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Remote Sens., Volume 9, Issue 9 (September 2017)

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Cover Story Satellite data from the polar-orbiting Landsat-8 and Sentinel-2 sensors offer multi-spectral global [...] Read more.
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Editorial

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Open AccessEditorial Remote Sensing of Above-Ground Biomass
Remote Sens. 2017, 9(9), 935; doi:10.3390/rs9090935
Received: 8 September 2017 / Revised: 8 September 2017 / Accepted: 8 September 2017 / Published: 10 September 2017
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(This article belongs to the Special Issue Remote Sensing of Above Ground Biomass)

Research

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Open AccessArticle The Combined Use of Remote Sensing and Social Sensing Data in Fine-Grained Urban Land Use Mapping: A Case Study in Beijing, China
Remote Sens. 2017, 9(9), 865; doi:10.3390/rs9090865
Received: 19 June 2017 / Revised: 6 August 2017 / Accepted: 16 August 2017 / Published: 23 August 2017
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Abstract
In light of the need for fine-grained, accurate, and timely urban land use information, a per-field classification approach was proposed in this paper to automatically map fine-grained urban land use in a study area within Haidian District, Beijing, China, in 2016. High-resolution remote
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In light of the need for fine-grained, accurate, and timely urban land use information, a per-field classification approach was proposed in this paper to automatically map fine-grained urban land use in a study area within Haidian District, Beijing, China, in 2016. High-resolution remote sensing imagery and multi-source social sensing data were used to provide both physical and socioeconomic information. Four categories of attributes were derived from both data sources for urban land use parcels segmented by the OpenStreetMap road network, including spectral/texture attributes, landscape metrics, Baidu Point-Of-Interest (POI) attributes, and Weibo attributes. The random forests technique was adopted to conduct the classification. The importance of each attribute, attribute category, and data source was evaluated for the classification as a whole and the classification of individual land use types. The results showed that a testing accuracy of 77.83% can be achieved. The approach is relatively good at classifying open space and residential parcels, and poor at classifying institutional parcels. While using solely remote sensing data or social sensing data can achieve equally high overall accuracy, their importance varies in terms of the classification of individual classes. Landscape metrics are the most important for open space parcels. Spectral/texture attributes are more important in identifying institutional and residential parcels. The classification of business parcels relies more on landscape metrics and social sensing data, and less on spectral/texture attributes. The classification accuracy can be potentially improved upon the acquisition of purer parcels and the addition of new attributes. It is expected that the proposed approach will be useful for the routine update of urban land use information and large-scale urban land use mapping. Full article
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Open AccessArticle Multiscale Union Regions Adaptive Sparse Representation for Hyperspectral Image Classification
Remote Sens. 2017, 9(9), 872; doi:10.3390/rs9090872
Received: 13 July 2017 / Revised: 14 August 2017 / Accepted: 21 August 2017 / Published: 23 August 2017
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Abstract
Sparse Representation has been widely applied to classification of hyperspectral images (HSIs). Besides spectral information, the spatial context in HSIs also plays an important role in the classification. The recently published Multiscale Adaptive Sparse Representation (MASR) classifier has shown good performance in exploiting
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Sparse Representation has been widely applied to classification of hyperspectral images (HSIs). Besides spectral information, the spatial context in HSIs also plays an important role in the classification. The recently published Multiscale Adaptive Sparse Representation (MASR) classifier has shown good performance in exploiting spatial information for HSI classification. But the spatial information is exploited by multiscale patches with fixed sizes of square windows. The patch can include all nearest neighbor pixels but these neighbor pixels may contain some noise pixels. Then another research proposed a Multiscale Superpixel-Based Sparse Representation (MSSR) classifier. Shape-adaptive superpixels can provide more accurate representation than patches. But it is difficult to select scales for superpixels. Therefore, inspired by the merits and demerits of multiscale patches and superpixels, we propose a novel algorithm called Multiscale Union Regions Adaptive Sparse Representation (MURASR). The union region, which is the overlap of patch and superpixel, can make full use of the advantages of both and overcome the weaknesses of each one. Experiments on several HSI datasets demonstrate that the proposed MURASR is superior to MASR and union region is better than the patch in the sparse representation. Full article
(This article belongs to the Special Issue Hyperspectral Imaging and Applications)
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Open AccessArticle Statistical Comparison between Low-Cost Methods for 3D Characterization of Cut-Marks on Bones
Remote Sens. 2017, 9(9), 873; doi:10.3390/rs9090873
Received: 22 June 2017 / Revised: 3 August 2017 / Accepted: 18 August 2017 / Published: 23 August 2017
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Abstract
In recent years, new techniques for the morphological study of cut marks have become essential for the interpretation of prehistoric butchering practices. Different criteria have been suggested for the description and classification of cut marks. The methods commonly used for the study of
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In recent years, new techniques for the morphological study of cut marks have become essential for the interpretation of prehistoric butchering practices. Different criteria have been suggested for the description and classification of cut marks. The methods commonly used for the study of cut marks rely on high-cost microscopy techniques with low portability (i.e., inability to work in situ), such as the 3D digital microscope (3D DM) or laser scanning confocal microscopy (LSCM). Recently, new algorithmic developments in the field of computer vision and photogrammetry, have achieved very high precision and resolution, offering a portable and low-cost alternative to microscopic techniques. However, the photogrammetric techniques present some disadvantages, such as longer data collection and processing time, and the requirement of some photogrammetric expertise for the calibration of the cameras and the acquisition of precise image orientation. In this paper, we compare two low-cost techniques and their application to cut mark studies: the micro-photogrammetry (M-PG) technique presented, developed, and validated previously, and a methodology based on the use of a structured light scanner (SLS). A total of 47 experimental cut marks, produced using a stainless steel knife, were analyzed. The data registered through virtual reconstruction was analyzed by means of three-dimensional geometric morphometrics (GMM). Full article
(This article belongs to the Special Issue Advances in Remote Sensing for Archaeological Heritage)
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Open AccessArticle On-Board Ortho-Rectification for Images Based on an FPGA
Remote Sens. 2017, 9(9), 874; doi:10.3390/rs9090874
Received: 16 June 2017 / Revised: 7 August 2017 / Accepted: 18 August 2017 / Published: 23 August 2017
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Abstract
The traditional ortho-rectification technique for remotely sensed (RS) images, which is performed on the basis of a ground image processing platform, has been unable to meet timeliness or near timeliness requirements. To solve this problem, this paper presents research on an ortho-rectification technique
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The traditional ortho-rectification technique for remotely sensed (RS) images, which is performed on the basis of a ground image processing platform, has been unable to meet timeliness or near timeliness requirements. To solve this problem, this paper presents research on an ortho-rectification technique based on a field programmable gate array (FPGA) platform that can be implemented on board spacecraft for (near) real-time processing. The proposed FPGA-based ortho-rectification method contains three modules, i.e., a memory module, a coordinate transformation module (including the transformation from geodetic coordinates to photo coordinates, and the transformation from photo coordinates to scanning coordinates), and an interpolation module. Two datasets, aerial images located in central Denver, Colorado, USA, and an aerial image from the example dataset of ERDAS IMAGINE 9.2, are used to validate the processing speed and accuracy. Compared to traditional ortho-rectification technology, the throughput from the proposed FPGA-based platform and the personal computer (PC)-based platform are 11,182.3 kilopixels per second and 2582.9 kilopixels per second, respectively. This means that the proposed FPGA-based platform is 4.3 times faster than the PC-based platform for processing the same RS images. In addition, the root-mean-square errors of the planimetric coordinates φX and φY and the distance φS are 1.09 m, 1.61 m, and 1.93 m, respectively, which can meet the requirements of correction accuracy in practice. Full article
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Open AccessArticle Classification of Tree Species in a Diverse African Agroforestry Landscape Using Imaging Spectroscopy and Laser Scanning
Remote Sens. 2017, 9(9), 875; doi:10.3390/rs9090875
Received: 14 July 2017 / Revised: 14 August 2017 / Accepted: 18 August 2017 / Published: 23 August 2017
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Abstract
Airborne imaging spectroscopy (IS) and laser scanning (ALS) have been explored widely for tree species classification during the past decades. However, African agroforestry areas, where a few exotic tree species are dominant and many native species occur less frequently, have not yet been
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Airborne imaging spectroscopy (IS) and laser scanning (ALS) have been explored widely for tree species classification during the past decades. However, African agroforestry areas, where a few exotic tree species are dominant and many native species occur less frequently, have not yet been studied. Obtaining maps of tree species would provide useful information for the characterization of agroforestry systems and detecting invasive species. Our objective was to study tree species classification in a diverse tropical landscape using IS and ALS data at the tree crown level, with primary interest in the exotic tree species. We performed multiple analyses based on different IS and ALS feature sets, identified important features using feature selection, and evaluated the impact of combining the two data sources. Given that a high number of tree species with limited sample size (499 samples for 31 species) was expected to limit the classification accuracy, we tested different approaches to group the species based on the frequency of their occurrence and Jeffries–Matusita (JM) distance. Surface reflectance at wavelengths between 400–450 nm and 750–800 nm, and height to crown width ratio, were identified as important features. Nonetheless, a selection of minimum noise fraction (MNF) transformed reflectance bands showed superior performance. Support vector machine classifier performed slightly better than the random forest classifier, but the improvement was not statistically significant for the best performing feature set. The highest F1-scores were achieved when each of the species was classified separately against a mixed group of all other species, which makes this approach suitable for invasive species detection. Our results are valuable for organizations working on biodiversity conservation and improving agroforestry practices, as we showed how the non-native Eucalyptus spp., Acacia mearnsii and Grevillea robusta (mean F1-scores 76%, 79% and 89%, respectively) trees can be mapped with good accuracy. We also found a group of six fruit bearing trees using JM distance, which was classified with mean F1-score of 65%. This was a useful finding, as these species could not be classified with acceptable accuracy individually, while they all share common economic and ecological importance. Full article
(This article belongs to the Special Issue Hyperspectral Imaging and Applications)
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Open AccessArticle Determining the Pixel-to-Pixel Uncertainty in Satellite-Derived SST Fields
Remote Sens. 2017, 9(9), 877; doi:10.3390/rs9090877
Received: 18 July 2017 / Revised: 21 August 2017 / Accepted: 22 August 2017 / Published: 23 August 2017
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Abstract
The primary measure of the quality of sea surface temperature (SST) fields obtained from satellite-borne infrared sensors has been the bias and variance of matchups with co-located in-situ values. Because such matchups tend to be widely separated, these bias and variance estimates are
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The primary measure of the quality of sea surface temperature (SST) fields obtained from satellite-borne infrared sensors has been the bias and variance of matchups with co-located in-situ values. Because such matchups tend to be widely separated, these bias and variance estimates are not necessarily a good measure of small scale (several pixels) gradients in these fields because one of the primary contributors to the uncertainty in satellite retrievals is atmospheric contamination, which tends to have large spatial scales compared with the pixel separation of infrared sensors. Hence, there is not a good measure to use in selecting SST fields appropriate for the study of submesoscale processes and, in particular, of processes associated with near-surface fronts, both of which have recently seen a rapid increase in interest. In this study, two methods are examined to address this problem, one based on spectra of the SST data and the other on their variograms. To evaluate the methods, instrument noise was estimated in Level-2 Visible-Infrared Imager-Radiometer Suite (VIIRS) and Advanced Very High Resolution Radiometer (AVHRR) SST fields of the Sargasso Sea. The two methods provided very nearly identical results for AVHRR: along-scan values of approximately 0.18 K for both day and night and along-track values of 0.21 K for day and night. By contrast, the instrument noise estimated for VIIRS varied by method, scan geometry and day-night. Specifically, daytime, along-scan (along-track), spectral estimates were found to be approximately 0.05 K (0.08 K) and the corresponding nighttime values of 0.02 K (0.03 K). Daytime estimates based on the variogram were found to be 0.08 K (0.10 K) with the corresponding nighttime values of 0.04 K (0.06 K). Taken together, AVHRR instrument noise is significantly larger than VIIRS instrument noise, along-track noise is larger than along-scan noise and daytime levels are higher than nighttime levels. Given the similarity of results and the less stringent preprocessing requirements, the variogram is the preferred method, although there is a suggestion that this approach overestimates the noise for high quality data in dynamically quiet regions. Finally, simulations of the impact of noise on the determination of SST gradients show that on average the gradient magnitude for typical ocean gradients will be accurately estimated with VIIRS but substantially overestimated with AVHRR. Full article
(This article belongs to the Special Issue Sea Surface Temperature Retrievals from Remote Sensing)
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Open AccessArticle Automatic Counting of Large Mammals from Very High Resolution Panchromatic Satellite Imagery
Remote Sens. 2017, 9(9), 878; doi:10.3390/rs9090878
Received: 22 June 2017 / Revised: 18 August 2017 / Accepted: 21 August 2017 / Published: 23 August 2017
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Abstract
Estimating animal populations by direct counting is an essential component of wildlife conservation and management. However, conventional approaches (i.e., ground survey and aerial survey) have intrinsic constraints. Advances in image data capture and processing provide new opportunities for using applied remote sensing to
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Estimating animal populations by direct counting is an essential component of wildlife conservation and management. However, conventional approaches (i.e., ground survey and aerial survey) have intrinsic constraints. Advances in image data capture and processing provide new opportunities for using applied remote sensing to count animals. Previous studies have demonstrated the feasibility of using very high resolution multispectral satellite images for animal detection, but to date, the practicality of detecting animals from space using panchromatic imagery has not been proven. This study demonstrates that it is possible to detect and count large mammals (e.g., wildebeests and zebras) from a single, very high resolution GeoEye-1 panchromatic image in open savanna. A novel semi-supervised object-based method that combines a wavelet algorithm and a fuzzy neural network was developed. To discern large mammals from their surroundings and discriminate between animals and non-targets, we used the wavelet technique to highlight potential objects. To make full use of geometric attributes, we carefully trained the classifier, using the adaptive-network-based fuzzy inference system. Our proposed method (with an accuracy index of 0.79) significantly outperformed the traditional threshold-based method (with an accuracy index of 0.58) detecting large mammals in open savanna. Full article
(This article belongs to the collection Learning to Understand Remote Sensing Images)
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Open AccessArticle Split-Band Interferometry-Assisted Phase Unwrapping for the Phase Ambiguities Correction
Remote Sens. 2017, 9(9), 879; doi:10.3390/rs9090879
Received: 20 July 2017 / Revised: 9 August 2017 / Accepted: 19 August 2017 / Published: 23 August 2017
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Abstract
Split-Band Interferometry (SBInSAR) exploits the large range bandwidth of the new generation of synthetic aperture radar (SAR) sensors to process images at subrange bandwidth. Its application to an interferometric pair leads to several lower resolution interferograms of the same scene with slightly shifted
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Split-Band Interferometry (SBInSAR) exploits the large range bandwidth of the new generation of synthetic aperture radar (SAR) sensors to process images at subrange bandwidth. Its application to an interferometric pair leads to several lower resolution interferograms of the same scene with slightly shifted central frequencies. When SBInSAR is applied to frequency-persistent scatterers, the linear trend of the phase through the stack of interferograms can be used to perform absolute and spatially independent phase unwrapping. While the height computation has been the main concern of studies on SBInSAR so far, we propose instead to use it to assist conventional phase unwrapping. During phase unwrapping, phase ambiguities are introduced when parts of the interferogram are separately unwrapped. The proposed method reduces the phase ambiguities so that the phase can be connected between separately unwrapped regions. The approach is tested on a pair of TerraSAR-X spotlight images of Copahue volcano, Argentina. In this framework, we propose two new criteria for the frequency-persistent scatterers detection, based respectively on the standard deviation of the slope of the linear regression and on the phase variance stability, and we compare them to the multifrequency phase error. Both new criteria appear to be more suited to our approach than the multifrequency phase error. We validate the SBInSAR-assisted phase unwrapping method by artificially splitting a continuous phase region into disconnected subzones. Despite the decorrelation and the steep topography affecting the volcanic test region, the expected phase ambiguities are successfully recovered whatever the chosen criterion to detect the frequency-persistent scatterers. Comparing the aspect ratio of the distributions of the computed phase ambiguities, the analysis shows that the phase variance stability is the most efficient criterion to select stable targets and the slope standard deviation gives satisfactory results. Full article
(This article belongs to the Special Issue Advances in SAR: Sensors, Methodologies, and Applications)
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Open AccessArticle A Microtopographic Feature Analysis-Based LiDAR Data Processing Approach for the Identification of Chu Tombs
Remote Sens. 2017, 9(9), 880; doi:10.3390/rs9090880
Received: 3 July 2017 / Revised: 10 August 2017 / Accepted: 18 August 2017 / Published: 24 August 2017
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Abstract
Most of the cultural sites hidden under dense vegetation in the mountains of China have been destroyed. In this paper, we present a microtopographic feature analysis (MFA)-based Light Detection and Ranging (LiDAR) data processing approach and an archaeological pattern-oriented point cloud segmentation (APoPCS)
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Most of the cultural sites hidden under dense vegetation in the mountains of China have been destroyed. In this paper, we present a microtopographic feature analysis (MFA)-based Light Detection and Ranging (LiDAR) data processing approach and an archaeological pattern-oriented point cloud segmentation (APoPCS) algorithm that we developed for the classification of archaeological objects and terrain points and the detection of archaeological remains. The archaeological features and patterns are interpreted and extracted from LiDAR point cloud data to construct an archaeological object pattern database. A microtopographic factor is calculated based on the archaeological object patterns, and this factor converts the massive point cloud data into a raster feature image. A fuzzy clustering algorithm based on the archaeological object patterns is presented for raster feature image segmentation and the detection of archaeological remains. Using the proposed approach, we investigated four typical areas with different types of Chu tombs in Central China, which had dense vegetation and high population densities. Our research results show that the proposed LiDAR data processing approach can identify archaeological remains from large-volume and massive LiDAR data, as well as in areas with dense vegetation and trees. The studies of different archaeological object patterns are important for improving the robustness of the proposed APoPCS algorithm for the extraction of archaeological remains. Full article
(This article belongs to the Special Issue Advances in Remote Sensing for Archaeological Heritage)
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Open AccessArticle Use of Proper Orthogonal Decomposition for Extraction of Ocean Surface Wave Fields from X-Band Radar Measurements of the Sea Surface
Remote Sens. 2017, 9(9), 881; doi:10.3390/rs9090881
Received: 24 June 2017 / Revised: 13 August 2017 / Accepted: 21 August 2017 / Published: 25 August 2017
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Abstract
Radar remote sensing of the sea surface for the extraction of ocean surface wave fields requires separating wave and non-wave contributions to the sea surface measurement. Conventional methods of extracting wave information from radar measurements of the sea surface rely on filtering the
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Radar remote sensing of the sea surface for the extraction of ocean surface wave fields requires separating wave and non-wave contributions to the sea surface measurement. Conventional methods of extracting wave information from radar measurements of the sea surface rely on filtering the wavenumber-frequency spectrum using the linear dispersion relationship for ocean surface waves. However, this technique has limitations, e.g., it isn’t suited for the inclusion of non-linear wave features. This study evaluates an alternative method called proper orthogonal decomposition (POD) for the extraction of ocean surface wave fields remotely sensed by marine radar. POD is an empirical and optimal linear method for representing non-linear processes. The method was applied to Doppler velocity data of the sea surface collected using two different radar systems during two different experiments that spanned a variety of environmental conditions. During both experiments, GPS mini-buoys simultaneously collected wave data. The POD method was used to generate phase-resolved wave orbital velocity maps that are statistically evaluated by comparing wave statistics computed from the buoy data to those obtained from these maps. The results show that leading POD modes contain energy associated with the peak wavelength(s) of the measured wave field, and consequently, wave contributions to the radar measurement of the sea surface can be separated based on modes. Wave statistics calculated from optimized POD reconstructions are comparable to those calculated from GPS wave buoys. The accuracy of the wave statistics generated from POD-reconstructed orbital velocity maps was not sensitive to the radar configuration or environmental conditions examined. Further research is needed to determine a rigorous method for selecting modes a priori. Full article
(This article belongs to the Special Issue Ocean Radar)
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Open AccessArticle Robust Feature Matching Method for SAR and Optical Images by Using Gaussian-Gamma-Shaped Bi-Windows-Based Descriptor and Geometric Constraint
Remote Sens. 2017, 9(9), 882; doi:10.3390/rs9090882
Received: 14 July 2017 / Revised: 22 August 2017 / Accepted: 23 August 2017 / Published: 25 August 2017
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Abstract
Improving the matching reliability of multi-sensor imagery is one of the most challenging issues in recent years, particularly for synthetic aperture radar (SAR) and optical images. It is difficult to deal with the noise influence, geometric distortions, and nonlinear radiometric difference between SAR
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Improving the matching reliability of multi-sensor imagery is one of the most challenging issues in recent years, particularly for synthetic aperture radar (SAR) and optical images. It is difficult to deal with the noise influence, geometric distortions, and nonlinear radiometric difference between SAR and optical images. In this paper, a method for SAR and optical images matching is proposed. First, interest points that are robust to speckle noise in SAR images are detected by improving the original phase-congruency-based detector. Second, feature descriptors are constructed for all interest points by combining a new Gaussian-Gamma-shaped bi-windows-based gradient operator and the histogram of oriented gradient pattern. Third, descriptor similarity and geometrical relationship are combined to constrain the matching processing. Finally, an approach based on global and local constraints is proposed to eliminate outliers. In the experiments, SAR images including COSMO-Skymed, RADARSAT-2, TerraSAR-X and HJ-1C images, and optical images including ZY-3 and Google Earth images are used to evaluate the performance of the proposed method. The experimental results demonstrate that the proposed method provides significant improvements in the number of correct matches and matching precision compared with the state-of-the-art SIFT-like methods. Near 1 pixel registration accuracy is obtained based on the matching results of the proposed method. Full article
(This article belongs to the Section Remote Sensing Image Processing)
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Open AccessArticle Quantifying Snow Albedo Radiative Forcing and Its Feedback during 2003–2016
Remote Sens. 2017, 9(9), 883; doi:10.3390/rs9090883
Received: 26 June 2017 / Revised: 10 August 2017 / Accepted: 22 August 2017 / Published: 25 August 2017
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Abstract
Snow albedo feedback is one of the most crucial feedback processes that control equilibrium climate sensitivity, which is a central parameter for better prediction of future climate change. However, persistent large discrepancies and uncertainties are found in snow albedo feedback estimations. Remotely sensed
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Snow albedo feedback is one of the most crucial feedback processes that control equilibrium climate sensitivity, which is a central parameter for better prediction of future climate change. However, persistent large discrepancies and uncertainties are found in snow albedo feedback estimations. Remotely sensed snow cover products, atmospheric reanalysis data and radiative kernel data are used in this study to quantify snow albedo radiative forcing and its feedback on both hemispheric and global scales during 2003–2016. The strongest snow albedo radiative forcing is located north of 30°N, apart from Antarctica. In general, it has large monthly variation and peaks in spring. Snow albedo feedback is estimated to be 0.18 ± 0.08 W∙m−2∙°C−1 and 0.04 ± 0.02 W∙m−2∙°C−1 on hemispheric and global scales, respectively. Compared to previous studies, this paper focuses specifically on quantifying snow albedo feedback and demonstrates three improvements: (1) used high spatial and temporal resolution satellite-based snow cover data to determine the areas of snow albedo radiative forcing and feedback; (2) provided detailed information for model parameterization by using the results from (1), together with accurate description of snow cover change and constrained snow albedo and snow-free albedo data; and (3) effectively reduced the uncertainty of snow albedo feedback and increased its confidence level through the block bootstrap test. Our results of snow albedo feedback agreed well with other partially observation-based studies and indicate that the 25 Coupled Model Intercomparison Project Phase 5 (CMIP5) models might have overestimated the snow albedo feedback, largely due to the overestimation of surface albedo change between snow-covered and snow-free surface in these models. Full article
(This article belongs to the Special Issue Earth Observations for Addressing Global Challenges)
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Open AccessArticle Reducing the Effect of the Endmembers’ Spectral Variability by Selecting the Optimal Spectral Bands
Remote Sens. 2017, 9(9), 884; doi:10.3390/rs9090884
Received: 6 June 2017 / Revised: 19 August 2017 / Accepted: 23 August 2017 / Published: 25 August 2017
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Abstract
Variable environmental conditions cause different spectral responses of scene endmembers. Ignoring these variations affects the accuracy of fractional abundances obtained from linear spectral unmixing. On the other hand, the correlation between the bands of hyperspectral data is not considered by conventional methods developed
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Variable environmental conditions cause different spectral responses of scene endmembers. Ignoring these variations affects the accuracy of fractional abundances obtained from linear spectral unmixing. On the other hand, the correlation between the bands of hyperspectral data is not considered by conventional methods developed for dealing with spectral variability. In this paper, a novel approach is proposed to simultaneously mitigate spectral variability and reduce correlation among different endmembers in hyperspectral datasets. The idea of the proposed method is to utilize the angular discrepancy of bands in the Prototype Space (PS), which is constructed using the endmembers of the image. Using the concepts of PS, in which each band is treated as a space point, we proposed a method to identify independent bands according to their angles. The proposed method comprised two main steps. In the first step, which aims to alleviate the spectral variability issue, image bands are prioritized based on their standard deviations computed over some sets of endmembers. Independent bands are then recognized in the prototype space, employing the angles between the prioritized bands. Finally, the unmixing process is done using the selected bands. In addition, the paper presents a technique to form a spectral library of endmembers’ variability (sets of endmembers). The proposed method extracts endmembers sets directly from the image data via a modified version of unsupervised spatial–spectral preprocessing. The performance of the proposed method was evaluated by five simulated images and three real hyperspectral datasets. The experiments show that the proposed method—using both groups of spectral variability reduction methods and independent band selection methods—produces better results compared to the conventional methods of each group. The improvement in the performance of the proposed method is observed in terms of more appropriate bands being selected and more accurate fractional abundance values being estimated. Full article
(This article belongs to the Special Issue Hyperspectral Imaging and Applications)
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Open AccessArticle Comparing Fuzzy Sets and Random Sets to Model the Uncertainty of Fuzzy Shorelines
Remote Sens. 2017, 9(9), 885; doi:10.3390/rs9090885
Received: 19 July 2017 / Revised: 17 August 2017 / Accepted: 21 August 2017 / Published: 25 August 2017
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Abstract
This paper addresses uncertainty modelling of shorelines by comparing fuzzy sets and random sets. Both methods quantify extensional uncertainty of shorelines extracted from remote sensing images. Two datasets were tested: pan-sharpened Pleiades with four bands (Pleiades) and pan-sharpened Pleiades stacked with
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This paper addresses uncertainty modelling of shorelines by comparing fuzzy sets and random sets. Both methods quantify extensional uncertainty of shorelines extracted from remote sensing images. Two datasets were tested: pan-sharpened Pleiades with four bands (Pleiades) and pan-sharpened Pleiades stacked with elevation data as the fifth band (Pleiades + DTM). Both fuzzy sets and random sets model the spatial extent of shoreline including its uncertainty. Fuzzy sets represent shorelines as a margin determined by upper and lower thresholds and their uncertainty as confusion indices. They do not consider randomness. Random sets fit the mixed Gaussian model to the image histogram. It represents shorelines as a transition zone between water and non-water. Their extensional uncertainty is assessed by the covering function. The results show that fuzzy sets and random sets resulted in shorelines that were closely similar. Kappa (κ) values were slightly different and McNemar’s test showed high p-values indicating a similar accuracy. Inclusion of the DTM (digital terrain model) improved the classification results, especially for roofs, inundated houses and inundated land. The shoreline model using Pleiades + DTM performed better than that of using Pleiades only, when using either fuzzy sets or random sets. It achieved κ values above 80%. Full article
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Open AccessArticle An Improved Tomography Approach Based on Adaptive Smoothing and Ground Meteorological Observations
Remote Sens. 2017, 9(9), 886; doi:10.3390/rs9090886
Received: 10 June 2017 / Revised: 20 July 2017 / Accepted: 23 August 2017 / Published: 25 August 2017
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Abstract
Using the Global Navigation Satellite System (GNSS) to sense three-dimensional water vapor (WV) has been intensively investigated. However, this technique still heavily relies on the a priori information. In this study, we propose an improved tomography approach based on adaptive Laplacian smoothing (ALS)
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Using the Global Navigation Satellite System (GNSS) to sense three-dimensional water vapor (WV) has been intensively investigated. However, this technique still heavily relies on the a priori information. In this study, we propose an improved tomography approach based on adaptive Laplacian smoothing (ALS) and ground meteorological observations. By using the proposed approach, the troposphere tomography is less dependent on a priori information and the ALS constraints match better with the actual situation than the constant constraints. Tomography experiments in Hong Kong during a heavy rainy period and a rainless period show that the ALS method gets superior results compared with the constant Laplacian smoothing (CLS) method. By validation with radiosonde and European Centre for Medium-Range Weather Forecasts (ECMWF) data, we found that the introduction of ground meteorological observations into tomography can solve the perennial problem of resolving the wet refractivity in the lower troposphere and thus significantly improve the tomography results. However, bad data quality and incompatibility of the ground meteorological observations may introduce errors into tomography results. Full article
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Open AccessArticle Sea Wind Measurement by Doppler Navigation System with X-Configured Beams in Rectilinear Flight
Remote Sens. 2017, 9(9), 887; doi:10.3390/rs9090887
Received: 7 June 2017 / Revised: 8 August 2017 / Accepted: 9 August 2017 / Published: 26 August 2017
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Abstract
We suggest a conceptual approach to the measurement of the near-surface wind vector over water using a Doppler navigation system, in addition to its standard navigation capabilities. We consider a Doppler navigation system with a track-stabilized antenna and x-configuration of its beams.
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We suggest a conceptual approach to the measurement of the near-surface wind vector over water using a Doppler navigation system, in addition to its standard navigation capabilities. We consider a Doppler navigation system with a track-stabilized antenna and x-configuration of its beams. For the measurement of the sea-surface wind, the system operates in the multi-beam scatterometer mode in rectilinear flight. The proposed conceptual design has been validated, and its accuracy for the wind vector measurement has been estimated using Monte Carlo computational simulations. Full article
(This article belongs to the Special Issue Instruments and Methods for Ocean Observation and Monitoring)
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Open AccessFeature PaperEditor’s ChoiceArticle The 2015 Surge of Hispar Glacier in the Karakoram
Remote Sens. 2017, 9(9), 888; doi:10.3390/rs9090888
Received: 14 June 2017 / Revised: 4 August 2017 / Accepted: 15 August 2017 / Published: 26 August 2017
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Abstract
The Karakoram mountain range is well known for its numerous surge-type glaciers of which several have recently surged or are still doing so. Analysis of multi-temporal satellite images and digital elevation models have revealed impressive details about the related changes (e.g., in glacier
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The Karakoram mountain range is well known for its numerous surge-type glaciers of which several have recently surged or are still doing so. Analysis of multi-temporal satellite images and digital elevation models have revealed impressive details about the related changes (e.g., in glacier length, surface elevation and flow velocities) and considerably expanded the database of known surge-type glaciers. One glacier that has so far only been reported as impacted by surging tributaries, rather than surging itself, is the 50 km long main trunk of Hispar Glacier in the Hunza catchment. We here present the evolution of flow velocities and surface features from its 2015/16 surge as revealed from a dense time series of SAR and optical images along with an analysis of historic satellite images. We observed maximum flow velocities of up to 14 m d−1 (5 km a−1) in spring 2015, sudden drops in summer velocities, a second increase in winter 2015/16 and a total advance of the surge front of about 6 km. During a few months the surge front velocity was much higher (about 90 m d−1) than the maximum flow velocity. We assume that one of its northern tributary glaciers, Yutmaru, initiated the surge at the end of summer 2014 and that the variability in flow velocities was driven by changes in the basal hydrologic regime (Alaska-type surge). We further provide evidence that Hispar Glacier has surged before (around 1960) over a distance of about 10 km so that it can also be regarded as a surge-type glacier. Full article
(This article belongs to the Special Issue Remote Sensing of Glaciers)
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Open AccessArticle Monitoring Rainfall Patterns in the Southern Amazon with PERSIANN-CDR Data: Long-Term Characteristics and Trends
Remote Sens. 2017, 9(9), 889; doi:10.3390/rs9090889
Received: 22 May 2017 / Revised: 17 August 2017 / Accepted: 22 August 2017 / Published: 27 August 2017
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Abstract
Satellite-derived estimates of precipitation are essential to compensate for missing rainfall measurements in regions where the homogeneous and continuous monitoring of rainfall remains challenging due to low density rain gauge networks. The Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks—Climate Data
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Satellite-derived estimates of precipitation are essential to compensate for missing rainfall measurements in regions where the homogeneous and continuous monitoring of rainfall remains challenging due to low density rain gauge networks. The Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks—Climate Data Record (PERSIANN-CDR) is a relatively new product (released in 2013) but that contains data since 1983, thus enabling long-term rainfall analysis. In this work, we used three decades (1983–2014) of PERSIANN-CDR daily rainfall data to characterize precipitation patterns in the southern part of the Amazon basin, which has been drastically impacted in recent decades by anthropogenic activities that exacerbate the spatio-temporal variability of rainfall regimes. We computed metrics for the rainy season (onset date, demise date and duration) on a pixel-to-pixel basis for each year in the time series. We identified significant trends toward a shortening of the rainy season in the southern Amazon, mainly linked to earlier demise dates. This work thus contributes to monitoring possible signs of climate change in the region and to assessing uncertainties in rainfall trends and their potential impacts on human activities and natural ecosystems. Full article
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Open AccessFeature PaperArticle Fusion of SAR, Optical Imagery and Airborne LiDAR for Surface Water Detection
Remote Sens. 2017, 9(9), 890; doi:10.3390/rs9090890
Received: 10 July 2017 / Revised: 21 August 2017 / Accepted: 22 August 2017 / Published: 28 August 2017
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Abstract
The detection and monitoring of surface water and its extent are critical for understanding floodwater hazards. Flooding and undermining caused by surface water flow can result in damage to critical infrastructure and changes in ecosystems. Along major transportation corridors, such as railways, even
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The detection and monitoring of surface water and its extent are critical for understanding floodwater hazards. Flooding and undermining caused by surface water flow can result in damage to critical infrastructure and changes in ecosystems. Along major transportation corridors, such as railways, even small bodies of water can pose significant hazards resulting in eroded or washed out tracks. In this study, heterogeneous data from synthetic aperture radar (SAR) satellite missions, optical satellite-based imagery and airborne light detection and ranging (LiDAR) were fused for surface water detection. Each dataset was independently classified for surface water and then fused classification models of the three datasets were created. A multi-level decision tree was developed to create an optimal water mask by minimizing the differences between models originating from single datasets. Results show a water classification uncertainty of 4–9% using the final fused models compared to 17–23% uncertainty using single polarization SAR. Of note is the use of a high resolution LiDAR digital elevation model (DEM) to remove shadow and layover effects in the SAR observations, which reduces overestimation of surface water with growing vegetation. Overall, the results highlight the advantages of fusing multiple heterogeneous remote sensing techniques to detect surface water in a predominantly natural landscape. Full article
(This article belongs to the Section Remote Sensing in Geology, Geomorphology and Hydrology)
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Open AccessArticle Linear and Non-Linear Trends for Seasonal NO2 and SO2 Concentrations in the Southern Hemisphere (2004−2016)
Remote Sens. 2017, 9(9), 891; doi:10.3390/rs9090891
Received: 30 June 2017 / Revised: 3 August 2017 / Accepted: 24 August 2017 / Published: 28 August 2017
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Abstract
In order to address the behaviour of nitrogen dioxide (NO2) and sulphur dioxide (SO2) in the context of a changing climate, linear and non-linear trends for the concentrations of these two trace gases were estimated over their seasonal standardised
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In order to address the behaviour of nitrogen dioxide (NO2) and sulphur dioxide (SO2) in the context of a changing climate, linear and non-linear trends for the concentrations of these two trace gases were estimated over their seasonal standardised variables in the Southern Hemisphere—between the Equator and 60° S—using data retrieved by the Ozone Monitoring Instrument, for the period 2004–2016. A rescaling was applied to the calculated linear trends so that they are expressed in Dobson units (DU) per decade. Separately, the existence of monotonic—not necessarily linear—trends was addressed by means of the Mann-Kendall test. Results indicate that the SO2 exhibits significant linear trends in the planetary boundary layer only; they are present in all the analysed seasons but just in a small number of grid cells that are generally located over the landmasses or close to them. The SO2 concentrations in the quarterly time series exhibit, on average, a linear trend that is just below 0.08 DU decade−1 when significant and not significant values are considered altogether, but this figure increases to 0.80 DU decade−1 when only the significant trends are included. On the other hand, an important number of pixels in the lower troposphere, the middle troposphere, and the lower stratosphere have significant monotonic upward or downward trends. As for the NO2, no significant linear trends were found either in the troposphere or in the stratosphere, yet monotonic upward and downward trends were observed in the former and latter layers, respectively. Unlike the linear trends, semi-linear and non-linear trends were seen over the continents and in remote regions over the oceans. This suggests that pollutants are transported away from their sources by large-scale circulation and redistributed hemispherically. The combination of regional meteorological phenomena with atmospheric chemistry was raised as a possible explanation for the observed trends. If extrapolated, these trends are in an overall contradiction with the projected emissions of both gases for the current century. Full article
(This article belongs to the Special Issue Remote Sensing of Atmospheric Pollution)
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Open AccessArticle Satellite Survey of Internal Waves in the Black and Caspian Seas
Remote Sens. 2017, 9(9), 892; doi:10.3390/rs9090892
Received: 30 June 2017 / Revised: 24 August 2017 / Accepted: 25 August 2017 / Published: 28 August 2017
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Abstract
The paper discusses the results of a study of short-period internal waves (IWs) in the Black and Caspian Seas from their surface manifestations in satellite imagery. Since tides are negligible in these seas, they can be considered non-tidal. Consequently, the main generation mechanism
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The paper discusses the results of a study of short-period internal waves (IWs) in the Black and Caspian Seas from their surface manifestations in satellite imagery. Since tides are negligible in these seas, they can be considered non-tidal. Consequently, the main generation mechanism of IWs in the ocean—interaction of barotropic tides with bathymetry—is irrelevant. A statistically significant survey of IW occurrences in various regions of the two seas is presented. Detailed maps of spatial distribution of surface manifestations of internal waves (SMIWs) are compiled. Factors facilitating generation of IWs are determined, and a comprehensive discussion of IW generation mechanisms is presented. In the eastern and western coastal zones of the Black Sea, where large rivers disembogue, intrusions of fresh water create hydrological fronts that are able to generate IWs. At the continental shelf edge, on the west and northwest of the Black Sea and near the Crimean Peninsula, IWs are generated primarily due to relaxation of coastal upwelling and inertial oscillations associated with hydrological fronts. In addition, IWs can be formed at sea fronts associated with the passage of cold eddies. In the Caspian Sea, seiches are the main source of the observed IWs. Full article
(This article belongs to the Special Issue Ocean Remote Sensing with Synthetic Aperture Radar)
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Open AccessArticle A Long-Term Vegetation Recovery Estimation for Mt. Jou-Jou Using Multi-Date SPOT 1, 2, and 4 Images
Remote Sens. 2017, 9(9), 893; doi:10.3390/rs9090893
Received: 25 June 2017 / Revised: 4 August 2017 / Accepted: 23 August 2017 / Published: 28 August 2017
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Abstract
Vegetation recovery monitoring is critical for assessing denudation areas after landslides have occurred. A long-term and broad area investigation using remote sensing techniques is an efficient and cost-effective approach incorporating the consideration of radiometric correction and seasonality variations across multi-date satellite images. This
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Vegetation recovery monitoring is critical for assessing denudation areas after landslides have occurred. A long-term and broad area investigation using remote sensing techniques is an efficient and cost-effective approach incorporating the consideration of radiometric correction and seasonality variations across multi-date satellite images. This paper investigates long-term vegetation recovery using 14 SPOT satellite images spanning from 1999 to 2011 over the landslide area of Mt. Jou-Jou in central Taiwan, which was caused by the Chi-Chi earthquake in 1999. The vegetation status was evaluated by the Normalized Difference Vegetation Index (NDVI) with radiometric correction between multi-date images based on pseudoinvariant features, and subsequently a vegetation recovery rate (VRR) model was empirically established after seasonality adjustment was performed on the multi-date NDVI images. An increasing tendency of the vegetation recovery in the landslide area of Mt. Jou-Jou appeared based on the NDVI value rising to 0.367 in March 2011 from −0.044 right after the catastrophic earthquake. The vegetation recovery rate with seasonality adjustment approached 81.5% for the total area and 81.3% for the landslide area through 12 years succession. The seasonality adjustment also enhanced the VRR model with a determination coefficient that increased from 0.883 to 0.916 for the landslide area and from 0.584 to 0.915 for the total area, highlighting the necessity of seasonality adjustment in multi-date vegetation observations using satellite images. Furthermore, the association between precipitation and NDVI was discussed, and the inverse relationship with the reoccurrence of high-intensity short-duration rainfall and yearly heavy rainfall was observed, in agreement with the on-site investigation. Full article
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Open AccessArticle Spatial Variability of L-Band Brightness Temperature during Freeze/Thaw Events over a Prairie Environment
Remote Sens. 2017, 9(9), 894; doi:10.3390/rs9090894
Received: 30 May 2017 / Revised: 10 August 2017 / Accepted: 22 August 2017 / Published: 29 August 2017
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Abstract
Passive microwave measurements from space are known to be sensitive to the freeze/thaw (F/T) state of the land surface. These measurements are at a coarse spatial resolution (~15–50 km) and the spatial variability of the microwave emissions within a pixel can have important
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Passive microwave measurements from space are known to be sensitive to the freeze/thaw (F/T) state of the land surface. These measurements are at a coarse spatial resolution (~15–50 km) and the spatial variability of the microwave emissions within a pixel can have important effects on the interpretation of the signal. An L-band ground-based microwave radiometer campaign was conducted in the Canadian Prairies during winter 2014–2015 to examine the spatial variability of surface emissions during frozen and thawed periods. Seven different sites within the Kenaston soil monitoring network were sampled five times between October 2014 and April 2015 with a mobile ground-based L-band radiometer system at approximately monthly intervals. The radiometer measurements showed that in a seemingly homogenous prairie landscape, the spatial variability of brightness temperature (TB) is non-negligible during both frozen and unfrozen soil conditions. Under frozen soil conditions, TB was negatively correlated with soil permittivity (εG). This correlation was related to soil moisture conditions before the main freezing event, showing that the soil ice volumetric content at least partly affects TB. However, because of the effect of snow on L-Band emission, the correlation between TB and εG decreased with snow accumulation. When compared to satellite measurements, the average TB of the seven plots were well correlated with the Soil Moisture Ocean Salinity (SMOS) TB with a root mean square difference of 8.1 K and consistent representation of the strong F/T signal (i.e., TB increases and decreases when soil freezing and thawing, respectively). This study allows better quantitative understanding of the spatial variability in L-Band emissions related to landscape F/T, and will help the calibration and validation of satellite-based F/T retrieval algorithms. Full article
(This article belongs to the Section Remote Sensing in Geology, Geomorphology and Hydrology)
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Open AccessArticle North Africa and Saudi Arabia Day/Night Sandstorm Survey (NASCube)
Remote Sens. 2017, 9(9), 896; doi:10.3390/rs9090896
Received: 7 July 2017 / Revised: 12 August 2017 / Accepted: 25 August 2017 / Published: 30 August 2017
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Abstract
The Meteosat Second Generation (MSG) geostationary platform equipped with the Spinning Enhanced Visible and Infrared Imager (SEVIRI) instrument provides observations of the Earth every 15 min since 2004. Based on those measurements, we present a new method called North African Sandstorm Survey (NASCube)
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The Meteosat Second Generation (MSG) geostationary platform equipped with the Spinning Enhanced Visible and Infrared Imager (SEVIRI) instrument provides observations of the Earth every 15 min since 2004. Based on those measurements, we present a new method called North African Sandstorm Survey (NASCube) to: (i) generate day/night remote sensing images in order to detect sandstorms over the Sahara and Saudi Arabia; and (ii) estimate day and night aerosol optical depth (AOD). This paper presents a method to create true color day and night images from the SEVIRI instrument level 1.5 products and the complete operational data processing system to detect sandstorms and quantify the AOD over the desert areas of North Africa and Saudi Arabia. The designed retrieval algorithms are essentially based on the use of artificial neural networks (ANN), which seems to be well suited to this issue. Our methods are validated against two different datasets, namely the Deep Blue NASA moderate-resolution imaging spectroradiometer (MODIS) product and AErosol RObotic NETwork (AERONET) acquisitions located in desert areas. It is shown that NASCube products deliver better estimations for high AOD (>0.2) over land areas than Deep Blue products. The open-public web platform will help researchers to identify, quantify and retrieve the impact of sandstorms over desert regions. Full article
(This article belongs to the Special Issue Aerosol Remote Sensing)
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Open AccessArticle Characteristics of Aerosol Types in Beijing and the Associations with Air Pollution from 2004 to 2015
Remote Sens. 2017, 9(9), 898; doi:10.3390/rs9090898
Received: 27 July 2017 / Revised: 25 August 2017 / Accepted: 28 August 2017 / Published: 30 August 2017
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Abstract
With the fast development of the economy and expansion, a large number of people have concentrated in Beijing over the past few decades, leading to the result that Beijing has become home to one of the most complex mixtures of aerosol types in
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With the fast development of the economy and expansion, a large number of people have concentrated in Beijing over the past few decades, leading to the result that Beijing has become home to one of the most complex mixtures of aerosol types in the world. The various aerosol types play different roles in the determination of global climate change, visibility, and human health. However, to the best of our knowledge, research has rarely analyzed the correlation between aerosol types and air quality index (AQI) in Beijing (urban and suburban) over a long-term series of observations. Therefore, in this study, we aim to identify and discuss the different aerosol types and AQI in Beijing from 2004 to 2015. The aerosol types are classified into six categories: dust, mixed, highly-absorbing, moderately-absorbing, slightly-absorbing, and scattering by a multiple clustering method with the fine mode fraction (FMF) and single scattering albedo (SSA) data of retrievals from the global Aerosol Robotic Network (AERONET) sun photometer sites. The AQI levels: are good (0–50); moderate (51–100); unhealthy for sensitive groups (101–150); unhealthy (151–200); very unhealthy (201–300); and hazardous (>300). The results show that a significant FMF variability occurred among different seasons in Beijing, with maximum values present in spring and minimum values in winter. The SSA values exhibit variation, with small fluctuations from season to season. In the case of BJ station, the scattering aerosols are more frequent in summer (39%) and less in winter (1%), while the coarse particles (dust) are more frequent in spring (18%) and less in autumn (6%). In contrast, the absorbing aerosols (especially slightly-absorbing) are more frequent in summer (35%) and winter (15%). However, the mixed aerosol types are more frequent in spring (38%) and less in summer (8%). There is a similar seasonal variation in XH. In the past 12 years, the slightly-absorbing aerosol type in Beijing has increased by approximately 14%, which is believed to be due to the rapid development of industrial cities. In addition, comparing the urban and suburban regions, the slightly-absorbing aerosol type is the dominant aerosol type in both areas. Furthermore, to identify the dominant aerosol types which lead to air pollution, a related analysis was carried out by analyzing different aerosol types and the relationship between aerosol types and AQI. The results indicate that the air pollution was strongly correlated to slightly-absorbing aerosols, in which the percentage of slightly-absorbing aerosols was about 49% during the hazardous days in 2013–2015, and the correlation between AQI and aerosol types is also strong (R2 = 0.76 and 0.97, in Beijing and Xianghe). Full article
(This article belongs to the Special Issue Aerosol Remote Sensing)
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Open AccessArticle Dynamic Post-Earthquake Image Segmentation with an Adaptive Spectral-Spatial Descriptor
Remote Sens. 2017, 9(9), 899; doi:10.3390/rs9090899
Received: 16 June 2017 / Revised: 15 August 2017 / Accepted: 28 August 2017 / Published: 30 August 2017
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Abstract
The region merging algorithm is a widely used segmentation technique for very high resolution (VHR) remote sensing images. However, the segmentation of post-earthquake VHR images is more difficult due to the complexity of these images, especially high intra-class and low inter-class variability among
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The region merging algorithm is a widely used segmentation technique for very high resolution (VHR) remote sensing images. However, the segmentation of post-earthquake VHR images is more difficult due to the complexity of these images, especially high intra-class and low inter-class variability among damage objects. Herein two key issues must be resolved: the first is to find an appropriate descriptor to measure the similarity of two adjacent regions since they exhibit high complexity among the diverse damage objects, such as landslides, debris flow, and collapsed buildings. The other is how to solve over-segmentation and under-segmentation problems, which are commonly encountered with conventional merging strategies due to their strong dependence on local information. To tackle these two issues, an adaptive dynamic region merging approach (ADRM) is introduced, which combines an adaptive spectral-spatial descriptor and a dynamic merging strategy to adapt to the changes of merging regions for successfully detecting objects scattered globally in a post-earthquake image. In the new descriptor, the spectral similarity and spatial similarity of any two adjacent regions are automatically combined to measure their similarity. Accordingly, the new descriptor offers adaptive semantic descriptions for geo-objects and thus is capable of characterizing different damage objects. Besides, in the dynamic region merging strategy, the adaptive spectral-spatial descriptor is embedded in the defined testing order and combined with graph models to construct a dynamic merging strategy. The new strategy can find the global optimal merging order and ensures that the most similar regions are merged at first. With combination of the two strategies, ADRM can identify spatially scattered objects and alleviates the phenomenon of over-segmentation and under-segmentation. The performance of ADRM has been evaluated by comparing with four state-of-the-art segmentation methods, including the fractal net evolution approach (FNEA, as implemented in the eCognition software, Trimble Inc., Westminster, CO, USA), the J-value segmentation (JSEG) method, the graph-based segmentation (GSEG) method, and the statistical region merging (SRM) approach. The experiments were conducted on six VHR subarea images captured by RGB sensors mounted on aerial platforms, which were acquired after the 2008 Wenchuan Ms 8.0 earthquake. Quantitative and qualitative assessments demonstrated that the proposed method offers high feasibility and improved accuracy in the segmentation of post-earthquake VHR aerial images. Full article
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Open AccessFeature PaperArticle The Impact of Mapping Error on the Performance of Upscaling Agricultural Maps
Remote Sens. 2017, 9(9), 901; doi:10.3390/rs9090901
Received: 19 July 2017 / Revised: 8 August 2017 / Accepted: 29 August 2017 / Published: 31 August 2017
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Abstract
Aggregation methods are the most common way of upscaling land cover maps. To analyze the impact of land cover mapping error on upscaling agricultural maps, we utilized the Cropland Data Layer (CDL) data with corresponding confidence level data and simulated eight levels of
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Aggregation methods are the most common way of upscaling land cover maps. To analyze the impact of land cover mapping error on upscaling agricultural maps, we utilized the Cropland Data Layer (CDL) data with corresponding confidence level data and simulated eight levels of error using a Monte Carlo simulation for two Agriculture Statistic Districts (ASD) in the U.S.A. The results of the simulations were used as base maps for subsequent upscaling, utilizing the majority rule based aggregation method. The results show that increasing error level resulted in higher proportional errors for each crop in both study areas. As a result of increasing error level, landscape characteristics of the base map also changed greatly resulting in higher proportional error in the upscaled maps. Furthermore, the proportional error is sensitive to the crop area proportion in the base map and decreases as the crop proportion increases. These findings indicate that three factors, the error level of the thematic map, the change in landscape pattern/characteristics of the thematic map, and the objective of the project, should be considered before performing any upscaling. The first two factors can be estimated by using pre-existing land cover maps with relatively high accuracy. The third factor is dependent on the project requirements (e.g., landscape characteristics, proportions of cover types, and use of the upscaled map). Overall, improving our understanding of the impacts of land cover mapping error is necessary to the proper design for upscaling and obtaining the optimal upscaled map. Full article
(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)
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Open AccessEditor’s ChoiceArticle A Global Analysis of Sentinel-2A, Sentinel-2B and Landsat-8 Data Revisit Intervals and Implications for Terrestrial Monitoring
Remote Sens. 2017, 9(9), 902; doi:10.3390/rs9090902
Received: 29 July 2017 / Revised: 22 August 2017 / Accepted: 22 August 2017 / Published: 31 August 2017
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Abstract
Combination of different satellite data will provide increased opportunities for more frequent cloud-free surface observations due to variable cloud cover at the different satellite overpass times and dates. Satellite data from the polar-orbiting Landsat-8 (launched 2013), Sentinel-2A (launched 2015) and Sentinel-2B (launched 2017)
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Combination of different satellite data will provide increased opportunities for more frequent cloud-free surface observations due to variable cloud cover at the different satellite overpass times and dates. Satellite data from the polar-orbiting Landsat-8 (launched 2013), Sentinel-2A (launched 2015) and Sentinel-2B (launched 2017) sensors offer 10 m to 30 m multi-spectral global coverage. Together, they advance the virtual constellation paradigm for mid-resolution land imaging. In this study, a global analysis of Landsat-8, Sentinel-2A and Sentinel-2B metadata obtained from the committee on Earth Observation Satellite (CEOS) Visualization Environment (COVE) tool for 2016 is presented. A global equal area projection grid defined every 0.05° is used considering each sensor and combined together. Histograms, maps and global summary statistics of the temporal revisit intervals (minimum, mean, and maximum) and the number of observations are reported. The temporal observation frequency improvements afforded by sensor combination are shown to be significant. In particular, considering Landsat-8, Sentinel-2A, and Sentinel-2B together will provide a global median average revisit interval of 2.9 days, and, over a year, a global median minimum revisit interval of 14 min (±1 min) and maximum revisit interval of 7.0 days. Full article
(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)
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Open AccessEditor’s ChoiceArticle Lidar Aboveground Vegetation Biomass Estimates in Shrublands: Prediction, Uncertainties and Application to Coarser Scales
Remote Sens. 2017, 9(9), 903; doi:10.3390/rs9090903
Received: 4 June 2017 / Revised: 11 August 2017 / Accepted: 29 August 2017 / Published: 31 August 2017
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Abstract
Our study objectives were to model the aboveground biomass in a xeric shrub-steppe landscape with airborne light detection and ranging (Lidar) and explore the uncertainty associated with the models we created. We incorporated vegetation vertical structure information obtained from Lidar with ground-measured biomass
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Our study objectives were to model the aboveground biomass in a xeric shrub-steppe landscape with airborne light detection and ranging (Lidar) and explore the uncertainty associated with the models we created. We incorporated vegetation vertical structure information obtained from Lidar with ground-measured biomass data, allowing us to scale shrub biomass from small field sites (1 m subplots and 1 ha plots) to a larger landscape. A series of airborne Lidar-derived vegetation metrics were trained and linked with the field-measured biomass in Random Forests (RF) regression models. A Stepwise Multiple Regression (SMR) model was also explored as a comparison. Our results demonstrated that the important predictors from Lidar-derived metrics had a strong correlation with field-measured biomass in the RF regression models with a pseudo R2 of 0.76 and RMSE of 125 g/m2 for shrub biomass and a pseudo R2 of 0.74 and RMSE of 141 g/m2 for total biomass, and a weak correlation with field-measured herbaceous biomass. The SMR results were similar but slightly better than RF, explaining 77–79% of the variance, with RMSE ranging from 120 to 129 g/m2 for shrub and total biomass, respectively. We further explored the computational efficiency and relative accuracies of using point cloud and raster Lidar metrics at different resolutions (1 m to 1 ha). Metrics derived from the Lidar point cloud processing led to improved biomass estimates at nearly all resolutions in comparison to raster-derived Lidar metrics. Only at 1 m were the results from the point cloud and raster products nearly equivalent. The best Lidar prediction models of biomass at the plot-level (1 ha) were achieved when Lidar metrics were derived from an average of fine resolution (1 m) metrics to minimize boundary effects and to smooth variability. Overall, both RF and SMR methods explained more than 74% of the variance in biomass, with the most important Lidar variables being associated with vegetation structure and statistical measures of this structure (e.g., standard deviation of height was a strong predictor of biomass). Using our model results, we developed spatially-explicit Lidar estimates of total and shrub biomass across our study site in the Great Basin, U.S.A., for monitoring and planning in this imperiled ecosystem. Full article
(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)
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Open AccessArticle A Small UAV Based Multi-Temporal Image Registration for Dynamic Agricultural Terrace Monitoring
Remote Sens. 2017, 9(9), 904; doi:10.3390/rs9090904
Received: 15 July 2017 / Revised: 21 August 2017 / Accepted: 21 August 2017 / Published: 31 August 2017
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Abstract
Terraces are the major land-use type of agriculture and support the main agricultural production in southeast and southwest China. However, due to smallholder farming, complex terrains, natural disasters and illegal land occupations, a light-weight and low cost dynamic monitoring of agricultural terraces has
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Terraces are the major land-use type of agriculture and support the main agricultural production in southeast and southwest China. However, due to smallholder farming, complex terrains, natural disasters and illegal land occupations, a light-weight and low cost dynamic monitoring of agricultural terraces has become a serious concern for smallholder production systems in the above area. In this work, we propose a small unmanned aerial vehicle (UAV) based multi-temporal image registration method that plays an important role in transforming multi-temporal images into one coordinate system and determines the effectiveness of the subsequent change detection for dynamic agricultural terrace monitoring. The proposed method consists of four steps: (i) guided image filtering based agricultural terrace image preprocessing, (ii) texture and geometric structure features extraction and combination, (iii) multi-feature guided point set registration, and (iv) feature points based image registration. We evaluated the performance of the proposed method by 20 pairs of aerial images captured from Longji and Yunhe terraces, China using a small UAV (the DJI Phantom 4 Pro), and also compared against four state-of-the-art methods where our method shows the best alignments in most cases. Full article
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Open AccessArticle Using Annual Landsat Time Series for the Detection of Dry Forest Degradation Processes in South-Central Angola
Remote Sens. 2017, 9(9), 905; doi:10.3390/rs9090905
Received: 29 June 2017 / Revised: 24 August 2017 / Accepted: 28 August 2017 / Published: 31 August 2017
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Abstract
Dry tropical forests undergo massive conversion and degradation processes. This also holds true for the extensive Miombo forests that cover large parts of Southern Africa. While the largest proportional area can be found in Angola, the country still struggles with food shortages, insufficient
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Dry tropical forests undergo massive conversion and degradation processes. This also holds true for the extensive Miombo forests that cover large parts of Southern Africa. While the largest proportional area can be found in Angola, the country still struggles with food shortages, insufficient medical and educational supplies, as well as the ongoing reconstruction of infrastructure after 27 years of civil war. Especially in rural areas, the local population is therefore still heavily dependent on the consumption of natural resources, as well as subsistence agriculture. This leads, on one hand, to large areas of Miombo forests being converted for cultivation purposes, but on the other hand, to degradation processes due to the selective use of forest resources. While forest conversion in south-central rural Angola has already been quantitatively described, information about forest degradation is not yet available. This is due to the history of conflicts and the therewith connected research difficulties, as well as the remote location of this area. We apply an annual time series approach using Landsat data in south-central Angola not only to assess the current degradation status of the Miombo forests, but also to derive past developments reaching back to times of armed conflicts. We use the Disturbance Index based on tasseled cap transformation to exclude external influences like inter-annual variation of rainfall. Based on this time series, linear regression is calculated for forest areas unaffected by conversion, but also for the pre-conversion period of those areas that were used for cultivation purposes during the observation time. Metrics derived from linear regression are used to classify the study area according to their dominant modification processes. We compare our results to MODIS latent integral trends and to further products to derive information on underlying drivers. Around 13% of the Miombo forests are affected by degradation processes, especially along streets, in villages, and close to existing agriculture. However, areas in presumably remote and dense forest areas are also affected to a significant extent. A comparison with MODIS derived fire ignition data shows that they are most likely affected by recurring fires and less by selective timber extraction. We confirm that areas that are used for agriculture are more heavily disturbed by selective use beforehand than those that remain unaffected by conversion. The results can be substantiated by the MODIS latent integral trends and we also show that due to extent and location, the assessment of forest conversion is most likely not sufficient to provide good estimates for the loss of natural resources. Full article
(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)
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Open AccessArticle Evaluation of Sentinel-2A Satellite Imagery for Mapping Cotton Root Rot
Remote Sens. 2017, 9(9), 906; doi:10.3390/rs9090906
Received: 30 June 2017 / Revised: 12 August 2017 / Accepted: 28 August 2017 / Published: 31 August 2017
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Abstract
Cotton (Gossypium hirsutum L.) is an economically important crop that is highly susceptible to cotton root rot. Remote sensing technology provides a useful and effective means for detecting and mapping cotton root rot infestations in cotton fields. This research assessed the potential
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Cotton (Gossypium hirsutum L.) is an economically important crop that is highly susceptible to cotton root rot. Remote sensing technology provides a useful and effective means for detecting and mapping cotton root rot infestations in cotton fields. This research assessed the potential of 10-m Sentinel-2A satellite imagery for cotton root rot detection and compared it with airborne multispectral imagery using unsupervised classification at both field and regional levels. Accuracy assessment showed that the classification maps from the Sentinel-2A imagery had an overall accuracy of 94.1% for field subset images and 91.2% for the whole image, compared with the airborne image classification results. However, some small cotton root rot areas were undetectable and some non-infested areas within large root rot areas were incorrectly classified as infested due to the images’ coarse spatial resolution. Classification maps based on field subset Sentinel-2A images missed 16.6% of the infested areas and the classification map based on the whole Sentinel-2A image for the study area omitted 19.7% of the infested areas. These results demonstrate that freely-available Sentinel-2 imagery can be used as an alternative data source for identifying cotton root rot and creating prescription maps for site-specific management of the disease. Full article
(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)
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Open AccessArticle Transfer Learning with Deep Convolutional Neural Network for SAR Target Classification with Limited Labeled Data
Remote Sens. 2017, 9(9), 907; doi:10.3390/rs9090907
Received: 7 July 2017 / Revised: 20 August 2017 / Accepted: 28 August 2017 / Published: 31 August 2017
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Abstract
Tremendous progress has been made in object recognition with deep convolutional neural networks (CNNs), thanks to the availability of large-scale annotated dataset. With the ability of learning highly hierarchical image feature extractors, deep CNNs are also expected to solve the Synthetic Aperture Radar
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Tremendous progress has been made in object recognition with deep convolutional neural networks (CNNs), thanks to the availability of large-scale annotated dataset. With the ability of learning highly hierarchical image feature extractors, deep CNNs are also expected to solve the Synthetic Aperture Radar (SAR) target classification problems. However, the limited labeled SAR target data becomes a handicap to train a deep CNN. To solve this problem, we propose a transfer learning based method, making knowledge learned from sufficient unlabeled SAR scene images transferrable to labeled SAR target data. We design an assembled CNN architecture consisting of a classification pathway and a reconstruction pathway, together with a feedback bypass additionally. Instead of training a deep network with limited dataset from scratch, a large number of unlabeled SAR scene images are used to train the reconstruction pathway with stacked convolutional auto-encoders (SCAE) at first. Then, these pre-trained convolutional layers are reused to transfer knowledge to SAR target classification tasks, with feedback bypass introducing the reconstruction loss simultaneously. The experimental results demonstrate that transfer learning leads to a better performance in the case of scarce labeled training data and the additional feedback bypass with reconstruction loss helps to boost the capability of classification pathway. Full article
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Open AccessArticle Improving Satellite-Driven PM2.5 Models with VIIRS Nighttime Light Data in the Beijing–Tianjin–Hebei Region, China
Remote Sens. 2017, 9(9), 908; doi:10.3390/rs9090908
Received: 11 August 2017 / Revised: 28 August 2017 / Accepted: 30 August 2017 / Published: 31 August 2017
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Abstract
Previous studies have estimated ground-level concentrations of particulate matter 2.5 (PM2.5) using satellite-derived aerosol optical depth (AOD) in conjunction with meteorological and land use variables. However, the impacts of urbanization on air pollution for predicting PM2.5 are seldom considered. Nighttime
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Previous studies have estimated ground-level concentrations of particulate matter 2.5 (PM2.5) using satellite-derived aerosol optical depth (AOD) in conjunction with meteorological and land use variables. However, the impacts of urbanization on air pollution for predicting PM2.5 are seldom considered. Nighttime light (NTL) data, acquired with the Visible Infrared Imaging Radiometer Suite (VIIRS) aboard the Suomi National Polar-orbiting Partnership (S-NPP) satellite, could be useful for predictions because they have been shown to be good indicators of the urbanization and human activity that can affect PM2.5 concentrations. This study investigated the potential of incorporating VIIRS NTL data in statistical models for PM2.5 concentration predictions. We developed a mixed-effects model to derive daily estimations of surface PM2.5 levels in the Beijing–Tianjin–Hebei region using 3 km resolution satellite AOD and VIIRS NTL data. The results showed the addition of NTL information could improve the performance of the PM2.5 prediction model. The NTL data revealed additional details for predication results in areas with low PM2.5 concentrations and greater apparent seasonal variation due to the seasonal variability of human activity. Comparison showed prediction accuracy was improved more substantially for the model using NTL directly than for the model using the vegetation-adjusted NTL urban index that included NTL. Our findings indicate that VIIRS NTL data have potential for predicting PM2.5 and that they could constitute a useful supplemental data source for estimating ground-level PM2.5 distributions. Full article
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Open AccessArticle Diurnal Cycle in Atmospheric Water over Switzerland
Remote Sens. 2017, 9(9), 909; doi:10.3390/rs9090909
Received: 21 June 2017 / Revised: 23 August 2017 / Accepted: 30 August 2017 / Published: 31 August 2017
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Abstract
The TROpospheric WAter RAdiometer (TROWARA) is a ground-based microwave radiometer with an additional infrared channel observing atmospheric water parameters in Bern, Switzerland. TROWARA measures with nearly all-weather capability during day- and nighttime with a high temporal resolution (about 10 s). Using the almost
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The TROpospheric WAter RAdiometer (TROWARA) is a ground-based microwave radiometer with an additional infrared channel observing atmospheric water parameters in Bern, Switzerland. TROWARA measures with nearly all-weather capability during day- and nighttime with a high temporal resolution (about 10 s). Using the almost complete data set from 2004 to 2016, we derive and discuss the diurnal cycles in cloud fraction (CF), integrated liquid water (ILW) and integrated water vapour (IWV) for different seasons and the annual mean. The amplitude of the mean diurnal cycle in IWV is 0.41 kg/m 2 . The sub-daily minimum of IWV is at 10:00 LT while the maximum of IWV occurs at 19:00 LT. The relative amplitudes of the diurnal cycle in ILW are up to 25% in October, November and January, which is possibly related to a breaking up of the cloud layer at 10:00 LT. The minimum of ILW occurs at 12:00 LT, which is due to cloud solar absorption. In case of cloud fraction of liquid water clouds, maximal values of +10% are reached at 07:00 LT and then a decrease starts towards the minimum of −10%, which is reached at 16:00 LT in autumn. This breakup of cloud layers in the late morning and early afternoon hours seems to be typical for the weather in Bern in autumn. Finally, the diurnal cycle in rain fraction is analysed, which shows an increase of a few percent in the late afternoon hours during summer. Full article
(This article belongs to the Special Issue Earth Observations for Addressing Global Challenges)
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Open AccessArticle Spatial-Spectral-Emissivity Land-Cover Classification Fusing Visible and Thermal Infrared Hyperspectral Imagery
Remote Sens. 2017, 9(9), 910; doi:10.3390/rs9090910
Received: 16 July 2017 / Revised: 25 August 2017 / Accepted: 30 August 2017 / Published: 5 September 2017
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Abstract
High-resolution visible remote sensing imagery and thermal infrared hyperspectral imagery are potential data sources for land-cover classification. In this paper, in order to make full use of these two types of imagery, a spatial-spectral-emissivity land-cover classification method based on the fusion of visible
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High-resolution visible remote sensing imagery and thermal infrared hyperspectral imagery are potential data sources for land-cover classification. In this paper, in order to make full use of these two types of imagery, a spatial-spectral-emissivity land-cover classification method based on the fusion of visible and thermal infrared hyperspectral imagery is proposed, namely, SSECRF (spatial-spectral-emissivity land-cover classification based on conditional random fields). A spectral-spatial feature set is constructed considering the spectral variability and spatial-contextual information, to extract features from the high-resolution visible image. The emissivity is retrieved from the thermal infrared hyperspectral image by the FLAASH-IR algorithm and firstly introduced in the fusion of the visible and thermal infrared hyperspectral imagery; also, the emissivity is utilized in SSECRF, which contributes to improving the identification of man-made objects, such as roads and roofs. To complete the land-cover classification, the spatial-spectral feature set and emissivity are integrated by constructing the SSECRF energy function, which relates labels to the spatial-spectral-emissivity features, to obtain an improved classification result. The classification map performs a good result in distinguishing some certain classes, such as roads and bare soil. Also, the experimental results show that the proposed SSECRF algorithm efficiently integrates the spatial, spectral, and emissivity information and performs better than the traditional methods using raw radiance from thermal infrared hyperspectral imagery data, with a kappa value of 0.9137. Full article
(This article belongs to the Section Remote Sensing Image Processing)
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Open AccessArticle Response of Canopy Solar-Induced Chlorophyll Fluorescence to the Absorbed Photosynthetically Active Radiation Absorbed by Chlorophyll
Remote Sens. 2017, 9(9), 911; doi:10.3390/rs9090911
Received: 21 June 2017 / Revised: 28 August 2017 / Accepted: 28 August 2017 / Published: 1 September 2017
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Abstract
Solar-induced chlorophyll fluorescence (SIF), which can be used as a novel proxy for estimating gross primary production (GPP), can be effectively retrieved using ground-based, airborne and satellite measurements. Absorbed photosynthetically active radiation (APAR) is the key bridge linking SIF and GPP. Remotely sensed
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Solar-induced chlorophyll fluorescence (SIF), which can be used as a novel proxy for estimating gross primary production (GPP), can be effectively retrieved using ground-based, airborne and satellite measurements. Absorbed photosynthetically active radiation (APAR) is the key bridge linking SIF and GPP. Remotely sensed SIF at the canopy level ( S I F c a n o p y ) is only a part of the total SIF emission at the photosystem level. An SIF-based model for GPP estimation would be strongly influenced by the fraction of SIF photons escaping from the canopy ( f e s c ). Understanding the response of S I F c a n o p y to the absorbed photosynthetically active radiation absorbed by chlorophyll ( A P A R c h l ) is a key step in estimating GPP but, as yet, this has not been well explored. In this study, we aim to investigate the relationship between remotely sensed S I F c a n o p y and A P A R c h l based on simulations made by the Soil Canopy Observation Photosynthesis Energy fluxes (SCOPE) model and field measurements. First, the ratio of the fraction of the absorbed photosynthetically active radiation absorbed by chlorophyll ( fPAR c h l ) to the fraction of absorbed photosynthetically active radiation absorbed by green leaves ( fPAR g r e e n ) is investigated using a dataset simulated by the SCOPE model. The results give a mean value of 0.722 for Cab at 5 μg cm−2, 0.761 for Cab at 10 μg cm−2 and 0.795 for other Cab content (ranging from 0.71 to 0.81). The response of S I F c a n o p y to A P A R c h l is then explored using simulations corresponding to different biochemical and biophysical conditions and it is found that S I F c a n o p y is well correlated with A P A R c h l . At the O2-A band, for a given plant type, the relationship between S I F c a n o p y and A P A R c h l can be approximately expressed by a linear statistical model even for different values of the leaf area index (LAI) and chlorophyll content, whereas the relationship varies with the LAI and chlorophyll content at the O2-B band. Finally, the response of S I F c a n o p y to A P A R c h l for different leaf angle distribution (LAD) functions is investigated using field observations and simulations; the results show that f e s c is larger for a planophile canopy structure. The values of the ratio of S I F c a n o p y to A P A R c h l are 0.0092 ± 0.0020 , 0.0076 ± 0.0036 and 0.0052 ± 0.0004 μm−1 sr−1 for planophile vegetables/crops, planophile grass and spherical winter wheat, respectively, at the O2-A band. At the O2-B band, the ratios are 0.0063 ± 0.0014 , 0.0049 ± 0.0030 and 0.0033 ± 0.0004 μm−1 sr−1, respectively. The values of this ratio derived from observations agree with simulations, giving values of 0.0055 ± 0.0002 and 0.0068 ± 0.0001 μm−1 sr−1 at the O2-A band and 0.0032 ± 0.0002 and 0.0047 ± 0.0001 μm−1 sr−1 at the O2-B band for spherical and planophile canopies, respectively. Therefore, both the simulations and observations confirm that the relationship between S I F c a n o p y and APAR c h l is species-specific and affected by biochemical components and canopy structure, especially at the O2-B band. It is also very important to correct for reabsorption and scattering of the SIF radiative transfer from the photosystem to the canopy level before the remotely sensed S I F c a n o p y is linked to the GPP. Full article
(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)
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Open AccessArticle Alike Scene Retrieval from Land-Cover Products Based on the Label Co-Occurrence Matrix (LCM)
Remote Sens. 2017, 9(9), 912; doi:10.3390/rs9090912
Received: 20 June 2017 / Revised: 21 August 2017 / Accepted: 28 August 2017 / Published: 2 September 2017
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Abstract
The management and application of remotely sensed data has become much more difficult due to the dramatically growing volume of remotely sensed imagery. To address this issue, content-based image retrieval (CBIR) has been applied to remote sensing image retrieval for information mining. As
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The management and application of remotely sensed data has become much more difficult due to the dramatically growing volume of remotely sensed imagery. To address this issue, content-based image retrieval (CBIR) has been applied to remote sensing image retrieval for information mining. As a consequence of the growing volume of remotely sensed imagery, the number of different types of image-derived products (such as land use/land cover (LULC) databases) is also increasing rapidly. Nevertheless, only a few studies have addressed the exploration and information mining of these products. In this letter, for the sake of making the most use of the LULC map, we propose an approach for the retrieval of alike scenes from it. Based on the proposed approach, we design a content-based map retrieval (CBMR) system for LULC. The main contributions of our work are listed below. Firstly, the proposed system can allow the user to select a region of interest as the reference scene with variable shape and size. In contrast, in the traditional CBIR/CBMR systems, the region of interest is usually of a fixed size, which is equal to the size of the analysis window for extracting features. In addition, the user can acquire various retrieval results by specifying the corresponding parameters. Finally, by combining the signatures in the base signature library, the user can acquire the retrieval result faster. Full article
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Open AccessArticle Mapping of the Invasive Species Hakea sericea Using Unmanned Aerial Vehicle (UAV) and WorldView-2 Imagery and an Object-Oriented Approach
Remote Sens. 2017, 9(9), 913; doi:10.3390/rs9090913
Received: 17 July 2017 / Revised: 23 August 2017 / Accepted: 30 August 2017 / Published: 1 September 2017
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Abstract
Invasive plants are non-native species that establish and spread in their new location, generating a negative impact on the local ecosystem and representing one of the most important causes of the extinction of local species. The first step for the control of invasion
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Invasive plants are non-native species that establish and spread in their new location, generating a negative impact on the local ecosystem and representing one of the most important causes of the extinction of local species. The first step for the control of invasion should be directed at understanding and quantification of their location, extent and evolution, namely the monitoring of the phenomenon. In this sense, the techniques and methods of remote sensing can be very useful. The aim of this paper was to identify and quantify the areas covered by the invasive plant Hakea sericea using high spatial resolution images obtained from aerial platforms (Unmanned Aerial Vehicle: UAV/drone) and orbital platforms (WorldView-2: WV2), following an object-oriented image analysis approach. The results showed that both data were suitable. WV2reached user and producer accuracies greater than 93% (Estimate of Kappa (KHAT): 0.95), while the classifications with the UAV orthophotographs obtained accuracies higher than 75% (KHAT: 0.51). The most suitable data to use as input consisted of using all of the multispectral bands that were available for each image. The addition of textural features did not increase the accuracies for the Hakea sericea class, but it did for the general classification using WV2. Full article
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Open AccessArticle Land Surface Phenology and Seasonality Using Cool Earthlight in Croplands of Eastern Africa and the Linkages to Crop Production
Remote Sens. 2017, 9(9), 914; doi:10.3390/rs9090914
Received: 30 June 2017 / Revised: 22 August 2017 / Accepted: 29 August 2017 / Published: 1 September 2017
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Abstract
Across Eastern Africa, croplands cover 45 million ha. The regional economy is heavily dependent on small holder traditional rain-fed peasant agriculture (up to 90%), which is vulnerable to extreme weather events such as drought and floods that leads to food insecurity. Agricultural production
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Across Eastern Africa, croplands cover 45 million ha. The regional economy is heavily dependent on small holder traditional rain-fed peasant agriculture (up to 90%), which is vulnerable to extreme weather events such as drought and floods that leads to food insecurity. Agricultural production in the region is moisture limited. Weather station data are scarce and access is limited, while optical satellite data are obscured by heavy clouds limiting their value to study cropland dynamics. Here, we characterized cropland dynamics in Eastern Africa for 2003–2015 using precipitation data from Tropical Rainfall Measuring Mission (TRMM) and a passive microwave dataset of land surface variables that blends data from the Advanced Microwave Scanning Radiometer (AMSR) on the Earth Observing System (AMSR-E) from 2002 to 2011 with data from AMSR2 from 2012 to 2015 with a Chinese microwave radiometer to fill the gap. These time series were analyzed in terms of either cumulative precipitable water vapor-days (CVDs) or cumulative actual evapotranspiration-days (CETaDs), rather than as days of the year. Time series of the land surface variables displayed unimodal seasonality at study sites in Ethiopia and South Sudan, in contrast to bimodality at sites in Tanzania. Interannual moisture variability was at its highest at the beginning of the growing season affecting planting times of crops, while it was lowest at the time of peak moisture. Actual evapotranspiration (ETa) from the simple surface energy balance (SSEB) model was sensitive to track both unimodal and bimodal rainfall patterns. ETa as a function of CETaD was better fitted by a quadratic model (r2 > 0.8) than precipitable water vapor was by CVDs (r2 > 0.6). Moisture time to peak (MTP) for the land surface variables showed strong, logical correspondence among variables (r2 > 0.73). Land surface parameters responded to El Niño-Southern Oscillation and the Indian Ocean Dipole forcings. Area under the curve of the diel difference in vegetation optical depth showed correspondence to crop production and yield data collected by local offices, but not to the data reported at the national scale. A long-term seasonal Mann–Kendall rainfall trend showed a significant decrease for Ethiopia, while the decrement was not significant for Tanzania. While there is significant potential for passive microwave data to augment cropland status and food security monitoring efforts in the region, more research is needed before these data can be used in an operational environment. Full article
(This article belongs to the Special Issue Ecophysiological Remote Sensing)
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Open AccessFeature PaperArticle Diurnal Air Temperature Modeling Based on the Land Surface Temperature
Remote Sens. 2017, 9(9), 915; doi:10.3390/rs9090915
Received: 27 July 2017 / Revised: 29 August 2017 / Accepted: 30 August 2017 / Published: 1 September 2017
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Abstract
The air temperature is an essential variable in many applications related to Earth science. Sporadic spatial distribution of weather stations causes a low spatial resolution of measured air temperatures. This study focused on modeling the air diurnal temperature cycle (DTC) based on the
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The air temperature is an essential variable in many applications related to Earth science. Sporadic spatial distribution of weather stations causes a low spatial resolution of measured air temperatures. This study focused on modeling the air diurnal temperature cycle (DTC) based on the land surface temperature (LST) DTC. The air DTC model parameters were estimated from LST DTC model parameters by a regression analysis. Here, the LST obtained from the INSAT-3D geostationary satellite and the air temperature extracted from weather stations were used within the time frame of 4 March 2015 to 22 May 2017 across Iran. Constant parameters of the air DTC model for each weather station were estimated based on an experimental approach over the time period. Results showed these parameters decrease as elevation increases. The mean absolute error (MAE) and the root mean square error (RMSE) for three hours sampling were calculated. The MAE and RMSE ranges were between [0.1, 4] °C and [0.1, 3.3] °C, respectively. Additionally, 95% of MAEs and RMSEs were less than 2.9 °C and 2.4 °C values, correspondingly. The range of the mean values of MAEs and RMSEs for a three-hour sampling time were [−0.29, 0.6] °C and [2, 2.11] °C. The DTC model results showed a meaningful statistical fitting in both air DTCs modeled from LST and weather station-based DTCs. The variability of mean error and RMSE in different land covers and elevation classes were also investigated. In spite of the complex behavior of the environmental variables in the study area, the model error bar did not show significantly biased estimations for various classes. Therefore, the developed model was less sensitive to variations of land covers and elevation changes. It can be conclude that the coefficients of regression between LST and air DTC could model properly the environmental factors. Full article
(This article belongs to the Special Issue Remote Sensing of Land-Atmosphere Interactions)
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Open AccessFeature PaperArticle “Kill Two Birds with One Stone”: Urban Tree Species Classification Using Bi-Temporal Pléiades Images to Study Nesting Preferences of an Invasive Bird
Remote Sens. 2017, 9(9), 916; doi:10.3390/rs9090916
Received: 19 June 2017 / Revised: 17 July 2017 / Accepted: 1 September 2017 / Published: 1 September 2017
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Abstract
This study presents the results of object-based classifications assessing the potential of bi-temporal Pléiades images for mapping broadleaf and coniferous tree species potentially used by the ring-necked parakeet Psittacula krameri for nesting in the urban area of Marseille, France. The first classification was
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This study presents the results of object-based classifications assessing the potential of bi-temporal Pléiades images for mapping broadleaf and coniferous tree species potentially used by the ring-necked parakeet Psittacula krameri for nesting in the urban area of Marseille, France. The first classification was performed based solely on a summer Pléiades image (acquired on 28 July 2015) and the second classification based on bi-temporal Pléiades images (a spring image acquired on 24 March 2016 and the summer image). An ensemble of spectral and textural features was extracted from both images and two machine-learning classifiers were used, Random Forest (RF) and Support Vector Machine (SVM). Regardless of the classifiers, model results suggest that classification based on bi-temporal Pléiades images produces more satisfying results, with an overall accuracy 11.5–13.9% higher than classification using the single-date image. Textural and spectral features extracted from the blue and the NIR bands were consistently ranked among the most important features. Regardless of the classification scheme, RF slightly outperforms SVM. RF classification using bi-temporal Pléiades images allows identifying 98.5% of the tree species used by the ring-necked parakeet for nesting, highlighting the promising value of remote sensing techniques to assess the ecological requirements of fauna in urban areas. Full article
(This article belongs to the Special Issue Remote Sensing of Urban Ecology)
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Open AccessArticle A Multiscale Deeply Described Correlatons-Based Model for Land-Use Scene Classification
Remote Sens. 2017, 9(9), 917; doi:10.3390/rs9090917
Received: 25 May 2017 / Revised: 16 August 2017 / Accepted: 30 August 2017 / Published: 2 September 2017
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Abstract
Research efforts in land-use scene classification is growing alongside the popular use of High-Resolution Satellite (HRS) images. The complex background and multiple land-cover classes or objects, however, make the classification tasks difficult and challenging. This article presents a Multiscale Deeply Described Correlatons (MDDC)-based
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Research efforts in land-use scene classification is growing alongside the popular use of High-Resolution Satellite (HRS) images. The complex background and multiple land-cover classes or objects, however, make the classification tasks difficult and challenging. This article presents a Multiscale Deeply Described Correlatons (MDDC)-based algorithm which incorporates appearance and spatial information jointly at multiple scales for land-use scene classification to tackle these problems. Specifically, we introduce a convolutional neural network to learn and characterize the dense convolutional descriptors at different scales. The resulting multiscale descriptors are used to generate visual words by a general mapping strategy and produce multiscale correlograms of visual words. Then, an adaptive vector quantization of multiscale correlograms, termed multiscale correlatons, are applied to encode the spatial arrangement of visual words at different scales. Experiments with two publicly available land-use scene datasets demonstrate that our MDDC model is discriminative for efficient representation of land-use scene images, and achieves competitive classification results with state-of-the-art methods. Full article
(This article belongs to the Special Issue Remote Sensing Big Data: Theory, Methods and Applications)
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Open AccessArticle Scale- and Region-Dependence in Landscape-PM2.5 Correlation: Implications for Urban Planning
Remote Sens. 2017, 9(9), 918; doi:10.3390/rs9090918
Received: 16 July 2017 / Revised: 15 August 2017 / Accepted: 31 August 2017 / Published: 2 September 2017
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Abstract
Under rapid urbanization, many cities in China suffer from serious fine particulate matter (PM2.5) pollution. As the emission sources or adsorption sinks, land use and the corresponding landscape pattern unavoidably affect the concentration. However, the correlation varies with different regions and
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Under rapid urbanization, many cities in China suffer from serious fine particulate matter (PM2.5) pollution. As the emission sources or adsorption sinks, land use and the corresponding landscape pattern unavoidably affect the concentration. However, the correlation varies with different regions and scales, leaving a significant gap for urban planning. This study clarifies the correlation with the aid of in situ and satellite-based spatial datasets over six urban agglomerations in China. Two coverage and four landscape indices are adopted to represent land use and landscape pattern. Specifically, the coverage indices include the area ratios of forest (F_PLAND) and built-up areas (C_PLAND). The landscape indices refer to the perimeter-area fractal dimension index (PAFRAC), interspersion and juxtaposition index (IJI), aggregation index (AI), Shannon’s diversity index (SHDI). Then, the correlation between PM2.5 concentration with the selected indices are evaluated from supporting the potential urban planning. Results show that the correlations are weak with the in situ PM2.5 concentration, which are significant with the regional value. It means that land use coverage and landscape pattern affect PM2.5 at a relatively large scale. Furthermore, regional PM2.5 concentration negatively correlate to F_PLAND and positively to C_PLAND (significance at p < 0.05), indicating that forest helps to improve air quality, while built-up areas worsen the pollution. Finally, the heterogeneous landscape presents positive correlation to the regional PM2.5 concentration in most regions, except for the urban agglomeration with highly-developed urban (i.e., the Jing-Jin-Ji and Chengdu-Chongqing urban agglomerations). It suggests that centralized urbanization would be helpful for PM2.5 pollution controlling by reducing the emission sources in most regions. Based on the results, the potential urban planning is proposed for controlling PM2.5 pollution for each urban agglomeration. Full article
(This article belongs to the Special Issue Earth Observation in Planning for Sustainable Urban Development)
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Open AccessArticle Diversification of Land Surface Temperature Change under Urban Landscape Renewal: A Case Study in the Main City of Shenzhen, China
Remote Sens. 2017, 9(9), 919; doi:10.3390/rs9090919
Received: 21 June 2017 / Revised: 8 August 2017 / Accepted: 31 August 2017 / Published: 2 September 2017
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Abstract
Unprecedented rapid urbanization in China during the past several decades has been accompanied by extensive urban landscape renewal, which has increased the urban thermal environmental risk. However, landscape change is a sufficient but not necessary condition for land surface temperature (LST) variation. Many
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Unprecedented rapid urbanization in China during the past several decades has been accompanied by extensive urban landscape renewal, which has increased the urban thermal environmental risk. However, landscape change is a sufficient but not necessary condition for land surface temperature (LST) variation. Many studies have merely highlighted the correlation between landscape pattern and LST, while neglecting to comprehensively present the spatiotemporal diversification of LST change under urban landscape renewal. Taking the main city of Shenzhen as a case study area, this study tracked the landscape renewal and LST variation for the period 1987–2015 using 49 Landsat images. A decision tree algorithm suitable for fast landscape type interpretation was developed to map the landscape renewal. Analytical tools that identified hot-cold spots, the gravity center, and transect of LST movement were adopted to identify LST changes. The results showed that the spatial variation of LST was not completely consistent with landscape change. The transformation from Green landscape to Grey landscape usually increased the LST within a median of 0.2 °C, while the reverse transformation did not obviously decrease the LST (the median was nearly 0 °C). The median of LST change from Blue landscape to Grey landscape was 1.0 °C, corresponding to 0.5 °C in the reverse transformation. The imbalance of LST change between the loss and gain of Green or Blue landscape indicates the importance of protecting natural space, where the benefits in terms of temperature mitigation cannot be completely substituted by reverse transformation. Full article
(This article belongs to the Special Issue Remote Sensing of Urban Ecology)
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Open AccessArticle Understanding How Low-Level Clouds and Fog Modify the Diurnal Cycle of Orographic Precipitation Using In Situ and Satellite Observations
Remote Sens. 2017, 9(9), 920; doi:10.3390/rs9090920
Received: 26 July 2017 / Revised: 25 August 2017 / Accepted: 30 August 2017 / Published: 2 September 2017
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Abstract
Satellite orographic precipitation estimates exhibit large errors with space-time structure tied to landform. Observations in the Southern Appalachian Mountains (SAM) suggest that low-level clouds and fog (LLCF) amplify mid-day rainfall via seeder-feeder interactions (SFI) at both high and low elevations. Here, a rainfall
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Satellite orographic precipitation estimates exhibit large errors with space-time structure tied to landform. Observations in the Southern Appalachian Mountains (SAM) suggest that low-level clouds and fog (LLCF) amplify mid-day rainfall via seeder-feeder interactions (SFI) at both high and low elevations. Here, a rainfall microphysics model constrained by fog observations was used first to reveal that fast SFI (2–5 min time-scales) modify the rain drop size distributions by increasing coalescence efficiency among small drops (<0.7 mm diameter), whereas competition between coalescence and filament-only breakup dominates for larger drops (3–5 mm diameter). The net result is a large increase in the number concentrations of intermediate size raindrops in the 0.7–3 mm range and up to a ten-fold increase in rainfall intensity. Next, a 10-year climatology of satellite observations was developed to map LLCF. Combined estimates from CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations) and CloudSat products reveal persistent shallower cloud base heights at high elevations enveloping the terrain. The regional cloud top height climatology derived from the MODIS (Moderate Resolution Imaging Spectroradiometer) shows high-frequency daytime LLCF over mountain ridges in the warm season shifting to river valleys at nighttime. In fall and winter, LLCF patterns define a cloud-shadow region east of the continental divide, consistent with downwind rain-shadow effects. Optical and microphysical properties from collocated MODIS and ground ceilometers indicate small values of vertically integrated cloud water path (CWP < 100 g/m2), optical thickness (COT < 15), and particle effective radius (CER) < 15 μm near cloud top whereas surface observed CER ~25 μm changes to ~150 μm and higher prior to the mid-day rainfall. The vertical stratification of LLCF microphysics and SFI at low levels pose a significant challenge to satellite-based remote sensing in complex topography. Full article
(This article belongs to the Section Atmosphere Remote Sensing)
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Open AccessArticle Self-Learning Based Land-Cover Classification Using Sequential Class Patterns from Past Land-Cover Maps
Remote Sens. 2017, 9(9), 921; doi:10.3390/rs9090921
Received: 11 July 2017 / Revised: 25 August 2017 / Accepted: 1 September 2017 / Published: 2 September 2017
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Abstract
To improve the accuracy of classification with a small amount of training data, this paper presents a self-learning approach that defines class labels from sequential patterns using a series of past land-cover maps. By stacking past land-cover maps, unique sequence rule information from
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To improve the accuracy of classification with a small amount of training data, this paper presents a self-learning approach that defines class labels from sequential patterns using a series of past land-cover maps. By stacking past land-cover maps, unique sequence rule information from sequential change patterns of land-covers is first generated, and a rule-based class label image is then prepared for a given time. After the most informative pixels with high uncertainty are selected from the initial classification, rule-based class labels are assigned to the selected pixels. These newly labeled pixels are added to training data, which then undergo an iterative classification process until a stopping criterion is reached. Time-series MODIS NDVI data sets and cropland data layers (CDLs) from the past five years are used for the classification of various crop types in Kansas. From the experiment results, it is found that once the rule-based labels are derived from past CDLs, the labeled informative pixels could be properly defined without analyst intervention. Regardless of different combinations of past CDLs, adding these labeled informative pixels to training data increased classification accuracy and the maximum improvement of 8.34 percentage points in overall accuracy was achieved when using three CDLs, compared to the initial classification result using a small amount of training data. Using more than three consecutive CDLs showed slightly better classification accuracy than when using two CDLs (minimum and maximum increases were 1.56 and 2.82 percentage points, respectively). From a practical viewpoint, using three or four CDLs was the best choice for this study area. Based on these experiment results, the presented approach could be applied effectively to areas with insufficient training data but access to past land-cover maps. However, further consideration should be given to select the optimal number of past land-cover maps and reduce the impact of errors of rule-based labels. Full article
(This article belongs to the Special Issue Earth Observations for Addressing Global Challenges)
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Open AccessArticle Monitoring the Response of Roads and Railways to Seasonal Soil Movement with Persistent Scatterers Interferometry over Six UK Sites
Remote Sens. 2017, 9(9), 922; doi:10.3390/rs9090922
Received: 8 June 2017 / Revised: 9 August 2017 / Accepted: 1 September 2017 / Published: 4 September 2017
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Abstract
Road and rail networks provide critical support for society, yet they can be degraded by seasonal soil movements. Currently, few transport network operators monitor small-scale soil movement, but understanding the conditions contributing to infrastructure failure can improve network resilience. Persistent Scatterers Interferometry (PSI)
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Road and rail networks provide critical support for society, yet they can be degraded by seasonal soil movements. Currently, few transport network operators monitor small-scale soil movement, but understanding the conditions contributing to infrastructure failure can improve network resilience. Persistent Scatterers Interferometry (PSI) is a remote sensing technique offering the potential for near real-time ground movement monitoring over wide areas. This study tests the use of PSI for monitoring the response of major roads, minor roads, and railways to ground movement across six study sites in England, using Sentinel 1 data in VV polarisation in ascending orbit. Some soils are more stable than others—a national soil map was used to quantify the relationships between infrastructure movement and major soil groups. Vertical movement of transport infrastructure is a function of engineering design, soil properties, and traffic loading. Roads and railways built on soil groups prone to seasonal water-logging (Ground-water Gley soils, Surface-water Gley soils, Pelosols, and Brown soils) demonstrated seasonal subsidence and heave, associated with an increased risk of infrastructure degradation. Roads and railways over Podzolic soils demonstrated relative stability. Railways on Peat soils exhibited the most extreme continual subsidence of up to 7.5 mm year−1. Limitations of this study include the short observation period (~13 months, due to satellite data availability) and the regional scale of the soil map—mapping units contain multiple soil types with different ground movement potentials. Future use of a higher resolution soil map over a longer period will advance this research. Nevertheless, this study demonstrates the viability of PSI as a technique for measuring both seasonal soil-related ground movement and the associated impacts on road and rail infrastructure. Full article
(This article belongs to the Section Remote Sensing in Geology, Geomorphology and Hydrology)
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Open AccessArticle Assessing Lodging Severity over an Experimental Maize (Zea mays L.) Field Using UAS Images
Remote Sens. 2017, 9(9), 923; doi:10.3390/rs9090923
Received: 5 June 2017 / Revised: 30 August 2017 / Accepted: 31 August 2017 / Published: 4 September 2017
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Abstract
Lodging has been recognized as one of the major destructive factors for crop quality and yield, resulting in an increasing need to develop cost-efficient and accurate methods for detecting crop lodging in a routine manner. Using structure-from-motion (SfM) and novel geospatial computing algorithms,
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Lodging has been recognized as one of the major destructive factors for crop quality and yield, resulting in an increasing need to develop cost-efficient and accurate methods for detecting crop lodging in a routine manner. Using structure-from-motion (SfM) and novel geospatial computing algorithms, this study investigated the potential of high resolution imaging with unmanned aircraft system (UAS) technology for detecting and assessing lodging severity over an experimental maize field at the Texas A&M AgriLife Research and Extension Center in Corpus Christi, Texas, during the 2016 growing season. The method was proposed to not only detect the occurrence of lodging at the field scale, but also to quantitatively estimate the number of lodged plants and the lodging rate within individual rows. Nadir-view images of the field trial were taken by multiple UAS platforms equipped with consumer grade red, green, and blue (RGB), and near-infrared (NIR) cameras on a routine basis, enabling a timely observation of the plant growth until harvesting. Models of canopy structure were reconstructed via an SfM photogrammetric workflow. The UAS-estimated maize height was characterized by polygons developed and expanded from individual row centerlines, and produced reliable accuracy when compared against field measures of height obtained from multiple dates. The proposed method then segmented the individual maize rows into multiple grid cells and determined the lodging severity based on the height percentiles against preset thresholds within individual grid cells. From the analysis derived from this method, the UAS-based lodging results were generally comparable in accuracy to those measured by a human data collector on the ground, measuring the number of lodging plants (R2 = 0.48) and the lodging rate (R2 = 0.50) on a per-row basis. The results also displayed a negative relationship of ground-measured yield with UAS-estimated and ground-measured lodging rate. Full article
(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)
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Open AccessArticle Hyperspectral Image Classification Based on Semi-Supervised Rotation Forest
Remote Sens. 2017, 9(9), 924; doi:10.3390/rs9090924
Received: 20 July 2017 / Revised: 23 August 2017 / Accepted: 1 September 2017 / Published: 6 September 2017
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Abstract
Ensemble learning is widely used to combine varieties of weak learners in order to generate a relatively stronger learner by reducing either the bias or the variance of the individual learners. Rotation forest (RoF), combining feature extraction and classifier ensembles, has been successfully
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Ensemble learning is widely used to combine varieties of weak learners in order to generate a relatively stronger learner by reducing either the bias or the variance of the individual learners. Rotation forest (RoF), combining feature extraction and classifier ensembles, has been successfully applied to hyperspectral (HS) image classification by promoting the diversity of base classifiers since last decade. Generally, RoF uses principal component analysis (PCA) as the rotation tool, which is commonly acknowledged as an unsupervised feature extraction method, and does not consider the discriminative information about classes. Sometimes, however, it turns out to be sub-optimal for classification tasks. Therefore, in this paper, we propose an improved RoF algorithm, in which semi-supervised local discriminant analysis is used as the feature rotation tool. The proposed algorithm, named semi-supervised rotation forest (SSRoF), aims to take advantage of both the discriminative information and local structural information provided by the limited labeled and massive unlabeled samples, thus providing better class separability for subsequent classifications. In order to promote the diversity of features, we also adjust the semi-supervised local discriminant analysis into a weighted form, which can balance the contributions of labeled and unlabeled samples. Experiments on several hyperspectral images demonstrate the effectiveness of our proposed algorithm compared with several state-of-the-art ensemble learning approaches. Full article
(This article belongs to the Special Issue Hyperspectral Imaging and Applications)
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Open AccessArticle Environmental Variability and Oceanographic Dynamics of the Central and Southern Coastal Zone of Sonora in the Gulf of California
Remote Sens. 2017, 9(9), 925; doi:10.3390/rs9090925
Received: 28 June 2017 / Revised: 21 August 2017 / Accepted: 21 August 2017 / Published: 6 September 2017
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Abstract
This study analyzed monthly and inter-annual variability of mesoscale phenomena, including the El Niño Southern Oscillation (ENSO), the Pacific Decadal Oscillation (PDO) climate indexes and wind intensity considering their influence on sea surface temperature (SST) and chlorophyll a (Chl-a). These analyses were performed
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This study analyzed monthly and inter-annual variability of mesoscale phenomena, including the El Niño Southern Oscillation (ENSO), the Pacific Decadal Oscillation (PDO) climate indexes and wind intensity considering their influence on sea surface temperature (SST) and chlorophyll a (Chl-a). These analyses were performed to determine the effects, if any, of climate indexes and oceanographic and environmental variability on the central and southern coastal ecosystem of Sonora in the Gulf of California (GC). Monthly satellite images of SST (°C) and Chl-a concentration were used with a 1-km resolution for oceanographic and environmental description, as well as monthly data of the climate indexes and wind intensity from 2002–2015. Significant differences (p > 0.05) were observed while analyzing the monthly variability results of mesoscale phenomena, SST and Chl-a, where the greatest percentage of anti-cyclonic gyres and filaments was correlated with a greater Chl-a concentration in the area of study, low temperatures and, thus, greater productivity. Moreover, the greatest percentage of intrusion was correlated with the increase in temperature and cyclonic gyres and a strong decrease of Chl-a concentration values, causing oligotrophic conditions in the ecosystem and a decrease in upwelling and filament occurrence. As for the analysis of the interannual variability of mesoscales phenomena, SST, Chl-a and winds, the variability between years was not significant (p > 0.05), so no correlation was observed between variabilities or phenomena. The results of the monthly analyses of climate indexes, environmental variables and wind intensity did not show significant differences for the ENSO and PDO indexes (p > 0.05). Nonetheless, an important correlation could be observed between the months of negative anomalies of the ENSO with high Chl-a concentration values and intense winds, as well as with low SST values. The months with positive ENSO anomalies were correlated with high SST values, low Chl-a concentration and moderate winds. Significant inter-annual differences were observed for climate indexes where the years with high SST values were related to the greatest positive anomaly of ENSO, of which 2002 and 2009 stood out, characterized as moderate Niño years, and 2015 as a strong El Niño year. The years with the negative ENSO anomaly were related to the years of lower SST values, of which 2007–2008 and 2010–2011 stood out, characterized as moderate Niñas. Thus, variability associated with mesoscale oceanographic phenomena and seasonal and inter-annual variations of climate indexes had a great influence on the environmental conditions of the coastal ecosystem of Sonora in the Gulf of California. Full article
(This article belongs to the Special Issue Sea Surface Temperature Retrievals from Remote Sensing)
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Open AccessArticle Fast and Efficient Correction of Ground Moving Targets in a Synthetic Aperture Radar, Single-Look Complex Image
Remote Sens. 2017, 9(9), 926; doi:10.3390/rs9090926
Received: 1 August 2017 / Revised: 18 August 2017 / Accepted: 28 August 2017 / Published: 6 September 2017
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Abstract
Ground moving targets distort normally-focused synthetic aperture radar (SAR) images. Since most high-resolution SAR data providers only offer single-look complex (SLC) data rather than raw signals to general users, they need to apply a simple and efficient residual SAR focusing to SLC data
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Ground moving targets distort normally-focused synthetic aperture radar (SAR) images. Since most high-resolution SAR data providers only offer single-look complex (SLC) data rather than raw signals to general users, they need to apply a simple and efficient residual SAR focusing to SLC data containing moving targets. This paper presents an efficient and effective SAR residual focusing method that is practically applicable to SLC data. The residual Doppler spectrum of the moving target is derived from a general SAR configuration and normal SAR focusing. The processing steps are simple and straightforward, with a limited size of the processing window, e.g., 64 × 64. Application results using simulation data and actual TerraSAR-X SLC data with a speed-controlled vehicle demonstrate the effectiveness of the method, which particularly improves the −3 dB width, integrated sidelobe ratio, and symmetry of the reconstructed signals. In particular, the azimuthal symmetry becomes seriously distorted when the target speed is higher than 8 m/s (or 28.8 km/h), and the symmetry is well recovered by the proposed method. Full article
(This article belongs to the Special Issue Advances in SAR: Sensors, Methodologies, and Applications)
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Open AccessArticle SCOPE-Based Emulators for Fast Generation of Synthetic Canopy Reflectance and Sun-Induced Fluorescence Spectra
Remote Sens. 2017, 9(9), 927; doi:10.3390/rs9090927
Received: 24 July 2017 / Revised: 25 August 2017 / Accepted: 1 September 2017 / Published: 6 September 2017
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Abstract
Progress in advanced radiative transfer models (RTMs) led to an improved understanding of reflectance (R) and sun-induced chlorophyll fluorescence (SIF) emission throughout the leaf and canopy. Among advanced canopy RTMs that have been recently modified to deliver SIF spectral
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Progress in advanced radiative transfer models (RTMs) led to an improved understanding of reflectance (R) and sun-induced chlorophyll fluorescence (SIF) emission throughout the leaf and canopy. Among advanced canopy RTMs that have been recently modified to deliver SIF spectral outputs are the energy balance model SCOPE and the 3D models DART and FLIGHT. The downside of these RTMs is that they are computationally expensive, which makes them impractical in routine processing, such as scene generation and retrieval applications. To bypass their computational burden, a computationally effective technique has been proposed by only using a limited number of model runs, called emulation. The idea of emulation is approximating the original RTM by a surrogate machine learning model with low computation time. However, a concern is whether the emulator reaches sufficient accuracy. To this end, we analyzed key aspects of emulator development that may impact the precision of emulating SCOPE-like R and SIF spectra, being: (1) type of machine learning, (2) type of dimensionality reduction (DR) method, and (3) number of components and lookup table (LUT) size. The machine learning family of Gaussian processes regression and neural networks were found best suited to function as emulators. The classical principal component analysis (PCA) remains a robust DR method, but the number of components needs to be optimized depending on the complexity of the spectral data. Based on a small Latin hypercube sampling LUT of 500 samples (70% used for training) covering a selection of SCOPE input variables, the best-performing emulators can reconstruct any combination for the selected SCOPE input variables with relative errors along the spectral range below 2% for R and 4% for SIF. That is sufficient for a precise reconstruction for the large majority of possible combinations, and errors can be further reduced when increasing LUT size for training. As a proof of concept, we imported the best-performing emulators into a newly developed Automated Scene Generator Module (A-SGM) to generate a R and SIF synthetic scene of a vegetated surface. Using emulators as alternative of SCOPE reduced the processing time from the order of days to the order of minutes while preserving sufficient accuracy. Full article
(This article belongs to the Special Issue Recent Progress and Developments in Imaging Spectroscopy)
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Open AccessArticle Spatial and Temporal Variability in Winter Precipitation across the Western United States during the Satellite Era
Remote Sens. 2017, 9(9), 928; doi:10.3390/rs9090928
Received: 31 May 2017 / Revised: 2 August 2017 / Accepted: 31 August 2017 / Published: 7 September 2017
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Abstract
The western United States is known for its water shortages due to large seasonal and inter-annual variability of precipitation as well as increasing demand. Climate change will impact the availability of water in the western United States through the modification of precipitation characteristics.
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The western United States is known for its water shortages due to large seasonal and inter-annual variability of precipitation as well as increasing demand. Climate change will impact the availability of water in the western United States through the modification of precipitation characteristics. Satellite data presents the opportunity to study these changes at a fine, continuous spatial resolution—particularly in places where no traditional ground observations exist. Utilizing the Tropical Rainfall Measuring Mission (TRMM) 3B42 version 7 precipitation data, this study examines the spatio-temporal changes in precipitation characteristics over the western United States between 1998 and 2015, and their connections with the atmospheric total column water vapor and the El Niño Southern Oscillation during the winter season. The results show that precipitation frequency in the western United States has been decreasing in general, precipitation totals and mean daily intensity are increasing in northwestern United States, but decreasing in the southwest United States during the 18 years of the study time period. Additionally, results show a strong relationship between total column water vapor and the precipitation characteristics, specifically in the southwestern United States. Full article
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Open AccessArticle Detecting Drought-Induced Tree Mortality in Sierra Nevada Forests with Time Series of Satellite Data
Remote Sens. 2017, 9(9), 929; doi:10.3390/rs9090929
Received: 22 July 2017 / Revised: 15 August 2017 / Accepted: 31 August 2017 / Published: 8 September 2017
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Abstract
A five-year drought in California led to a significant increase in tree mortality in the Sierra Nevada forests from 2012 to 2016. Landscape level monitoring of forest health and tree dieback is critical for vegetation and disaster management strategies. We examined the capability
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A five-year drought in California led to a significant increase in tree mortality in the Sierra Nevada forests from 2012 to 2016. Landscape level monitoring of forest health and tree dieback is critical for vegetation and disaster management strategies. We examined the capability of multispectral imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS) in detecting and explaining the impacts of the recent severe drought in Sierra Nevada forests. Remote sensing metrics were developed to represent baseline forest health conditions and drought stress using time series of MODIS vegetation indices (VIs) and a water index. We used Random Forest algorithms, trained with forest aerial detection surveys data, to detect tree mortality based on the remote sensing metrics and topographical variables. Map estimates of tree mortality demonstrated that our two-stage Random Forest models were capable of detecting the spatial patterns and severity of tree mortality, with an overall producer’s accuracy of 96.3% for the classification Random Forest (CRF) and a RMSE of 7.19 dead trees per acre for the regression Random Forest (RRF). The overall omission errors of the CRF ranged from 19% for the severe mortality class to 27% for the low mortality class. Interpretations of the models revealed that forests with higher productivity preceding the onset of drought were more vulnerable to drought stress and, consequently, more likely to experience tree mortality. This method highlights the importance of incorporating baseline forest health data and measurements of drought stress in understanding forest response to severe drought. Full article
(This article belongs to the Section Forest Remote Sensing)
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Open AccessEditor’s ChoiceArticle Mapping Smallholder Yield Heterogeneity at Multiple Scales in Eastern Africa
Remote Sens. 2017, 9(9), 931; doi:10.3390/rs9090931
Received: 3 August 2017 / Revised: 1 September 2017 / Accepted: 4 September 2017 / Published: 8 September 2017
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Abstract
Accurate measurements of crop production in smallholder farming systems are critical to the understanding of yield constraints and, thus, setting the appropriate agronomic investments and policies for improving food security and reducing poverty. Nevertheless, mapping the yields of smallholder farms is challenging because
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Accurate measurements of crop production in smallholder farming systems are critical to the understanding of yield constraints and, thus, setting the appropriate agronomic investments and policies for improving food security and reducing poverty. Nevertheless, mapping the yields of smallholder farms is challenging because of factors such as small field sizes and heterogeneous landscapes. Recent advances in fine-resolution satellite sensors offer promise for monitoring and characterizing the production of smallholder farms. In this study, we investigated the utility of different sensors, including the commercial Skysat and RapidEye satellites and the publicly accessible Sentinel-2, for tracking smallholder maize yield variation throughout a ~40,000 km2 western Kenya region. We tested the potential of two types of multiple regression models for predicting yield: (i) a “calibrated model”, which required ground-measured yield and weather data for calibration, and (ii) an “uncalibrated model”, which used a process-based crop model to generate daily vegetation index and end-of-season biomass and/or yield as pseudo training samples. Model performance was evaluated at the field, division, and district scales using a combination of farmer surveys and crop cuts across thousands of smallholder plots in western Kenya. Results show that the “calibrated” approach captured a significant fraction (R2 between 0.3 and 0.6) of yield variations at aggregated administrative units (e.g., districts and divisions), while the “uncalibrated” approach performed only slightly worse. For both approaches, we found that predictions using the MERIS Terrestrial Chlorophyll Index (MTCI), which included the red edge band available in RapidEye and Sentinel-2, were superior to those made using other commonly used vegetation indices. We also found that multiple refinements to the crop simulation procedures led to improvements in the “uncalibrated” approach. We identified the prevalence of small field sizes, intercropping management, and cloudy satellite images as major challenges to improve the model performance. Overall, this study suggested that high-resolution satellite imagery can be used to map yields of smallholder farming systems, and the methodology presented in this study could serve as a good foundation for other smallholder farming systems in the world. Full article
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Open AccessArticle Hindcasting and Forecasting of Surface Flow Fields through Assimilating High Frequency Remotely Sensing Radar Data
Remote Sens. 2017, 9(9), 932; doi:10.3390/rs9090932
Received: 4 July 2017 / Revised: 24 August 2017 / Accepted: 6 September 2017 / Published: 8 September 2017
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Abstract
In order to improve the forecasting ability of numerical models, a sequential data assimilation scheme, nudging, was applied to blend remotely sensing high-frequency (HF) radar surface currents with results from a three-dimensional numerical, EFDC (Environmental Fluid Dynamics Code) model. For the first time,
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In order to improve the forecasting ability of numerical models, a sequential data assimilation scheme, nudging, was applied to blend remotely sensing high-frequency (HF) radar surface currents with results from a three-dimensional numerical, EFDC (Environmental Fluid Dynamics Code) model. For the first time, this research presents the most appropriate nudging parameters, which were determined from sensitivity experiments. To examine the influence of data assimilation cycle lengths on forecasts and to extend forecasting improvements, the duration of data assimilation cycles was studied through assimilating linearly interpolated temporal radar data. Data assimilation nudging parameters have not been previously analyzed. Assimilation of HF radar measurements at each model computational timestep outperformed those assimilation models using longer data assimilation cycle lengths; root-mean-square error (RMSE) values of both surface velocity components during a 12 h model forecasting period indicated that surface flow fields were significantly improved when implementing nudging assimilation at each model computational timestep. The Data Assimilation Skill Score (DASS) technique was used to quantitatively evaluate forecast improvements. The averaged values of DASS over the data assimilation domain were 26% and 33% for east–west and north–south velocity components, respectively, over the half-day forecasting period. Correlation of Averaged Kinetic Energy (AKE) was improved by more than 10% in the best data assimilation model. Time series of velocity components and surface flow fields were presented to illustrate the improvement resulting from data assimilation application over time. Full article
(This article belongs to the Special Issue Radar Remote Sensing of Oceans and Coastal Areas)
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Open AccessArticle Performance Analysis of Ocean Surface Topography Altimetry by Ku-Band Near-Nadir Interferometric SAR
Remote Sens. 2017, 9(9), 933; doi:10.3390/rs9090933
Received: 14 August 2017 / Revised: 4 September 2017 / Accepted: 6 September 2017 / Published: 9 September 2017
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Abstract
Interferometric imaging radar altimeter (InIRA) is the first spaceborne Ku-band interferometric synthetic aperture radar (InSAR) which is specially designed for ocean surface topography altimetry. It is on the Tiangong II space laboratory, which was launched on 15 September 2016. Different from any other
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Interferometric imaging radar altimeter (InIRA) is the first spaceborne Ku-band interferometric synthetic aperture radar (InSAR) which is specially designed for ocean surface topography altimetry. It is on the Tiangong II space laboratory, which was launched on 15 September 2016. Different from any other spaceborne synthetic aperture radar (SAR), InIRA chooses a near-nadir incidence of 1°~8° in order to increase the altimetric precision and swath width. Limited by the size of the Tiangong II capsule, the baseline length of InIRA is only 2.3 m. However, benefitting from the low orbit, the signal-to-noise ratio of InIRA-acquired data is above 10 dB in most of the swath, which, to a certain extent, compensates for the short baseline deficiency. The altimetric precision is simulated based on the system parameters of InIRA. Results show that it is better than 7 cm on a 5-km grid and improves to 3 cm on a 10-km grid when the incidence is below 7.4°. The interferometric data of InIRA are processed to estimate the altimetric precision after a series of procedures (including image coregistration, flat-earth-phase removal, system parameters calibration and phase noise suppression). Results show that the estimated altimetric precision is close to but lower than the simulated precision among most of the swath. The intensity boundary phenomenon is first found between the near range and far range of the SAR images of InIRA. It can be explained by the modulation of ocean internal waves or oil slick, which smooths ocean surface roughness and causes the modulated area to appear either brighter or darker than its surroundings. This intensity boundary phenomenon indicates that the available swath of high altimetric precision will be narrower than expected. Full article
(This article belongs to the Special Issue Ocean Remote Sensing with Synthetic Aperture Radar)
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Open AccessArticle Spatio-Temporal Variability and Model Parameter Sensitivity Analysis of Ice Production in Ross Ice Shelf Polynya from 2003 to 2015
Remote Sens. 2017, 9(9), 934; doi:10.3390/rs9090934
Received: 4 May 2017 / Revised: 21 August 2017 / Accepted: 5 September 2017 / Published: 10 September 2017
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Abstract
Antarctic sea ice formation is strongly influenced by polynyas occurring in austral winter. The sea ice production of Ross Ice Shelf Polynya (RISP) located in the Ross Sea is the highest among coastal polynyas around the Southern Ocean. In this paper, daily sea
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Antarctic sea ice formation is strongly influenced by polynyas occurring in austral winter. The sea ice production of Ross Ice Shelf Polynya (RISP) located in the Ross Sea is the highest among coastal polynyas around the Southern Ocean. In this paper, daily sea ice production distribution of RISP in wintertime is estimated during 2003–2015, and the spatial and temporal trends of ice production are explored. Moreover, the sensitivity of the ice production model to parameterization is tested. To define the extent of RISP, this study uses sea ice concentration (SIC) maps mainly derived from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSRE) and the Advanced Microwave Scanning Radiometer 2 (AMSR2) by ARTIST (Arctic Radiation and Turbulence Interaction Study) sea ice algorithm (ASI) and constrains the ice production estimation to areas with SIC less than 75%. ERA-Interim reanalysis meteorological data are applied to a thermodynamic model to estimate daily ice production distribution between April and October during 2003–2015 for the open water fractions within the polynya. This estimation is conducted under the assumption that the meteorological data represent the reality. We further analyzed the spatial variability, monthly trend, and interannual trend for wintertime of the total RISP sea ice production. The results show that the ocean surface produces ice at a high rate within the distance of 20–30 km from the ice shelf front. In most high production areas, the ice production significantly increases. Some local regions show a contrarily significant decreasing trend as a result of ice shelf expansion and iceberg events. The monthly total RISP ice production ranges from 14 to 76 km3, showing substantial fluctuations in each month during 2003–2015. The seasonal variation of each year also shows substantial fluctuations. The wintertime total ice productions of RISP for 2003–2015 range 164–313 km3 with an average of 219 km3, showing no obvious temporal trend. More importantly, we conducted ten sensitivity tests, aiming to illustrate the sensitivity of the ice production model to parameterization. The output of the ice production model is sensitive to the value of the bulk transfer coefficients ( C s and C e ), latent heat of sea ice fusion ( L f ), and the threshold of SIC for RISP extent definition. C s and C e have the greatest influence, leading to a variation of average wintertime total RISP ice production results as high as 87.1%. A set of optimal local parameter values are recommended, including C s and C e = 0.002 and L f = 2.79 × 105 J·kg−1. L f is calculated by the salinity and temperature of sea ice, the value of which may lead to potential influence to the value of L f and the following ice production results. Full article
(This article belongs to the Special Issue Cryospheric Remote Sensing II)
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Open AccessArticle A Convolutional Neural Network-Based 3D Semantic Labeling Method for ALS Point Clouds
Remote Sens. 2017, 9(9), 936; doi:10.3390/rs9090936
Received: 1 August 2017 / Revised: 23 August 2017 / Accepted: 8 September 2017 / Published: 10 September 2017
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Abstract
3D semantic labeling is a fundamental task in airborne laser scanning (ALS) point clouds processing. The complexity of observed scenes and the irregularity of point distributions make this task quite challenging. Existing methods rely on a large number of features for the LiDAR
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3D semantic labeling is a fundamental task in airborne laser scanning (ALS) point clouds processing. The complexity of observed scenes and the irregularity of point distributions make this task quite challenging. Existing methods rely on a large number of features for the LiDAR points and the interaction of neighboring points, but cannot exploit the potential of them. In this paper, a convolutional neural network (CNN) based method that extracts the high-level representation of features is used. A point-based feature image-generation method is proposed that transforms the 3D neighborhood features of a point into a 2D image. First, for each point in the ALS data, the local geometric features, global geometric features and full-waveform features of its neighboring points within a window are extracted and transformed into an image. Then, the feature images are treated as the input of a CNN model for a 3D semantic labeling task. Finally, to allow performance comparisons with existing approaches, we evaluate our framework on the publicly available datasets provided by the International Society for Photogrammetry and Remote Sensing Working Groups II/4 (ISPRS WG II/4) benchmark tests on 3D labeling. The experiment results achieve 82.3% overall accuracy, which is the best among all considered methods. Full article
(This article belongs to the Special Issue Remote Sensing for 3D Urban Morphology)
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Open AccessArticle Integration of Information Theory, K-Means Cluster Analysis and the Logistic Regression Model for Landslide Susceptibility Mapping in the Three Gorges Area, China
Remote Sens. 2017, 9(9), 938; doi:10.3390/rs9090938
Received: 23 July 2017 / Revised: 30 August 2017 / Accepted: 6 September 2017 / Published: 11 September 2017
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Abstract
In this work, an effective framework for landslide susceptibility mapping (LSM) is presented by integrating information theory, K-means cluster analysis and statistical models. In general, landslides are triggered by many causative factors at a local scale, and the impact of these factors is
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In this work, an effective framework for landslide susceptibility mapping (LSM) is presented by integrating information theory, K-means cluster analysis and statistical models. In general, landslides are triggered by many causative factors at a local scale, and the impact of these factors is closely related to geographic locations and spatial neighborhoods. Based on these facts, the main idea of this research is to group a study area into several clusters to ensure that landslides in each cluster are affected by the same set of selected causative factors. Based on this idea, the proposed predictive method is constructed for accurate LSM at a regional scale by applying a statistical model to each cluster of the study area. Specifically, each causative factor is first classified by the natural breaks method with the optimal number of classes, which is determined by adopting Shannon’s entropy index. Then, a certainty factor (CF) for each class of factors is estimated. The selection of the causative factors for each cluster is determined based on the CF values of each factor. Furthermore, the logistic regression model is used as an example of statistical models in each cluster using the selected causative factors for landslide prediction. Finally, a global landslide susceptibility map is obtained by combining the regional maps. Experimental results based on both qualitative and quantitative analysis indicated that the proposed framework can achieve more accurate landslide susceptibility maps when compared to some existing methods, e.g., the proposed framework can achieve an overall prediction accuracy of 91.76%, which is 7.63–11.5% higher than those existing methods. Therefore, the local scale LSM technique is very promising for further improvement of landslide prediction. Full article
(This article belongs to the Special Issue Remote Sensing of Landslides)
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Open AccessArticle Feature Selection Solution with High Dimensionality and Low-Sample Size for Land Cover Classification in Object-Based Image Analysis
Remote Sens. 2017, 9(9), 939; doi:10.3390/rs9090939
Received: 18 July 2017 / Revised: 5 September 2017 / Accepted: 8 September 2017 / Published: 11 September 2017
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Abstract
Land cover information extraction through object-based image analysis (OBIA) has become an important trend in remote sensing, thanks to the increasing availability of high-resolution imagery. Segmented objects have a large number of features that cause high-dimension and low-sample size problems in the classification
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Land cover information extraction through object-based image analysis (OBIA) has become an important trend in remote sensing, thanks to the increasing availability of high-resolution imagery. Segmented objects have a large number of features that cause high-dimension and low-sample size problems in the classification process. In this study, on the basis of a partial least squares generalized linear regression (PLSGLR), we propose a group corrected PLSGLR, known as G-PLSGLR, that aims to reduce the redundancy of object features for land cover identifications. Using Gaofen-2 images, the area of interest was segmented and sampled to generate small sample-size training datasets with 51 object features. The features selected by G-PLSGLR were compared against a guided regularized random forest (GRRF) in metrics of reduction rate, feature redundancy, and accuracy assessment of classification. Three indicators of overall accuracy (OA), user’s accuracy (UA), and producer’s accuracy (PA) were applied for accuracy assessment in this paper. The result shows that the G-PLSGLR achieved a reduction rate of 9.27 with a feature redundancy of 0.29, and a value of OA 90.63%. The GRRF achieved a reduction rate of 1.61 with a feature redundancy of 0.42, and a value of OA 85.56%. The PA of each land cover category was more than 95% using features selected by G-PLSGLR, while the PA ranged from 77 to 96% using features selected by GRRF. The UA of G-PLSGLR-selected features ranged from 70 to 80% except for grass land and bare land, which achieved 10% higher UA than GRRF-selected features. The G-PLSGLR method we proposed has the advantages of a large reduction rate, low feature redundancy, and high classification performance, which can be applied in OBIA-based land cover classification. Full article
(This article belongs to the Section Remote Sensing Image Processing)
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Open AccessArticle Estimating Forest Structural Parameters Using Canopy Metrics Derived from Airborne LiDAR Data in Subtropical Forests
Remote Sens. 2017, 9(9), 940; doi:10.3390/rs9090940
Received: 14 June 2017 / Revised: 6 September 2017 / Accepted: 6 September 2017 / Published: 11 September 2017
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Abstract
Accurate and timely estimation of forest structural parameters plays a key role in the management of forest resources, as well as studies on the carbon cycle and biodiversity. Light Detection and Ranging (LiDAR) is a promising active remote sensing technology capable of providing
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Accurate and timely estimation of forest structural parameters plays a key role in the management of forest resources, as well as studies on the carbon cycle and biodiversity. Light Detection and Ranging (LiDAR) is a promising active remote sensing technology capable of providing highly accurate three dimensional and wall-to-wall forest structural characteristics. In this study, we evaluated the utility of standard metrics and canopy metrics derived from airborne LiDAR data for estimating plot-level forest structural parameters individually and in combination, over a subtropical forest in Yushan forest farm, southeastern China. Standard metrics, i.e., height-based and density-based metrics, and canopy metrics extracted from canopy vertical profiles, i.e., canopy volume profile (CVP), canopy height distribution (CHD), and foliage profile (FP), were extracted from LiDAR point clouds. Then the standard metrics and canopy metrics were used for estimating forest structural parameters individually and in combination by multiple regression models, including forest type-specific (coniferous forest, broad-leaved forest, mixed forest) models and general models. Additionally, the synergy of standard metrics and canopy metrics for estimating structural parameters was evaluated using field measured data. Finally, the sensitivity of vertical and horizontal resolution of voxel size for estimating forest structural parameters was assessed. The results showed that, in general, the accuracies of forest type-specific models (Adj-R2 = 0.44–0.88) were relatively higher than general models (Adj-R2 = 0.39–0.77). For forest structural parameters, the estimation accuracies of Lorey’s mean height (Adj-R2 = 0.61–0.88) and aboveground biomass (Adj-R2 = 0.54–0.81) models were the highest, followed by volume (Adj-R2 = 0.42–0.78), DBH (Adj-R2 = 0.48–0.74), basal area (Adj-R2 = 0.41–0.69), whereas stem density (Adj-R2 = 0.39–0.64) models were relatively lower. The combination models (Adj-R2 = 0.45–0.88) had higher performance compared with models developed using standard metrics (only) (Adj-R2 = 0.42–0.84) and canopy metrics (only) (Adj-R2 = 0.39–0.83). The results also demonstrated that the optimal voxel size was 5 × 5 × 0.5 m3 for estimating most of the parameters. This study demonstrated that canopy metrics based on canopy vertical profiles can be effectively used to enhance the estimation accuracies of forest structural parameters in subtropical forests. Full article
(This article belongs to the Section Forest Remote Sensing)
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Open AccessArticle A Modified Normalized Difference Impervious Surface Index (MNDISI) for Automatic Urban Mapping from Landsat Imagery
Remote Sens. 2017, 9(9), 942; doi:10.3390/rs9090942
Received: 16 August 2017 / Revised: 8 September 2017 / Accepted: 8 September 2017 / Published: 12 September 2017
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Abstract
Impervious surface area (ISA) is a key factor for monitoring urban environment and land development. Automatic mapping of impervious surfaces has attracted growing attention in recent years. Spectral built-up indices are considered promising to map ISA distributions due to their easy, parameter-free implementations.
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Impervious surface area (ISA) is a key factor for monitoring urban environment and land development. Automatic mapping of impervious surfaces has attracted growing attention in recent years. Spectral built-up indices are considered promising to map ISA distributions due to their easy, parameter-free implementations. This study explores the potentials of impervious surface indices for ISA mapping from Landsat imagery using a case study area in Boston, USA. A modified normalized difference impervious surface index (MNDISI) is proposed, and a Gaussian-based automatic threshold selection method is used to identify the optimal MNDISI threshold for delineating impervious surfaces from background features. To evaluate its effectiveness, comparison analysis is conducted between MNDISI and the original NDISI using Landsat images from three sensors (TM/ETM+/OLI-TIRS) acquired in four seasons. Our results suggest that built-up indices are sensitive to image seasonality, and summer is the best time phase for ISA mapping. With reduced uncertainties from automatic threshold selection, the MNDISI extracts impervious surfaces from all Landsat images in summer with an overall accuracy higher than 87% and an overall Kappa coefficient higher than 0.74. The proposed method is superior to previous index-based ISA mapping from the enhanced thermal integration and automatic threshold selection. The ISA maps from the TM, ETM+ and OLI-TIRS images are not significantly different. With enlarged data pool when all Landsat sensors are considered and automation of threshold selection proposed in this study, the MNDISI could be an effective built-up index for rapid and automatic ISA mapping at regional and global scales. Full article
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Open AccessArticle Evaluation of Remote-Sensing-Based Landslide Inventories for Hazard Assessment in Southern Kyrgyzstan
Remote Sens. 2017, 9(9), 943; doi:10.3390/rs9090943
Received: 10 June 2017 / Revised: 6 September 2017 / Accepted: 8 September 2017 / Published: 15 September 2017
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Abstract
Large areas in southern Kyrgyzstan are subjected to high and ongoing landslide activity; however, an objective and systematic assessment of landslide susceptibility at a regional level has not yet been conducted. In this paper, we investigate the contribution that remote sensing can provide
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Large areas in southern Kyrgyzstan are subjected to high and ongoing landslide activity; however, an objective and systematic assessment of landslide susceptibility at a regional level has not yet been conducted. In this paper, we investigate the contribution that remote sensing can provide to facilitate a quantitative landslide hazard assessment at a regional scale under the condition of data scarcity. We performed a landslide susceptibility and hazard assessment based on a multi-temporal landslide inventory that was derived from a 30-year time series of satellite remote sensing data using an automated identification approach. To evaluate the effect of the resulting inventory on the landslide susceptibility assessment, we calculated an alternative susceptibility model using a historical inventory that was derived by an expert through combining visual interpretation of remote sensing data with already existing knowledge on landslide activity in this region. For both susceptibility models, the same predisposing factors were used: geology, stream power index, absolute height, aspect and slope. A comparison of the two models revealed that using the multi-temporal landslide inventory covering the 30-year period results in model coefficients and susceptibility values that more strongly reflect the properties of the most recent landslide activity. Overall, both susceptibility maps present the highest susceptibility values for similar regions and are characterized by acceptable to high predictive performances. We conclude that the results of the automated landslide detection provide a suitable landslide inventory for a reliable large-area landslide susceptibility assessment. We also used the temporal information of the automatically detected multi-temporal landslide inventory to assess the temporal component of landslide hazard in the form of exceedance probability. The results show the great potential of satellite remote sensing for deriving detailed and systematic spatio-temporal information on landslide occurrences, which can significantly improve landslide susceptibility and hazard assessment at a regional scale, particularly in data-scarce regions such as Kyrgyzstan. Full article
(This article belongs to the Special Issue Remote Sensing of Landslides)
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Open AccessArticle An Alternative Approach to Using LiDAR Remote Sensing Data to Predict Stem Diameter Distributions across a Temperate Forest Landscape
Remote Sens. 2017, 9(9), 944; doi:10.3390/rs9090944
Received: 29 May 2017 / Accepted: 19 June 2017 / Published: 12 September 2017
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Abstract
We apply a spatially-implicit, allometry-based modelling approach to predict stem diameter distributions (SDDs) from low density airborne LiDAR data in a heterogeneous, temperate forest in Ontario, Canada. Using a recently published algorithm that relates the density, size, and species of individual trees to
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We apply a spatially-implicit, allometry-based modelling approach to predict stem diameter distributions (SDDs) from low density airborne LiDAR data in a heterogeneous, temperate forest in Ontario, Canada. Using a recently published algorithm that relates the density, size, and species of individual trees to the height distribution of first returns, we estimated parameters that succinctly describe SDDs that are most consistent with each 0.25-ha LiDAR tile across a 30,000 ha forest landscape. Tests with independent validation plots showed that the diameter distribution of stems was predicted with reasonable accuracy in most cases (half of validation plots had R2 ≥ 0.75, and another 23% had 0.5 ≤ R2 < 0.75). The predicted frequency of larger stems was much better than that of small stems (8 ≤ x < 11 cm diameter), particularly small conifers. We used the predicted SDDs to calculate aboveground carbon density (ACD; RMSE = 21.4 Mg C/ha), quadratic mean diameter (RMSE = 3.64 cm), basal area (RMSE = 6.99 m2/ha) and stem number (RMSE = 272 stems/ha). The accuracy of our predictions compared favorably with previous studies that have generally been undertaken in simpler conifer-dominated forest types. We demonstrate the utility of our results to spatial forest management planning by mapping SDDs, the proportion of broadleaves, and ACD at a 0.25 ha resolution. Full article
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Open AccessArticle Applicability of Earth Observation for Identifying Small-Scale Mining Footprints in a Wet Tropical Region
Remote Sens. 2017, 9(9), 945; doi:10.3390/rs9090945
Received: 22 August 2017 / Revised: 2 September 2017 / Accepted: 8 September 2017 / Published: 12 September 2017
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Abstract
The unpredictable climate in wet tropical regions along with the spatial resolution limitations of some satellite imageries make detecting and mapping artisanal and small-scale mining (ASM) challenging. The objective of this study was to test the utility of Pleiades and SPOT imagery with
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The unpredictable climate in wet tropical regions along with the spatial resolution limitations of some satellite imageries make detecting and mapping artisanal and small-scale mining (ASM) challenging. The objective of this study was to test the utility of Pleiades and SPOT imagery with an object-based support vector machine (OB-SVM) classifier for the multi-temporal remote sensing of ASM and other land cover including a large-scale mine in the Didipio catchment in the Philippines. Historical spatial data on location and type of ASM mines were collected from the field and were utilized as training data for the OB-SVM classifier. The classification had an overall accuracy between 87% and 89% for the three different images—Pleiades-1A for the 2013 and 2014 images and SPOT-6 for the 2016 image. The main land use features, particularly the Didipio large-scale mine, were well identified by the OB-SVM classifier, however there were greater commission errors for the mapping of small-scale mines. The lack of consistency in their shape and their small area relative to pixel sizes meant they were often not distinguished from other land clearance types (i.e., open land). To accurately estimate the total area of each land cover class, we calculated bias-adjusted surface areas based on misclassification values. The analysis showed an increase in small-scale mining areas from 91,000 m2—or 0.2% of the total catchment area—in March 2013 to 121,000 m2—or 0.3%—in May 2014, and then a decrease to 39,000 m2—or 0.1%—in January 2016. Full article
(This article belongs to the Special Issue GIS and Remote Sensing advances in Land Change Science)
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