Next Issue
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 10 (October 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 (view full-size image) Africa has potential to provide solution to the global food-security challenges of the twenty-first [...] Read more.
View options order results:
result details:
Displaying articles 1-115
Export citation of selected articles as:
Open AccessLetter Multi-Layer Model Based on Multi-Scale and Multi-Feature Fusion for SAR Images
Remote Sens. 2017, 9(10), 1085; https://doi.org/10.3390/rs9101085
Received: 16 August 2017 / Revised: 12 October 2017 / Accepted: 18 October 2017 / Published: 24 October 2017
PDF Full-text (7820 KB) | HTML Full-text | XML Full-text
Abstract
A multi-layer classification approach based on multi-scales and multi-features (ML–MFM) for synthetic aperture radar (SAR) images is proposed in this paper. Firstly, the SAR image is partitioned into superpixels, which are local, coherent regions that preserve most of the characteristics necessary for extracting
[...] Read more.
A multi-layer classification approach based on multi-scales and multi-features (ML–MFM) for synthetic aperture radar (SAR) images is proposed in this paper. Firstly, the SAR image is partitioned into superpixels, which are local, coherent regions that preserve most of the characteristics necessary for extracting image information. Following this, a new sparse representation-based classification is used to express sparse multiple features of the superpixels. Moreover, a multi-scale fusion strategy is introduced into ML–MFM to construct the dictionary, which allows complementation between sample information. Finally, the multi-layer operation is used to refine the classification results of superpixels by adding a threshold decision condition to sparse representation classification (SRC) in an iterative way. Compared with traditional SRC and other existing methods, the experimental results of both synthetic and real SAR images have shown that the proposed method not only shows good performance in quantitative evaluation, but can also obtain satisfactory and cogent visualization of classification results. Full article
(This article belongs to the Special Issue Advances in SAR: Sensors, Methodologies, and Applications)
Figures

Graphical abstract

Open AccessArticle A Region-Based Hierarchical Cross-Section Analysis for Individual Tree Crown Delineation Using ALS Data
Remote Sens. 2017, 9(10), 1084; https://doi.org/10.3390/rs9101084
Received: 31 August 2017 / Revised: 30 September 2017 / Accepted: 19 October 2017 / Published: 24 October 2017
Cited by 2 | PDF Full-text (23517 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
In recent years, airborne Light Detection and Ranging (LiDAR) that provided three-dimensional forest information has been widely applied in forest inventory and has shown great potential in automatic individual tree crown delineation (ITCD). Usually, ITCD algorithms include treetop detection and crown boundary delineation
[...] Read more.
In recent years, airborne Light Detection and Ranging (LiDAR) that provided three-dimensional forest information has been widely applied in forest inventory and has shown great potential in automatic individual tree crown delineation (ITCD). Usually, ITCD algorithms include treetop detection and crown boundary delineation procedures. In this study, we proposed a novel method called region-based hierarchical cross-section analysis (RHCSA), which combined the two procedures together based on a canopy height model (CHM) derived from airborne LiDAR data for ITCD. This method considers the CHM as a three-dimensional topological surface, simulates stereoscopic scanning from top to bottom using an iterative process, and utilizes the individual crown and vertical structure of crowns to progressively detect individual treetops and delineate crown boundaries. The proposed method was tested in natural forest stands with high canopy densities in Liangshui National Nature Reserve and Maoershan Forest Farm, Heilongjiang Province, China. Its performance was evaluated by an accuracy procedure that considered both the relative position of treetops and overlapped area of crowns. The average overall accuracy achieved was 85.12% for coniferous plots, 83.86% for deciduous plots and 86.44% for coniferous and broad-leaved mixed forest plots. The results revealed that the RHCSA method can detect and delineate individual tree crowns with little influence from forest types and crown size. It could provide technical support for individual tree crown delineation in coniferous, deciduous and mixed forests with high canopy densities. Full article
(This article belongs to the Section Forest Remote Sensing)
Figures

Graphical abstract

Open AccessArticle Computing Coastal Ocean Surface Currents from MODIS and VIIRS Satellite Imagery
Remote Sens. 2017, 9(10), 1083; https://doi.org/10.3390/rs9101083
Received: 26 August 2017 / Revised: 1 October 2017 / Accepted: 5 October 2017 / Published: 24 October 2017
Cited by 1 | PDF Full-text (8571 KB) | HTML Full-text | XML Full-text
Abstract
We explore the potential of computing coastal ocean surface currents from Moderate-Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) satellite imagery using the maximum cross-correlation (MCC) method. To improve on past versions of this method, we evaluate combining MODIS and
[...] Read more.
We explore the potential of computing coastal ocean surface currents from Moderate-Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) satellite imagery using the maximum cross-correlation (MCC) method. To improve on past versions of this method, we evaluate combining MODIS and VIIRS thermal infrared (IR) and ocean color (OC) imagery to map the coastal surface currents and discuss the benefits of this combination of sensors and optical channels. By combining these two sensors, the total number of vectors increases by 58.3 % . In addition, we also make use of the different surface patterns of IR and OC imagery to improve the tracking performance of the MCC method. By merging the MCC velocity fields inferred from IR and OC products, the spatial coverage of each individual MCC field is increased by 65.8 % relative to the vectors derived from OC images. The root mean square (RMS) error of the merged currents is 18 cm · s 1 compared with coincident HF radar surface currents. A 5-year long time serious of merged MCC computed currents was used to investigate the current structure of the California Current (CC). Weekly, seasonal, and 5-year mean flows provide a unique space-time picture of the oceanographic variability of the CC. Full article
(This article belongs to the Special Issue Ocean Surface Currents: Progress in Remote Sensing and Validation)
Figures

Graphical abstract

Open AccessArticle A 33-Year NPP Monitoring Study in Southwest China by the Fusion of Multi-Source Remote Sensing and Station Data
Remote Sens. 2017, 9(10), 1082; https://doi.org/10.3390/rs9101082
Received: 24 July 2017 / Revised: 7 October 2017 / Accepted: 20 October 2017 / Published: 24 October 2017
Cited by 2 | PDF Full-text (3812 KB) | HTML Full-text | XML Full-text
Abstract
Knowledge of regional net primary productivity (NPP) is important for the systematic understanding of the global carbon cycle. In this study, multi-source data were employed to conduct a regional NPP study in southwest China, with a 33-year time span and a 1-km scale.
[...] Read more.
Knowledge of regional net primary productivity (NPP) is important for the systematic understanding of the global carbon cycle. In this study, multi-source data were employed to conduct a regional NPP study in southwest China, with a 33-year time span and a 1-km scale. A multi-sensor fusion framework was applied to obtain a new normalized difference vegetation index (NDVI) time series from 1982 to 2014, combining the advantages of different remote sensing datasets. As another key parameter for NPP modeling, the total solar radiation was calculated utilizing the improved Yang hybrid model (YHM), based on meteorological station data. The accuracy of the data processes is proved reliable by verification experiments. Moreover, NPP estimated by fused NDVI shows an obvious improved accuracy than that based on the original data. The spatio-temporal analysis results indicated that 67% of the study area showed an increasing NPP trend over the past three decades. The correlation between NPP and precipitation was significant heterogeneous at the monthly scale; specifically, the correlation is negative in the growing season and positive in the dry season. Meanwhile, the lagged positive correlation in the growing season and no lag in the dry season indicated the important impacts of precipitation on NPP. What is more, we found that there are three distinct stages during the variation of NPP, which were driven by different climatic factors. Significant climate warming led to a great increase of NPP from 1992 to 2002, while NPP clearly decreased during 1982–1992 and 2002–2014 due to the frequent droughts caused by the precipitation decrease. Full article
Figures

Figure 1

Open AccessLetter Vis-NIR Spectroscopy and PLS Regression with Waveband Selection for Estimating the Total C and N of Paddy Soils in Madagascar
Remote Sens. 2017, 9(10), 1081; https://doi.org/10.3390/rs9101081
Received: 20 September 2017 / Revised: 19 October 2017 / Accepted: 20 October 2017 / Published: 23 October 2017
Cited by 4 | PDF Full-text (2272 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Visible and near-infrared (Vis-NIR) diffuse reflectance spectroscopy with partial least squares (PLS) regression is a quick, cost-effective, and promising technology for predicting soil properties. The advantage of PLS regression is that all available wavebands can be incorporated in the model, while earlier studies
[...] Read more.
Visible and near-infrared (Vis-NIR) diffuse reflectance spectroscopy with partial least squares (PLS) regression is a quick, cost-effective, and promising technology for predicting soil properties. The advantage of PLS regression is that all available wavebands can be incorporated in the model, while earlier studies indicate that PLS models include redundant wavelengths, and selecting specific wavebands can refine PLS analyses. This study evaluated the performance of PLS regression with waveband selection using Vis-NIR reflectance spectra to estimate the total carbon (TC) and total nitrogen (TN) in soils collected mainly from the surface of upland and lowland rice fields in Madagascar (n = 59; after outliers were removed). We used iterative stepwise elimination-based PLS (ISE-PLS) to estimate soil TC and TN and compared the predictive ability with standard full-spectrum PLS (FS-PLS). The predictive abilities were assessed using the coefficient of determination (R2), the root mean squared error of cross-validation (RMSECV), and the residual predictive deviation (RPD). Overall, ISE-PLS using first derivative reflectance (FDR) showed a better predictive accuracy than ISE-PLS for both TC (R2 = 0.972, RMSECV = 0.194, RPD = 5.995) and TN (R2 = 0.949, RMSECV = 0.019, RPD = 4.416) in the soil of Madagascar. The important wavebands for estimating TC (12.59% of all wavebands) and TN (3.55% of all wavebands) were selected from all 2001 wavebands over the 400–2400 nm range using ISE-PLS. These findings suggest that ISE-PLS based on Vis-NIR diffuse reflectance spectra can be used to estimate soil TC and TN contents in Madagascar with an improved predictive accuracy. Full article
(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)
Figures

Graphical abstract

Open AccessArticle Sharpening the VNIR and SWIR Bands of Sentinel-2A Imagery through Modified Selected and Synthesized Band Schemes
Remote Sens. 2017, 9(10), 1080; https://doi.org/10.3390/rs9101080
Received: 19 July 2017 / Revised: 16 October 2017 / Accepted: 16 October 2017 / Published: 23 October 2017
Cited by 3 | PDF Full-text (4803 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
In this work, the bands of a Sentinel-2A image with spatial resolutions of 20 m and 60 m are sharpened to a spatial resolution of 10 m to obtain visible and near-infrared (VNIR) and shortwave infrared (SWIR) spectral bands with a spatial resolution
[...] Read more.
In this work, the bands of a Sentinel-2A image with spatial resolutions of 20 m and 60 m are sharpened to a spatial resolution of 10 m to obtain visible and near-infrared (VNIR) and shortwave infrared (SWIR) spectral bands with a spatial resolution of 10 m. In particular, we propose a two-step sharpening algorithm for Sentinel-2A imagery based on modified, selected, and synthesized band schemes using layer-stacked bands to sharpen Sentinel-2A images. The modified selected and synthesized band schemes proposed in this study extend the existing band schemes for sharpening Sentinel-2A images with spatial resolutions of 20 m and 60 m to improve the pan-sharpening accuracy by changing the combinations of bands used for multiple linear regression analysis through band-layer stacking. The proposed algorithms are applied to the pan-sharpening algorithm based on component substitution (CS) and a multiresolution analysis (MRA), and our results are then compared to the sharpening results when using sharpening algorithms based on existing band schemes. The experimental results show that the sharpening results from the proposed algorithm are improved in terms of the spatial and spectral properties when compared to existing methods. However, the results of the sharpening algorithm when applied to our modified band schemes show differing tendencies. With the modified, selected band scheme, the sharpening result when applying the CS-based algorithm is higher than the result when applying the MRA-based algorithm. However, the quality of the sharpening results when using the MRA-based algorithm with the modified synthesized band scheme is higher than that when using the CS-based algorithm. Full article
Figures

Graphical abstract

Open AccessArticle Data Synergy between Altimetry and L-Band Passive Microwave Remote Sensing for the Retrieval of Sea Ice Parameters—A Theoretical Study of Methodology
Remote Sens. 2017, 9(10), 1079; https://doi.org/10.3390/rs9101079
Received: 6 August 2017 / Revised: 18 October 2017 / Accepted: 20 October 2017 / Published: 23 October 2017
Cited by 1 | PDF Full-text (18607 KB) | HTML Full-text | XML Full-text
Abstract
Accurate knowledge of the sea ice parameters, including the thickness and the snow depth over sea ice, are key to both climate change studies and operational forecast in polar regions. The estimation of these parameters mainly relies on satellite based remote sensing, and
[...] Read more.
Accurate knowledge of the sea ice parameters, including the thickness and the snow depth over sea ice, are key to both climate change studies and operational forecast in polar regions. The estimation of these parameters mainly relies on satellite based remote sensing, and current retrieval algorithms usually focus on the retrieval of a single parameter under simple assumptions over the other. In this article, we explore the potential of combined retrieval of both sea ice thickness and snow depth through the data synergy two types of concurrent observations of the sea ice cover: the active altimetry and the L-band passive remote sensing. The data synergy is based on two physical constrains: (1) L-band (1.4 GHz) radiation model for the sea ice cover, and (2) the hydrostatic equilibrium as used in satellite altimetry. Two schemes of data synergy are proposed: (1) the synergy between L-band brightness temperature ( T B ) from passive microwave remote sensing and sea ice freeboard ( F B i c e ) as measured by radar altimetry, and (2) the synergy between L-band T B and snow freeboard ( F B s n o w ) as measured by laser altimetry. Based on retrievability studies, we show that both parameters can be retrieved using the two sets of data. Specifically, we show that there is potential problem of ill-posedness for the synergy between L-band T B and F B s n o w , with two possible retrieval solutions for a small portion of the solution space. On the other hand, the synergy between L-band T B and F B i c e is always well-posed. In terms of sensitivity, lower uncertainty is witnessed for thin ice for the retrieval with F B i c e , while the retrieval with F B s n o w shows advantage for thick ice. Besides the input parameters of T B , F B i c e and F B s n o w , the uncertainty associated with certain model parameters such as snow and ice densities is not negligible for the uncertainty estimation of the retrieved parameters. Verification is carried out with observational data from Operation IceBridge (OIB) campaigns and SMOS satellite, showing that both sea ice thickness and snow depth can be attained by the proposed retrieval algorithms. These algorithms serve as the basis for large-scale retrieval with satellite remote sensing data, including concurrent observation of the Arctic Ocean by independent satellite campaigns such as SMOS, CryoSat-2 and ICESat. Full article
(This article belongs to the Section Ocean Remote Sensing)
Figures

Graphical abstract

Open AccessArticle Agricultural Soil Spectral Response and Properties Assessment: Effects of Measurement Protocol and Data Mining Technique
Remote Sens. 2017, 9(10), 1078; https://doi.org/10.3390/rs9101078
Received: 11 September 2017 / Revised: 15 October 2017 / Accepted: 20 October 2017 / Published: 23 October 2017
Cited by 3 | PDF Full-text (4172 KB) | HTML Full-text | XML Full-text
Abstract
Soil spectroscopy has shown to be a fast, cost-effective, environmentally friendly, non-destructive, reproducible and repeatable analytical technique. Soil components, as well as types of instruments, protocols, sampling methods, sample preparation, spectral acquisition techniques and analytical algorithms have a combined influence on the final
[...] Read more.
Soil spectroscopy has shown to be a fast, cost-effective, environmentally friendly, non-destructive, reproducible and repeatable analytical technique. Soil components, as well as types of instruments, protocols, sampling methods, sample preparation, spectral acquisition techniques and analytical algorithms have a combined influence on the final performance. Therefore, it is important to characterize these differences and to introduce an effective approach in order to minimize the technical factors that alter reflectance spectra and consequent prediction. To quantify this alteration, a joint project between Czech University of Life Sciences Prague (CULS) and Tel-Aviv University (TAU) was conducted to estimate Cox, pH-H2O, pH-KCl and selected forms of Fe and Mn. Two different soil spectral measurement protocols and two data mining techniques were used to examine seventy-eight soil samples from five agricultural areas in different parts of the Czech Republic. Spectral measurements at both laboratories were made using different ASD spectroradiometers. The CULS protocol was based on employing a contact probe (CP) spectral measurement scheme, while the TAU protocol was carried out using a CP measurement method, accompanied with the internal soil standard (ISS) procedure. Two spectral datasets, acquired from different protocols, were both analyzed using partial least square regression (PLSR) technique as well as the PARACUDA II®, a new data mining engine for optimizing PLSR models. The results showed that spectra based on the CULS setup (non-ISS) demonstrated significantly higher albedo intensity and reflectance values relative to the TAU setup with ISS. However, the majority of statistics using the TAU protocol was not noticeably better than the CULS spectra. The paper also highlighted that under both measurement protocols, the PARACUDA II® engine proved to be a powerful tool for providing better results than PLSR. Such initiative is not only a way to unlock current limitations of soil spectroscopy, but also offers considerable efficiency and cost- and time-saving possibilities, which lead to further improvements in prediction performance of spectral models. Full article
Figures

Figure 1

Open AccessArticle Improving Jason-2 Sea Surface Heights within 10 km Offshore by Retracking Decontaminated Waveforms
Remote Sens. 2017, 9(10), 1077; https://doi.org/10.3390/rs9101077
Received: 7 June 2017 / Revised: 5 October 2017 / Accepted: 19 October 2017 / Published: 23 October 2017
Cited by 2 | PDF Full-text (5739 KB) | HTML Full-text | XML Full-text
Abstract
It is widely believed that altimetry-derived sea surface heights (SSHs) in coastal zones are seriously degraded due to land contamination in altimeter waveforms from non-marine surfaces or due to inhomogeneous sea state conditions. Spurious peaks superimposed in radar waveforms adversely impact waveform retracking
[...] Read more.
It is widely believed that altimetry-derived sea surface heights (SSHs) in coastal zones are seriously degraded due to land contamination in altimeter waveforms from non-marine surfaces or due to inhomogeneous sea state conditions. Spurious peaks superimposed in radar waveforms adversely impact waveform retracking and hence require tailored algorithms to mitigate this problem. Here, we present an improved method to decontaminate coastal waveforms based on the waveform modification concept. SSHs within 10 km offshore are calculated from Jason-2 data by a 20% threshold retracker using decontaminated waveforms (DW-TR) and compared with those using original waveforms and modified waveforms in four study regions. We then compare our results with retracked SSHs in the sensor geophysical data record (SGDR) and with the state-of-the-art PISTACH (Prototype Innovant de Système de Traitement pour les Applications Côtières et l’Hydrologie) and ALES (Adaptive Leading Edge Subwaveform) products. Our result indicates that the DW-TR is the most robust retracker in the 0–10 km coastal band and provides consistent accuracy up to 1 km away from the coastline. In the four test regions, the DW-TR retracker outperforms other retrackers, with the smallest averaged standard deviations at 15 cm and 20 cm, as compared against the EGM08 (Earth Gravitational Model 2008) geoid model and tide gauge data, respectively. For the SGDR products, only the ICE retracker provides competitive SSHs for coastal applications. Subwaveform retrackers such as ICE3, RED3 and ALES perform well beyond 8 km offshore, but seriously degrade in the 0–8 km strip along the coast. Full article
(This article belongs to the Special Issue Satellite Altimetry for Earth Sciences)
Figures

Graphical abstract

Open AccessArticle Impacts of Airborne Lidar Pulse Density on Estimating Biomass Stocks and Changes in a Selectively Logged Tropical Forest
Remote Sens. 2017, 9(10), 1068; https://doi.org/10.3390/rs9101068
Received: 3 September 2017 / Revised: 4 October 2017 / Accepted: 18 October 2017 / Published: 23 October 2017
Cited by 4 | PDF Full-text (6203 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Airborne lidar is a technology well-suited for mapping many forest attributes, including aboveground biomass (AGB) stocks and changes in selective logging in tropical forests. However, trade-offs still exist between lidar pulse density and accuracy of AGB estimates. We assessed the impacts of lidar
[...] Read more.
Airborne lidar is a technology well-suited for mapping many forest attributes, including aboveground biomass (AGB) stocks and changes in selective logging in tropical forests. However, trade-offs still exist between lidar pulse density and accuracy of AGB estimates. We assessed the impacts of lidar pulse density on the estimation of AGB stocks and changes using airborne lidar and field plot data in a selectively logged tropical forest located near Paragominas, Pará, Brazil. Field-derived AGB was computed at 85 square 50 × 50 m plots in 2014. Lidar data were acquired in 2012 and 2014, and for each dataset the pulse density was subsampled from its original density of 13.8 and 37.5 pulses·m−2 to lower densities of 12, 10, 8, 6, 4, 2, 0.8, 0.6, 0.4 and 0.2 pulses·m−2. For each pulse density dataset, a power-law model was developed to estimate AGB stocks from lidar-derived mean height and corresponding changes between the years 2012 and 2014. We found that AGB change estimates at the plot level were only slightly affected by pulse density. However, at the landscape level we observed differences in estimated AGB change of >20 Mg·ha−1 when pulse density decreased from 12 to 0.2 pulses·m−2. The effects of pulse density were more pronounced in areas of steep slope, especially when the digital terrain models (DTMs) used in the lidar derived forest height were created from reduced pulse density data. In particular, when the DTM from high pulse density in 2014 was used to derive the forest height from both years, the effects on forest height and the estimated AGB stock and changes did not exceed 20 Mg·ha−1. The results suggest that AGB change can be monitored in selective logging in tropical forests with reasonable accuracy and low cost with low pulse density lidar surveys if a baseline high-quality DTM is available from at least one lidar survey. We recommend the results of this study to be considered in developing projects and national level MRV systems for REDD+ emission reduction programs for tropical forests. Full article
(This article belongs to the Special Issue Biomass Remote Sensing in Forest Landscapes)
Figures

Graphical abstract

Open AccessArticle Using Satellite Data for the Characterization of Local Animal Reservoir Populations of Hantaan Virus on the Weihe Plain, China
Remote Sens. 2017, 9(10), 1076; https://doi.org/10.3390/rs9101076
Received: 19 September 2017 / Revised: 15 October 2017 / Accepted: 18 October 2017 / Published: 22 October 2017
Cited by 1 | PDF Full-text (3472 KB) | HTML Full-text | XML Full-text
Abstract
Striped field mice (Apodemus agrarius) are the main host for the Hantaan virus (HTNV), the cause of hemorrhagic fever with renal syndrome (HFRS) in central China. It has been shown that host population density is associated with pathogen dynamics and disease
[...] Read more.
Striped field mice (Apodemus agrarius) are the main host for the Hantaan virus (HTNV), the cause of hemorrhagic fever with renal syndrome (HFRS) in central China. It has been shown that host population density is associated with pathogen dynamics and disease risk. Thus, a higher population density of A. agrarius in an area might indicate a higher risk for an HFRS outbreak. Here, we surveyed the A. agrarius population density between 2005 and 2012 on the Weihe Plain, Shaanxi Province, China, and used this monitoring data to examine the relationships between the dynamics of A. agrarius populations and environmental conditions of crop-land, represented by remote sensing based indicators. These included the normalized difference vegetation index, leaf area index, fraction of photosynthetically active radiation absorbed by vegetation, net photosynthesis (PsnNet), gross primary productivity, and land surface temperature. Structural equation modeling (SEM) was applied to detect the possible causal relationship between PsnNet, A. agrarius population density and HFRS risk. The results showed that A. agrarius was the most frequently captured species with a capture rate of 0.9 individuals per hundred trap-nights, during 96 months of trapping in the study area. The risk of HFRS was highly associated with the abundance of A. agrarius, with a 1–5-month lag. The breeding season of A. agrarius was also found to coincide with agricultural activity and seasons with high PsnNet. The SEM indicated that PsnNet had an indirect positive effect on HFRS incidence via rodents. In conclusion, the remote sensing-based environmental indicator, PsnNet, was highly correlated with HTNV reservoir population dynamics with a 3-month lag (r = 0.46, p < 0.01), and may serve as a predictor of potential HFRS outbreaks. Full article
(This article belongs to the Special Issue Remote Sensing Applications to Human Health)
Figures

Figure 1

Open AccessTechnical Note Usability Study to Assess the IGBP Land Cover Classification for Singapore
Remote Sens. 2017, 9(10), 1075; https://doi.org/10.3390/rs9101075
Received: 7 September 2017 / Revised: 11 October 2017 / Accepted: 11 October 2017 / Published: 22 October 2017
PDF Full-text (2333 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Our research focuses on assessing the usability of the International Geosphere Biosphere Programme (IGBP) classification scheme provided in the MODIS MCD12Q1-1 dataset for assessing the land cover of the city-state, Singapore. We conducted a user study with responses from 33 users by providing
[...] Read more.
Our research focuses on assessing the usability of the International Geosphere Biosphere Programme (IGBP) classification scheme provided in the MODIS MCD12Q1-1 dataset for assessing the land cover of the city-state, Singapore. We conducted a user study with responses from 33 users by providing them with Google Earth images from different parts of Singapore, asking survey-takers to classify these images according to their understanding by the IGBP definitions provided. We also conducted interviews with experts from major governmental agencies working with satellite imagery, which highlighted the need for a detailed land classification for Singapore. In addition to the qualitative analysis of the IGBP land classification scheme, we carried out a validation of the MCD12Q1-1 remote sensing product against SPOT-5 imagery for our study area. The user study revealed that survey-takers were able to correctly classify urban areas, as well as densely forested areas. Misclassifications between Cropland and Mixed Forest classes were highest and were attributed by users to the broad terminology of the IGBP of the two land cover class definitions. For the accuracy assessment, we obtained validation points using weighted and unweighted stratified sampling. The overall classification accuracy for all 17 IGBP land classes is 62%. Upon selecting only the four most occurring IGBP land classes in Singapore, the classification accuracy improved to 71%. Validation of the MCD12Q1-1 against ground truth for Singapore revealed less-common land classes that may be of importance in a global context but are sources of error when the same product is applied at a smaller scale. Combining the user study with the accuracy assessment gives a comprehensive overview of the challenges associated with using global-level land cover data to derive localized land cover information specifically for smaller land masses like Singapore. Full article
(This article belongs to the Special Issue GIS and Remote Sensing advances in Land Change Science)
Figures

Graphical abstract

Open AccessArticle Nonnegative Matrix Factorization With Data-Guided Constraints For Hyperspectral Unmixing
Remote Sens. 2017, 9(10), 1074; https://doi.org/10.3390/rs9101074
Received: 7 August 2017 / Revised: 16 October 2017 / Accepted: 18 October 2017 / Published: 21 October 2017
Cited by 3 | PDF Full-text (4302 KB) | HTML Full-text | XML Full-text
Abstract
Hyperspectral unmixing aims to estimate a set of endmembers and corresponding abundances in pixels. Nonnegative matrix factorization (NMF) and its extensions with various constraints have been widely applied to hyperspectral unmixing. L1/2 and L2 regularizers can be added to
[...] Read more.
Hyperspectral unmixing aims to estimate a set of endmembers and corresponding abundances in pixels. Nonnegative matrix factorization (NMF) and its extensions with various constraints have been widely applied to hyperspectral unmixing. L 1 / 2 and L 2 regularizers can be added to NMF to enforce sparseness and evenness, respectively. In practice, a region in a hyperspectral image may possess different sparsity levels across locations. The problem remains as to how to impose constraints accordingly when the level of sparsity varies. We propose a novel nonnegative matrix factorization with data-guided constraints (DGC-NMF). The DGC-NMF imposes on the unknown abundance vector of each pixel with either an L 1 / 2 constraint or an L 2 constraint according to its estimated mixture level. Experiments on the synthetic data and real hyperspectral data validate the proposed algorithm. Full article
(This article belongs to the Special Issue Hyperspectral Imaging and Applications)
Figures

Graphical abstract

Open AccessArticle The Methane Isotopologues by Solar Occultation (MISO) Nanosatellite Mission: Spectral Channel Optimization and Early Performance Analysis
Remote Sens. 2017, 9(10), 1073; https://doi.org/10.3390/rs9101073
Received: 23 August 2017 / Revised: 17 October 2017 / Accepted: 18 October 2017 / Published: 21 October 2017
Cited by 1 | PDF Full-text (4961 KB) | HTML Full-text | XML Full-text
Abstract
MISO is an in-orbit demonstration mission that focuses on improving the representation of the methane distribution throughout the upper troposphere and stratosphere, to complement and augment the nadir- and zenith-looking methane observing system for a better understanding of the methane budget. MISO also
[...] Read more.
MISO is an in-orbit demonstration mission that focuses on improving the representation of the methane distribution throughout the upper troposphere and stratosphere, to complement and augment the nadir- and zenith-looking methane observing system for a better understanding of the methane budget. MISO also aims to raise to space mission readiness the concept of laser heterodyne spectro-radiometry (LHR) and associated miniaturization technologies, through demonstration of Doppler-limited atmospheric transmittance spectroscopy of methane from a nanosatellite platform suitable for future constellation deployment. The instrumental and engineering approach to MISO is briefly presented to demonstrate the technical feasibility of the mission. LHR operates using narrow spectral coverage (<1 cm−1) focusing on a few carefully chosen individual ro-vibrational transitions. A line-by-line spectral channel selection methodology is developed and used to optimize spectral channel selection relevant to methane isotopologue sounding from co-registered thermal infrared and short-wave infrared LHR. One of the selected windows is then used to carry out a first performance analysis of methane retrievals based on measurement noise propagation. This preliminary analysis of a single observation demonstrates an ideal instrumental precision of <1% for altitudes in the range 8–20 km, <5% for 20–30 km and <10% up to 37 km on a single isotopologue profile, which leaves a significant reserve for real-world error budget degradation and bodes well for the mission feasibility. MISO could realistically demonstrate methane limb sounding at Doppler-limited spectral resolution, even from a cost-effective 6 dm3 nanosatellite. Full article
(This article belongs to the Special Issue Remote Sensing of Greenhouse Gases)
Figures

Graphical abstract

Open AccessArticle The Geometry of Large Tundra Lakes Observed in Historical Maps and Satellite Images
Remote Sens. 2017, 9(10), 1072; https://doi.org/10.3390/rs9101072
Received: 9 September 2017 / Revised: 10 October 2017 / Accepted: 18 October 2017 / Published: 21 October 2017
PDF Full-text (4483 KB) | HTML Full-text | XML Full-text
Abstract
The climate of the Arctic is warming rapidly and this is causing major changes to the cycling of carbon and the distribution of permafrost in this region. Tundra lakes are key components of the Arctic climate system because they represent a source of
[...] Read more.
The climate of the Arctic is warming rapidly and this is causing major changes to the cycling of carbon and the distribution of permafrost in this region. Tundra lakes are key components of the Arctic climate system because they represent a source of methane to the atmosphere. In this paper, we aim to analyze the geometry of the patterns formed by large (> 0.8 km 2 ) tundra lakes in the Russian High Arctic. We have studied images of tundra lakes in historical maps from the State Hydrological Institute, Russia (date 1977; scale 0.21166 km/pixel) and in Landsat satellite images derived from the Google Earth Engine (G.E.E.; date 2016; scale 0.1503 km/pixel). The G.E.E. is a cloud-based platform for planetary-scale geospatial analysis on over four decades of Landsat data. We developed an image-processing algorithm to segment these maps and images, measure the area and perimeter of each lake, and compute the fractal dimension of the lakes in the images we have studied. Our results indicate that as lake size increases, their fractal dimension bifurcates. For lakes observed in historical maps, this bifurcation occurs among lakes larger than 100 km 2 (fractal dimension 1.43 to 1.87 ). For lakes observed in satellite images this bifurcation occurs among lakes larger than ∼100 km 2 (fractal dimension 1.31 to 1.95 ). Tundra lakes with a fractal dimension close to 2 have a tendency to be self-similar with respect to their area–perimeter relationships. Area–perimeter measurements indicate that lakes with a length scale greater than 70 km 2 are power-law distributed. Preliminary analysis of changes in lake size over time in paired lakes (lakes that were visually matched in both the historical map and the satellite imagery) indicate that some lakes in our study region have increased in size over time, whereas others have decreased in size over time. Lake size change during this 39-year time interval can be up to half the size of the lake as recorded in the historical map. Full article
(This article belongs to the Special Issue Remote Sensing of Arctic Tundra)
Figures

Graphical abstract

Back to Top