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Remote Sens., Volume 8, Issue 8 (August 2016)

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Cover Story (view full-size image) Unmanned Aerial Vehicles (UAVs) have emerged as a rapid, low-cost and flexible acquisition system [...] Read more.
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Open AccessArticle Degradation of Non-Photosynthetic Vegetation in a Semi-Arid Rangeland
Remote Sens. 2016, 8(8), 692; https://doi.org/10.3390/rs8080692
Received: 1 June 2016 / Revised: 28 July 2016 / Accepted: 10 August 2016 / Published: 24 August 2016
Cited by 5 | Viewed by 1909 | PDF Full-text (4177 KB) | HTML Full-text | XML Full-text
Abstract
Land degradation in drylands is the process in which undesirable conditions emerge due to human and natural causes. Despite the particularly deleterious effects of degradation, and it’s potentially irreversible nature, regional assessments have provided conflicting extents, rates, and severities of degradation, both globally [...] Read more.
Land degradation in drylands is the process in which undesirable conditions emerge due to human and natural causes. Despite the particularly deleterious effects of degradation, and it’s potentially irreversible nature, regional assessments have provided conflicting extents, rates, and severities of degradation, both globally and regionally. Current monitoring of degradation relies upon the detection of green, photosynthetically active parts of vegetation (e.g., leaves). Less is known, however, about the effect of degradation on the non-photosynthetic components of vegetation (e.g., wood, stems, leaf litter) and the relationship between photosynthetic vegetation (PV), non-photosynthetic vegetation (NPV), and bare soil under degraded conditions (BS). The major objective of the study was to evaluate regional patterns of fractional cover (i.e., PV, NPV, BS) under degraded and non-degraded NPP conditions in a managed rangeland in north Queensland, Australia. Homogenous environmental conditions were identified and each of NPP, PV, NPV, and BS were scaled according to their potential, reference values. We found a strong spatial and temporal correlation between scaled NPP with both scaled PV and scaled BS. Drastic differences were also found for PV and BS between degraded and non-degraded conditions. NPV displayed similarity to both PV and BS, however no clear relationship was found for NPV in all areas, irrespective of degradation conditions. Full article
(This article belongs to the Special Issue Remote Sensing of Land Degradation and Drivers of Change)
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Open AccessArticle Consistent Classification of Landsat Time Series with an Improved Automatic Adaptive Signature Generalization Algorithm
Remote Sens. 2016, 8(8), 691; https://doi.org/10.3390/rs8080691
Received: 27 June 2016 / Revised: 25 July 2016 / Accepted: 15 August 2016 / Published: 24 August 2016
Cited by 8 | Viewed by 2353 | PDF Full-text (2446 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Classifying land cover is perhaps the most common application of remote sensing, yet classification at frequent temporal intervals remains a challenging task due to radiometric differences among scenes, time and budget constraints, and semantic differences among class definitions from different dates. The automatic [...] Read more.
Classifying land cover is perhaps the most common application of remote sensing, yet classification at frequent temporal intervals remains a challenging task due to radiometric differences among scenes, time and budget constraints, and semantic differences among class definitions from different dates. The automatic adaptive signature generalization (AASG) algorithm overcomes many of these limitations by locating stable sites between two images and using them to adapt class spectral signatures from a high-quality reference classification to a new image, which mitigates the impacts of radiometric and phenological differences between images and ensures that class definitions remain consistent between the two classifications. We refined AASG to adapt stable site identification parameters to each individual land cover class, while also incorporating improved input data and a random forest classifier. In the Research Triangle region of North Carolina, our new version of AASG demonstrated an improved ability to update existing land cover classifications compared to the initial version of AASG, particularly for low intensity developed, mixed forest, and woody wetland classes. Topographic indices were particularly important for distinguishing woody wetlands from other forest types, while multi-seasonal imagery contributed to improved classification of water, developed, forest, and hay/pasture classes. These results demonstrate both the flexibility of the AASG algorithm and the potential for using it to produce high-quality land cover classifications that can utilize the entire temporal range of the Landsat archive in an automated fashion while maintaining consistent class definitions through time. Full article
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Open AccessReview Review of Automatic Feature Extraction from High-Resolution Optical Sensor Data for UAV-Based Cadastral Mapping
Remote Sens. 2016, 8(8), 689; https://doi.org/10.3390/rs8080689
Received: 30 June 2016 / Revised: 3 August 2016 / Accepted: 11 August 2016 / Published: 22 August 2016
Cited by 23 | Viewed by 4419 | PDF Full-text (5051 KB) | HTML Full-text | XML Full-text
Abstract
Unmanned Aerial Vehicles (UAVs) have emerged as a rapid, low-cost and flexible acquisition system that appears feasible for application in cadastral mapping: high-resolution imagery, acquired using UAVs, enables a new approach for defining property boundaries. However, UAV-derived data are arguably not exploited to [...] Read more.
Unmanned Aerial Vehicles (UAVs) have emerged as a rapid, low-cost and flexible acquisition system that appears feasible for application in cadastral mapping: high-resolution imagery, acquired using UAVs, enables a new approach for defining property boundaries. However, UAV-derived data are arguably not exploited to its full potential: based on UAV data, cadastral boundaries are visually detected and manually digitized. A workflow that automatically extracts boundary features from UAV data could increase the pace of current mapping procedures. This review introduces a workflow considered applicable for automated boundary delineation from UAV data. This is done by reviewing approaches for feature extraction from various application fields and synthesizing these into a hypothetical generalized cadastral workflow. The workflow consists of preprocessing, image segmentation, line extraction, contour generation and postprocessing. The review lists example methods per workflow step—including a description, trialed implementation, and a list of case studies applying individual methods. Furthermore, accuracy assessment methods are outlined. Advantages and drawbacks of each approach are discussed in terms of their applicability on UAV data. This review can serve as a basis for future work on the implementation of most suitable methods in a UAV-based cadastral mapping workflow. Full article
(This article belongs to the Special Issue Recent Trends in UAV Remote Sensing)
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Open AccessArticle Polar Sea Ice Monitoring Using HY-2A Scatterometer Measurements
Remote Sens. 2016, 8(8), 688; https://doi.org/10.3390/rs8080688
Received: 25 April 2016 / Revised: 4 August 2016 / Accepted: 17 August 2016 / Published: 22 August 2016
Cited by 1 | Viewed by 2114 | PDF Full-text (6545 KB) | HTML Full-text | XML Full-text
Abstract
A sea ice detection algorithm based on Fisher’s linear discriminant analysis is developed to segment sea ice and open water for the Ku-band scatterometer onboard the China’s Hai Yang 2A Satellite (HY-2A/SCAT). Residual classification errors are reduced through image erosion/dilation techniques and sea [...] Read more.
A sea ice detection algorithm based on Fisher’s linear discriminant analysis is developed to segment sea ice and open water for the Ku-band scatterometer onboard the China’s Hai Yang 2A Satellite (HY-2A/SCAT). Residual classification errors are reduced through image erosion/dilation techniques and sea ice growth/retreat constraint methods. The arctic sea-ice-type classification is estimated via a time-dependent threshold derived from the annual backscatter trends based on previous HY-2A/SCAT derived sea ice extent. The extent and edge of the sea ice obtained in this study is compared with the Special Sensor Microwave Imager/Sounder (SSMIS) sea ice concentration data and the Sentinel-1 SAR imagery for verification, respectively. Meanwhile, the classified sea ice type is compared with a multi-sensor sea ice type product based on data from the Advanced Scatterometer (ASCAT) and SSMIS. Results show that HY-2A/SCAT is powerful in providing sea ice extent and type information, while differences in the sensitivities of active/passive products are found. In addition, HY-2A/SCAT derived sea ice products are also proved to be valuable complements for existing polar sea ice data products. Full article
(This article belongs to the Special Issue Sea Ice Remote Sensing and Analysis)
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Open AccessFeature PaperArticle Landsat Imagery Spectral Trajectories—Important Variables for Spatially Predicting the Risks of Bark Beetle Disturbance
Remote Sens. 2016, 8(8), 687; https://doi.org/10.3390/rs8080687
Received: 30 May 2016 / Revised: 12 August 2016 / Accepted: 16 August 2016 / Published: 22 August 2016
Cited by 5 | Viewed by 2379 | PDF Full-text (3372 KB) | HTML Full-text | XML Full-text
Abstract
Tree mortality caused by bark beetle infestation has significant effects on the ecology and value of both natural and commercial forests. Therefore, prediction of bark beetle infestations is critical in forest management. Existing predictive models, however, rarely consider the influence of long-term stressors [...] Read more.
Tree mortality caused by bark beetle infestation has significant effects on the ecology and value of both natural and commercial forests. Therefore, prediction of bark beetle infestations is critical in forest management. Existing predictive models, however, rarely consider the influence of long-term stressors on forest susceptibility to bark beetle infestation. In this study we introduce pre-disturbance spectral trajectories from Landsat Thematic Mapper (TM) imagery as an indicator of long-term stress into models of bark beetle infestation together with commonly used environmental predictors. Observations for this study come from forests in the central part of the Šumava Mountains, in the border region between the Czech Republic and Germany, Central Europe. The areas of bark beetle-infested forest were delineated from aerial photographs taken in 1991 and in every year from 1994 to 2000. The environmental predictors represent forest stand attributes (e.g., tree density and distance to the infested forest from previous year) and common abiotic factors, such as topography, climate, geology, and soil. Pre-disturbance spectral trajectories were defined by the linear regression slope of Tasseled Cap components (Wetness, Brightness and Greenness) calculated from a time series of 16 Landsat TM images across years from 1984 until one year before the bark beetle infestation. Using logistic regression and multimodel inference, we calculated predictive models separately for each single year from 1994 to 2000 to account for a possible shift in importance of individual predictors during disturbance. Inclusion of two pre-disturbance spectral trajectories (Wetness slope and Brightness slope) significantly improved predictive ability of bark beetle infestation models. Wetness slope had the greatest predictive power, even relative to environmental predictors, and was relatively stable in its power over the years. Brightness slope improved the model only in the middle of the disturbance period (1996). Importantly, these pre-disturbance predictors were not correlated with other predictors, and therefore bring additional explanatory power to the model. Generally, the predictive power of most fitted model decreases as time progresses and models describing the initial phase of bark beetle outbreaks appear more reliable for conducting near-future predictions. The pre-disturbance spectral trajectories are valuable not only for assessing the risk of bark beetle infestation, but also for detection of long-term gradual changes even in non-forest ecosystems. Full article
(This article belongs to the Special Issue Remote Sensing of Forest Health)
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Open AccessArticle Methodology for Detection and Interpretation of Ground Motion Areas with the A-DInSAR Time Series Analysis
Remote Sens. 2016, 8(8), 686; https://doi.org/10.3390/rs8080686
Received: 30 June 2016 / Revised: 5 August 2016 / Accepted: 16 August 2016 / Published: 22 August 2016
Cited by 15 | Viewed by 2099 | PDF Full-text (11998 KB) | HTML Full-text | XML Full-text
Abstract
Recent improvement to Advanced Differential Interferometric SAR (A-DInSAR) time series quality enhances the knowledge of various geohazards. Ground motion studies need an appropriate methodology to exploit the great potential contained in the A-DInSAR time series. Here, we propose a methodology to analyze multi-sensors [...] Read more.
Recent improvement to Advanced Differential Interferometric SAR (A-DInSAR) time series quality enhances the knowledge of various geohazards. Ground motion studies need an appropriate methodology to exploit the great potential contained in the A-DInSAR time series. Here, we propose a methodology to analyze multi-sensors and multi-temporal A-DInSAR data for the geological interpretation of areas affected by land subsidence/uplift and seasonal movements. The methodology was applied in the plain area of the Oltrepo Pavese (Po Plain, Italy) using ERS-1/2 and Radarsat data, processed using the SqueeSAR™ algorithm, and covering time spans, respectively, from 1992 to 2000 and from 2003 to 2010. The test area is a representative site of the Po Plain, affected by various geohazards and characterized by moderate rates of motion, ranging from −10 to 4 mm/yr. Different components of motion were recognized: linear, non-linear, and seasonal deformational behaviors. Natural and man-induced processes were identified such as swelling/shrinkage of clayey soils, land subsidence due to load of new buildings, moderate tectonic uplift, and seasonal ground motion due to seasonal groundwater level variations. Full article
(This article belongs to the Special Issue Earth Observations for Geohazards) Printed Edition available
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Open AccessArticle Built-up Area Extraction from PolSAR Imagery with Model-Based Decomposition and Polarimetric Coherence
Remote Sens. 2016, 8(8), 685; https://doi.org/10.3390/rs8080685
Received: 8 July 2016 / Revised: 5 August 2016 / Accepted: 18 August 2016 / Published: 22 August 2016
Cited by 10 | Viewed by 1468 | PDF Full-text (5499 KB) | HTML Full-text | XML Full-text
Abstract
Built-up area extraction from polarimetric SAR (PolSAR) imagery has a close relationship with urban planning, disaster management, etc. Since the buildings have complex geometries and may be misclassified as forests due to the significant cross-polarized scattering, built-up area extraction from PolSAR data is [...] Read more.
Built-up area extraction from polarimetric SAR (PolSAR) imagery has a close relationship with urban planning, disaster management, etc. Since the buildings have complex geometries and may be misclassified as forests due to the significant cross-polarized scattering, built-up area extraction from PolSAR data is still a challenging problem. This paper proposes a new urban extraction method for PolSAR data. First, a multiple-component model-based decomposition method, which was previously proposed by us, is applied to detect the urban areas using the scattering powers. Second, with the sub-aperture decomposition, a new average polarimetric coherence coefficient ratio is proposed to discriminate the urban and natural areas. Finally, these two preliminary detection results are fused on the decision level to improve the overall detection accuracy. We validate our method using one dataset acquired with the Phased Array type L-band Synthetic Aperture Radar (PALSAR) system. Experimental results demonstrate that the decomposed scattering powers and the proposed polarimetric coherence coefficient ratio are both capable of distinguishing urban areas from natural areas with accuracy about 83.1% and 80.1%, respectively. The overall detection accuracy can further increase to 86.9% with the fusion of two detection results. Full article
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Open AccessArticle Second-Order Polynomial Equation-Based Block Adjustment for Orthorectification of DISP Imagery
Remote Sens. 2016, 8(8), 680; https://doi.org/10.3390/rs8080680
Received: 4 July 2016 / Revised: 12 August 2016 / Accepted: 17 August 2016 / Published: 22 August 2016
Cited by 4 | Viewed by 1788 | PDF Full-text (5914 KB) | HTML Full-text | XML Full-text
Abstract
Due to the lack of ground control points (GCPs) and parameters of satellite orbits, as well as the interior and exterior orientation parameters of cameras in historical declassified intelligence satellite photography (DISP) imagery, a second order polynomial equation-based block adjustment model is proposed [...] Read more.
Due to the lack of ground control points (GCPs) and parameters of satellite orbits, as well as the interior and exterior orientation parameters of cameras in historical declassified intelligence satellite photography (DISP) imagery, a second order polynomial equation-based block adjustment model is proposed for orthorectification of DISP imagery. With the proposed model, 355 DISP images from four missions and five orbits are orthorectified, with an approximate accuracy of 2.0–3.0 m. The 355 orthorectified images are assembled into a seamless, full-coverage mosaic image map of the karst area of Guangxi, China. The accuracy of the mosaicked image map is within 2.0–4.0 m when compared to 78 checkpoints measured by Real–Time Kinematic (RTK) GPS surveys. The assembled image map will be delivered to the Guangxi Geological Library and released to the public domain and the research community. Full article
(This article belongs to the Special Issue Earth Observations for Geohazards) Printed Edition available
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Open AccessArticle Three-Dimensional Body and Centre of Mass Kinematics in Alpine Ski Racing Using Differential GNSS and Inertial Sensors
Remote Sens. 2016, 8(8), 671; https://doi.org/10.3390/rs8080671
Received: 30 June 2016 / Revised: 2 August 2016 / Accepted: 16 August 2016 / Published: 22 August 2016
Cited by 22 | Viewed by 2685 | PDF Full-text (2653 KB) | HTML Full-text | XML Full-text
Abstract
A key point in human movement analysis is measuring the trajectory of a person’s center of mass (CoM). For outdoor applications, differential Global Navigation Satellite Systems (GNSS) can be used for tracking persons since they allow measuring the trajectory and speed of the [...] Read more.
A key point in human movement analysis is measuring the trajectory of a person’s center of mass (CoM). For outdoor applications, differential Global Navigation Satellite Systems (GNSS) can be used for tracking persons since they allow measuring the trajectory and speed of the GNSS antenna with centimeter accuracy. However, the antenna cannot be placed exactly at the person’s CoM, but rather on the head or upper back. Thus, a model is needed to relate the measured antenna trajectory to the CoM trajectory. In this paper we propose to estimate the person’s posture based on measurements obtained from inertial sensors. From this estimated posture the CoM is computed relative to the antenna position and finally fused with the GNSS trajectory information to obtain the absolute CoM trajectory. In a biomechanical field experiment, the method has been applied to alpine ski racing and validated against a camera-based stereo photogrammetric system. CoM position accuracy and precision was found to be 0.08 m and 0.04 m, respectively. CoM speed accuracy and precision was 0.04 m/s and 0.14 m/s, respectively. The observed accuracy and precision might be sufficient for measuring performance- or equipment-related trajectory differences in alpine ski racing. Moreover, the CoM estimation was not based on a movement-specific model and could be used for other skiing disciplines or sports as well. Full article
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Open AccessArticle Best Accuracy Land Use/Land Cover (LULC) Classification to Derive Crop Types Using Multitemporal, Multisensor, and Multi-Polarization SAR Satellite Images
Remote Sens. 2016, 8(8), 684; https://doi.org/10.3390/rs8080684
Received: 15 March 2016 / Revised: 6 July 2016 / Accepted: 13 August 2016 / Published: 20 August 2016
Cited by 21 | Viewed by 4209 | PDF Full-text (5310 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
When using microwave remote sensing for land use/land cover (LULC) classifications, there are a wide variety of imaging parameters to choose from, such as wavelength, imaging mode, incidence angle, spatial resolution, and coverage. There is still a need for further study of the [...] Read more.
When using microwave remote sensing for land use/land cover (LULC) classifications, there are a wide variety of imaging parameters to choose from, such as wavelength, imaging mode, incidence angle, spatial resolution, and coverage. There is still a need for further study of the combination, comparison, and quantification of the potential of multiple diverse radar images for LULC classifications. Our study site, the Qixing farm in Heilongjiang province, China, is especially suitable to demonstrate this. As in most rice growing regions, there is a high cloud cover during the growing season, making LULC from optical images unreliable. From the study year 2009, we obtained nine TerraSAR-X, two Radarsat-2, one Envisat-ASAR, and an optical FORMOSAT-2 image, which is mainly used for comparison, but also for a combination. To evaluate the potential of the input images and derive LULC with the highest possible precision, two classifiers were used: the well-established Maximum Likelihood classifier, which was optimized to find those input bands, yielding the highest precision, and the random forest classifier. The resulting highly accurate LULC-maps for the whole farm with a spatial resolution as high as 8 m demonstrate the beneficial use of a combination of x- and c-band microwave data, the potential of multitemporal very high resolution multi-polarization TerraSAR-X data, and the profitable integration and comparison of microwave and optical remote sensing images for LULC classifications. Full article
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Open AccessArticle SAR Target Recognition via Supervised Discriminative Dictionary Learning and Sparse Representation of the SAR-HOG Feature
Remote Sens. 2016, 8(8), 683; https://doi.org/10.3390/rs8080683
Received: 25 May 2016 / Revised: 22 July 2016 / Accepted: 17 August 2016 / Published: 20 August 2016
Cited by 24 | Viewed by 1980 | PDF Full-text (873 KB) | HTML Full-text | XML Full-text
Abstract
Automatic target recognition (ATR) in synthetic aperture radar (SAR) images plays an important role in both national defense and civil applications. Although many methods have been proposed, SAR ATR is still very challenging due to the complex application environment. Feature extraction and classification [...] Read more.
Automatic target recognition (ATR) in synthetic aperture radar (SAR) images plays an important role in both national defense and civil applications. Although many methods have been proposed, SAR ATR is still very challenging due to the complex application environment. Feature extraction and classification are key points in SAR ATR. In this paper, we first design a novel feature, which is a histogram of oriented gradients (HOG)-like feature for SAR ATR (called SAR-HOG). Then, we propose a supervised discriminative dictionary learning (SDDL) method to learn a discriminative dictionary for SAR ATR and propose a strategy to simplify the optimization problem. Finally, we propose a SAR ATR classifier based on SDDL and sparse representation (called SDDLSR), in which both the reconstruction error and the classification error are considered. Extensive experiments are performed on the MSTAR database under standard operating conditions and extended operating conditions. The experimental results show that SAR-HOG can reliably capture the structures of targets in SAR images, and SDDL can further capture subtle differences among the different classes. By virtue of the SAR-HOG feature and SDDLSR, the proposed method achieves the state-of-the-art performance on MSTAR database. Especially for the extended operating conditions (EOC) scenario “Training 17 —Testing 45 ”, the proposed method improves remarkably with respect to the previous works. Full article
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Open AccessArticle Comparison of Four Machine Learning Methods for Generating the GLASS Fractional Vegetation Cover Product from MODIS Data
Remote Sens. 2016, 8(8), 682; https://doi.org/10.3390/rs8080682
Received: 18 May 2016 / Revised: 15 August 2016 / Accepted: 17 August 2016 / Published: 20 August 2016
Cited by 10 | Viewed by 1716 | PDF Full-text (3463 KB) | HTML Full-text | XML Full-text
Abstract
Long-term global land surface fractional vegetation cover (FVC) products are essential for various applications. Currently, several global FVC products have been generated from medium spatial resolution remote sensing data. However, validation results indicate that there are inconsistencies and spatial and temporal discontinuities in [...] Read more.
Long-term global land surface fractional vegetation cover (FVC) products are essential for various applications. Currently, several global FVC products have been generated from medium spatial resolution remote sensing data. However, validation results indicate that there are inconsistencies and spatial and temporal discontinuities in the current FVC products. Therefore, the Global LAnd Surface Satellite (GLASS) FVC product algorithm using general regression neural networks (GRNNs), which achieves an FVC estimation accuracy comparable to that of the GEOV1 FVC product with much improved spatial and temporal continuities, was developed. However, the computational efficiency of the GRNNs method is low and unsatisfactory for generating the long-term GLASS FVC product. Therefore, the objective of this study was to discover an alternative algorithm for generating the GLASS FVC product that has both an accuracy comparable to that of the GRNNs method and adequate computational efficiency. Four commonly used machine learning methods, back-propagation neural networks (BPNNs), GRNNs, support vector regression (SVR), and multivariate adaptive regression splines (MARS), were evaluated. After comparing its performance of training accuracy and computational efficiency with the other three methods, the MARS model was preliminarily selected as the most suitable algorithm for generating the GLASS FVC product. Direct validation results indicated that the performance of the MARS model (R2 = 0.836, RMSE = 0.1488) was comparable to that of the GRNNs method (R2 = 0.8353, RMSE = 0.1495), and the global land surface FVC generated from the MARS model had good spatial and temporal consistency with that generated from the GRNNs method. Furthermore, the computational efficiency of MARS was much higher than that of the GRNNs method. Therefore, the MARS model is a suitable algorithm for generating the GLASS FVC product from Moderate Resolution Imaging Spectroradiometer (MODIS) data. Full article
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Open AccessArticle Mapping Decadal Land Cover Changes in the Woodlands of North Eastern Namibia from 1975 to 2014 Using the Landsat Satellite Archived Data
Remote Sens. 2016, 8(8), 681; https://doi.org/10.3390/rs8080681
Received: 11 June 2016 / Revised: 11 August 2016 / Accepted: 15 August 2016 / Published: 20 August 2016
Cited by 10 | Viewed by 2617 | PDF Full-text (11098 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Woodlands and savannahs provide essential ecosystem functions and services to communities. On the African continent, they are widely utilized and converted to subsistence and intensive agriculture or urbanized. This study investigates changes in land cover over four administrative regions of North Eastern Namibia [...] Read more.
Woodlands and savannahs provide essential ecosystem functions and services to communities. On the African continent, they are widely utilized and converted to subsistence and intensive agriculture or urbanized. This study investigates changes in land cover over four administrative regions of North Eastern Namibia within the Kalahari woodland savannah biome, covering a total of 107,994 km2. Land cover is mapped using multi-sensor Landsat imagery at decadal intervals from 1975 to 2014, with a post-classification change detection method. The dominant change observed was a reduction in the area of woodland savannah due to the expansion of agriculture, primarily in the form of small-scale cereal and pastoral production. More specifically, woodland savannah area decreased from 90% of the study area in 1975 to 83% in 2004, and then increased to 86% in 2014, while agricultural land increased from 6% to 12% between 1975 and 2014. We assess land cover changes in relation to towns, villages, rivers and roads and find most changes occurred in proximity to these. In addition, we find that most land cover changes occur within land designated as communally held, followed by state protected land. With widespread changes occurring across the African continent, this study provides important data for understanding drivers of change in the region and their impacts on the distribution of woodland savannahs. Full article
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Open AccessArticle An Assessment of Pre- and Post Fire Near Surface Fuel Hazard in an Australian Dry Sclerophyll Forest Using Point Cloud Data Captured Using a Terrestrial Laser Scanner
Remote Sens. 2016, 8(8), 679; https://doi.org/10.3390/rs8080679
Received: 20 June 2016 / Revised: 5 August 2016 / Accepted: 16 August 2016 / Published: 20 August 2016
Cited by 5 | Viewed by 1810 | PDF Full-text (5164 KB) | HTML Full-text | XML Full-text
Abstract
Assessment of ecological and structrual changes induced by fire events is important for understanding the effects of fire, and planning future ecological and risk mitigation strategies. This study employs Terrestrial Laser Scanning (TLS) data captured at multiple points in time to monitor the [...] Read more.
Assessment of ecological and structrual changes induced by fire events is important for understanding the effects of fire, and planning future ecological and risk mitigation strategies. This study employs Terrestrial Laser Scanning (TLS) data captured at multiple points in time to monitor the changes in a dry sclerophyll forest induced by a prescribed burn. Point cloud data was collected for two plots; one plot undergoing a fire treatment, and the second plot remaining untreated, thereby acting as the control. Data was collected at three epochs (pre-fire, two weeks post fire and two years post fire). Coregistration of these multitemporal point clouds to within an acceptable tolerance was achieved through a two step process utilising permanent infield markers and manually extracted stem objects as reference targets. Metrics describing fuel height and fuel fragmentation were extracted from the point clouds for direct comparison with industry standard visual assessments. Measurements describing the change (or lack thereof) in the control plot indicate that the method of data capture and coregistration were achieved with the required accuracy to monitor fire induced change. Results from the fire affected plot show that immediately post fire 67% of area had been burnt with the average fuel height decreasing from 0.33 to 0.13 m. At two years post-fire the fuel remained signicantly lower (0.11 m) and more fragmented in comparison to pre-fire levels. Results in both the control and fire altered plot were comparable to synchronus onground visual assessment. The advantage of TLS over the visual assessment method is, however, demonstrated through the use of two physical and spatially quantifiable metrics to describe fuel change. These results highlight the capabilities of multitemporal TLS data for measuring and mapping changes in the three dimensional structure of vegetation. Metrics from point clouds can be derived to provide quantified estimates of surface and near-surface fuel loss and accumulation, and inform prescribed burn efficacy and burn severity reporting. Full article
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Open AccessArticle Assessing Global Forest Land-Use Change by Object-Based Image Analysis
Remote Sens. 2016, 8(8), 678; https://doi.org/10.3390/rs8080678
Received: 4 March 2016 / Revised: 12 August 2016 / Accepted: 15 August 2016 / Published: 20 August 2016
Cited by 6 | Viewed by 2244 | PDF Full-text (3589 KB) | HTML Full-text | XML Full-text
Abstract
Consistent estimates of forest land-use and change over time are important for understanding and managing human activities on the Earth’s surface, parameterizing models used for global and regional climate change analyses and a critical component of reporting requirements faced by countries as part [...] Read more.
Consistent estimates of forest land-use and change over time are important for understanding and managing human activities on the Earth’s surface, parameterizing models used for global and regional climate change analyses and a critical component of reporting requirements faced by countries as part of the international effort to Reduce Emissions from Deforestation and Degradation (REDD). In this study, object-based image analysis methods were applied to a global sample of Landsat imagery from years 1990, 2000 and 2005 to produce a land cover classification suitable for expert human review, revision and translation into forest and non-forest land use classes. We describe and analyse here the derivation and application of an automated, multi-date image segmentation, neural network classification method and independent, automated change detection procedure to all sample sites. The automated results were compared against expert human interpretation and found to have an overall agreement of ~76% for a 5-class land cover classification and ~88% agreement for change/no-change assessment. The establishment of a 5 ha minimum mapping unit affected the ability of the segmentation methods to detect small or irregularly-shaped land cover change and, combined with aggregation rules that favour forest, added bias to the automated results. However, the OBIA methods provided an efficient means of processing over 11,000 sample sites, 33,000 Landsat 20 × 20 km sample tiles and more than 6.5 million individual polygons over three epochs and adequately facilitated human expert review, revision and conversion to a global forest land-use product. Full article
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Open AccessArticle Thermal Imagery-Derived Surface Inundation Modeling to Assess Flood Risk in a Flood-Pulsed Savannah Watershed in Botswana and Namibia
Remote Sens. 2016, 8(8), 676; https://doi.org/10.3390/rs8080676
Received: 22 April 2016 / Revised: 4 August 2016 / Accepted: 15 August 2016 / Published: 20 August 2016
Cited by 4 | Viewed by 1704 | PDF Full-text (6996 KB) | HTML Full-text | XML Full-text
Abstract
The Chobe River Basin (CRB), a sub-basin of the Upper Zambezi Basin shared by Namibia and Botswana, is a complex hydrologic system that lies at the center of the world’s largest transfrontier conservation area. Despite its regional importance for livelihoods and biodiversity, its [...] Read more.
The Chobe River Basin (CRB), a sub-basin of the Upper Zambezi Basin shared by Namibia and Botswana, is a complex hydrologic system that lies at the center of the world’s largest transfrontier conservation area. Despite its regional importance for livelihoods and biodiversity, its hydrology, controlled by the timing and relative contributions of water from two regional rivers, remains poorly understood. An increase in the magnitude of flooding in this region since 2009 has resulted in significant displacements of rural communities. We use an innovative approach that employs time-series of thermal imagery and station discharge data to model seasonal flooding patterns, identify the driving forces that control the magnitude of flooding and the high population density areas that are most at risk of high magnitude floods throughout the watershed. Spatio-temporal changes in surface inundation determined using NASA Moderate-resolution Imaging Spectroradiometer (MODIS) thermal imagery (2000–2015) revealed that flooding extent in the CRB is extremely variable, ranging from 401 km2 to 5779 km2 over the last 15 years. A multiple regression model of lagged discharge of surface contributor basins and flooding extent in the CRB indicated that the best predictor of flooding in this region is the discharge of the Zambezi River 64 days prior to flooding. The seasonal floods have increased drastically in magnitude since 2008 causing large populations to be displaced. Over 46,000 people (53% of Zambezi Region population) are living in high magnitude flood risk areas, making the need for resettlement planning and mitigation strategies increasingly important. Full article
(This article belongs to the Special Issue Earth Observations for a Better Future Earth)
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Open AccessArticle Ground Subsidence in the Beijing-Tianjin-Hebei Region from 1992 to 2014 Revealed by Multiple SAR Stacks
Remote Sens. 2016, 8(8), 675; https://doi.org/10.3390/rs8080675
Received: 6 April 2016 / Revised: 25 July 2016 / Accepted: 15 August 2016 / Published: 20 August 2016
Cited by 15 | Viewed by 2148 | PDF Full-text (16851 KB) | HTML Full-text | XML Full-text
Abstract
The coordinated development of the Beijing-Tianjin-Hebei has become a national strategy with Beijing and Tianjin as twin engines driving the regional development. However, the Beijing-Tianjin-Hebei region has suffered dramatic ground subsidence during last two to three decades, mainly due to long-term groundwater withdrawal. [...] Read more.
The coordinated development of the Beijing-Tianjin-Hebei has become a national strategy with Beijing and Tianjin as twin engines driving the regional development. However, the Beijing-Tianjin-Hebei region has suffered dramatic ground subsidence during last two to three decades, mainly due to long-term groundwater withdrawal. Although, annual spirit leveling has been conducted routinely in some parts of Beijing and Tianjin, and InSAR technique has also been used to monitor ground subsidence in some local areas of the region, there is a lack of a complete survey of ground subsidence over the whole region. In this paper, we report a research on mapping ground subsidence in the Beijing-Tianjin-Hebei region over a long time span from 1992 to 2014. Three SAR datasets from four satellites are used: ERS-1/2 SAR images from 1992 to 2000, ENVISAT ASAR images from 2003 to 2010, and RADARSAT-2 images from 2012 to 2014. An improved multi-temporal InSAR method, namely “Multiple-master Coherent Target Small-Baseline InSAR” (MCTSB-InSAR), has been developed to process the datasets. A unique feature of MCTSB-InSAR is the adjustment process useful for wide area monitoring which provides an integrated solution for both calibration of InSAR-derived deformation and the harmonization of the deformation estimates from overlapping SAR frames. Three maps of the subsidence rate corresponding to the three periods over the wide Beijing-Tianjin-Hebei region are generated, with respective accuracy of 8.7 mm/year (1992–2000), 4.7 mm/year (2003–2010), and 5.4 mm/year (2012–2014) validated by more than 120 leveling measurements. The spatial-temporal characteristics of the development of ground subsidence in Beijing and Tianjin are analyzed. This research represents a first-ever effort on mapping ground subsidence over very large area and over long time span in China. The result is of significance to serve the decision-making on ground subsidence mitigation in the Beijing-Tianjin-Hebei region. Full article
(This article belongs to the Special Issue Earth Observations for Geohazards) Printed Edition available
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Open AccessArticle Distributed-Temperature-Sensing Using Optical Methods: A First Application in the Offshore Area of Campi Flegrei Caldera (Southern Italy) for Volcano Monitoring
Remote Sens. 2016, 8(8), 674; https://doi.org/10.3390/rs8080674
Received: 16 May 2016 / Revised: 12 August 2016 / Accepted: 15 August 2016 / Published: 19 August 2016
Cited by 5 | Viewed by 2597 | PDF Full-text (14305 KB) | HTML Full-text | XML Full-text
Abstract
A temperature profile 2400 m along the off-shore active caldera of Campi Flegrei (Gulf of Pozzuoli) was obtained by the installation of a permanent fiber-optic monitoring system within the framework of the Innovative Monitoring for Coastal and Marine Environment (MON.I.C.A) project. The system [...] Read more.
A temperature profile 2400 m along the off-shore active caldera of Campi Flegrei (Gulf of Pozzuoli) was obtained by the installation of a permanent fiber-optic monitoring system within the framework of the Innovative Monitoring for Coastal and Marine Environment (MON.I.C.A) project. The system consists of a submerged, reinforced, multi-fiber cable containing six single-mode telecom grade optical fibers that, exploiting the stimulated Brillouin scattering, provide distributed temperature sensing (DTS) with 1 m of spatial resolution. The obtained data show that the offshore caldera, at least along the monitored profile, has many points of heat discharge associated with fluid emission. A loose association between the temperature profile and the main structural features of the offshore caldera was also evidenced by comparing DTS data with a high-resolution reflection seismic survey. This represents an important advancement in the monitoring of this high-risk volcanic area, since temperature variations are among the precursors of magma migration towards the surface and are also crucial data in the study of caldera dynamics. The adopted system can also be applied to many other calderas which are often partially or largely submerged and hence difficult to monitor. Full article
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Open AccessArticle Emulation of Leaf, Canopy and Atmosphere Radiative Transfer Models for Fast Global Sensitivity Analysis
Remote Sens. 2016, 8(8), 673; https://doi.org/10.3390/rs8080673
Received: 9 April 2016 / Revised: 1 August 2016 / Accepted: 16 August 2016 / Published: 19 August 2016
Cited by 18 | Viewed by 2509 | PDF Full-text (4229 KB) | HTML Full-text | XML Full-text
Abstract
Physically-based radiative transfer models (RTMs) help understand the interactions of radiation with vegetation and atmosphere. However, advanced RTMs can be computationally burdensome, which makes them impractical in many real applications, especially when many state conditions and model couplings need to be studied. To [...] Read more.
Physically-based radiative transfer models (RTMs) help understand the interactions of radiation with vegetation and atmosphere. However, advanced RTMs can be computationally burdensome, which makes them impractical in many real applications, especially when many state conditions and model couplings need to be studied. To overcome this problem, it is proposed to substitute RTMs through surrogate meta-models also named emulators. Emulators approximate the functioning of RTMs through statistical learning regression methods, and can open many new applications because of their computational efficiency and outstanding accuracy. Emulators allow fast global sensitivity analysis (GSA) studies on advanced, computationally expensive RTMs. As a proof-of-concept, three machine learning regression algorithms (MLRAs) were tested to function as emulators for the leaf RTM PROSPECT-4, the canopy RTM PROSAIL, and the computationally expensive atmospheric RTM MODTRAN5. Selected MLRAs were: kernel ridge regression (KRR), neural networks (NN) and Gaussian processes regression (GPR). For each RTM, 500 simulations were generated for training and validation. The majority of MLRAs were excellently validated to function as emulators with relative errors well below 0.2%. The emulators were then put into a GSA scheme and compared against GSA results as generated by original PROSPECT-4 and PROSAIL runs. NN and GPR emulators delivered identical GSA results, while processing speed compared to the original RTMs doubled for PROSPECT-4 and tripled for PROSAIL. Having the emulator-GSA concept successfully tested, for six MODTRAN5 atmospheric transfer functions (outputs), i.e., direct and diffuse at-surface solar irradiance ( E d i f , E d i r ), direct and diffuse upward transmittance ( T d i r , T d i f ), spherical albedo (S) and path radiance ( L 0 ), the most accurate MLRA’s were subsequently applied as emulator into the GSA scheme. The sensitivity analysis along the 400–2500 nm spectral range took no more than a few minutes on a contemporary computer—in comparison, the same analysis in the original MODTRAN5 would have taken over a month. Key atmospheric drivers were identified, which are on the one hand aerosol optical properties, i.e., aerosol optical thickness (AOT), Angstrom coefficient (AMS) and scattering asymmetry variable (G), mostly driving diffuse atmospheric components, E d i f and T d i f ; and those affected by atmospheric scattering, L 0 and S. On the other hand, as expected, AOT, AMS and columnar water vapor (CWV) in the absorption regions mostly drive E d i r and T d i r atmospheric functions. The presented emulation schemes showed very promising results in replacing costly RTMs, and we think they can contribute to the adoption of machine learning techniques in remote sensing and environmental applications. Full article
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Open AccessArticle An Image Matching Algorithm Integrating Global SRTM and Image Segmentation for Multi-Source Satellite Imagery
Remote Sens. 2016, 8(8), 672; https://doi.org/10.3390/rs8080672
Received: 7 May 2016 / Revised: 9 August 2016 / Accepted: 16 August 2016 / Published: 19 August 2016
Cited by 8 | Viewed by 1983 | PDF Full-text (8605 KB) | HTML Full-text | XML Full-text
Abstract
This paper presents a novel image matching method for multi-source satellite images, which integrates global Shuttle Radar Topography Mission (SRTM) data and image segmentation to achieve robust and numerous correspondences. This method first generates the epipolar lines as a geometric constraint assisted by [...] Read more.
This paper presents a novel image matching method for multi-source satellite images, which integrates global Shuttle Radar Topography Mission (SRTM) data and image segmentation to achieve robust and numerous correspondences. This method first generates the epipolar lines as a geometric constraint assisted by global SRTM data, after which the seed points are selected and matched. To produce more reliable matching results, a region segmentation-based matching propagation is proposed in this paper, whereby the region segmentations are extracted by image segmentation and are considered to be a spatial constraint. Moreover, a similarity measure integrating Distance, Angle and Normalized Cross-Correlation (DANCC), which considers geometric similarity and radiometric similarity, is introduced to find the optimal correspondences. Experiments using typical satellite images acquired from Resources Satellite-3 (ZY-3), Mapping Satellite-1, SPOT-5 and Google Earth demonstrated that the proposed method is able to produce reliable and accurate matching results. Full article
(This article belongs to the Special Issue Multi-Sensor and Multi-Data Integration in Remote Sensing)
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Open AccessArticle Advanced Three-Dimensional Finite Element Modeling of a Slow Landslide through the Exploitation of DInSAR Measurements and in Situ Surveys
Remote Sens. 2016, 8(8), 670; https://doi.org/10.3390/rs8080670
Received: 10 June 2016 / Revised: 10 August 2016 / Accepted: 16 August 2016 / Published: 19 August 2016
Cited by 9 | Viewed by 1853 | PDF Full-text (10328 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
In this paper, we propose an advanced methodology to perform three-dimensional (3D) Finite Element (FE) modeling to investigate the kinematical evolution of a slow landslide phenomenon. Our approach benefits from the effective integration of the available geological, geotechnical and satellite datasets to perform [...] Read more.
In this paper, we propose an advanced methodology to perform three-dimensional (3D) Finite Element (FE) modeling to investigate the kinematical evolution of a slow landslide phenomenon. Our approach benefits from the effective integration of the available geological, geotechnical and satellite datasets to perform an accurate simulation of the landslide process. More specifically, we fully exploit the capability of the advanced Differential Synthetic Aperture Radar Interferometry (DInSAR) technique referred to as the Small BAseline Subset (SBAS) approach to provide spatially dense surface displacement information. Subsequently, we analyze the physical behavior characterizing the observed landslide phenomenon by means of an inverse analysis based on an optimization procedure. We focus on the Ivancich landslide phenomenon, which affects a residential area outside the historical center of the town of Assisi (Central Italy). Thanks to the large amount of available information, we have selected this area as a representative case study highlighting the capability of advanced 3D FE modeling to perform effective risk analyses of slow landslide processes and accurate urban development planning. In particular, the FE modeling is constrained by using the data from 7 litho-stratigraphic cross-sections and 62 stratigraphic boreholes; and the optimization procedure is carried out using the SBAS-DInSAR retrieved results by processing 39 SAR images collected by the Cosmo-SkyMed (CSK) constellation in the 2009–2012 time span. The achieved results allow us to explore the spatial and temporal evolution of the slow-moving phenomenon and via comparison with the geomorphological data, to derive a synoptic view of the kinematical activity of the urban area affected by the Ivancich landslide. Full article
(This article belongs to the Special Issue Earth Observations for Geohazards) Printed Edition available
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Open AccessArticle Fusion of WorldView-2 and LiDAR Data to Map Fuel Types in the Canary Islands
Remote Sens. 2016, 8(8), 669; https://doi.org/10.3390/rs8080669
Received: 6 June 2016 / Revised: 19 July 2016 / Accepted: 16 August 2016 / Published: 18 August 2016
Cited by 5 | Viewed by 1848 | PDF Full-text (3654 KB) | HTML Full-text | XML Full-text
Abstract
Wildland fires are one of the factors causing the deepest disturbances on the natural environment and severely threatening many ecosystems, as well as economic welfare and public health. Having accurate and up-to-date fuel type maps is essential to properly manage wildland fire risk [...] Read more.
Wildland fires are one of the factors causing the deepest disturbances on the natural environment and severely threatening many ecosystems, as well as economic welfare and public health. Having accurate and up-to-date fuel type maps is essential to properly manage wildland fire risk areas. This research aims to assess the viability of combining Geographic Object-Based Image Analysis (GEOBIA) and the fusion of a WorldView-2 (WV2) image and low density Light Detection and Ranging (LiDAR) data in order to produce fuel type maps within an area of complex orography and vegetation distribution located in the island of Tenerife (Spain). Independent GEOBIAs were applied to four datasets to create four fuel type maps according to the Prometheus classification. The following fusion methods were compared: Image Stack (IS), Principal Component Analysis (PCA) and Minimum Noise Fraction (MNF), as well as the WV2 image alone. Accuracy assessment of the maps was conducted by comparison against the fuel types assessed in the field. Besides global agreement, disagreement measures due to allocation and quantity were estimated, both globally and by fuel type. This made it possible to better understand the nature of disagreements linked to each map. The global agreement of the obtained maps varied from 76.23% to 85.43%. Maps obtained through data fusion reached a significantly higher global agreement than the map derived from the WV2 image alone. By integrating LiDAR information with the GEOBIAs, global agreement improvements by over 10% were attained in all cases. No significant differences in global agreement were found among the three classifications performed on WV2 and LiDAR fusion data (IS, PCA, MNF). These study’s findings show the validity of the combined use of GEOBIA, high-spatial resolution multispectral data and low density LiDAR data in order to generate fuel type maps in the Canary Islands. Full article
(This article belongs to the Special Issue Fusion of LiDAR Point Clouds and Optical Images)
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Open AccessArticle Do Agrometeorological Data Improve Optical Satellite-Based Estimations of the Herbaceous Yield in Sahelian Semi-Arid Ecosystems?
Remote Sens. 2016, 8(8), 668; https://doi.org/10.3390/rs8080668
Received: 23 April 2016 / Revised: 26 July 2016 / Accepted: 10 August 2016 / Published: 18 August 2016
Cited by 9 | Viewed by 1764 | PDF Full-text (5703 KB) | HTML Full-text | XML Full-text
Abstract
Quantitative estimates of forage availability at the end of the growing season in rangelands are helpful for pastoral livestock managers and for local, national and regional stakeholders in natural resource management. For this reason, remote sensing data such as the Fraction of Absorbed [...] Read more.
Quantitative estimates of forage availability at the end of the growing season in rangelands are helpful for pastoral livestock managers and for local, national and regional stakeholders in natural resource management. For this reason, remote sensing data such as the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) have been widely used to assess Sahelian plant productivity for about 40 years. This study combines traditional FAPAR-based assessments with agrometeorological variables computed by the geospatial water balance program, GeoWRSI, using rainfall and potential evapotranspiration satellite gridded data to estimate the annual herbaceous yield in the semi-arid areas of Senegal. It showed that a machine-learning model combining FAPAR seasonal metrics with various agrometeorological data provided better estimations of the in situ annual herbaceous yield (R2 = 0.69; RMSE = 483 kg·DM/ha) than models based exclusively on FAPAR metrics (R2 = 0.63; RMSE = 550 kg·DM/ha) or agrometeorological variables (R2 = 0.55; RMSE = 585 kg·DM/ha). All the models provided reasonable outputs and showed a decrease in the mean annual yield with increasing latitude, together with an increase in relative inter-annual variation. In particular, the additional use of agrometeorological information mitigated the saturation effects that characterize the plant indices of areas with high plant productivity. In addition, the date of the onset of the growing season derived from smoothed FAPAR seasonal dynamics showed no significant relationship (0.05 p-level) with the annual herbaceous yield across the whole studied area. The date of the onset of rainfall however, was significantly related to the herbaceous yield and its inclusion in fodder biomass models could constitute a significant improvement in forecasting risks of a mass herbaceous deficit at an early stage of the year. Full article
(This article belongs to the Special Issue Remote Sensing of Vegetation Structure and Dynamics)
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Open AccessLetter Experiments on a Ground-Based Tomographic Synthetic Aperture Radar
Remote Sens. 2016, 8(8), 667; https://doi.org/10.3390/rs8080667
Received: 16 June 2016 / Revised: 10 August 2016 / Accepted: 16 August 2016 / Published: 18 August 2016
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Abstract
This paper presents the development and experiment of three-dimensional image formation by using a ground-based tomographic synthetic aperture radar (GB-TomoSAR) system. GB-TomoSAR formulates two-dimensional synthetic aperture by the motion of antennae, both in azimuth and vertical directions. After range compression, three-dimensional image focusing [...] Read more.
This paper presents the development and experiment of three-dimensional image formation by using a ground-based tomographic synthetic aperture radar (GB-TomoSAR) system. GB-TomoSAR formulates two-dimensional synthetic aperture by the motion of antennae, both in azimuth and vertical directions. After range compression, three-dimensional image focusing is performed by applying Deramp-FFT (Fast Fourier Transform) algorithms, both in azimuth and vertical directions. Geometric and radiometric calibrations were applied to make an image cube, which is then projected into range-azimuth and range-vertical cross-sections for visualization. An experiment with a C-band GB-TomoSAR system with a scan length of 2.49 m and 1.86 m in azimuth and vertical-direction, respectively, shows distinctive three-dimensional radar backscattering of stable buildings and roads with resolutions similar to the theoretical values. Unstable objects such as trees and moving cars generate severe noise due to decorrelation during the eight-hour image-acquisition time. Full article
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Open AccessArticle Ready-to-Use Methods for the Detection of Clouds, Cirrus, Snow, Shadow, Water and Clear Sky Pixels in Sentinel-2 MSI Images
Remote Sens. 2016, 8(8), 666; https://doi.org/10.3390/rs8080666
Received: 27 April 2016 / Revised: 29 July 2016 / Accepted: 1 August 2016 / Published: 18 August 2016
Cited by 17 | Viewed by 5000 | PDF Full-text (4410 KB) | HTML Full-text | XML Full-text
Abstract
Classification of clouds, cirrus, snow, shadows and clear sky areas is a crucial step in the pre-processing of optical remote sensing images and is a valuable input for their atmospheric correction. The Multi-Spectral Imager on board the Sentinel-2’s of the Copernicus program offers [...] Read more.
Classification of clouds, cirrus, snow, shadows and clear sky areas is a crucial step in the pre-processing of optical remote sensing images and is a valuable input for their atmospheric correction. The Multi-Spectral Imager on board the Sentinel-2’s of the Copernicus program offers optimized bands for this task and delivers unprecedented amounts of data regarding spatial sampling, global coverage, spectral coverage, and repetition rate. Efficient algorithms are needed to process, or possibly reprocess, those big amounts of data. Techniques based on top-of-atmosphere reflectance spectra for single-pixels without exploitation of external data or spatial context offer the largest potential for parallel data processing and highly optimized processing throughput. Such algorithms can be seen as a baseline for possible trade-offs in processing performance when the application of more sophisticated methods is discussed. We present several ready-to-use classification algorithms which are all based on a publicly available database of manually classified Sentinel-2A images. These algorithms are based on commonly used and newly developed machine learning techniques which drastically reduce the amount of time needed to update the algorithms when new images are added to the database. Several ready-to-use decision trees are presented which allow to correctly label about 91 % of the spectra within a validation dataset. While decision trees are simple to implement and easy to understand, they offer only limited classification skill. It improves to 98 % when the presented algorithm based on the classical Bayesian method is applied. This method has only recently been used for this task and shows excellent performance concerning classification skill and processing performance. A comparison of the presented algorithms with other commonly used techniques such as random forests, stochastic gradient descent, or support vector machines is also given. Especially random forests and support vector machines show similar classification skill as the classical Bayesian method. Full article
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Open AccessArticle Analyzing Landscape Trends on Agriculture, Introduced Exotic Grasslands and Riparian Ecosystems in Arid Regions of Mexico
Remote Sens. 2016, 8(8), 664; https://doi.org/10.3390/rs8080664
Received: 4 May 2016 / Revised: 13 July 2016 / Accepted: 1 August 2016 / Published: 18 August 2016
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Abstract
Riparian Zones are considered biodiversity and ecosystem services hotspots. In arid environments, these ecosystems represent key habitats, since water availability makes them unique in terms of fauna, flora and ecological processes. Simple yet powerful remote sensing techniques were used to assess how spatial [...] Read more.
Riparian Zones are considered biodiversity and ecosystem services hotspots. In arid environments, these ecosystems represent key habitats, since water availability makes them unique in terms of fauna, flora and ecological processes. Simple yet powerful remote sensing techniques were used to assess how spatial and temporal land cover dynamics, and water depth reflect distribution of key land cover types in riparian areas. Our study area includes the San Miguel and Zanjon rivers in Northwest Mexico. We used a supervised classification and regression tree (CART) algorithm to produce thematic classifications (with accuracies higher than 78%) for 1993, 2002 and 2011 using Landsat TM scenes. Our results suggest a decline in agriculture (32.5% area decrease) and cultivated grasslands (21.1% area decrease) from 1993 to 2011 in the study area. We found constant fluctuation between adjacent land cover classes and riparian habitat. We also found that water depth restricts Riparian Vegetation distribution but not agricultural lands or induced grasslands. Using remote sensing combined with spatial analysis, we were able to reach a better understanding of how riparian habitats are being modified in arid environments and how they have changed through time. Full article
(This article belongs to the Special Issue Monitoring of Land Changes)
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Open AccessEditorial Preface: Remote Sensing in Coastal Environments
Remote Sens. 2016, 8(8), 665; https://doi.org/10.3390/rs8080665
Received: 12 August 2016 / Accepted: 15 August 2016 / Published: 17 August 2016
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Abstract
The Special Issue (SI) on “Remote Sensing in Coastal Environments” presents a wide range of articles focusing on a variety of remote sensing models and techniques to address coastal issues and processes ranging for wetlands and water quality to coral reefs and kelp [...] Read more.
The Special Issue (SI) on “Remote Sensing in Coastal Environments” presents a wide range of articles focusing on a variety of remote sensing models and techniques to address coastal issues and processes ranging for wetlands and water quality to coral reefs and kelp habitats. The SI is comprised of twenty-one papers, covering a broad range of research topics that employ remote sensing imagery, models, and techniques to monitor water quality, vegetation, habitat suitability, and geomorphology in the coastal zone. This preface provides a brief summary of each article published in the SI. Full article
(This article belongs to the Special Issue Remote Sensing in Coastal Environments)
Open AccessArticle Highlighting Biome-Specific Sensitivity of Fire Size Distributions to Time-Gap Parameter Using a New Algorithm for Fire Event Individuation
Remote Sens. 2016, 8(8), 663; https://doi.org/10.3390/rs8080663
Received: 4 June 2016 / Revised: 5 August 2016 / Accepted: 11 August 2016 / Published: 17 August 2016
Cited by 4 | Viewed by 1838 | PDF Full-text (5542 KB) | HTML Full-text | XML Full-text
Abstract
Detailed spatial-temporal characterization of individual fire dynamics using remote sensing data is important to understand fire-environment relationships, to support landscape-scale fire risk management, and to obtain improved statistics on fire size distributions over broad areas. Previously, individuation of events to quantify fire size [...] Read more.
Detailed spatial-temporal characterization of individual fire dynamics using remote sensing data is important to understand fire-environment relationships, to support landscape-scale fire risk management, and to obtain improved statistics on fire size distributions over broad areas. Previously, individuation of events to quantify fire size distributions has been performed with the flood-fill algorithm. A key parameter of such algorithms is the time-gap used to cluster spatially adjacent fire-affected pixels and declare them as belonging to the same event. Choice of a time-gap to define a fire event entails several assumptions affecting the degree of clustering/fragmentation of the individual events. We evaluate the impact of different time-gaps on the number, size and spatial distribution of active fire clusters, using a new algorithm. The information produced by this algorithm includes number, size, and ignition date of active fire clusters. The algorithm was tested at a global scale using active fire observations from the Moderate Resolution Imaging Spectroradiometer (MODIS). Active fire cluster size distributions were characterized with the Gini coefficient, and the impact of changing time-gap values was analyzed on a 0.5° cell grid. As expected, the number of active fire clusters decreased and their mean size increased with the time-gap value. The largest sensitivity of fire size distributions to time-gap was observed in African tropical savannas and, to a lesser extent, in South America, Southeast Asia, and eastern Siberia. Sensitivity of fire individuation, and thus Gini coefficient values, to time-gap demonstrate the difficulty of individuating fire events in tropical savannas, where coalescence of flame fronts with distinct ignition locations and dates is very common, and fire size distributions strongly depend on algorithm parameterization. Thus, caution should be exercised when attempting to individualize fire events, characterizing their size distributions, and addressing their management implications, particularly in the African savannas. Full article
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Open AccessArticle Hyper-Temporal C-Band SAR for Baseline Woody Structural Assessments in Deciduous Savannas
Remote Sens. 2016, 8(8), 661; https://doi.org/10.3390/rs8080661
Received: 28 June 2016 / Accepted: 3 August 2016 / Published: 17 August 2016
Cited by 5 | Viewed by 1788 | PDF Full-text (3481 KB) | HTML Full-text | XML Full-text
Abstract
Savanna ecosystems and their woody vegetation provide valuable resources and ecosystem services. Locally calibrated and cost effective estimates of these resources are required in order to satisfy commitments to monitor and manage change within them. Baseline maps of woody resources are important for [...] Read more.
Savanna ecosystems and their woody vegetation provide valuable resources and ecosystem services. Locally calibrated and cost effective estimates of these resources are required in order to satisfy commitments to monitor and manage change within them. Baseline maps of woody resources are important for analyzing change over time. Freely available, and highly repetitive, C-band data has the potential to be a viable alternative to high-resolution commercial SAR imagery (e.g., RADARSAT-2, ALOS2) in generating large-scale woody resources maps. Using airborne LiDAR as calibration, we investigated the relationships between hyper-temporal C-band ASAR data and woody structural parameters, namely total canopy cover (TCC) and total canopy volume (TCV), in a deciduous savanna environment. Results showed that: the temporal filter reduced image variance; the random forest model out-performed the linear model; while the TCV metric consistently showed marginally higher accuracies than the TCC metric. Combinations of between 6 and 10 images could produce results comparable to high resolution commercial (C- & L-band) SAR imagery. The approach showed promise for producing a regional scale, locally calibrated, baseline maps for the management of deciduous savanna resources, and lay a foundation for monitoring using time series of data from newer C-band SAR sensors (e.g., Sentinel1). Full article
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Open AccessArticle Spectral Indices to Improve Crop Residue Cover Estimation under Varying Moisture Conditions
Remote Sens. 2016, 8(8), 660; https://doi.org/10.3390/rs8080660
Received: 23 June 2016 / Revised: 8 August 2016 / Accepted: 10 August 2016 / Published: 17 August 2016
Cited by 8 | Viewed by 1797 | PDF Full-text (5044 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Crop residues on the soil surface protect the soil against erosion, increase water infiltration and reduce agrochemicals in runoff water. Crop residues and soils are spectrally different in the absorption features associated with cellulose and lignin. Our objectives were to: (1) assess the [...] Read more.
Crop residues on the soil surface protect the soil against erosion, increase water infiltration and reduce agrochemicals in runoff water. Crop residues and soils are spectrally different in the absorption features associated with cellulose and lignin. Our objectives were to: (1) assess the impact of water on the spectral indices for estimating crop residue cover (fR); (2) evaluate spectral water indices for estimating the relative water content (RWC) of crop residues and soils; and (3) propose methods that mitigate the uncertainty caused by variable moisture conditions on estimates of fR. Reflectance spectra of diverse crops and soils were acquired in the laboratory over the 400–2400-nm wavelength region. Using the laboratory data, a linear mixture model simulated the reflectance of scenes with various fR and levels of RWC. Additional reflectance spectra were acquired over agricultural fields with a wide range of crop residue covers and scene moisture conditions. Spectral indices for estimating crop residue cover that were evaluated in this study included the Normalized Difference Tillage Index (NDTI), the Shortwave Infrared Normalized Difference Residue Index (SINDRI) and the Cellulose Absorption Index (CAI). Multivariate linear models that used pairs of spectral indices—one for RWC and one for fR—significantly improved estimates of fR using CAI and SINDRI. For NDTI to reliably assess fR, scene RWC should be relatively dry (RWC < 0.25). These techniques provide the tools needed to monitor the spatial and temporal changes in crop residue cover and help determine where additional conservation practices may be required. Full article
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