This article belongs to the Special Issue Advances in SAR: Sensors, Methodologies, and Applications)
Figures

Open AccessArticle Fluorescence Imaging Spectrometer (FLORIS) for ESA FLEX Mission
Remote Sens. 2017, 9(7), 649; doi:10.3390/rs9070649
Received: 10 May 2017 / Revised: 9 June 2017 / Accepted: 18 June 2017 / Published: 23 June 2017
PDF Full-text (8263 KB) | HTML Full-text | XML Full-text
Abstract
The Fluorescence Explorer (FLEX) mission has been selected as ESA’s 8th Earth Explorer mission. The primary objectives of the mission are to provide global estimates of vegetation fluorescence, actual photosynthetic activity, and vegetation stress. FLEX will fly in tandem formation with Sentinel-3 providing
[...] Read more.
The Fluorescence Explorer (FLEX) mission has been selected as ESA’s 8th Earth Explorer mission. The primary objectives of the mission are to provide global estimates of vegetation fluorescence, actual photosynthetic activity, and vegetation stress. FLEX will fly in tandem formation with Sentinel-3 providing ancillary data for atmospheric characterization and correction, vegetation related spectral indices, and land surface temperature. The purpose of this manuscript is to present its scientific payload, FLORIS, which is a push-broom hyperspectral imager, flying on a medium size platform. FLORIS will measure the vegetation fluorescence in the spectral range between 500 nm and 780 nm at medium spatial resolution (300 m) and over a swath of 150 km. It accommodates an imaging spectrometer with a very high spectral resolution (0.3 nm), to measure the fluorescence spectrum within two oxygen absorption bands (O2A and O2B), and a second spectrometer with lower spectral resolution to derive additional atmospheric and vegetation parameters. A compact opto-mechanical solution is the current instrument baseline. A polarization scrambler is placed in front of a common dioptric telescope serving both spectrometers to minimize the polarization sensitivity. The telescope images the ground scene onto a double slit assembly. The radiation is spectrally dispersed onto the focal planes of the grating spectrometers. Special attention has been given to the mitigation of stray-light which is a key factor to reach good accuracy of the fluorescence measurement. The absolute radiometric calibration is achieved by observing a dedicated Sun illuminated Lambertian diffuser, while the spectral calibration in flight is performed by means of vicarious techniques. The thermal stabilization is achieved by using two passive radiators looking directly to the cold space, counterbalanced by heaters in a closed loop system. The focal planes are based on custom developed CCDs. The opto-mechanical design is robust, stable vs. temperature and easy to align. The optical quality is very good as recently demonstrated by the latest tests of an elegant breadboard. The scientific data products comprise the Top Of Atmosphere (TOA) radiance measurements as well as fluorescence estimates and higher-level products related to the health status of the vegetation addressing a wide range of applications from agriculture to forestry and climate. Full article
Figures

Open AccessArticle Suitability Assessment of Satellite-Derived Drought Indices for Mongolian Grassland
Remote Sens. 2017, 9(7), 650; doi:10.3390/rs9070650
Received: 14 February 2017 / Revised: 12 May 2017 / Accepted: 5 June 2017 / Published: 26 June 2017
PDF Full-text (18472 KB) | HTML Full-text | XML Full-text
Abstract
In Mongolia, drought is a major natural disaster that can influence and devastate large regions, reduce livestock production, cause economic damage, and accelerate desertification in association with destructive human activities. The objective of this article is to determine the optimal satellite-derived drought indices
[...] Read more.
In Mongolia, drought is a major natural disaster that can influence and devastate large regions, reduce livestock production, cause economic damage, and accelerate desertification in association with destructive human activities. The objective of this article is to determine the optimal satellite-derived drought indices for accurate and real-time expression of grassland drought in Mongolia. Firstly, an adaptability analysis was performed by comparing nine remote sensing-derived drought indices with reference indicators obtained from field observations using several methods (correlation, consistency percentage (CP), and time-space analysis). The reference information included environmental data, vegetation growth status, and region drought-affected (RDA) information at diverse scales (pixel, county, and region) for three types of land cover (forest steppe, steppe, and desert steppe). Second, a meteorological index (PED), a normalized biomass (NorBio) reference indicator, and the RDA-based drought CP method were adopted for describing Mongolian drought. Our results show that in forest steppe regions the normalized difference water index (NDWI) is most sensitive to NorBio (maximum correlation coefficient (MAX_R): up to 0.92) and RDA (maximum CP is 87%), and is most consistent with RDA spatial distribution. The vegetation health index (VHI) and temperature condition index (TCI) are most correlated with the PED index (MAX_R: 0.75) and soil moisture (MAX_R: 0.58), respectively. In steppe regions, the NDWI is most closely related to soil moisture (MAX_R: 0.69) and the VHI is most related to the PED (MAX_R: 0.76), NorBio (MCC: 0.95), and RDA data (maximum CP is 89%), exhibiting the most consistency with RDA spatial distribution. In desert steppe areas, the vegetation condition index (VCI) correlates best with NorBio (MAX_R: 0.92), soil moisture (MAX_R: 0.61), and RDA spatial distribution, while TCI correlates best with the PED (MAX_R: 0.75) and the RDA data (maximum CP is 79%). The VHI is a combination of constructed VCI and TCI, and can be used instead of them. Finally, the mode method was adopted to identify appropriate drought indices. The best two indices (VHI and NDWI) can be utilized to develop a combination drought model for accurately monitoring and quantifying drought in the future. Additionally, the new framework can be adopted to investigate and analyze the suitability of satellite-derived drought indices and determine the most appropriate index/indices for other countries or areas. Full article
(This article belongs to the Special Issue Earth Observations for a Better Future Earth)
Figures

Open AccessArticle Circular Regression Applied to GNSS-R Phase Altimetry
Remote Sens. 2017, 9(7), 651; doi:10.3390/rs9070651
Received: 5 May 2017 / Revised: 15 June 2017 / Accepted: 20 June 2017 / Published: 23 June 2017
PDF Full-text (3025 KB) | HTML Full-text | XML Full-text
Abstract
This article is dedicated to the design of a linear-circular regression technique and to its application to ground-based GNSS-Reflectometry (GNSS-R) altimetry. The altimetric estimation is based on the observation of the phase delay between a GNSS signal sensed directly and after a reflection
[...] Read more.
This article is dedicated to the design of a linear-circular regression technique and to its application to ground-based GNSS-Reflectometry (GNSS-R) altimetry. The altimetric estimation is based on the observation of the phase delay between a GNSS signal sensed directly and after a reflection off of the Earth’s surface. This delay evolves linearly with the sine of the emitting satellite elevation, with a slope proportional to the height between the reflecting surface and the receiving antenna. However, GNSS-R phase delay observations are angular and affected by a noise assumed to follow the von Mises distribution. In order to estimate the phase delay slope, a linear-circular regression estimator is thus defined in the maximum likelihood sense. The proposed estimator is able to fuse phase observations obtained from several satellite signals. Moreover, unlike the usual unwrapping approach, the proposed estimator allows the sea-surface height to be estimated from datasets with large data gaps. The proposed regression technique and altimeter performances are studied theoretically, with further assessment on both synthetic and real data. Full article
(This article belongs to the Section Ocean Remote Sensing)
Figures

Open AccessArticle Semi-Automated Monitoring of a Mega-Scale Beach Nourishment Using High-Resolution TerraSAR-X Satellite Data
Remote Sens. 2017, 9(7), 653; doi:10.3390/rs9070653
Received: 25 April 2017 / Revised: 21 June 2017 / Accepted: 23 June 2017 / Published: 24 June 2017
PDF Full-text (4146 KB) | HTML Full-text | XML Full-text
Abstract
This paper presents a semi-automated approach to detecting coastal shoreline change with high spatial- and temporal-resolution using X-band synthetic aperture radar (SAR) data. The method was applied at the Sand Motor, a “mega-scale” beach nourishment project in the Netherlands. Natural processes, like waves,
[...] Read more.
This paper presents a semi-automated approach to detecting coastal shoreline change with high spatial- and temporal-resolution using X-band synthetic aperture radar (SAR) data. The method was applied at the Sand Motor, a “mega-scale” beach nourishment project in the Netherlands. Natural processes, like waves, wind, and tides, gradually distribute the highly concentrated sand to adjacent beaches. Currently, various in-situ techniques are used to monitor the Sand Motor on a monthly basis. Meanwhile, the TerraSAR-X satellite collects two high-resolution (3 × 3 m), cloud-penetrating SAR images every 11 days. This study investigates whether shorelines detected in TerraSAR-X imagery are accurate enough to monitor the shoreline dynamics of a project like the Sand Motor. The study proposes and implements a semi-automated workflow to extract shorelines from all 182 available TerraSAR-X images acquired between 2011 and 2014. The shorelines are validated using bi-monthly RTK-GPS topographic surveys and nearby wave and tide measurements. A valid shoreline could be extracted from 54% of the images. The horizontal accuracy of these shorelines is approximately 50 m, which is sufficient to assess the larger scale shoreline dynamics of the Sand Motor. The accuracy is affected strongly by sea state and partly by acquisition geometry. We conclude that using frequent, high-resolution TerraSAR-X imagery is a valid option for assessing coastal dynamics on the order of tens of meters at approximately monthly intervals. Full article
(This article belongs to the Section Remote Sensing in Geology, Geomorphology and Hydrology)
Figures

Open AccessArticle Identification of Hazard and Risk for Glacial Lakes in the Nepal Himalaya Using Satellite Imagery from 2000–2015
Remote Sens. 2017, 9(7), 654; doi:10.3390/rs9070654
Received: 19 April 2017 / Revised: 7 June 2017 / Accepted: 21 June 2017 / Published: 26 June 2017
PDF Full-text (800 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Glacial lakes in the Nepal Himalaya can threaten downstream communities and have large socio-economic consequences if an outburst flood occurs. This study identified 131 glacial lakes in Nepal in 2015 that are greater than 0.1 km2 and performed a first-pass hazard and
[...] Read more.
Glacial lakes in the Nepal Himalaya can threaten downstream communities and have large socio-economic consequences if an outburst flood occurs. This study identified 131 glacial lakes in Nepal in 2015 that are greater than 0.1 km2 and performed a first-pass hazard and risk assessment for each lake. The hazard assessment included mass entering the lake, the moraine stability, and how lake expansion will alter the lake’s hazard in the next 15–30 years. A geometric flood model was used to quantify potential hydropower systems, buildings, agricultural land, and bridges that could be affected by a glacial lake outburst flood. The hazard and downstream impacts were combined to classify the risk associated with each lake. 11 lakes were classified as very high risk and 31 as high risk. The potential flood volume was also estimated and used to prioritize the glacial lakes that are the highest risk, which included Phoksundo Tal, Tsho Rolpa, Chamlang North Tsho, Chamlang South Tsho, and Lumding Tsho. These results are intended to assist stakeholders and decision makers in making well-informed decisions with respect to the glacial lakes that should be the focus of future field studies, modeling efforts, and risk-mitigation actions. Full article
(This article belongs to the Special Issue Remote Sensing of Climate Change and Water Resources)
Figures

Open AccessArticle Ground Truth of Passive Microwave Radiative Transfer on Vegetated Land Surfaces
Remote Sens. 2017, 9(7), 655; doi:10.3390/rs9070655
Received: 15 May 2017 / Revised: 12 June 2017 / Accepted: 21 June 2017 / Published: 26 June 2017
PDF Full-text (3541 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
In this paper, we implemented the in-situ observation of surface soil moisture (SSM), vegetation water content (VWC), and microwave brightness temperatures. By analyzing this in-situ observation dataset and the numerical simulation, we investigated the source of the uncertainty of the current
[...] Read more.
In this paper, we implemented the in-situ observation of surface soil moisture (SSM), vegetation water content (VWC), and microwave brightness temperatures. By analyzing this in-situ observation dataset and the numerical simulation, we investigated the source of the uncertainty of the current algorithms for Advanced Microwave Scanning Radiometer for Earth observation system (AMSR-E) and AMSR2 to retrieve SSM and vegetation dynamics. Our findings are: (1) the microwave radiative transfer at C-band and X-band is not strongly affected by the shape of vegetation and the existing algorithm can be applied to a wide variety of plant types; (2) the diversity of surface soil roughness significantly affects the indices which are used by the current algorithms and addressing the uncertainty of surface soil roughness is necessary to improve the retrieval algorithms; (3) At C-band, SSM of the homogeneous vegetated land surfaces can be detected only when their VWC is less than approximately 0.25 (kg/m2); (4) the state-of-the-art Radiative Transfer Model (RTM) can predict our observed dataset although we have some biases in simulating brightness temperatures at a higher frequency. The new in-situ observation dataset produced by this study can be the guideline for both developers and users of passive microwave land observations to consider the uncertainties of their products. Full article
(This article belongs to the Section Remote Sensing in Geology, Geomorphology and Hydrology)
Figures

Open AccessArticle Estimation of FAPAR over Croplands Using MISR Data and the Earth Observation Land Data Assimilation System (EO-LDAS)
Remote Sens. 2017, 9(7), 656; doi:10.3390/rs9070656
Received: 24 March 2017 / Revised: 20 June 2017 / Accepted: 22 June 2017 / Published: 27 June 2017
PDF Full-text (1786 KB) | HTML Full-text | XML Full-text
Abstract
The Fraction of Absorbed Photosynthetically-Active Radiation (FAPAR) is an important parameter in climate and carbon cycle studies. In this paper, we use the Earth Observation Land Data Assimilation System (EO-LDAS) framework to retrieve FAPAR from observations of directional surface reflectance measurements from the
[...] Read more.
The Fraction of Absorbed Photosynthetically-Active Radiation (FAPAR) is an important parameter in climate and carbon cycle studies. In this paper, we use the Earth Observation Land Data Assimilation System (EO-LDAS) framework to retrieve FAPAR from observations of directional surface reflectance measurements from the Multi-angle Imaging SpectroRadiometer(MISR) instrument. The procedure works by interpreting the reflectance data via the semi-discrete Radiative Transfer (RT) model, supported by a prior parameter distribution and a dynamic regularisation model and resulting in an inference of land surface parameters, such as effective Leaf Area Index (LAI), leaf chlorophyll concentration and fraction of senescent leaves, with full uncertainty quantification. The method is demonstrated over three agricultural FLUXNET sites, and the EO-LDAS results are compared with eight years of in situ measurements of FAPAR and LAI, resulting in a total of 24 site years. We additionally compare three other widely-used EO FAPAR products, namely the MEdium Resolution Imaging Spectrometer (MERIS) Full Resolution, the MISR High Resolution (HR) Joint Research Centre Two-stream Inversion Package (JRC-TIP) and MODIS MCD15 FAPAR products. The EO-LDAS MISR FAPAR retrievals show a high correlation with the ground measurements ( r 2 > 0.8), as well as the lowest average R M S E (0.14), in line with the MODIS product. As the EO-LDAS solution is effectively interpolated, if only measurements that are coincident with MISR observations are considered, the correlation increases ( r 2 > 0.85); the R M S E is lower by 4–5%; and the bias is 2% and 7%. The EO-LDAS MISR LAI estimates show a strong correlation with ground-based LAI (average r 2 = 0.76), but an underestimate of LAI for optically-thick canopies due to saturation (average R M S E = 2.23). These results suggest that the EO-LDAS approach is successful in retrieving both FAPAR and other land surface parameters. A large part of this success is based on the use of a dynamic regularisation model that counteracts the poor temporal sampling from the MISR instrument. Full article
Figures

Open AccessArticle Adaptive Unscented Kalman Filter for Target Tracking in the Presence of Nonlinear Systems Involving Model Mismatches
Remote Sens. 2017, 9(7), 657; doi:10.3390/rs9070657
Received: 25 April 2017 / Revised: 24 June 2017 / Accepted: 25 June 2017 / Published: 27 June 2017
PDF Full-text (1812 KB) | HTML Full-text | XML Full-text
Abstract
In order to improve filtering precision and restrain divergence caused by sensor faults or model mismatches for target tracking, a new adaptive unscented Kalman filter (N-AUKF) algorithm is proposed. First of all, the unscented Kalman filter (UKF) problem to be solved for systems
[...] Read more.
In order to improve filtering precision and restrain divergence caused by sensor faults or model mismatches for target tracking, a new adaptive unscented Kalman filter (N-AUKF) algorithm is proposed. First of all, the unscented Kalman filter (UKF) problem to be solved for systems involving model mismatches is described, after that, the necessary and sufficient condition with third order accuracy of the standard UKF is given and proven by using the matrix theory. In the filtering process of N-AUKF, an adaptive matrix gene is introduced to the standard UKF to adjust the covariance matrixes of the state vector and innovation vector in real time, which makes full use of normal innovations. Then, a covariance matching criterion is designed to judge the filtering divergence. On this basis, an adaptive weighted coefficient is applied to restrain the divergence. Compared with the standard UKF and existing adaptive UKF, the proposed UKF algorithm improves the filtering accuracy, rapidity and numerical stability remarkably, moreover, it has a good adaptive capability to deal with sensor faults or model mismatches. The performance and effectiveness of the proposed UKF is verified in a target tracking mission. Full article
Figures

Open AccessArticle Geometric Potential Assessment for ZY3-02 Triple Linear Array Imagery
Remote Sens. 2017, 9(7), 658; doi:10.3390/rs9070658
Received: 13 May 2017 / Revised: 21 June 2017 / Accepted: 26 June 2017 / Published: 28 June 2017
PDF Full-text (37151 KB) | HTML Full-text | XML Full-text
Abstract
ZiYuan3-02 (ZY3-02) is the first remote sensing satellite for the development of China’s civil space infrastructure (CCSI) and the second satellite in the ZiYuan3 series; it was launched successfully on 30 May 2016, aboard the CZ-4B rocket at the Taiyuan Satellite Launch Center
[...] Read more.
ZiYuan3-02 (ZY3-02) is the first remote sensing satellite for the development of China’s civil space infrastructure (CCSI) and the second satellite in the ZiYuan3 series; it was launched successfully on 30 May 2016, aboard the CZ-4B rocket at the Taiyuan Satellite Launch Center (TSLC) in China. Core payloads of ZY3-02 include a triple linear array camera (TLC) and a multi-spectral camera, and this equipment will be used to acquire space geographic information with high-resolution and stereoscopic observations. Geometric quality is a key factor that affects the performance and potential of satellite imagery. For the purpose of evaluating comprehensively the geometric potential of ZY3-02, this paper introduces the method used for geometric calibration of the TLC onboard the satellite and a model for sensor corrected (SC) products that serve as basic products delivered to users. Evaluation work was conducted by making a full assessment of the geometric performance. Furthermore, images of six regions and corresponding reference data were collected to implement the geometric calibration technique and evaluate the resulting geometric accuracy. Experimental results showed that the direct location performance and internal accuracy of SC products increased remarkably after calibration, and the planimetric and vertical accuracies with relatively few ground control points (GCPs) were demonstrated to be better than 2.5 m and 2 m, respectively. Additionally, the derived digital surface model (DSM) accuracy was better than 3 m (RMSE) for flat terrain and 5 m (RMSE) for mountainous terrain. However, given that several variations such as changes in the thermal environment can alter the camera’s installation angle, geometric performance will vary with the geographical location and imaging time changes. Generally, ZY3-02 can be used for 1:50,000 stereo mapping and can produce (and update) larger-scale basic geographic information products. Full article
(This article belongs to the Section Atmosphere Remote Sensing)
Figures

Open AccessArticle Comparing Sentinel-2A and Landsat 7 and 8 Using Surface Reflectance over Australia
Remote Sens. 2017, 9(7), 659; doi:10.3390/rs9070659
Received: 5 May 2017 / Revised: 22 June 2017 / Accepted: 25 June 2017 / Published: 27 June 2017
PDF Full-text (795 KB) | HTML Full-text | XML Full-text
Abstract
The new Sentinel-2 Multi Spectral Imager instrument has a set of bands with very similar spectral windows to the main bands of the Landsat Thematic Mapper family of instruments. While these should, in principle, give broadly comparable measurements, any differences are a function
[...] Read more.
The new Sentinel-2 Multi Spectral Imager instrument has a set of bands with very similar spectral windows to the main bands of the Landsat Thematic Mapper family of instruments. While these should, in principle, give broadly comparable measurements, any differences are a function not only of the differences in the sensor responses, but also of the spectral characteristics of the target pixels. In order to test for and quantify differences between these sensors, a large set of coincident imagery was assembled for the Australian landscape. Comparisons were carried out in terms of surface reflectance, and also in terms of biophysical quantities estimated from the reflectances. Small but consistent differences were found, and suitable adjustment equations fitted to enable transformation of Sentinel-2A reflectance values to more closely match Landsat-7 or Landsat-8 values. This is useful if trying to take models and thresholds fitted from Landsat and use them with Sentinel-2. The fitted adjustment equations were also compared against those fitted globally for NASA’s Harmonized Landsat-8 Sentinel-2 product, and found to be substantially different, raising the possibility that such adjustments need to be fitted on a regional basis. Full article
Figures

Open AccessArticle PolSAR Land Cover Classification Based on Roll-Invariant and Selected Hidden Polarimetric Features in the Rotation Domain
Remote Sens. 2017, 9(7), 660; doi:10.3390/rs9070660
Received: 8 May 2017 / Revised: 9 June 2017 / Accepted: 15 June 2017 / Published: 1 July 2017
PDF Full-text (10804 KB) | HTML Full-text | XML Full-text
Abstract
Land cover classification is an important application for polarimetric synthetic aperture radar (PolSAR). Target polarimetric response is strongly dependent on its orientation. Backscattering responses of the same target with different orientations to the SAR flight path may be quite different. This target orientation
[...] Read more.
Land cover classification is an important application for polarimetric synthetic aperture radar (PolSAR). Target polarimetric response is strongly dependent on its orientation. Backscattering responses of the same target with different orientations to the SAR flight path may be quite different. This target orientation diversity effect hinders PolSAR image understanding and interpretation. Roll-invariant polarimetric features such as entropy, anisotropy, mean alpha angle, and total scattering power are independent of the target orientation and are commonly adopted for PolSAR image classification. On the other aspect, target orientation diversity also contains rich information which may not be sensed by roll-invariant polarimetric features. In this vein, only using the roll-invariant polarimetric features may limit the final classification accuracy. To address this problem, this work uses the recently reported uniform polarimetric matrix rotation theory and a visualization and characterization tool of polarimetric coherence pattern to investigate hidden polarimetric features in the rotation domain along the radar line of sight. Then, a feature selection scheme is established and a set of hidden polarimetric features are selected in the rotation domain. Finally, a classification method is developed using the complementary information between roll-invariant and selected hidden polarimetric features with a support vector machine (SVM)/decision tree (DT) classifier. Comparison experiments are carried out with NASA/JPL AIRSAR and multi-temporal UAVSAR data. For AIRSAR data, the overall classification accuracy of the proposed classification method is 95.37% (with SVM)/96.38% (with DT), while that of the conventional classification method is 93.87% (with SVM)/94.12% (with DT), respectively. Meanwhile, for multi-temporal UAVSAR data, the mean overall classification accuracy of the proposed method is up to 97.47% (with SVM)/99.39% (with DT), which is also higher than the mean accuracy of 89.59% (with SVM)/97.55% (with DT) from the conventional method. The comparison studies clearly demonstrate the efficiency and advantage of the proposed classification methodology. In addition, the proposed classification method achieves better robustness for the multi-temporal PolSAR data. This work also further validates that added benefits can be gained for PolSAR data investigation by mining and utilization of hidden polarimetric information in the rotation domain. Full article
(This article belongs to the Special Issue Advances in SAR: Sensors, Methodologies, and Applications)
Figures

Open AccessArticle Developing the Remote Sensing-Gash Analytical Model for Estimating Vegetation Rainfall Interception at Very High Resolution: A Case Study in the Heihe River Basin
Remote Sens. 2017, 9(7), 661; doi:10.3390/rs9070661
Received: 10 April 2017 / Revised: 16 June 2017 / Accepted: 21 June 2017 / Published: 27 June 2017
PDF Full-text (4009 KB) | HTML Full-text | XML Full-text
Abstract
Accurately quantifying the vegetation rainfall interception at a high resolution is critical for rainfall-runoff modeling and flood forecasting, and is also essential for understanding its further impact on local, regional, and even global water cycle dynamics. In this study, the Remote Sensing-based Gash
[...] Read more.
Accurately quantifying the vegetation rainfall interception at a high resolution is critical for rainfall-runoff modeling and flood forecasting, and is also essential for understanding its further impact on local, regional, and even global water cycle dynamics. In this study, the Remote Sensing-based Gash model (RS-Gash model) is developed based on a modified Gash model for interception loss estimation using remote sensing observations at the regional scale, and has been applied and validated in the upper reach of the Heihe River Basin of China for different types of vegetation. To eliminate the scale error and the effect of mixed pixels, the RS-Gash model is applied at a fine scale of 30 m with the high resolution vegetation area index retrieved by using the unified model of bidirectional reflectance distribution function (BRDF-U) for the vegetation canopy. Field validation shows that the RMSE and R2 of the interception ratio are 3.7% and 0.9, respectively, indicating the model’s strong stability and reliability at fine scale. The temporal variation of vegetation rainfall interception and its relationship with precipitation are further investigated. In summary, the RS-Gash model has demonstrated its effectiveness and reliability in estimating vegetation rainfall interception. When compared to the coarse resolution results, the application of this model at 30-m fine resolution is necessary to resolve the scaling issues as shown in this study. Full article
(This article belongs to the Section Remote Sensing in Geology, Geomorphology and Hydrology)
Figures