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Remote Sens., Volume 9, Issue 2 (February 2017)

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Cover Story An algorithm, based on RGB imagery, acquired with a UAV is proposed to characterize the 3D [...] Read more.
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Open AccessFeature PaperArticle A Convolutional Neural Network Approach for Assisting Avalanche Search and Rescue Operations with UAV Imagery
Remote Sens. 2017, 9(2), 100; doi:10.3390/rs9020100
Received: 11 November 2016 / Revised: 29 December 2016 / Accepted: 14 January 2017 / Published: 24 January 2017
Cited by 4 | PDF Full-text (6738 KB) | HTML Full-text | XML Full-text
Abstract
Following an avalanche, one of the factors that affect victims’ chance of survival is the speed with which they are located and dug out. Rescue teams use techniques like trained rescue dogs and electronic transceivers to locate victims. However, the resources and time
[...] Read more.
Following an avalanche, one of the factors that affect victims’ chance of survival is the speed with which they are located and dug out. Rescue teams use techniques like trained rescue dogs and electronic transceivers to locate victims. However, the resources and time required to deploy rescue teams are major bottlenecks that decrease a victim’s chance of survival. Advances in the field of Unmanned Aerial Vehicles (UAVs) have enabled the use of flying robots equipped with sensors like optical cameras to assess the damage caused by natural or manmade disasters and locate victims in the debris. In this paper, we propose assisting avalanche search and rescue (SAR) operations with UAVs fitted with vision cameras. The sequence of images of the avalanche debris captured by the UAV is processed with a pre-trained Convolutional Neural Network (CNN) to extract discriminative features. A trained linear Support Vector Machine (SVM) is integrated at the top of the CNN to detect objects of interest. Moreover, we introduce a pre-processing method to increase the detection rate and a post-processing method based on a Hidden Markov Model to improve the prediction performance of the classifier. Experimental results conducted on two different datasets at different levels of resolution show that the detection performance increases with an increase in resolution, while the computation time increases. Additionally, they also suggest that a significant decrease in processing time can be achieved thanks to the pre-processing step. Full article
(This article belongs to the Special Issue Recent Trends in UAV Remote Sensing)
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Open AccessArticle Analysis of Ocean Tide Loading in Differential InSAR Measurements
Remote Sens. 2017, 9(2), 101; doi:10.3390/rs9020101
Received: 19 August 2016 / Revised: 24 December 2016 / Accepted: 18 January 2017 / Published: 24 January 2017
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Abstract
Ocean tide loading (OTL) causes crustal displacements in coastal regions, and the relative variation of these ground displacements may reach several centimeters across differential interferometric synthetic aperture radar (DInSAR) interferograms. However, orbit errors seriously affect the analysis of long-wavelength crustal deformation signals such
[...] Read more.
Ocean tide loading (OTL) causes crustal displacements in coastal regions, and the relative variation of these ground displacements may reach several centimeters across differential interferometric synthetic aperture radar (DInSAR) interferograms. However, orbit errors seriously affect the analysis of long-wavelength crustal deformation signals such as the OTL effect because of their similar signatures in DInSAR interferograms. To correct the orbit errors, we used a linear surface model to model the relative displacements of the Global Positioning System (GPS) precise point positioning (PPP) in the line of sight (LOS) direction as a priori parameter of the long-wavelength crustal deformation signals. After correcting the orbit errors, an ocean tide model was applied to correct the OTL effect in the DInSAR interferograms. The proposed approach was verified with the DInSAR interferograms from the Los Angeles basin. The experimental results confirm that the real orbit errors can be modeled by the bilinear ramp function under the constraint of the priori parameter. Moreover, after removing the orbit errors, the OTL effect, which is dominant in the long-wavelength crustal deformation signals, can be revealed, and then be effectively eliminated by the FES2004 tide model. Full article
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Open AccessArticle Assessment of Altimetric Range and Geophysical Corrections and Mean Sea Surface Models—Impacts on Sea Level Variability around the Indonesian Seas
Remote Sens. 2017, 9(2), 102; doi:10.3390/rs9020102
Received: 14 October 2016 / Revised: 28 December 2016 / Accepted: 18 January 2017 / Published: 24 January 2017
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Abstract
The focus of this study is the assessment of the main range and geophysical corrections needed to derive accurate sea level time series from satellite altimetry in the Indonesia seas, the ultimate aim being the determination of sea level trend for this region.
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The focus of this study is the assessment of the main range and geophysical corrections needed to derive accurate sea level time series from satellite altimetry in the Indonesia seas, the ultimate aim being the determination of sea level trend for this region. Due to its island nature, this is an area of large complexity for altimetric studies, a true laboratory for coastal altimetry. For this reason, the selection of the best corrections for sea level anomaly estimation from satellite altimetry is of particular relevance in the Indonesian seas. The same happens with the mean sea surface adopted in the sea level anomaly computation due to the large gradients of the mean sea surface in this part of the ocean. This study has been performed using altimetric data from the three reference missions, TOPEX/Poseidon, Jason-1 and Jason-2, extracted from the Radar Altimeter Database System. Analyses of sea level anomaly variance differences, function of distance from the coast and at altimeter crossovers were used to assess the quality of the various corrections and mean sea surface models. The selected set of corrections and mean sea surface have been used to estimate the sea level anomaly time series. The rate of sea level rise for the Indonesian seas was found to be 4.2 ± 0.2 mm/year over the 23-year period (1993–2015). Full article
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Open AccessArticle Validation of Spaceborne and Modelled Surface Soil Moisture Products with Cosmic-Ray Neutron Probes
Remote Sens. 2017, 9(2), 103; doi:10.3390/rs9020103
Received: 11 November 2016 / Revised: 13 January 2017 / Accepted: 19 January 2017 / Published: 25 January 2017
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Abstract
The scale difference between point in situ soil moisture measurements and low resolution satellite products limits the quality of any validation efforts in heterogeneous regions. Cosmic Ray Neutron Probes (CRNP) could be an option to fill the scale gap between both systems, as
[...] Read more.
The scale difference between point in situ soil moisture measurements and low resolution satellite products limits the quality of any validation efforts in heterogeneous regions. Cosmic Ray Neutron Probes (CRNP) could be an option to fill the scale gap between both systems, as they provide area-average soil moisture within a 150–250 m radius footprint. In this study, we evaluate differences and similarities between CRNP observations, and surface soil moisture products from the Advanced Microwave Scanning Radiometer 2 (AMSR2), the METOP-A/B Advanced Scatterometer (ASCAT), the Soil Moisture Active and Passive (SMAP), the Soil Moisture and Ocean Salinity (SMOS), as well as simulations from the Global Land Data Assimilation System Version 2 (GLDAS2). Six CRNPs located on five continents have been selected as test sites: the Rur catchment in Germany, the COSMOS sites in Arizona and California (USA), and Kenya, one CosmOz site in New South Wales (Australia), and a site in Karnataka (India). Standard validation scores as well as the Triple Collocation (TC) method identified SMAP to provide a high accuracy soil moisture product with low noise or uncertainties as compared to CRNPs. The potential of CRNPs for satellite soil moisture validation has been proven; however, biomass correction methods should be implemented to improve its application in regions with large vegetation dynamics. Full article
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Open AccessArticle Validation Analysis of SMAP and AMSR2 Soil Moisture Products over the United States Using Ground-Based Measurements
Remote Sens. 2017, 9(2), 104; doi:10.3390/rs9020104
Received: 10 November 2016 / Revised: 16 January 2017 / Accepted: 23 January 2017 / Published: 25 January 2017
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Abstract
Soil moisture products acquired from passive satellite missions have been widely applied in environmental processes. A primary challenge for the use of soil moisture products from passive sensors is their reliability. It is crucial to evaluate the reliability of those products before they
[...] Read more.
Soil moisture products acquired from passive satellite missions have been widely applied in environmental processes. A primary challenge for the use of soil moisture products from passive sensors is their reliability. It is crucial to evaluate the reliability of those products before they can be routinely used at a global scale. In this paper, we evaluated the Soil Moisture Active/Passive (SMAP) and the Advanced Microwave Scanning Radiometer (AMSR2) radiometer soil moisture products against in situ measurements collected from American networks with four statistics, including the mean difference (MD), the root mean squared difference (RMSD), the unbiased root mean square error (ubRMSE) and the correlation coefficient (R). The evaluation results of SMAP and AMSR2 soil moisture products were compared. Moreover, the triple collocation (TC) error model was used to assess the error among AMSR2, SMAP and in situ data. The monthly average and daily AMSR2 and SMAP soil moisture data were analyzed. Different spatial series, temporal series and combined spatial-temporal analysis were carried out. The results reveal that SMAP soil moisture retrievals are generally better than AMSR2 soil moisture data. The remotely sensed retrievals show the best agreement with in situ measurements over the central Great Plains and cultivated crops throughout the year. In particular, SMAP soil moisture data shows a stable pattern for capturing the spatial distribution of surface soil moisture. Further studies are required for better understanding the SMAP soil moisture product. Full article
(This article belongs to the Special Issue Remote Sensing Applied to Soils: From Ground to Space)
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Open AccessEditor’s ChoiceArticle Integrating Radarsat-2, Lidar, and Worldview-3 Imagery to Maximize Detection of Forested Inundation Extent in the Delmarva Peninsula, USA
Remote Sens. 2017, 9(2), 105; doi:10.3390/rs9020105
Received: 30 September 2016 / Revised: 9 January 2017 / Accepted: 20 January 2017 / Published: 25 January 2017
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Abstract
Natural variability in surface-water extent and associated characteristics presents a challenge to gathering timely, accurate information, particularly in environments that are dominated by small and/or forested wetlands. This study mapped inundation extent across the Upper Choptank River Watershed on the Delmarva Peninsula, occurring
[...] Read more.
Natural variability in surface-water extent and associated characteristics presents a challenge to gathering timely, accurate information, particularly in environments that are dominated by small and/or forested wetlands. This study mapped inundation extent across the Upper Choptank River Watershed on the Delmarva Peninsula, occurring within both Maryland and Delaware. We integrated six quad-polarized Radarsat-2 images, Worldview-3 imagery, and an enhanced topographic wetness index in a random forest model. Output maps were filtered using light detection and ranging (lidar)-derived depressions to maximize the accuracy of forested inundation extent. Overall accuracy within the integrated and filtered model was 94.3%, with 5.5% and 6.0% errors of omission and commission for inundation, respectively. Accuracy of inundation maps obtained using Radarsat-2 alone were likely detrimentally affected by less than ideal angles of incidence and recent precipitation, but were likely improved by targeting the period between snowmelt and leaf-out for imagery collection. Across the six Radarsat-2 dates, filtering inundation outputs by lidar-derived depressions slightly elevated errors of omission for water (+1.0%), but decreased errors of commission (−7.8%), resulting in an average increase of 5.4% in overall accuracy. Depressions were derived from lidar datasets collected under both dry and average wetness conditions. Although antecedent wetness conditions influenced the abundance and total area mapped as depression, the two versions of the depression datasets showed a similar ability to reduce error in the inundation maps. Accurate mapping of surface water is critical to predicting and monitoring the effect of human-induced change and interannual variability on water quantity and quality. Full article
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Open AccessArticle Potential and Limitation of SPOT-5 Ortho-Image Correlation to Investigate the Cinematics of Landslides: The Example of “Mare à Poule d’Eau” (Réunion, France)
Remote Sens. 2017, 9(2), 106; doi:10.3390/rs9020106
Received: 6 November 2016 / Revised: 19 January 2017 / Accepted: 20 January 2017 / Published: 26 January 2017
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Abstract
Over the last 10 years, the accessibility of high spatial resolution remote sensing images has strongly increased. These images are available in ortho-rectified format which do not necessitate any further geometrical processing to be analyzed. In parallel, image correlation software has become more
[...] Read more.
Over the last 10 years, the accessibility of high spatial resolution remote sensing images has strongly increased. These images are available in ortho-rectified format which do not necessitate any further geometrical processing to be analyzed. In parallel, image correlation software has become more efficient and friendly. In this paper, image correlation methods are tested to evaluate their potential and limitations to measure the surface displacements in a complex case of a landslide located in a tropical environment. This studied landslide, called “Mare à Poule d’Eau”, is located in the Salazie erosion watershed in Réunion Island (France). This landslide is monitored daily by a DGPS station which registers the south-north displacements. Two pairs of ortho-rectified SPOT-5 images at 2.5 m resolution provided by Kalideos (http://kalideos.cnes.fr) were selected. The first pair frames the period between 2002 and 2005 during which the landslide activity was low. The second pair of images (2006–2008) frames a period of time during which the landslide was more active. Fifty-nine Image Control Points (ICP) were selected on the images by the SIFT method (Scale Invariant Feature Transform) and visually controlled. The shifts of these points used as external control are estimated for the two time periods. Two image correlator softwares are used: MicMac and Cosi-Corr. The results obtained by the two correlators are similar. For the 2002–2005 period, the shift measured by correlators in the landslide is similar to the shift outside the landslide. This means that the displacement cannot be detected and estimated during periods of low activity of the landslide. The shift of the landslide for the 2006–2008 period is out of noise and reaches 8.5 m. The displacement can be estimated by applying a correction factor extracted from the ICP located in the stable areas. The potential and limits of the image correlation in such complex environments is discussed. A strategy is proposed to evaluate the quality of the results and to extract the displacement signal from the shift measurements. Full article
(This article belongs to the Special Issue Remote Sensing of Landslides)
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Open AccessArticle Single-Sensor Solution to Tree Species Classification Using Multispectral Airborne Laser Scanning
Remote Sens. 2017, 9(2), 108; doi:10.3390/rs9020108
Received: 20 October 2016 / Accepted: 20 January 2017 / Published: 27 January 2017
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Abstract
This paper investigated the potential of multispectral airborne laser scanning (ALS) data for individual tree detection and tree species classification. The aim was to develop a single-sensorsolution for forest mapping that is capable of providing species-specific information, required for forest management and planning
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This paper investigated the potential of multispectral airborne laser scanning (ALS) data for individual tree detection and tree species classification. The aim was to develop a single-sensorsolution for forest mapping that is capable of providing species-specific information, required for forest management and planning purposes. Experiments were conducted using 1903 ground measured trees from 22 sample plots and multispectral ALS data, acquired with an Optech Titan scanner over a boreal forest, mainly consisting of Scots pine (Pinus Sylvestris), Norway spruce (Picea Abies), and birch (Betula sp.), in southern Finland. ALS-features used as predictors for tree species were extracted from segmented tree objects and used in random forest classification. Different combinations of features, including point cloud features, and intensity features of single and multiple channels, were tested. Among the field-measured trees, 61.3% were correctly detected. The best overall accuracy (OA) of tree species classification achieved for correctly-detected trees was 85.9% (Kappa = 0.75), using a point cloud and single-channel intensity features combination, which was not significantly different from the ones that were obtained either using all features (OA = 85.6%, Kappa = 0.75), or single-channel intensity features alone (OA = 85.4%, Kappa = 0.75). Point cloud features alone achieved the lowest accuracy, with an OA of 76.0%. Field-measured trees were also divided into four categories. An examination of the classification accuracy for four categories of trees showed that isolated and dominant trees can be detected with a detection rate of 91.9%, and classified with a high overall accuracy of 90.5%. The corresponding detection rate and accuracy were 81.5% and 89.8% for a group of trees, 26.4% and 79.1% for trees next to a larger tree, and 7.2% and 53.9% for trees situated under a larger tree, respectively. The results suggest that Channel 2 (1064 nm) contains more information for separating pine, spruce, and birch, followed by channel 1 (1550 nm) and channel 3 (532 nm) with an overall accuracy of 81.9%, 78.3%, and 69.1%, respectively. Our results indicate that the use of multispectral ALS data has great potential to lead to a single-sensor solution for forest mapping. Full article
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Open AccessArticle Multi-Label Classification Based on Low Rank Representation for Image Annotation
Remote Sens. 2017, 9(2), 109; doi:10.3390/rs9020109
Received: 7 November 2016 / Accepted: 22 January 2017 / Published: 27 January 2017
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Abstract
Annotating remote sensing images is a challenging task for its labor demanding annotation process and requirement of expert knowledge, especially when images can be annotated with multiple semantic concepts (or labels). To automatically annotate these multi-label images, we introduce an approach called Multi-Label
[...] Read more.
Annotating remote sensing images is a challenging task for its labor demanding annotation process and requirement of expert knowledge, especially when images can be annotated with multiple semantic concepts (or labels). To automatically annotate these multi-label images, we introduce an approach called Multi-Label Classification based on Low Rank Representation (MLC-LRR). MLC-LRR firstly utilizes low rank representation in the feature space of images to compute the low rank constrained coefficient matrix, then it adapts the coefficient matrix to define a feature-based graph and to capture the global relationships between images. Next, it utilizes low rank representation in the label space of labeled images to construct a semantic graph. Finally, these two graphs are exploited to train a graph-based multi-label classifier. To validate the performance of MLC-LRR against other related graph-based multi-label methods in annotating images, we conduct experiments on a public available multi-label remote sensing images (Land Cover). We perform additional experiments on five real-world multi-label image datasets to further investigate the performance of MLC-LRR. Empirical study demonstrates that MLC-LRR achieves better performance on annotating images than these comparing methods across various evaluation criteria; it also can effectively exploit global structure and label correlations of multi-label images. Full article
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Open AccessArticle Cross-Comparison of Albedo Products for Glacier Surfaces Derived from Airborne and Satellite (Sentinel-2 and Landsat 8) Optical Data
Remote Sens. 2017, 9(2), 110; doi:10.3390/rs9020110
Received: 21 October 2016 / Accepted: 19 January 2017 / Published: 27 January 2017
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Abstract
Surface albedo partitions the amount of energy received by glacier surfaces from shortwave fluxes and modulates the energy available for melt processes. The ice-albedo feedback, influenced by the contamination of bare-ice surfaces with light-absorbing impurities, plays a major role in the melting of
[...] Read more.
Surface albedo partitions the amount of energy received by glacier surfaces from shortwave fluxes and modulates the energy available for melt processes. The ice-albedo feedback, influenced by the contamination of bare-ice surfaces with light-absorbing impurities, plays a major role in the melting of mountain glaciers in a warming climate. However, little is known about the spatial and temporal distribution and variability of bare-ice glacier surface albedo under changing conditions. In this study, we focus on two mountain glaciers located in the western Swiss Alps and perform a cross-comparison of different albedo products. We take advantage of high spectral and spatial resolution (284 bands, 2 m) imaging spectrometer data from the Airborne Prism Experiment (APEX) and investigate the applicability and potential of Sentinel-2 and Landsat 8 data to derive broadband albedo products. The performance of shortwave broadband albedo retrievals is tested and we assess the reliability of published narrow-to-broadband conversion algorithms. The resulting albedo products from the three sensors and different algorithms are further cross-compared. Moreover, the impact of the anisotropy correction is analysed depending on different surface types. While degradation of the spectral resolution impacted glacier-wide mean albedo by about 5%, reducing the spatial resolution resulted in changes of less than 1%. However, in any case, coarser spatial resolution was no longer able to represent small-scale variability of albedo on glacier surfaces. We discuss the implications when using Sentinel-2 and Landsat 8 to map dynamic glaciological processes and to monitor glacier surface albedo on larger spatial and more frequent temporal scales. Full article
(This article belongs to the Special Issue Remote Sensing of Glaciers)
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Open AccessArticle Using 3D Point Clouds Derived from UAV RGB Imagery to Describe Vineyard 3D Macro-Structure
Remote Sens. 2017, 9(2), 111; doi:10.3390/rs9020111
Received: 27 November 2016 / Revised: 13 January 2017 / Accepted: 23 January 2017 / Published: 28 January 2017
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Abstract
In the context of precision viticulture, remote sensing in the optical domain offers a potential way to map crop structure characteristics, such as vegetation cover fraction, row orientation or leaf area index, that are later used in decision support tools. A method based
[...] Read more.
In the context of precision viticulture, remote sensing in the optical domain offers a potential way to map crop structure characteristics, such as vegetation cover fraction, row orientation or leaf area index, that are later used in decision support tools. A method based on the RGB color model imagery acquired with an unmanned aerial vehicle (UAV) is proposed to describe the vineyard 3D macro-structure. The dense point cloud is first extracted from the overlapping RGB images acquired over the vineyard using the Structure from Motion algorithm implemented in the Agisoft PhotoScan software. Then, the terrain altitude extracted from the dense point cloud is used to get the 2D distribution of height of the vineyard. By applying a threshold on the height, the rows are separated from the row spacing. Row height, width and spacing are then estimated as well as the vineyard cover fraction and the percentage of missing segments along the rows. Results are compared with ground measurements with root mean square error (RMSE) = 9.8 cm for row height, RMSE = 8.7 cm for row width and RMSE = 7 cm for row spacing. The row width, cover fraction, as well as the percentage of missing row segments, appear to be sensitive to the quality of the dense point cloud. Optimal flight configuration and camera setting are therefore mandatory to access these characteristics with a good accuracy. Full article
(This article belongs to the Special Issue Recent Trends in UAV Remote Sensing)
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Open AccessFeature PaperArticle Cutbank Geophysics: A New Method for Expanding Magnetic Investigations to the Subsurface Using Magnetic Susceptibility Testing at an Awatixa Hidatsa Village, North Dakota
Remote Sens. 2017, 9(2), 112; doi:10.3390/rs9020112
Received: 30 September 2016 / Revised: 6 January 2017 / Accepted: 20 January 2017 / Published: 28 January 2017
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Abstract
Magnetic susceptibility investigations were conducted at an Awatixa Hidatsa village (32ME11, also known as Sakakawea Village) along a cutbank at the Knife River Indian Villages National Historic Site (KNRI) in central North Dakota, USA. This extensive exposure provided a superb opportunity to correlate
[...] Read more.
Magnetic susceptibility investigations were conducted at an Awatixa Hidatsa village (32ME11, also known as Sakakawea Village) along a cutbank at the Knife River Indian Villages National Historic Site (KNRI) in central North Dakota, USA. This extensive exposure provided a superb opportunity to correlate magnetic susceptibility measurements with a variety of subsurface features. These features were visible in the cutbank, and also recorded in cutbank profiles completed in the late 1970s in work supervised by Robert Nickel and Stanley Ahler. The susceptibility studies are part of a larger program of geophysics at KNRI that commenced with pioneering surveys of John Weymouth and Robert Nickel, also in the 1970s, and continued with extensive surface-based magnetic surveys over the interior portion of the site in 2012 by the National Park Service. Our magnetic susceptibility study differs from other geophysical efforts in that measurements were collected from the vertical cutbank, not from the surface, to investigate different feature types within their stratigraphic context and to map small-scale vertical changes in susceptibility. In situ measurements of volume magnetic susceptibility were accomplished on the cutbank at six areas within the village and a control location off-site. Samples were collected for use in soil magnetic studies aimed at providing an understanding of susceptibility contrasts in terms of magnetic mineralogy, grain size, and concentration. Distinctive susceptibility signatures for natural and cultural soils, different feature types, and buried soils, suggest that down-hole susceptibility surveys could be usefully paired with surface-based geophysics and soil magnetic studies to explore interior areas of this and other KNRI sites, mapping vertical and horizontal site limits, activity areas, features, and perhaps even earlier occupations. This study showcases the potential of cutbank studies for future geophysical survey design and interpretation, and also underscores the importance of information gained through pioneering studies of the past. Full article
(This article belongs to the Special Issue Archaeological Prospecting and Remote Sensing)
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Open AccessArticle Uncertainty in Terrestrial Laser Scanner Surveys of Landslides
Remote Sens. 2017, 9(2), 113; doi:10.3390/rs9020113
Received: 29 October 2016 / Revised: 20 January 2017 / Accepted: 23 January 2017 / Published: 29 January 2017
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Abstract
Terrestrial laser scanning (TLS) is a relatively new, versatile, and efficient technology for landslide monitoring. The evaluation of uncertainty of the surveyed data is not trivial because the final accuracy of the point position is unknown. An a priori evaluation of the accuracy
[...] Read more.
Terrestrial laser scanning (TLS) is a relatively new, versatile, and efficient technology for landslide monitoring. The evaluation of uncertainty of the surveyed data is not trivial because the final accuracy of the point position is unknown. An a priori evaluation of the accuracy of the observed points can be made based on both the footprint size and of the resolution, as well as in terms of effective instantaneous field of view (EIFOV). Such evaluations are surely helpful for a good survey design, but the further operations, such as cloud co-registration, georeferencing and editing, digital elevation model (DEM) creation, and so on, cause uncertainty which is difficult to evaluate. An assessment of the quality of the survey can be made evaluating the goodness of fit between the georeferenced point cloud and the terrain model built using it. In this article, we have considered a typical survey of a landsliding slope. We have presented an a priori quantitative assessment and we eventually analyzed how good the comparison is of the computed point cloud to the actual ground points. We have used the method of cross-validation to eventually suggest the use of a robust parameter for estimating the reliability of the fitting procedure. This statistic can be considered for comparing methods and parameters used to interpolate the DEM. Using kriging allows one to account for the spatial distribution of the data (including the typical anisotropy of the survey of a slope) and to obtain a map of the uncertainties over the height of the grid nodes. This map can be used to compute the estimated error over the DEM-derived quantities, and also represents an “objective” definition of the area of the survey that can be trusted for further use. Full article
(This article belongs to the Special Issue Uncertainties in Remote Sensing)
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Open AccessArticle Remote Sensing of Glacier Change in the Central Qinghai-Tibet Plateau and the Relationship with Changing Climate
Remote Sens. 2017, 9(2), 114; doi:10.3390/rs9020114
Received: 21 October 2016 / Revised: 16 January 2017 / Accepted: 23 January 2017 / Published: 29 January 2017
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Abstract
The widely distributed glaciers over the Qinghai-Tibet Plateau (QTP) represent important freshwater reserves and the meltwater feeds many major rivers of Asia. Glacier change over the QTP has shown high temporal and spatial variability in recent decades, and the driving forces of the
[...] Read more.
The widely distributed glaciers over the Qinghai-Tibet Plateau (QTP) represent important freshwater reserves and the meltwater feeds many major rivers of Asia. Glacier change over the QTP has shown high temporal and spatial variability in recent decades, and the driving forces of the variability are not yet clear. This study examines the area and thickness change of glaciers in the Dongkemadi (DKMD) region over central QTP by exploring all available Landsat images from 1976 to 2013 and satellite altimetry data over 2003–2008, and then analyzes the relationships between glacier variation and local and macroscale climate factors based on various remote sensing and re-analysis data. Results show that the variation of glacier area over 1976–2013 is characterized by significant shrinkage at a linear rate of −0.31 ± 0.04 km2·year−1. Glacier retreat slightly accelerated in the 2000s, and the mean glacier surface elevation lowered at a rate of −0.56 m·year−1 over 2003–2008. During the past 38 years, glacier change in the DKMD area was dominated by the variation of mean annual temperature, and was influenced by the state of the North Atlantic Oscillation (NAO). The mechanism linking climate variability over the central QTP and the state of NAO is most likely via changes in the strength of westerlies and Siberian High. We found no evidence supporting the role of summer monsoons (Indian summer monsoon and East Asian monsoon) in driving local climate and glacier changes. In addition, El Niño Southern Oscillation (ENSO) may be associated with the extreme weather (snow storm) in October 1986 and 2000 which might have led to significant glacier expansion in the following years. Further research is needed to better understand the physical mechanisms linking NAO, ENSO and climate variability over the mid-latitude central QTP. Full article
(This article belongs to the Special Issue Remote Sensing of Climate Change and Water Resources)
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Open AccessArticle Mining Coastal Land Use Sequential Pattern and Its Land Use Associations Based on Association Rule Mining
Remote Sens. 2017, 9(2), 116; doi:10.3390/rs9020116
Received: 23 October 2016 / Revised: 6 January 2017 / Accepted: 24 January 2017 / Published: 29 January 2017
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Abstract
Abstract: Research on the land use of the coastal zone in the sea–land direction will not only reveal its land use distribution, but may also indicate the interactions between inland land use and the ocean through associations between inland land use and
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Abstract: Research on the land use of the coastal zone in the sea–land direction will not only reveal its land use distribution, but may also indicate the interactions between inland land use and the ocean through associations between inland land use and seaward land use indirectly. However, in the existing research, few have paid attention to the land use in sea–land direction, let alone the sequential relationship between land-use types. The sequential relationship would be useful in land use planning and rehabilitation of the landscape in the sea–land direction, and the association between land-use types, particularly the inland land use and seaward land use, is not discussed. Therefore, This study presents a model named ARCLUSSM (Association Rules-based Coastal Land use Spatial Sequence Model) to mine the sequential pattern of land use with interesting associations in the sea–land direction of the coastal zone. As a case study, the typical coastal zone of Bohai Bay and the Yellow River delta in China was used. The results are as follows: firstly, 27 interesting association patterns of land use in the sea–land direction of the coastal zone were mined easily. Both sequential relationship and distance between land-use types for 27 patterns among six land-use types were mined definitely, and the sequence of the six land-use types tended to be tidal flat > shrimp pond > reservoir/artificial pond > settlement > river > dry land in sea–land direction. These patterns would offer specific support for land-use planning and rehabilitation of the coastal zone. There were 19 association patterns between seaward and landward land-use types. These patterns showed strong associations between seaward and landward land-use types. It indicated that the landward land use might have some impacts on the seaward land use, or in the other direction, which may help to reveal the interactions between inland land use and the ocean. Thus, the ARCLUSSM was an efficient tool to mine the sequential relationship and distance between land-use types with interesting association rules in the sea–land direction, which would offer practicable advice to appropriate coastal zone management and planning, and might reveal the interactions between inland land use and the ocean. Full article
(This article belongs to the Special Issue Remote Sensing Big Data: Theory, Methods and Applications)
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Open AccessArticle On the Use of Generalized Volume Scattering Models for the Improvement of General Polarimetric Model-Based Decomposition
Remote Sens. 2017, 9(2), 117; doi:10.3390/rs9020117
Received: 1 December 2016 / Accepted: 16 January 2017 / Published: 30 January 2017
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Abstract
Recently, a general polarimetric model-based decomposition framework was proposed by Chen et al., which addresses several well-known limitations in previous decomposition methods and implements a simultaneous full-parameter inversion by using complete polarimetric information. However, it only employs four typical models to characterize the
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Recently, a general polarimetric model-based decomposition framework was proposed by Chen et al., which addresses several well-known limitations in previous decomposition methods and implements a simultaneous full-parameter inversion by using complete polarimetric information. However, it only employs four typical models to characterize the volume scattering component, which limits the parameter inversion performance. To overcome this issue, this paper presents two general polarimetric model-based decomposition methods by incorporating the generalized volume scattering model (GVSM) or simplified adaptive volume scattering model, (SAVSM) proposed by Antropov et al. and Huang et al., respectively, into the general decomposition framework proposed by Chen et al. By doing so, the final volume coherency matrix structure is selected from a wide range of volume scattering models within a continuous interval according to the data itself without adding unknowns. Moreover, the new approaches rely on one nonlinear optimization stage instead of four as in the previous method proposed by Chen et al. In addition, the parameter inversion procedure adopts the modified algorithm proposed by Xie et al. which leads to higher accuracy and more physically reliable output parameters. A number of Monte Carlo simulations of polarimetric synthetic aperture radar (PolSAR) data are carried out and show that the proposed method with GVSM yields an overall improvement in the final accuracy of estimated parameters and outperforms both the version using SAVSM and the original approach. In addition, C-band Radarsat-2 and L-band AIRSAR fully polarimetric images over the San Francisco region are also used for testing purposes. A detailed comparison and analysis of decomposition results over different land-cover types are conducted. According to this study, the use of general decomposition models leads to a more accurate quantitative retrieval of target parameters. However, there exists a trade-off between parameter accuracy and model complexity which constrains the physical validity of solutions and must be further investigated. Full article
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Open AccessArticle Detection of Archaeological Residues in Vegetated Areas Using Satellite Synthetic Aperture Radar
Remote Sens. 2017, 9(2), 118; doi:10.3390/rs9020118
Received: 3 November 2016 / Accepted: 22 January 2017 / Published: 30 January 2017
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Abstract
Buried archaeological structures, such as earthworks and buildings, often leave traces at the surface by altering the properties of overlying material, such as soil and vegetation. These traces may be better visible from a remote perspective than on the surface. Active and passive
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Buried archaeological structures, such as earthworks and buildings, often leave traces at the surface by altering the properties of overlying material, such as soil and vegetation. These traces may be better visible from a remote perspective than on the surface. Active and passive airborne and spaceborne sensors acquiring imagery from the ultraviolet to infrared have been shown to reveal these archaeological residues following the application of various processing techniques. While the active microwave region of the spectrum, in the form of Synthetic Aperture Radar (SAR) has been used for archaeological prospection, particularly in desert regions, it has yet to be fully exploited to detect buried structures indirectly though proxy indicators in overlying materials in vegetated areas. Studies so far have tended to focus on the intensity of the SAR signal, without making full use of the phase. This paper demonstrates that SAR backscatter intensity, coherence and interferometry can be used to identify archaeological residues over a number of areas in the vicinity of Rome, Italy. SAR imagery from the COnstellation of small Satellites for the Mediterranean basin Observation (COSMO-SkyMed) have been obtained for the analysis: 77 scenes in Stripmap and 27 in Spotlight mode. Processing included multitemporal speckle filtering, coherence generation and Digital Elevation Model (DEM) creation from Small Baseline Subsets (SBAS). Comparison of these datasets with archaeological, geological, soil, vegetation and meteorological data reveal that several products derived from SAR data can expose various types of archaeological residues under different environmental conditions. Full article
(This article belongs to the Special Issue Remote Sensing for Cultural Heritage)
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Open AccessEditor’s ChoiceArticle Monitoring Rice Agriculture across Myanmar Using Time Series Sentinel-1 Assisted by Landsat-8 and PALSAR-2
Remote Sens. 2017, 9(2), 119; doi:10.3390/rs9020119
Received: 22 December 2016 / Accepted: 24 January 2017 / Published: 1 February 2017
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Abstract
Assessment and monitoring of rice agriculture over large areas has been limited by cloud cover, optical sensor spatial and temporal resolutions, and lack of systematic or open access radar. Dense time series of open access Sentinel-1 C-band data at moderate spatial resolution offers
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Assessment and monitoring of rice agriculture over large areas has been limited by cloud cover, optical sensor spatial and temporal resolutions, and lack of systematic or open access radar. Dense time series of open access Sentinel-1 C-band data at moderate spatial resolution offers new opportunities for monitoring agriculture. This is especially pertinent in South and Southeast Asia where rice is critical to food security and mostly grown during the rainy seasons when high cloud cover is present. In this research application, time series Sentinel-1A Interferometric Wide images (632) were utilized to map rice extent, crop calendar, inundation, and cropping intensity across Myanmar. An updated (2015) land use land cover map fusing Sentinel-1, Landsat-8 OLI, and PALSAR-2 were integrated and classified using a randomforest algorithm. Time series phenological analyses of the dense Sentinel-1 data were then executed to assess rice information across all of Myanmar. The broad land use land cover map identified 186,701 km2 of cropland across Myanmar with mean out-of-sample kappa of over 90%. A phenological time series analysis refined the cropland class to create a rice mask by extrapolating unique indicators tied to the rice life cycle (dynamic range, inundation, growth stages) from the dense time series Sentinel-1 to map rice paddy characteristics in an automated approach. Analyses show that the harvested rice area was 6,652,111 ha with general (R2 = 0.78) agreement with government census statistics. The outcomes show strong ability to assess and monitor rice production at moderate scales over a large cloud-prone region. In countries such as Myanmar with large populations and governments dependent upon rice production, more robust and transparent monitoring and assessment tools can help support better decision making. These results indicate that systematic and open access Synthetic Aperture Radar (SAR) can help scale information required by food security initiatives and Monitoring, Reporting, and Verification programs. Full article
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Open AccessArticle The Highest Gradient Model: A New Method for Analytical Assessment of the Efficiency of LiDAR-Derived Visualization Techniques for Landform Detection and Mapping
Remote Sens. 2017, 9(2), 120; doi:10.3390/rs9020120
Received: 16 November 2016 / Revised: 20 January 2017 / Accepted: 26 January 2017 / Published: 10 February 2017
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Abstract
ALS-derived raster visualization techniques have become common in recent years, opening up new possibilities for subtle landform detection in earth sciences and archaeology, but they have also introduced confusion for users. As a consequence, the choice between these visualization techniques is still mostly
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ALS-derived raster visualization techniques have become common in recent years, opening up new possibilities for subtle landform detection in earth sciences and archaeology, but they have also introduced confusion for users. As a consequence, the choice between these visualization techniques is still mostly supported by empirical knowledge. Some attempts have been made to compare these techniques, but there is still a lack of analytical data. This work proposes a new method, based on gradient modelling and spatial statistics, to analytically assess the efficacy of these visualization techniques. A selected panel of outstanding visualization techniques was assessed first by a classic non-analytical approach, and secondly by the proposed new analytical approach. The comparison of results showed that the latter provided more detailed and objective data, not always consistent with previous empirical knowledge. These data allowed us to characterize with precision the terrain for which each visualization technique performs best. A combination of visualization techniques based on DEM manipulation (Slope and Local Relief Model) appeared to be the best choice for normal terrain morphometry, occasionally supported by illumination techniques such as Sky-View Factor or Negative Openness as a function of terrain characteristics. Full article
(This article belongs to the Special Issue Remote Sensing for Cultural Heritage)
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Open AccessArticle Different Patterns in Daytime and Nighttime Thermal Effects of Urbanization in Beijing-Tianjin-Hebei Urban Agglomeration
Remote Sens. 2017, 9(2), 121; doi:10.3390/rs9020121
Received: 21 November 2016 / Accepted: 27 January 2017 / Published: 1 February 2017
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Abstract
Surface urban heat island (SUHI) in the context of urbanization has gained much attention in recent decades; however, the seasonal variations of SUHI and their drivers are still not well documented. In this study, the Beijing-Tianjin-Hebei (BTH) urban agglomeration, one of the most
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Surface urban heat island (SUHI) in the context of urbanization has gained much attention in recent decades; however, the seasonal variations of SUHI and their drivers are still not well documented. In this study, the Beijing-Tianjin-Hebei (BTH) urban agglomeration, one of the most typical areas experiencing drastic urbanization in China, was selected to study the SUHI intensity (SUHII) based on remotely sensed land surface temperature (LST) data. Pure and unchanged urban and rural pixels from 2000 to 2010 were chosen to avoid non-concurrency between land cover data and LST data and to estimate daytime and nighttime thermal effects of urbanization. Different patterns of the seasonal variations were found in daytime and nighttime SUHIIs. Specifically, the daytime SUHII in summer (4 °C) was more evident than in other seasons while a cold island phenomenon was found in winter; the nighttime SUHII was always positive and higher than the daytime one in all the seasons except summer. Moreover, we found the highest daytime SUHII in August, which is the growing peak stage of summer maize, while nighttime SUHII showed a trough in the same month. Seasonal variations of daytime SUHII showed higher significant correlations with the seasonal variations of ∆LAI (leaf area index) (R2 = 0.81, r = −0.90) compared with ∆albedo (R2 = 0.61, r = −0.78) and background daytime LST (R2 = 0.69, r = 0.83); moreover, agricultural practices (double-cropping system) played an important role in the seasonal variations of daytime SUHII. Seasonal variations of the nighttime SUHII did not show significant correlations with either of seasonal variations of ∆LAI, ∆albedo, and background nighttime LST, which implies different mechanisms in nighttime SUHII variation needing future studies. Full article
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Open AccessArticle Studying Vegetation Salinity: From the Field View to a Satellite-Based Perspective
Remote Sens. 2017, 9(2), 122; doi:10.3390/rs9020122
Received: 6 December 2016 / Accepted: 19 January 2017 / Published: 1 February 2017
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Abstract
Salinization of irrigated lands in the semi-arid Jezreel Valley, Northern Israel results in soil-structure deterioration and crop damage. We formulated a generic rule for estimating salinity of different vegetation types by studying the relationship between Cl/Na and different spectral slopes in the visible–near
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Salinization of irrigated lands in the semi-arid Jezreel Valley, Northern Israel results in soil-structure deterioration and crop damage. We formulated a generic rule for estimating salinity of different vegetation types by studying the relationship between Cl/Na and different spectral slopes in the visible–near infrared–shortwave infrared (VIS–NIR–SWIR) spectral range using both field measurements and satellite imagery (Sentinel-2). For the field study, the slope-based model was integrated with conventional partial least squares (PLS) analyses. Differences in 14 spectral ranges, indicating changes in salinity levels, were identified across the VIS–NIR–SWIR region (350–2500 nm). Next, two different models were run using PLS regression: (i) using spectral slope data across these ranges; and (ii) using preprocessed spectral reflectance. The best model for predicting Cl content was based on continuum removal reflectance (R2 = 0.84). Satisfactory correlations were obtained using the slope-based PLS model (R2 = 0.77 for Cl and R2 = 0.63 for Na). Thus, salinity contents in fresh plants could be estimated, despite masking of some spectral regions by water absorbance. Finally, we estimated the most sensitive spectral channels for monitoring vegetation salinity from a satellite perspective. We evaluated the recently available Sentinel-2 imagery’s ability to distinguish variability in vegetation salinity levels. The best estimate of a Sentinel-2-based vegetation salinity index was generated based on a ratio between calculated slopes: the 490–665 nm and 705–1610 nm. This index was denoted as the Sentinel-2-based vegetation salinity index (SVSI) (band 4 − band 2)/(band 5 + band 11). Full article
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Open AccessArticle Predictions of Tropical Forest Biomass and Biomass Growth Based on Stand Height or Canopy Area Are Improved by Landsat-Scale Phenology across Puerto Rico and the U.S. Virgin Islands
Remote Sens. 2017, 9(2), 123; doi:10.3390/rs9020123
Received: 22 November 2016 / Accepted: 25 January 2017 / Published: 1 February 2017
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Abstract
Remotely-sensed estimates of forest biomass are usually based on various measurements of canopy height, area, volume or texture, as derived from LiDAR, radar or fine spatial resolution imagery. These measurements are then calibrated to estimates of stand biomass that are primarily based on
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Remotely-sensed estimates of forest biomass are usually based on various measurements of canopy height, area, volume or texture, as derived from LiDAR, radar or fine spatial resolution imagery. These measurements are then calibrated to estimates of stand biomass that are primarily based on tree stem diameters. Although humid tropical forest seasonality can have low amplitudes compared with temperate regions, seasonal variations in growth-related factors like temperature, humidity, rainfall, wind speed and day length affect both tropical forest deciduousness and tree height-diameter relationships. Consequently, seasonal patterns in spectral measures of canopy greenness derived from satellite imagery should be related to tree height-diameter relationships and hence to estimates of forest biomass or biomass growth that are based on stand height or canopy area. In this study, we tested whether satellite image-based measures of tropical forest phenology, as estimated by indices of seasonal patterns in canopy greenness constructed from Landsat satellite images, can explain the variability in forest deciduousness, forest biomass and net biomass growth after already accounting for stand height or canopy area. Models of forest biomass that added phenology variables to structural variables similar to those that can be estimated by LiDAR or very high-spatial resolution imagery, like canopy height and crown area, explained up to 12% more variation in biomass. Adding phenology to structural variables explained up to 25% more variation in Net Biomass Growth (NBG). In all of the models, phenology contributed more as interaction terms than as single-effect terms. In addition, models run on only fully-forested plots performed better than models that included partially-forested plots. For forest NBG, the models produced better results when only those plots with a positive growth, from Inventory Cycle 1 to Inventory Cycle 2, were analyzed, as compared to models that included all plots Full article
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Open AccessArticle Soil Carbon Stock and Particle Size Fractions in the Central Amazon Predicted from Remotely Sensed Relief, Multispectral and Radar Data
Remote Sens. 2017, 9(2), 124; doi:10.3390/rs9020124
Received: 28 October 2016 / Revised: 12 January 2017 / Accepted: 22 January 2017 / Published: 3 February 2017
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Abstract
Soils from the remote areas of the Amazon Rainforest in Brazil are poorly mapped due to the presence of dense forest and lack of access routes. The use of covariates derived from multispectral and radar remote sensors allows mapping large areas and has
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Soils from the remote areas of the Amazon Rainforest in Brazil are poorly mapped due to the presence of dense forest and lack of access routes. The use of covariates derived from multispectral and radar remote sensors allows mapping large areas and has the potential to improve the accuracy of soil attribute maps. The objectives of this study were to: (a) evaluate the addition of relief, and vegetation covariates derived from multispectral images with distinct spatial and spectral resolutions (Landsat 8 and RapidEye) and L-band radar (ALOS PALSAR) for the prediction of soil organic carbon stock (CS) and particle size fractions; and (b) evaluate the performance of four geostatistical methods to map these soil properties. Overall, the results show that, even under forest coverage, the Normalized Difference Vegetation Index (NDVI) and ALOS PALSAR backscattering coefficient improved the accuracy of CS and subsurface clay content predictions. The NDVI derived from RapidEye sensor improved the prediction of CS using isotopic cokriging, while the NDVI derived from Landsat 8 and backscattering coefficient were selected to predict clay content at the subsurface using regression kriging (RK). The relative improvement of applying cokriging and RK over ordinary kriging were lower than 10%, indicating that further analyses are necessary to connect soil proxies (vegetation and relief types) with soil attributes. Full article
(This article belongs to the Special Issue Remote Sensing Applied to Soils: From Ground to Space)
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Open AccessArticle Deriving Dynamic Subsidence of Coal Mining Areas Using InSAR and Logistic Model
Remote Sens. 2017, 9(2), 125; doi:10.3390/rs9020125
Received: 17 October 2016 / Revised: 12 January 2017 / Accepted: 30 January 2017 / Published: 3 February 2017
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Abstract
The seasonal variation of land cover and the large deformation gradients in coal mining areas often give rise to severe temporal and geometrical decorrelation in interferometric synthetic aperture radar (InSAR) interferograms. Consequently, it is common that the available InSAR pairs do not cover
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The seasonal variation of land cover and the large deformation gradients in coal mining areas often give rise to severe temporal and geometrical decorrelation in interferometric synthetic aperture radar (InSAR) interferograms. Consequently, it is common that the available InSAR pairs do not cover the entire time period of SAR acquisitions, i.e., temporal gaps exist in the multi-temporal InSAR observations. In this case, it is very difficult to accurately estimate mining-induced dynamic subsidence using the traditional time-series InSAR techniques. In this investigation, we employ a logistic model which has been widely applied to describe mining-related dynamic subsidence, to bridge the temporal gaps in multi-temporal InSAR observations. More specifically, we first construct a functional relationship between the InSAR observations and the logistic model, and we then develop a method to estimate the model parameters of the logistic model from the InSAR observations with temporal gaps. Having obtained these model parameters, the dynamic subsidence can be estimated with the logistic model. Simulated and real data experiments in the Datong coal mining area, China, were carried out in this study, in order to test the proposed method. The results show that the maximum subsidence in the Datong coal mining area reached about 1.26 m between 1 July 2007 and 28 February 2009, and the accuracy of the estimated dynamic subsidence is about 0.017 m. Compared with the linear and cubic polynomial models of the traditional time-series InSAR techniques, the accuracy of dynamic subsidence derived by the logistic model is increased by about 50.0% and 45.2%, respectively. Full article
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Open AccessArticle Validation of PROBA-V GEOV1 and MODIS C5 & C6 fAPAR Products in a Deciduous Beech Forest Site in Italy
Remote Sens. 2017, 9(2), 126; doi:10.3390/rs9020126
Received: 15 November 2016 / Revised: 17 January 2017 / Accepted: 24 January 2017 / Published: 4 February 2017
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Abstract
The availability of new fAPAR satellite products requires simultaneous efforts in validation to provide users with a better comprehension of product performance and evaluation of uncertainties. This study aimed to validate three fAPAR satellite products, GEOV1, MODIS C5, and MODIS C6, against ground
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The availability of new fAPAR satellite products requires simultaneous efforts in validation to provide users with a better comprehension of product performance and evaluation of uncertainties. This study aimed to validate three fAPAR satellite products, GEOV1, MODIS C5, and MODIS C6, against ground references to determine to what extent the GCOS requirements on accuracy (maximum 10% or 5%) can be met in a deciduous beech forest site in a gently and variably sloped mountain site. Three ground reference fAPAR, differing for temporal (continuous or campaign mode) and spatial sampling (single points or Elementary Sampling Units—ESUs), were collected using different devices: (1) Apogee (defined as benchmark in this study); (2) PASTIS; and (3) Digital cameras for collecting hemispherical photographs (DHP). A bottom-up approach for the upscaling process was used in the present study. Radiometric values of decametric images (Landsat-8) were extracted over the ESUs and used to develop empirical transfer functions for upscaling the ground measurements. The resulting high-resolution ground-based maps were aggregated to the spatial resolution of the satellite product to be validated considering the equivalent point spread function of the satellite sensors, and a correlation analysis was performed to accomplish the accuracy assessment. PASTIS sensors showed good performance as fAPARPASTIS appropriately followed the seasonal trends depicted by fAPARAPOGEE (benchmark) (R2 = 0.84; RMSE = 0.01). Despite small dissimilarities, mainly attributed to different sampling schemes and errors in DHP classification process, the agreement between fAPARPASTIS and fAPARDHP was noticeable considering all the differences between both approaches. The temporal courses of the three satellite products were found to be consistent with both Apogee and PASTIS, except at the end of the summer season when ground data were more affected by senescent leaves, with both MODIS C5 and C6 displaying larger short-term variability due to their shorter temporal composite period. MODIS C5 and C6 retrievals were obtained with the backup algorithm in most cases. The three green fAPAR satellite products under study showed good agreement with ground-based maps of canopy fAPAR at 10 h, with RMSE values lower than 0.06, very low systematic differences, and more than 85% of the pixels within GCOS requirements. Among them, GEOV1 fAPAR showed up to 98% of the points lying within the GCOS requirements, and slightly lower values (mean bias = −0.02) as compared with the ground canopy fAPAR, which is expected to be only slightly higher than green fAPAR in the peak season. Full article
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Open AccessFeature PaperArticle Soybean Disease Monitoring with Leaf Reflectance
Remote Sens. 2017, 9(2), 127; doi:10.3390/rs9020127
Received: 5 December 2016 / Revised: 19 January 2017 / Accepted: 26 January 2017 / Published: 4 February 2017
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Abstract
Crop disease detection with remote sensing is a challenging area that can have significant economic and environmental impact on crop disease management. Spectroscopic remote sensing in the visible and near-infrared (NIR) region has the potential to detect crop changes due to diseases. Soybean
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Crop disease detection with remote sensing is a challenging area that can have significant economic and environmental impact on crop disease management. Spectroscopic remote sensing in the visible and near-infrared (NIR) region has the potential to detect crop changes due to diseases. Soybean cyst nematode (SCN) and sudden death syndrome (SDS) are two common soybean diseases that are extremely difficult to detect in the early stages under mild to moderate infestation levels. The objective of this research study was to relate leaf reflectance to disease conditions and to identify wavebands that best discriminated these crop diseases. A microplot experiment was conducted. Data collected included 800 leaf spectra, corresponding leaf chlorophyll content and disease rating of four soybean cultivars grown under different disease conditions. Disease conditions were created by introducing four disease treatments of control (no disease), SCN, SDS, and SCN+SDS. Crop data were collected on a weekly basis over a 10-week period, starting from 71 days after planting (DAP). The correlation between disease rating and selected vegetation indices (VI) were evaluated. Wavebands with the most disease discrimination capability were identified with stepwise linear discriminant analysis (LDA), logistic discriminant analysis (LgDA) and linear correlation analysis of pooled data. The identified band combinations were used to develop a classification function to identify plant disease condition. The best correlation (>0.8) between disease rating and VI occurred during 112 DAP. Both LDA and LgDA identified several bands in the NIR, red, green and blue regions as critical for disease discrimination. The discriminant models were able to detect over 80% of the healthy plants accurately under cross-validation but showed poor accuracy in discriminating individual diseases. A two-class discriminant model was able to identify 97% of the healthy plants and 58% of the infested plants as having some disease from the plant spectra. Full article
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Open AccessArticle A MODIS-Based Robust Satellite Technique (RST) for Timely Detection of Oil Spilled Areas
Remote Sens. 2017, 9(2), 128; doi:10.3390/rs9020128
Received: 31 October 2016 / Revised: 23 January 2017 / Accepted: 30 January 2017 / Published: 4 February 2017
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Abstract
Natural crude-oil seepages, together with the oil released into seawater as a consequence of oil exploration/production/transportation activities, and operational discharges from tankers (i.e., oil dumped during cleaning actions) represent the main sources of sea oil pollution. Satellite remote sensing can be a useful
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Natural crude-oil seepages, together with the oil released into seawater as a consequence of oil exploration/production/transportation activities, and operational discharges from tankers (i.e., oil dumped during cleaning actions) represent the main sources of sea oil pollution. Satellite remote sensing can be a useful tool for the management of such types of marine hazards, namely oil spills, mainly owing to the synoptic view and the good trade-off between spatial and temporal resolution, depending on the specific platform/sensor system used. In this paper, an innovative satellite-based technique for oil spill detection, based on the general robust satellite technique (RST) approach, is presented. It exploits the multi-temporal analysis of data acquired in the visible channels of the Moderate Resolution Imaging Spectroradiometer (MODIS) on board the Aqua satellite in order to automatically and quickly detect the presence of oil spills on the sea surface, with an attempt to minimize “false detections” caused by spurious effects associated with, for instance, cloud edges, sun/satellite geometries, sea currents, etc. The oil spill event that occurred in June 2007 off the south coast of Cyprus in the Mediterranean Sea has been considered as a test case. The resulting data, the reliability of which has been evaluated by both carrying out a confutation analysis and comparing them with those provided by the application of another independent MODIS-based method, showcase the potential of RST in identifying the presence of oil with a high level of accuracy. Full article
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Open AccessArticle Initial Radiometric Characteristics of KOMPSAT-3A Multispectral Imagery Using the 6S Radiative Transfer Model, Well-Known Radiometric Tarps, and MFRSR Measurements
Remote Sens. 2017, 9(2), 130; doi:10.3390/rs9020130
Received: 23 September 2016 / Revised: 17 January 2017 / Accepted: 26 January 2017 / Published: 4 February 2017
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Abstract
On-orbit radiometric characterization of the multispectral (MS) imagery of the Korea Aerospace Research Institute (KARI)’s Korea Multi-Purpose Satellite-3A (KOMPSAT-3A), which was launched on 25 March 2015, was conducted to provide quantitative radiometric information about KOMPSAT-3A. During the in-orbit test (IOT), vicarious radiometric calibration
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On-orbit radiometric characterization of the multispectral (MS) imagery of the Korea Aerospace Research Institute (KARI)’s Korea Multi-Purpose Satellite-3A (KOMPSAT-3A), which was launched on 25 March 2015, was conducted to provide quantitative radiometric information about KOMPSAT-3A. During the in-orbit test (IOT), vicarious radiometric calibration of KOMPSAT-3A was performed using the Second Simulation of a Satellite Signal in the Solar Spectrum (6S) radiative transfer model. The characteristics of radiometric tarps, the atmospheric optical depth from multi-filter rotating shadowband radiometer (MFRSR) measurements, and sun–sensor–geometry were carefully considered, in order to calculate the exact top of atmosphere (TOA) radiance received by KOMPSAT-3A MS bands. In addition, the bidirectional reflectance distribution function (BRDF) behaviors of the radiometric tarps were measured in the laboratory with a two-dimensional hyperspectral gonioradiometer, to compensate for the geometry discrepancy between the satellite and the ASD FieldSpec® 3 spectroradiometer. The match-up datasets between the TOA radiance and the digital number (DN) from KOMPSAT-3A were used to determine DN-to-radiance conversion factors, based on linear least squares fitting for two field campaigns. The final results showed that the R2 values between the observed and simulated radiances for the blue, green, red, and near-infrared (NIR) bands, are greater than 0.998. An approximate error budget analysis for the vicarious calibration of KOMPSAT-3A showed an error of less than 6.8%. When applying the laboratory-based BRDF correction to the case of higher viewing zenith angle geometry, the gain ratio was improved, particularly for the blue (1.3%) and green (1.2%) bands, which exhibit high sensitivity to the BRDF of radiometric tarps during the backward-scattering phase. The calculated gain ratio between the first and second campaigns showed a less than 5% discrepancy, indicating that the determined radiometric characteristics of KOMPSAT-3A are reliable and useful to the user group for quantitative applications. Full article
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Open AccessArticle Salt Marsh Monitoring in Jamaica Bay, New York from 2003 to 2013: A Decade of Change from Restoration to Hurricane Sandy
Remote Sens. 2017, 9(2), 131; doi:10.3390/rs9020131
Received: 26 December 2016 / Revised: 26 December 2016 / Accepted: 24 January 2017 / Published: 6 February 2017
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Abstract
This study used Quickbird-2 and Worldview-2, high resolution satellite imagery, in a multi-temporal salt marsh mapping and change analysis of Jamaica Bay, New York. An object-based image analysis methodology was employed. The study seeks to understand both natural and anthropogenic changes caused by
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This study used Quickbird-2 and Worldview-2, high resolution satellite imagery, in a multi-temporal salt marsh mapping and change analysis of Jamaica Bay, New York. An object-based image analysis methodology was employed. The study seeks to understand both natural and anthropogenic changes caused by Hurricane Sandy and salt marsh restoration, respectively. The objectives of this study were to: (1) document salt marsh change in Jamaica Bay from 2003 to 2013; (2) determine the impact of Hurricane Sandy on salt marshes within Jamaica Bay; (3) evaluate this long term monitoring methodology; and (4) evaluate the use of multiple sensor derived classifications to conduct change analysis. The study determined changes from 2003 to 2008, 2008 to 2012 and 2012 to 2013 to better understand the impact of restoration and natural disturbances. The study found that 21 ha of salt marsh vegetation was lost from 2003 to 2013. From 2012 to 2013, restoration efforts resulted in an increase of 10.6 ha of salt marsh. Hurricane Sandy breached West Pond, a freshwater environment, causing 3.1 ha of freshwater wetland loss. The natural salt marsh showed a decreasing trend in loss. Larger salt marshes in 2012 tended to add vegetation in 2012–2013 (F4,6 = 13.93, p = 0.0357 and R2 = 0.90). The study provides important information for the resource management of Jamaica Bay. Full article
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Open AccessArticle Monitoring Agricultural Expansion in Burkina Faso over 14 Years with 30 m Resolution Time Series: The Role of Population Growth and Implications for the Environment
Remote Sens. 2017, 9(2), 132; doi:10.3390/rs9020132
Received: 21 December 2016 / Revised: 25 January 2017 / Accepted: 27 January 2017 / Published: 5 February 2017
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Abstract
Burkina Faso ranges amongst the fastest growing countries in the world with an annual population growth rate of more than three percent. This trend has consequences for food security since agricultural productivity is still on a comparatively low level in Burkina Faso. In
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Burkina Faso ranges amongst the fastest growing countries in the world with an annual population growth rate of more than three percent. This trend has consequences for food security since agricultural productivity is still on a comparatively low level in Burkina Faso. In order to compensate for the low productivity, the agricultural areas are expanding quickly. The mapping and monitoring of this expansion is difficult, even on the basis of remote sensing imagery, since the extensive farming practices and frequent cloud coverage in the area make the delineation of cultivated land from other land cover and land use types a challenging task. However, as the rapidly increasing population could have considerable effects on the natural resources and on the regional development of the country, methods for improved mapping of LULCC (land use and land cover change) are needed. For this study, we applied the newly developed ESTARFM (Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model) framework to generate high temporal (8-day) and high spatial (30 m) resolution NDVI time series for all of Burkina Faso for the years 2001, 2007, and 2014. For this purpose, more than 500 Landsat scenes and 3000 MODIS scenes were processed with this automated framework. The generated ESTARFM NDVI time series enabled extraction of per-pixel phenological features that all together served as input for the delineation of agricultural areas via random forest classification at 30 m spatial resolution for entire Burkina Faso and the three years. For training and validation, a randomly sampled reference dataset was generated from Google Earth images and based on expert knowledge. The overall accuracies of 92% (2001), 91% (2007), and 91% (2014) indicate the well-functioning of the applied methodology. The results show an expansion of agricultural area of 91% between 2001 and 2014 to a total of 116,900 km². While rainfed agricultural areas account for the major part of this trend, irrigated areas and plantations also increased considerably, primarily promoted by specific development projects. This expansion goes in line with the rapid population growth in most provinces of Burkina Faso where land was still available for an expansion of agricultural area. The analysis of agricultural encroachment into protected areas and their surroundings highlights the increased human pressure on these areas and the challenges of environmental protection for the future. Full article
(This article belongs to the Special Issue Monitoring Agricultural Land-Use Change and Land-Use Intensity)
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Open AccessArticle A MODIS-Based Novel Method to Distinguish Surface Cyanobacterial Scums and Aquatic Macrophytes in Lake Taihu
Remote Sens. 2017, 9(2), 133; doi:10.3390/rs9020133
Received: 30 August 2016 / Revised: 22 January 2017 / Accepted: 26 January 2017 / Published: 6 February 2017
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Abstract
Satellite remote sensing can be an effective alternative for mapping cyanobacterial scums and aquatic macrophyte distribution over large areas compared with traditional ship’s site-specific samplings. However, similar optical spectra characteristics between aquatic macrophytes and cyanobacterial scums in red and near infrared (NIR) wavebands
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Satellite remote sensing can be an effective alternative for mapping cyanobacterial scums and aquatic macrophyte distribution over large areas compared with traditional ship’s site-specific samplings. However, similar optical spectra characteristics between aquatic macrophytes and cyanobacterial scums in red and near infrared (NIR) wavebands create a barrier to their discrimination when they co-occur. We developed a new cyanobacteria and macrophytes index (CMI) based on a blue, a green, and a shortwave infrared band to separate waters with cyanobacterial scums from those dominated by aquatic macrophytes, and a turbid water index (TWI) to avoid interference from high turbid waters typical of shallow lakes. Combining CMI, TWI, and the floating algae index (FAI), we used a novel classification approach to discriminate lake water, cyanobacteria blooms, submerged macrophytes, and emergent/floating macrophytes using MODIS imagery in the large shallow and eutrophic Lake Taihu (China). Thresholds for CMI, TWI, and FAI were determined by statistical analysis for a 2010–2016 MODIS Aqua time series. We validated the accuracy of our approach by in situ reflectance spectra, field investigations and high spatial resolution HJ-CCD data. The overall classification accuracy was 86% in total, and the user’s accuracy was 88%, 79%, 85%, and 93% for submerged macrophytes, emergent/floating macrophytes, cyanobacterial scums and lake water, respectively. The estimated aquatic macrophyte distributions gave consistent results with that based on HJ-CCD data. This new approach allows for the coincident determination of the distributions of cyanobacteria blooms and aquatic macrophytes in eutrophic shallow lakes. We also discuss the utility of the approach with respect to masking clouds, black waters, and atmospheric effects, and its mixed-pixel effects. Full article
(This article belongs to the Special Issue Water Optics and Water Colour Remote Sensing)
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Open AccessArticle Modelling Diverse Soil Attributes with Visible to Longwave Infrared Spectroscopy Using PLSR Employed by an Automatic Modelling Engine
Remote Sens. 2017, 9(2), 134; doi:10.3390/rs9020134
Received: 28 October 2016 / Revised: 18 January 2017 / Accepted: 27 January 2017 / Published: 6 February 2017
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Abstract
The study tested a data mining engine (PARACUDA®) to predict various soil attributes (BC, CEC, BS, pH, Corg, Pb, Hg, As, Zn and Cu) using reflectance data acquired for both optical and thermal infrared regions. The engine was designed
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The study tested a data mining engine (PARACUDA®) to predict various soil attributes (BC, CEC, BS, pH, Corg, Pb, Hg, As, Zn and Cu) using reflectance data acquired for both optical and thermal infrared regions. The engine was designed to utilize large data in parallel and automatic processing to build and process hundreds of diverse models in a unified manner while avoiding bias and deviations caused by the operator(s). The system is able to systematically assess the effect of diverse preprocessing techniques; additionally, it analyses other parameters, such as different spectral resolutions and spectral coverages that affect soil properties. Accordingly, the system was used to extract models across both optical and thermal infrared spectral regions, which holds significant chromophores. In total, 2880 models were evaluated where each model was generated with a different preprocessing scheme of the input spectral data. The models were assessed using statistical parameters such as coefficient of determination (R2), square error of prediction (SEP), relative percentage difference (RPD) and by physical explanation (spectral assignments). It was found that the smoothing procedure is the most beneficial preprocessing stage, especially when combined with spectral derivation (1st or 2nd derivatives). Automatically and without the need of an operator, the data mining engine enabled the best prediction models to be found from all the combinations tested. Furthermore, the data mining approach used in this study and its processing scheme proved to be efficient tools for getting a better understanding of the geochemical properties of the samples studied (e.g., mineral associations). Full article
(This article belongs to the Special Issue Remote Sensing Applied to Soils: From Ground to Space)
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Open AccessArticle Assessing Light Pollution in China Based on Nighttime Light Imagery
Remote Sens. 2017, 9(2), 135; doi:10.3390/rs9020135
Received: 19 November 2016 / Revised: 17 January 2017 / Accepted: 24 January 2017 / Published: 6 February 2017
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Abstract
Rapid urbanization and economic development inevitably lead to light pollution, which has become a universal environmental issue. In order to reveal the spatiotemporal patterns and evolvement rules of light pollution in China, images from 1992 to 2012 were selected from the Defense Meteorological
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Rapid urbanization and economic development inevitably lead to light pollution, which has become a universal environmental issue. In order to reveal the spatiotemporal patterns and evolvement rules of light pollution in China, images from 1992 to 2012 were selected from the Defense Meteorological Satellite Program Operational Linescan System (DMSP/OLS) and systematically corrected to ensure consistency. Furthermore, we employed a linear regression trend method and nighttime light index method to demonstrate China’s light pollution characteristics across national, regional, and provincial scales, respectively. We found that: (1) China’s light pollution expanded significantly in provincial capital cities over the past 21 years and hot-spots of light pollution were located in the eastern coastal region. The Yangtze River Delta, Pearl River Delta, and Beijing–Tianjin–Hebei regions have formed light pollution stretch areas; (2) China’s light pollution was mainly focused in areas of north China (NC) and east China (EC), which, together, accounted for over 50% of the light pollution for the whole country. The fastest growth of light pollution was observed in northwest China (NWC), followed by southwest China (SWC). The growth rates of east China (EC), central China (CC), and northeast China (NEC) were stable, while those of north China (NC) and south China (SC) declined; (3) Light pollution at the provincial scale was mainly located in the Shandong, Guangdong, and Hebei provinces, whereas the fastest growth of light pollution was in Tibet and Hainan. However, light pollution levels in the developed provinces (Hong Kong, Macao, Shanghai, and Tianjin) were higher than those of the undeveloped provinces. Similarly, the light pollution heterogeneities of Taiwan, Beijing, and Shanghai were higher than those of undeveloped western provinces. Full article
(This article belongs to the Special Issue Recent Advances in Remote Sensing with Nighttime Lights)
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Open AccessArticle Identifying the Relative Contributions of Climate and Grazing to Both Direction and Magnitude of Alpine Grassland Productivity Dynamics from 1993 to 2011 on the Northern Tibetan Plateau
Remote Sens. 2017, 9(2), 136; doi:10.3390/rs9020136
Received: 20 November 2016 / Revised: 23 January 2017 / Accepted: 27 January 2017 / Published: 7 February 2017
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Abstract
Alpine grasslands on the Tibetan Plateau are claimed to be sensitive and vulnerable to climate change and human disturbance. The mechanism, direction and magnitude of climatic and anthropogenic influences on net primary productivity (NPP) of various alpine pastures remain under debate. Here, we
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Alpine grasslands on the Tibetan Plateau are claimed to be sensitive and vulnerable to climate change and human disturbance. The mechanism, direction and magnitude of climatic and anthropogenic influences on net primary productivity (NPP) of various alpine pastures remain under debate. Here, we simulated the potential productivity (with only climate variables being considered as drivers; NPPP) and actual productivity (based on remote sensing dataset including both climate and anthropogenic drivers; NPPA) from 1993 to 2011. We denoted the difference between NPPP and NPPA as NPPpc to quantify how much forage can be potentially consumed by livestock. The actually consumed productivity (NPPac) by livestock were estimated based on meat production and daily forage consumption per standardized sheep unit. We hypothesized that the gap between NPPpc and NPPac (NPPgap) indicates the direction of vegetation dynamics, restoration or degradation. Our results show that growing season precipitation rather than temperature significantly relates with NPPgap, although warming was significant for the entire study region while precipitation only significantly increased in the northeastern places. On the Northern Tibetan Plateau, 69.05% of available alpine pastures showed a restoration trend with positive NPPgap, and for 58.74% of alpine pastures, stocking rate is suggested to increase in the future because of the positive mean NPPgap and its increasing trend. This study provides a potential framework for regionally regulating grazing management with aims to restore the degraded pastures and sustainable management of the healthy pastures on the Tibetan Plateau. Full article
(This article belongs to the Special Issue Remote Sensing of Above Ground Biomass)
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Open AccessArticle Urban Expansion and Its Impact on the Land Use Pattern in Xishuangbanna since the Reform and Opening up of China
Remote Sens. 2017, 9(2), 137; doi:10.3390/rs9020137
Received: 4 November 2016 / Revised: 17 January 2017 / Accepted: 25 January 2017 / Published: 7 February 2017
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Abstract
Since the Chinese government carried out the reform and opening up policy, Xishuangbanna Dai Autonomous Prefecture has experienced rapid urbanization and dramatic land use change. This research aims at analyzing urban expansion in Xishuangbanna and its impact on the land use pattern using
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Since the Chinese government carried out the reform and opening up policy, Xishuangbanna Dai Autonomous Prefecture has experienced rapid urbanization and dramatic land use change. This research aims at analyzing urban expansion in Xishuangbanna and its impact on the land use pattern using combined methods, including radar graph, the gradient-direction method and landscape metrics. Seven land use maps from 1976 to 2015 were generated and analyzed, respectively. The results showed that urban and rubber expanded rapidly, while forest decreased during the last 40 years. The city proper, the county town of Menghai and the county town of Mengla showed the most significant and fastest urban expansion rates. In response to rapid urban expansion, land use types outside urban areas changed dramatically. In Jinghong and Mengla, urban areas were usually surrounded by paddy, shrub, rubber and forest in 1976, while most areas were dominated by rubber by 2015. With the development of Xishuangbanna, landscape diversity increased along urban-rural gradients, but decreased in some key urban areas. Urban expansion slightly reduced the connectivity of forest and increased agglomeration of rubber at the same time. Based on the analyses above, we moved forward to discuss the consequences of urban expansion, rubber plantations and land fragmentation. Full article
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Open AccessArticle Investigation of Potential Volcanic Risk from Mt. Baekdu by DInSAR Time Series Analysis and Atmospheric Correction
Remote Sens. 2017, 9(2), 138; doi:10.3390/rs9020138
Received: 29 September 2016 / Revised: 25 January 2017 / Accepted: 26 January 2017 / Published: 7 February 2017
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Abstract
Mt. Baekdu is a volcano near the North Korea-Chinese border that experienced a few destructive eruptions over the course of its history, including the well-known 1702 A.D eruption. However, signals of unrest, including seismic activity, gas emission and intense geothermal activity, have been
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Mt. Baekdu is a volcano near the North Korea-Chinese border that experienced a few destructive eruptions over the course of its history, including the well-known 1702 A.D eruption. However, signals of unrest, including seismic activity, gas emission and intense geothermal activity, have been occurring with increasing frequency over the last few years. Due to its close vicinity to a densely populated area and the high magnitude of historical volcanic eruptions, its potential for destructive volcanic activity has drawn wide public attention. However, direct field surveying in the area is limited due to logistic challenges. In order to compensate for the limited coverage of ground observations, comprehensive measurements using remote sensing techniques are required. Among these techniques, Differential Interferometric SAR (DInSAR) analysis is the most effective method for monitoring surface deformation and is employed in this study. Through advanced atmospheric error correction and time series analysis, the accuracy of the detected displacements was improved. As a result, clear uplift up to 20 mm/year was identified around Mt. Baekdu and was further used to estimate the possible deformation source, which is considered as a consequence of magma and fault interaction. Since the method for tracing deformation was proved feasible, continuous DInSAR monitoring employing upcoming SAR missions and advanced error regulation algorithms will be of great value in monitoring comprehensive surface deformation over Mt. Baekdu and in general world-wide active volcanoes. Full article
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Open AccessEditor’s ChoiceArticle Multiscale Superpixel-Based Sparse Representation for Hyperspectral Image Classification
Remote Sens. 2017, 9(2), 139; doi:10.3390/rs9020139
Received: 30 November 2016 / Revised: 18 January 2017 / Accepted: 25 January 2017 / Published: 7 February 2017
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Abstract
Recently, superpixel segmentation has been proven to be a powerful tool for hyperspectral image (HSI) classification. Nonetheless, the selection of the optimal superpixel size is a nontrivial task. In addition, compared with single-scale superpixel segmentation, the same image segmented on a different scale
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Recently, superpixel segmentation has been proven to be a powerful tool for hyperspectral image (HSI) classification. Nonetheless, the selection of the optimal superpixel size is a nontrivial task. In addition, compared with single-scale superpixel segmentation, the same image segmented on a different scale can obtain different structure information. To overcome such a drawback also utilizing the structural information, a multiscale superpixel-based sparse representation (MSSR) algorithm for the HSI classification is proposed. Specifically, a modified segmentation strategy of multiscale superpixels is firstly applied on the HSI. Once the superpixels on different scales are obtained, the joint sparse representation classification is used to classify the multiscale superpixels. Furthermore, majority voting is utilized to fuse the labels of different scale superpixels and to obtain the final classification result. Two merits are realized by the MSSR. First, multiscale information fusion can more effectively explore the spatial information of HSI. Second, in the multiscale superpixel segmentation, except for the first scale, the superpixel number on a different scale for different HSI datasets can be adaptively changed based on the spatial complexity of the corresponding HSI. Experiments on four real HSI datasets demonstrate the qualitative and quantitative superiority of the proposed MSSR algorithm over several well-known classifiers. Full article
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Open AccessArticle Topology Adaptive Water Boundary Extraction Based on a Modified Balloon Snake: Using GF-1 Satellite Images as an Example
Remote Sens. 2017, 9(2), 140; doi:10.3390/rs9020140
Received: 13 December 2016 / Revised: 27 January 2017 / Accepted: 3 February 2017 / Published: 10 February 2017
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Abstract
Topology adaptive water boundary extraction from satellite images using parametric snakes remains challenging in the domain of image segmentation. This paper proposed a modified balloon snake (MB-Snake) method based on the balloon snake (B-Snake) method, overcoming the B-Snake’s drawbacks of inaccurate positioning, topology
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Topology adaptive water boundary extraction from satellite images using parametric snakes remains challenging in the domain of image segmentation. This paper proposed a modified balloon snake (MB-Snake) method based on the balloon snake (B-Snake) method, overcoming the B-Snake’s drawbacks of inaccurate positioning, topology inflexibility, and non-automatic contour evolution termination. Six satellite images, acquired by the GF-1 wide field of view sensor and with water bodies of different types, inner land numbers, areas, boundary and background complexities, and digital number value contrasts, were used as experimental images, in which the MB-Snake method, and two comparison methods, the B-Snake and the orthogonal topology adaptive snake (OT-Snake) methods, were applied for water boundary extraction. All the extracted results were first qualitatively assessed and further quantitatively evaluated via three indexes, including correctness, completeness, and area overlap measure. Both of the qualitative and quantitative evaluation results consistently demonstrated that the MB-Snake method can efficiently improve the positioning accuracy, detect and dispose of topology collisions, and perform automatic contour evolution termination, successfully meeting its design objectives, and exhibiting great superiority to the existing topology-flexible parametric snakes. The sensitivity to initial contours, the effects of model parameters, and spatial resolutions of satellite images, and image demands of the MB-Snake method was also analyzed. Full article
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Open AccessArticle The Estimation of the North American Great Lakes Turbulent Fluxes Using Satellite Remote Sensing and MERRA Reanalysis Data
Remote Sens. 2017, 9(2), 141; doi:10.3390/rs9020141
Received: 17 October 2016 / Revised: 14 January 2017 / Accepted: 4 February 2017 / Published: 8 February 2017
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Abstract
This study provides the first technique to investigate the turbulent fluxes over the Great Lakes from July 2001 to December 2014 using a combination of data from satellite remote sensing, reanalysis data sets, and direct measurements. Turbulent fluxes including latent heat flux (Q
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This study provides the first technique to investigate the turbulent fluxes over the Great Lakes from July 2001 to December 2014 using a combination of data from satellite remote sensing, reanalysis data sets, and direct measurements. Turbulent fluxes including latent heat flux (QE) and sensible heat flux (QH) were estimated using the bulk aerodynamic approach, then compared with the direct eddy covariance measurements from the rooftop of three lighthouses—Stannard Rock Lighthouse (SR) in Lake Superior, White Shoal Lighthouse (WS) in Lake Michigan, and Spectacle Reef Lighthouse (SP) in Lake Huron. The relationship between modeled and measured QE and QH were in a good statistical agreement, for QE, R2 varied from 0.41 (WS), 0.74 (SR), and 0.87 (SP) with RMSE of 5.68, 6.93, and 4.67 W·m−2, respectively, while QH, R2 ranged from 0.002 (WS), 0.8030 (SP) and 0.94 (SR) with RMSE of 6.97, 4.39 and 4.90 W·m−2 respectively. Both monthly mean QE and QH were highest in January for all lakes except Lake Ontario, which was highest in early December. The turbulent fluxes then sharply drop in March and are negligible during June and July. The evaporation processes continue again in August. Full article
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Open AccessArticle Multivariate Spatial Data Fusion for Very Large Remote Sensing Datasets
Remote Sens. 2017, 9(2), 142; doi:10.3390/rs9020142
Received: 26 August 2016 / Accepted: 23 January 2017 / Published: 9 February 2017
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Abstract
Global maps of total-column carbon dioxide (CO2) mole fraction (in units of parts per million) are important tools for climate research since they provide insights into the spatial distribution of carbon intake and emissions as well as their seasonal and annual
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Global maps of total-column carbon dioxide (CO2) mole fraction (in units of parts per million) are important tools for climate research since they provide insights into the spatial distribution of carbon intake and emissions as well as their seasonal and annual evolutions. Currently, two main remote sensing instruments for total-column CO2 are the Orbiting Carbon Observatory-2 (OCO-2) and the Greenhouse gases Observing SATellite (GOSAT), both of which produce estimates of CO2 concentration, called profiles, at 20 different pressure levels. Operationally, each profile estimate is then convolved into a single estimate of column-averaged CO2 using a linear pressure weighting function. This total-column CO2 is then used for subsequent analyses such as Level 3 map generation and colocation for validation. In principle, total-column CO2 in these applications may be more efficiently estimated by making optimal estimates of the vector-valued CO2 profiles and applying the pressure weighting function afterwards. These estimates will be more efficient if there is multivariate dependence between CO2 values in the profile. In this article, we describe a methodology that uses a modified Spatial Random Effects model to account for the multivariate nature of the data fusion of OCO-2 and GOSAT. We show that multivariate fusion of the profiles has improved mean squared error relative to scalar fusion of the column-averaged CO2 values from OCO-2 and GOSAT. The computations scale linearly with the number of data points, making it suitable for the typically massive remote sensing datasets. Furthermore, the methodology properly accounts for differences in instrument footprint, measurement-error characteristics, and data coverages. Full article
(This article belongs to the Special Issue Multi-Sensor and Multi-Data Integration in Remote Sensing)
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Open AccessArticle Mapping Seasonal Inundation Frequency (1985–2016) along the St-John River, New Brunswick, Canada using the Landsat Archive
Remote Sens. 2017, 9(2), 143; doi:10.3390/rs9020143
Received: 14 December 2016 / Revised: 24 January 2017 / Accepted: 4 February 2017 / Published: 10 February 2017
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Abstract
Extreme flood events in recent years in Canada have highlighted the need for historical information to better manage future flood risk. In this paper, a methodology to generate flood maps from Landsat to determine historical inundation frequency is presented for a region along
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Extreme flood events in recent years in Canada have highlighted the need for historical information to better manage future flood risk. In this paper, a methodology to generate flood maps from Landsat to determine historical inundation frequency is presented for a region along the St-John River, New Brunswick, Canada that experiences annual springtime flooding from snowmelt and river ice. 1985–2016 Landsat data from the USGS archive were classified by combining See5 decision trees to map spectrally variable water due to spring ice and sediment, and image thresholding to map inundated floodplains. Multiple scenes representing each year were overlaid to produce seasonal time-series of spring (March–May) and summer (June–August) maximum annual water extents. Comparisons of annual surface water maps were conducted separately for each season against historical hydrometric water depth as a measure of relative springtime flood severity, and 1 m water masks from digital orthophotos were used to perform a formal accuracy assessment of summer water. Due to Landsat’s 16-day revisit time, peak flood depth was poorly related to flood extent; however, spring depth measured during Landsat acquisitions was significantly related to extent (tau = 0.6, p-value < 0.001). Further, summer maps validated against 30 m water fractions scaled from 1 m water masks were over 97% accurate. Limitations with respect to the assessment of flood extent from depth, timing differences between peak flood depth and extent due to Landsat revisit time and cloud cover, and suggestions to overcome limitations through multi-sensor integration including radar are discussed. Full article
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Open AccessArticle Spatio-Temporal LAI Modelling by Integrating Climate and MODIS LAI Data in a Mesoscale Catchment
Remote Sens. 2017, 9(2), 144; doi:10.3390/rs9020144
Received: 27 June 2016 / Revised: 23 January 2017 / Accepted: 25 January 2017 / Published: 10 February 2017
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Abstract
Vegetation is often represented by the leaf area index (LAI) in many ecological, hydrological and meteorological land surface models. However, the spatio-temporal dynamics of the vegetation are important to represent in these models. While the widely applied methods, such as the Canopy Structure
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Vegetation is often represented by the leaf area index (LAI) in many ecological, hydrological and meteorological land surface models. However, the spatio-temporal dynamics of the vegetation are important to represent in these models. While the widely applied methods, such as the Canopy Structure Dynamic Model (CSDM) and the Double Logistic Model (DLM) are solely based on cumulative daily mean temperature data as input, a new spatio-temporal LAI prediction model referred to as the Temperature Precipitation Vegetation Model (TPVM) is developed that also considers cumulative precipitation data as input into the modelling process. TPVM as well as CDSM and DLM model performances are compared and evaluated against filtered LAI data from the Moderate Resolution Imaging Spectroradiometer (MODIS). The calibration/validation results of a cross-validation performed in the meso-scale Attert catchment in Luxembourg indicated that the DLM and TPVM generally provided more realistic and accurate LAI data. The TPVM performed superiorly for the agricultural land cover types compared to the other two models, which only used the temperature data. The Pearson's correlation coefficient (CC) between TPVM and the field measurement is 0.78, compared to 0.73 and 0.69 for the DLM and CSDM, respectively. The phenological metrics were derived from the TPVM model to investigate the interaction between the climate variables and the LAI variations. These interactions illustrated the dominant control of temperature on the LAI dynamics for deciduous forest cover, and a combined influence of temperature with precipitation for the agricultural land use areas. Full article
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Open AccessArticle An Improved Shape Contexts Based Ship Classification in SAR Images
Remote Sens. 2017, 9(2), 145; doi:10.3390/rs9020145
Received: 8 December 2016 / Accepted: 4 February 2017 / Published: 10 February 2017
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Abstract
In synthetic aperture radar (SAR) imagery, relating to maritime surveillance studies, the ship has always been the main focus of study. In this letter, a method of ship classification in SAR images is proposed to enhance classification accuracy. In the proposed method, to
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In synthetic aperture radar (SAR) imagery, relating to maritime surveillance studies, the ship has always been the main focus of study. In this letter, a method of ship classification in SAR images is proposed to enhance classification accuracy. In the proposed method, to fully exploit the distinguishing characters of the ship targets, both topology and intensity of the scattering points of the ship are considered. The results of testing the proposed method on a data set of three types of ships, collected via a space-borne SAR sensor designed by the Institute of Electronics, Chinese Academy of Sciences (IECAS), establish that the proposed method is superior to several existing methods, including the original shape contexts method, traditional invariant moments and the recent approach. Full article
(This article belongs to the Special Issue Ocean Remote Sensing with Synthetic Aperture Radar)
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Open AccessArticle A Novel and Inexpensive Method for Measuring Volcanic Plume Water Fluxes at High Temporal Resolution
Remote Sens. 2017, 9(2), 146; doi:10.3390/rs9020146
Received: 7 November 2016 / Revised: 11 January 2017 / Accepted: 6 February 2017 / Published: 10 February 2017
Cited by 3 | PDF Full-text (3519 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Water vapour (H2O) is the dominant species in volcanic gas plumes. Therefore, measurements of H2O fluxes could provide valuable constraints on subsurface degassing and magmatic processes. However, due to the large and variable concentration of this species in the
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Water vapour (H2O) is the dominant species in volcanic gas plumes. Therefore, measurements of H2O fluxes could provide valuable constraints on subsurface degassing and magmatic processes. However, due to the large and variable concentration of this species in the background atmosphere, little attention has been devoted to monitoring the emission rates of this species from volcanoes. Instead, the focus has been placed on remote measurements of SO2, which is present in far lower abundances in plumes, and therefore provides poorer single flux proxies for overall degassing conditions. Here, we present a new technique for the measurement of H2O emissions at degassing volcanoes at high temporal resolution (≈1 Hz), via remote sensing with low cost digital cameras. This approach is analogous to the use of dual band ultraviolet (UV) cameras for measurements of volcanic SO2 release, but is focused on near infrared absorption by H2O. We report on the field deployment of these devices on La Fossa crater, Vulcano Island, and the North East Crater of Mt. Etna, during which in-plume calibration was performed using a humidity sensor, resulting in estimated mean H2O fluxes of ≈15 kg·s−1 and ≈34 kg·s−1, respectively, in accordance with previously reported literature values. By combining the Etna data with parallel UV camera and Multi-GAS observations, we also derived, for the first time, a combined record of 1 Hz gas fluxes for the three most abundant volcanic gas species: H2O, CO2, and SO2. Spectral analysis of the Etna data revealed oscillations in the passive emissions of all three species, with periods spanning ≈40–175 s, and a strong degree of correlation between the periodicity manifested in the SO2 and H2O data, potentially related to the similar exsolution depths of these two gases. In contrast, there was a poorer linkage between oscillations in these species and those of CO2, possibly due to the deeper exsolution of carbon dioxide, giving rise to distinct periodic degassing behaviour. Full article
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Open AccessArticle Change Vector Analysis to Monitor the Changes in Fuzzy Shorelines
Remote Sens. 2017, 9(2), 147; doi:10.3390/rs9020147
Received: 23 November 2016 / Accepted: 6 February 2017 / Published: 10 February 2017
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Abstract
Mapping of shorelines and monitoring of their changes is challenging due to the large variation in shoreline position related to seasonal and tidal patterns. This study focused on a flood-prone area in the north of Java. We show the possibility of using fuzzy-crisp
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Mapping of shorelines and monitoring of their changes is challenging due to the large variation in shoreline position related to seasonal and tidal patterns. This study focused on a flood-prone area in the north of Java. We show the possibility of using fuzzy-crisp objects to derive shoreline positions as the transition zone between the classes water and non-water. Fuzzy c-means classification (FCM) was used to estimate the membership of pixels to these classes. A transition zone between the classes represents the shoreline, and its spatial extent was estimated using fuzzy-crisp objects. In change vector analysis (CVA) applied to water membership of successive shorelines, a change category was defined if the change magnitude between two years, T1 and T2, differed from zero, while zero magnitude corresponded to no-change category. Over several years, overall change magnitude and change directions of the shoreline allowed us to identify the trend of the fluctuating shoreline and the uncertainty distribution. The fuzzy error matrix (FERM) showed overall accuracies between 0.84 and 0.91. Multi-year patterns of water membership changes could indicate coastal processes such as: (a) high change direction and high change magnitude with a consistent positive direction probably corresponding to land subsidence and coastal inundation, while a consistent negative direction probably indicates a success in a shoreline protection scheme; (b) low change direction and high change magnitude indicating an abrupt change which may result from spring tides, extreme waves and winds; (c) high change direction and low change magnitude which could be due to cyclical tides and coastal processes; and (d) low change direction and low change magnitude probably indicating an undisturbed environment, such as changes in water turbidity or changes in soil moisture. The proposed method provided a way to analyze changes of shorelines as fuzzy objects and could be well-suited to apply to coastal areas around the globe. Full article
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Open AccessArticle Adaptive Mean Shift-Based Identification of Individual Trees Using Airborne LiDAR Data
Remote Sens. 2017, 9(2), 148; doi:10.3390/rs9020148
Received: 30 August 2016 / Accepted: 3 February 2017 / Published: 10 February 2017
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Abstract
Identifying individual trees and delineating their canopy structures from the forest point clouddataacquiredbyanairborneLiDAR(LightDetectionAndRanging)hassignificantimplications in forestry inventory. Once accurately identified, tree structural attributes such as tree height, crown diameter, canopy based height and diameter at breast height can be derived. This paper focuses on
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Identifying individual trees and delineating their canopy structures from the forest point clouddataacquiredbyanairborneLiDAR(LightDetectionAndRanging)hassignificantimplications in forestry inventory. Once accurately identified, tree structural attributes such as tree height, crown diameter, canopy based height and diameter at breast height can be derived. This paper focuses on a novel computationally efficient method to adaptively calibrate the kernel bandwidth of a computational scheme based on mean shift—a non-parametric probability density-based clustering technique—to segment the 3D (three-dimensional) forest point clouds and identify individual tree crowns. The basic concept of this method is to partition the 3D space over each test plot into small vertical units (irregular columns containing 3D spatial features from one or more trees) first, by using a fixed bandwidth mean shift procedure and a small square grouping technique, and then rough estimation of crown sizes for distinct trees within a unit, based on an original 2D (two-dimensional) incremental grid projection technique, is applied to provide a basis for dynamical calibration of the kernel bandwidth for an adaptive mean shift procedure performed in each partition. The adaptive mean shift-based scheme, which incorporates our proposed bandwidth calibration method, is validated on 10 test plots of a dense, multi-layered evergreen broad-leaved forest located in South China. Experimental results reveal that this approach can work effectively and when compared to the conventional point-based approaches (e.g., region growing, k-means clustering, fixed bandwidth or multi-scale mean shift), its accuracies are relatively high: it detects 86 percent of the trees (“recall”) and 92 percent of the identified trees are correct (“precision”), showing good potential for use in the area of forest inventory. Full article
(This article belongs to the Special Issue Digital Forest Resource Monitoring and Uncertainty Analysis)
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Open AccessArticle The Evaluation of Single-Sensor Surface Soil Moisture Anomalies over the Mainland of the People’s Republic of China
Remote Sens. 2017, 9(2), 149; doi:10.3390/rs9020149
Received: 12 December 2016 / Revised: 31 January 2017 / Accepted: 9 February 2017 / Published: 13 February 2017
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Abstract
In recent years, different space agencies have launched satellite missions that carry passive microwave instruments on-board that can measure surface soil moisture. Three currently operational missions are the Soil Moisture and Ocean Salinity (SMOS) mission developed by the European Space Agency (ESA), the
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In recent years, different space agencies have launched satellite missions that carry passive microwave instruments on-board that can measure surface soil moisture. Three currently operational missions are the Soil Moisture and Ocean Salinity (SMOS) mission developed by the European Space Agency (ESA), the Advanced Microwave Scanning Radiometer 2 (AMSR2) developed by the Japan Aerospace Exploration Agency (JAXA), and the Microwave Radiation Imager (MWRI) from China’s National Satellite Meteorological Centre (NSMC). In this study, the quality of surface soil moisture anomalies derived from these passive microwave instruments was sequentially assessed over the mainland of the People’s Republic of China. First, the impact of a recent update in the Land Parameter Retrieval Model (LPRM) was assessed for MWRI observations. Then, the soil moisture measurements retrieved from the X-band observations of MWRI were compared with those of AMSR2, followed by an internal comparison of the multiple frequencies of AMSR2. Finally, SMOS retrievals from two different algorithms were also included in the comparison. For each sequential step, processing and verification chains were specifically designed to isolate the impact of algorithm (version), observation frequency or instrument characteristics. Two verification techniques are used: the statistical Triple Collocation technique is used as the primary verification tool, while the precipitation-based Rvalue technique is used to confirm key results. Our results indicate a consistently better performance throughout the entire study area after the implementation of an update of the LPRM. We also find that passive microwave observations in the AMSR2 C-band frequency (6.9 GHz) have an advantage over the AMSR2 X-band frequency (10.7 GHz) over moderate to densely vegetated regions. This finding is in line with theoretical expectations as emitted soil radiation will become masked under a dense canopy with stricter thresholds for higher passive microwave frequencies. Both AMSR2 and MWRI make X-band observations; a direct comparison between them reveals a consistently higher quality obtained by AMSR2, specifically over semi-arid climate regimes. Unfortunately, Radio Frequency Interference hampers the usefulness of soil moisture products for the SMOS L-band mission, leading to a significantly reduced revisit time over the densely populated eastern part of the country. Nevertheless, our analysis demonstrates that soil moisture products from a number of multi-frequency microwave sensors are credible alternatives for this dedicated L-band mission over the mainland of the People’s Republic of China. Full article
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Open AccessArticle Spatial-Temporal Characteristics and Climatic Responses of Water Level Fluctuations of Global Major Lakes from 2002 to 2010
Remote Sens. 2017, 9(2), 150; doi:10.3390/rs9020150
Received: 17 December 2016 / Revised: 6 February 2017 / Accepted: 9 February 2017 / Published: 13 February 2017
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Abstract
As one of the most important geographical units affected by global climate change, lakes are sensitive to climatic changes and are considered “indicators” of climate and the environment. In this study, changes in the spatial-temporal characteristics of the water levels of 204 global
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As one of the most important geographical units affected by global climate change, lakes are sensitive to climatic changes and are considered “indicators” of climate and the environment. In this study, changes in the spatial-temporal characteristics of the water levels of 204 global major lakes are systematically analyzed using satellite altimetry data (Hydroweb product) from 2002 to 2010. Additionally, the responses of the major global lake levels to climatic fluctuations are analyzed using Global Land Surface Assimilation System (GLDAS) data (temperature and precipitation). The results show that the change rates of most global lakes exceed 0, which means that the lake levels of these lakes are rising. The change rates of the lake levels are between −0.3~0.3 m/a, which indicates that the rate of change in the water-level of most lakes is not obvious. A few lakes have a particularly sharp change rate, between −5.84~−2 m/a or 0.7~1.87 m/a. Lakes with increasing levels are mainly located in the mountain and plateau regions, and the change rates in the coastal highlands are more evident. The global temperatures rise by a change rate of 0.0058 °C/a, while the global precipitation decreases by a change rate of −0.6697 mm/a. However, there are significant regional differences in both temperature and precipitation. In addition, the impact of precipitation on the water level of lakes is significant and straightforward, while the impact of temperature is more complex. A study of lake levels on a global scale would be quite useful for a better understanding of the impact which climate change has on surface water resources. Full article
(This article belongs to the Special Issue Remote Sensing of Climate Change and Water Resources)
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Open AccessArticle A New Propagation Channel Synthesizer for UAVs in the Presence of Tree Canopies
Remote Sens. 2017, 9(2), 151; doi:10.3390/rs9020151
Received: 25 November 2016 / Revised: 15 January 2017 / Accepted: 9 February 2017 / Published: 13 February 2017
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Abstract
Following the increasing popularity of unmanned aerial vehicles (UAVs) for remote sensing applications, the reliable operation under a number of various radio wave propagation conditions is required. Assuming common outdoor scenarios, the presence of trees in the vicinity of a UAV or its
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Following the increasing popularity of unmanned aerial vehicles (UAVs) for remote sensing applications, the reliable operation under a number of various radio wave propagation conditions is required. Assuming common outdoor scenarios, the presence of trees in the vicinity of a UAV or its ground terminal is highly probable. However, such a scenario is very difficult to address from a radio wave propagation point of view. Recently, an approach based on physical optics (PO) and the multiple scattering theory (MST) has been proposed by the authors, which enables fast and straightforward predictions of tree-scattered fields at microwave frequencies. In this paper, this approach is developed further into a generative model capable of providing both the narrowband and wideband synthetic time series of received/transmitted signals which are needed for both UAV communications and remote sensing applications in the presence of scattering from tree canopies. The proposed channel synthesizer is validated using both an artificially-generated scenario and actual experimental dataset. Full article
(This article belongs to the Special Issue Recent Trends in UAV Remote Sensing)
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Open AccessArticle Validation and Analysis of Long-Term AATSR Land Surface Temperature Product in the Heihe River Basin, China
Remote Sens. 2017, 9(2), 152; doi:10.3390/rs9020152
Received: 13 December 2016 / Revised: 6 February 2017 / Accepted: 9 February 2017 / Published: 13 February 2017
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Abstract
The Advanced Along-Track Scanning Radiometer (AATSR) land surface temperature (LST) product has a long-term time series of data from 20 May 2002 to 8 April 2012 and is a crucial dataset for global change studies. Accuracy and uncertainty assessment of satellite derived LST
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The Advanced Along-Track Scanning Radiometer (AATSR) land surface temperature (LST) product has a long-term time series of data from 20 May 2002 to 8 April 2012 and is a crucial dataset for global change studies. Accuracy and uncertainty assessment of satellite derived LST is important for its use in studying land–surface–atmosphere interactions. However, the validation of AATSR-derived LST products is scarce in China, especially in arid and semi-arid areas. In this study, we evaluated the accuracy of the AATSR LST product using ground-based measurements from 2007 to 2011 in the Heihe River Basin (HRB), China. The AATSR-derived LST results over Yingke site are closer to ground measurements than those over A’rou site for both daytime and nighttime temperatures. For nighttime, the averaged bias, STD, RMSE and R2 over both sites are 0.67 K, 3.03 K, 3.13 K and 0.93 K, respectively. Based on the accuracy assessment, we analyzed the AATSR-derived annual LST variations both in the HRB region and the two validation sites for the period of 2003 to 2011. The results at the A’rou site show an obvious increasing trend for daytime from 2003 to 2011. For the whole HRB region, the warming trend is clearly shown in the downstream of HRB. Full article
(This article belongs to the Special Issue Earth Observations for Precision Farming in China (EO4PFiC))
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Open AccessArticle LiDAR-Assisted Multi-Source Program (LAMP) for Measuring Above Ground Biomass and Forest Carbon
Remote Sens. 2017, 9(2), 154; doi:10.3390/rs9020154
Received: 2 September 2016 / Accepted: 4 February 2017 / Published: 14 February 2017
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Abstract
Forest measurement for purposes like harvesting planning, biomass estimation and mitigating climate change through carbon capture by forests call for increasingly frequent forest measurement campaigns that need to balance cost with accuracy and precision. Often this implies the use of remote sensing based
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Forest measurement for purposes like harvesting planning, biomass estimation and mitigating climate change through carbon capture by forests call for increasingly frequent forest measurement campaigns that need to balance cost with accuracy and precision. Often this implies the use of remote sensing based measurement methods. For any remote-sensing based methods to be accurate, they must be validated against field data. We present a method that combines field measurements with two layers of remote sensing data: sampling of forests by airborne laser scanning (LiDAR) and Landsat imagery. The Bayesian model-based framework presented here is called Lidar-Assisted Multi-source Programme—or LAMP—for Above Ground Biomass estimation. The method has two variants: LAMP2 which splits the biomass estimation task into two separate stages: forest type stratification from Landsat imagery and mean biomass density estimation of each forest type by LiDAR models calibrated on field plots. LAMP3, on the other hand, estimates first the biomass on a LiDAR sample using models calibrated with field plots and then uses these LiDAR-based models to generate biomass density estimates on thousands of surrogate plots, with which a satellite image based model is calibrated and subsequently used to estimate biomass density on the entire forest area. Both LAMP methods have been applied to a 2 million hectare area in Southern Nepal, the Terai Arc Landscape or TAL to calculate the emission Reference Levels (RLs) that are required for the UN REDD+ program that was accepted as part of the Paris Climate Agreement. The uncertainty of these estimates is studied with error variance estimation, cross-validation and Monte Carlo simulation. The relative accuracy of activity data at pixel level was found to be 14 per cent at 95 per cent confidence level and the root mean squared error of biomass estimates to be between 35 and 39 per cent at 1 ha resolution. Full article
(This article belongs to the Special Issue Digital Forest Resource Monitoring and Uncertainty Analysis)
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Open AccessArticle A Parameterized Microwave Emissivity Model for Bare Soil Surfaces
Remote Sens. 2017, 9(2), 155; doi:10.3390/rs9020155
Received: 19 October 2016 / Revised: 31 January 2017 / Accepted: 10 February 2017 / Published: 15 February 2017
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Abstract
Due to the difficulty in accurately interpreting surface emissivity spectra, problems remain in the application of passive microwave satellite observations over land surfaces. This study develops a parameterized soil surface emissivity model to quantify the microwave emissivity accurately and rapidly for Gaussian-correlated rough
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Due to the difficulty in accurately interpreting surface emissivity spectra, problems remain in the application of passive microwave satellite observations over land surfaces. This study develops a parameterized soil surface emissivity model to quantify the microwave emissivity accurately and rapidly for Gaussian-correlated rough surfaces. We first analyze the sensitivity of surface emissivity to parameters using the advanced integral equation model (AIEM) simulated data. On the basis of the analysis and previous empirical models, two function factors that consider the polarization dependence of surface reflectivity are developed in the parameterized soil surface emissivity model. These factors also comprehensively account for the effects of surface roughness, soil moisture, and incident angle. A comparison with the AIEM simulated data indicates that the absolute error of effective reflectivity estimated by the parameterized soil surface emissivity model is small with a magnitude of 10−2. Validation through experimental measurements suggests that a good agreement could be obtained. The parameterized soil surface emissivity model is applied to simulate satellite measurements of the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E). Compared with the commonly-used microwave land emissivity model developed by Weng et al. (2001), the simulation results using the parameterized soil surface emissivity model yield a lower root-mean-square error (RMSE) and the overall errors are reduced, particularly for horizontal polarization. The newly-developed parameterized soil surface emissivity model should be useful in improving our understanding and modeling the measurements of passive microwave radiometers. Full article
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Open AccessArticle Combining Airborne Laser Scanning and Aerial Imagery Enhances Echo Classification for Invasive Conifer Detection
Remote Sens. 2017, 9(2), 156; doi:10.3390/rs9020156
Received: 6 November 2016 / Revised: 7 February 2017 / Accepted: 9 February 2017 / Published: 15 February 2017
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Abstract
The spread of exotic conifers from commercial plantation forests has significant economic and ecological implications. Accurate methods for invasive conifer detection are required to enable monitoring and guide control. In this research, we combined spectral information from aerial imagery with data from airborne
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The spread of exotic conifers from commercial plantation forests has significant economic and ecological implications. Accurate methods for invasive conifer detection are required to enable monitoring and guide control. In this research, we combined spectral information from aerial imagery with data from airborne laser scanning (ALS) to develop methods to identify invasive conifers using remotely-sensed data. We examined the effect of ALS pulse density and the height threshold of the training dataset on classification accuracy. The results showed that adding spectral values to the ALS metrics/variables in the training dataset led to significant increases in classification accuracy. The most accurate models (kappa range of 0.773–0.837) had either four or five explanatory variables, including ALS elevation, the near-infrared band and different combinations of ALS intensity and red and green bands. The best models were found to be relatively invariant to changes in pulse density (1–21 pls/m2) or the height threshold (0–2 m) used for the inclusion of data in the training dataset. This research has extended and improved the methods for scattered single tree detection and offered valuable insight into campaign settings for the monitoring of invasive conifers (tree weeds) using remote sensing approaches. Full article
(This article belongs to the Special Issue Fusion of LiDAR Point Clouds and Optical Images)
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Open AccessFeature PaperArticle Seasonal Change in Wetland Coherence as an Aid to Wetland Monitoring
Remote Sens. 2017, 9(2), 158; doi:10.3390/rs9020158
Received: 29 November 2016 / Revised: 22 January 2017 / Accepted: 9 February 2017 / Published: 15 February 2017
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Abstract
Water is an essential natural resource, and information about surface water conditions can support a wide variety of applications, including urban planning, agronomy, hydrology, electrical power generation, disaster relief, ecology and preservation of natural areas. Synthetic Aperture Radar (SAR) is recognized as an
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Water is an essential natural resource, and information about surface water conditions can support a wide variety of applications, including urban planning, agronomy, hydrology, electrical power generation, disaster relief, ecology and preservation of natural areas. Synthetic Aperture Radar (SAR) is recognized as an important source of data for monitoring surface water, especially under inclement weather conditions, and is used operationally for flood mapping applications. The canopy penetration capability of the microwaves also allows for mapping of flooded vegetation as a result of enhanced backscatter from what is generally believed to be a double-bounce scattering mechanism between the water and emergent vegetation. Recent investigations have shown that, under certain conditions, the SAR response signal from flooded vegetation may remain coherent during repeat satellite over-passes, which can be exploited for interferometric SAR (InSAR) measurements to estimate changes in water levels and water topography. InSAR results also suggest that coherence change detection (CCD) might be applied to wetland monitoring applications. This study examines wetland vegetation characteristics that lead to coherence in RADARSAT-2 InSAR data of an area in eastern Canada with many small wetlands, and determines the annual variation in the coherence of these wetlands using multi-temporal radar data. The results for a three-year period demonstrate that most swamps and marshes maintain coherence throughout the ice-/snow-free time period for the 24-day repeat cycle of RADARSAT-2. However, open water areas without emergent aquatic vegetation generally do not have suitable coherence for CCD or InSAR water level estimation. We have found that wetlands with tree cover exhibit the highest coherence and the least variance; wetlands with herbaceous cover exhibit high coherence, but also high variability of coherence; and wetlands with shrub cover exhibit high coherence, but variability intermediate between treed and herbaceous wetlands. From this knowledge, we have developed a novel image product that combines information about the magnitude of coherence and its variability with radar brightness (backscatter intensity). This product clearly displays the multitude of small wetlands over a wide area. With an interpretation key we have also developed, it is possible to distinguish different wetland types and assess year-to-year changes. In the next few years, satellite SAR systems, such as the European Sentinel and the Canadian RADARSAT Constellation Mission (RCM), will provide rapid revisit capabilities and standard data collection modes, enhancing the operational application of SAR data for assessing wetland conditions and monitoring water levels using InSAR techniques. Full article
(This article belongs to the Special Issue Remote Sensing of Climate Change and Water Resources)
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Open AccessArticle Using UAS Hyperspatial RGB Imagery for Identifying Beach Zones along the South Texas Coast
Remote Sens. 2017, 9(2), 159; doi:10.3390/rs9020159
Received: 18 November 2016 / Revised: 25 January 2017 / Accepted: 10 February 2017 / Published: 15 February 2017
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Abstract
Shoreline information is fundamental for understanding coastal dynamics and for implementing environmental policy. The analysis of shoreline variability usually uses a group of shoreline indicators visibly discernible in coastal imagery, such as the seaward vegetation line, wet beach/dry beach line, and instantaneous water
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Shoreline information is fundamental for understanding coastal dynamics and for implementing environmental policy. The analysis of shoreline variability usually uses a group of shoreline indicators visibly discernible in coastal imagery, such as the seaward vegetation line, wet beach/dry beach line, and instantaneous water line. These indicators partition a beach into four zones: vegetated land, dry sand or debris, wet sand, and water. Unmanned aircraft system (UAS) remote sensing that can acquire imagery with sub-decimeter pixel size provides opportunities to map these four beach zones. This paper attempts to delineate four beach zones based on UAS hyperspatial RGB (Red, Green, and Blue) imagery, namely imagery of sub-decimeter pixel size, and feature textures. Besides the RGB images, this paper also uses USGS (the United States Geological Survey) Munsell HSV (Hue, Saturation, and Value) and CIELUV (the CIE 1976 (L*, u*, v*) color space) images transformed from an RGB image. The four beach zones are identified based on the Gray Level Co-Occurrence Matrix (GLCM) and Local Binary Pattern (LBP) textures. Experiments were conducted with South Padre Island photos acquired by a Nikon D80 camera mounted on the US-16 UAS during March 2014. The results show that USGS Munsell hue can separate land and water reliably. GLCM and LBP textures can slightly improve classification accuracies by both unsupervised and supervised classification techniques. The experiments also indicate that we could reach acceptable results on different photos while using training data from another photo for site-specific UAS remote sensing. The findings imply that parallel processing of classification is feasible. Full article
(This article belongs to the Special Issue Recent Trends in UAV Remote Sensing)
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Open AccessArticle Assessing Orographic Variability in Glacial Thickness Changes at the Tibetan Plateau Using ICESat Laser Altimetry
Remote Sens. 2017, 9(2), 160; doi:10.3390/rs9020160
Received: 29 September 2016 / Accepted: 9 February 2017 / Published: 15 February 2017
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Abstract
Monitoring glacier changes is essential for estimating the water mass balance of the Tibetan Plateau. In this study, we exploit ICESat laser altimetry data in combination with the SRTM DEM and the GLIMS glacier mask to estimate trends in change in glacial thickness
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Monitoring glacier changes is essential for estimating the water mass balance of the Tibetan Plateau. In this study, we exploit ICESat laser altimetry data in combination with the SRTM DEM and the GLIMS glacier mask to estimate trends in change in glacial thickness between 2003 and 2009 on the whole Tibetan Plateau. Considering acquisition conditions of ICESat measurements and terrain surface characteristics, annual glacier elevation trends were estimated for 15 different settings with respect to terrain slope and roughness. In the end, we only included ICESat elevations acquired over terrain with a slope below 20° and a roughness at the footprint scale below 15 m. With this setting, 90 glaciated areas could be distinguished. The results show that most of observed glaciated areas on the Tibetan Plateau are thinning, except for some glaciers in the northwest. In general, glacier elevations on the whole Tibetan Plateau decreased at an average rate of -0.17± 0.47 m per year (m a-1) between 2003 and 2009, taking together glaciers of any size, distribution, and location of the observed glaciated area. Both rate and rate error estimates are obtained by accumulating results from individual regions using least squares techniques. Our results notably show that trends in glacier thickness change indeed strongly depend on the relative position in a mountain range. Full article
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Open AccessArticle Algorithm Development for Land Surface Temperature Retrieval: Application to Chinese Gaofen-5 Data
Remote Sens. 2017, 9(2), 161; doi:10.3390/rs9020161
Received: 7 December 2016 / Revised: 2 February 2017 / Accepted: 13 February 2017 / Published: 16 February 2017
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Abstract
Land surface temperature (LST) is a key variable in the study of the energy exchange between the land surface and the atmosphere. Among the different methods proposed to estimate LST, the quadratic split-window (SW) method has achieved considerable popularity. This method works well
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Land surface temperature (LST) is a key variable in the study of the energy exchange between the land surface and the atmosphere. Among the different methods proposed to estimate LST, the quadratic split-window (SW) method has achieved considerable popularity. This method works well when the emissivities are high in both channels. Unfortunately, it performs poorly for low land surface emissivities (LSEs). To solve this problem, assuming that the LSE is known, the constant in the quadratic SW method was calculated by maintaining the other coefficients the same as those obtained for the black body condition. This procedure permits transfer of the emissivity effect to the constant. The result demonstrated that the constant was influenced by both atmospheric water vapour content (W) and atmospheric temperature (T0) in the bottom layer. To parameterize the constant, an exponential approximation between W and T0 was used. A LST retrieval algorithm was proposed. The error for the proposed algorithm was RMSE = 0.70 K. Sensitivity analysis results showed that under the consideration of NEΔT = 0.2 K, 20% uncertainty in W and 1% uncertainties in the channel mean emissivity and the channel emissivity difference, the RMSE was 1.29 K. Compared with AST 08 product, the proposed algorithm underestimated LST by about 0.8 K for both study areas when ASTER L1B data was used as a proxy of Gaofen-5 (GF-5) satellite data. The GF-5 satellite is scheduled to be launched in 2017. Full article
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Open AccessArticle From Space to the Rocky Intertidal: Using NASA MODIS Sea Surface Temperature and NOAA Water Temperature to Predict Intertidal Logger Temperature
Remote Sens. 2017, 9(2), 162; doi:10.3390/rs9020162
Received: 3 November 2016 / Revised: 30 January 2017 / Accepted: 9 February 2017 / Published: 16 February 2017
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Abstract
The development of satellite-derived datasets has greatly facilitated large-scale ecological studies, as in situ observations are spatially sparse and expensive undertakings. We tested the efficacy of using satellite sea surface temperature (SST) collected by NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) and local water
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The development of satellite-derived datasets has greatly facilitated large-scale ecological studies, as in situ observations are spatially sparse and expensive undertakings. We tested the efficacy of using satellite sea surface temperature (SST) collected by NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) and local water temperature collected from NOAA buoys and onshore stations to estimate submerged intertidal mussel logger temperatures. Daily SST and local water temperatures were compared to mussel logger temperatures at five study sites located along the Oregon coastline. We found that satellite-derived SSTs and local water temperatures were similarly correlated to the submerged mussel logger temperatures. This finding suggests that satellite-derived SSTs may be used in conjunction with local water temperatures to understand the temporal and spatial variation of mussel logger temperatures. While there are limitations to using satellite-derived temperature for ecological studies, including issues with temporal and spatial resolution, our results are promising. Full article
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Open AccessArticle Quantifying the Effects of Normalisation of Airborne LiDAR Intensity on Coniferous Forest Leaf Area Index Estimations
Remote Sens. 2017, 9(2), 163; doi:10.3390/rs9020163
Received: 9 December 2016 / Revised: 9 February 2017 / Accepted: 14 February 2017 / Published: 16 February 2017
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Abstract
The range between a sensor and the target, the incidence angle, and the target reflectance, are known factors that can influence the intensity values of LiDAR data and consequently, its use in many applications. However, very few studies have provided a quantitative analysis
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The range between a sensor and the target, the incidence angle, and the target reflectance, are known factors that can influence the intensity values of LiDAR data and consequently, its use in many applications. However, very few studies have provided a quantitative analysis of the effects of normalisation of these three factors on forest leaf area index (LAI) estimations. In this paper, using two coniferous tree species (i.e., Scotch pine and Larch pine) as a case study, the effects of intensity normalisation on coniferous forest LAI estimations have, for the first time, been systematically examined and quantified. It was found that the intensity normalisation had a generally positive effect on the improvement of coniferous forest LAI estimations. However, the improvements were very minor. Specifically, the range normalisation did not improve the accuracy of the LAI estimation for either of the two coniferous tree species. The incidence angle and reflectance normalisation improved the accuracy of the LAI estimation for Scotch pine forests; however, they had no effect on the improvement of the LAI estimation for Larch pine forests. This experimental study suggests that range normalisation is not required for forest LAI estimations in areas with small elevation differences (i.e., less than 114 m). The incidence angle and target reflectance normalisation can marginally improve the accuracy of coniferous forest LAI estimations. However, the extent of this improvement varies among species, depending on the choice of incidence angle and reflectance coefficient. Overall, the effects of normalisation of airborne LiDAR intensity on coniferous forest LAI estimations are closely related to topographic conditions (i.e., elevation and slope), the tree species composition, and its associated structural attributes. Therefore, further research should explore the effects of LiDAR intensity normalisation on forest LAI estimations in regions with large elevation differences and diverse forest structures. Full article
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Open AccessArticle Analyzing Parcel-Level Relationships between Urban Land Expansion and Activity Changes by Integrating Landsat and Nighttime Light Data
Remote Sens. 2017, 9(2), 164; doi:10.3390/rs9020164
Received: 17 December 2016 / Revised: 8 February 2017 / Accepted: 14 February 2017 / Published: 16 February 2017
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Abstract
Urban growth is a process that imposes profound physical and socioeconomic restructuring on cities. Urban land expansion as an immediate physical manifestation of urban growth has been extensively studied using a variety of remote sensing methods. However, little research addresses the interactions between
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Urban growth is a process that imposes profound physical and socioeconomic restructuring on cities. Urban land expansion as an immediate physical manifestation of urban growth has been extensively studied using a variety of remote sensing methods. However, little research addresses the interactions between urban land expansion and corresponding activity changes, especially at local scales. We propose an innovative analytical framework that integrates Landsat and nighttime light data to capture the parcel-level relationships between urban land expansion and activity changes. The urban land data are acquired based on the classification of Landsat images, whereas the activity changes are approximated by the nighttime light data. Using the Local Indicator of Spatial Association (LISA) (local Moran’s I) approach, four types of local relationships between land expansion and activity changes are defined at the parcel level. The proposed analytical framework is applied in Guangzhou, China, as a case study. The results reveal the mismatched growth between urban land and activity intensity at the parcel level, where the increase in urban land area outpaces the increase of activity intensity. Such results are expected to provide a more comprehensive understanding of urban growth, and can be used to assist urban planning and related decision-making. Full article
(This article belongs to the Special Issue Recent Advances in Remote Sensing with Nighttime Lights)
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Open AccessArticle Monitoring the Rapid-Moving Reactivation of Earth Flows by Means of GB-InSAR: The April 2013 Capriglio Landslide (Northern Appennines, Italy)
Remote Sens. 2017, 9(2), 165; doi:10.3390/rs9020165
Received: 23 November 2016 / Revised: 31 January 2017 / Accepted: 13 February 2017 / Published: 17 February 2017
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Abstract
This paper presents the main results of the GB-InSAR (ground based interferometric synthetic aperture radar) monitoring of the Capriglio landslide (Northern Apennines, Emilia Romagna Region, Italy), activated on 6 April 2013. The landslide, triggered by prolonged rainfall, is constituted by two main adjacent
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This paper presents the main results of the GB-InSAR (ground based interferometric synthetic aperture radar) monitoring of the Capriglio landslide (Northern Apennines, Emilia Romagna Region, Italy), activated on 6 April 2013. The landslide, triggered by prolonged rainfall, is constituted by two main adjacent enlarging bodies with a roto-translational kinematics. They activated in sequence and subsequently joined into a large earth flow, channelizing downstream of the Bardea Creek, for a total length of about 3600 m. The displacement rate of this combined mass was quite high, so that the landslide toe evolved with velocities of several tens of meters per day (with peaks of 70–80 m/day) in the first month, and of several meters per day (with peaks of 13–14 m/day) from early May to mid-July 2013. In the crown area, the landslide completely destroyed a 450 m sector of provincial roadway S.P. 101, and its retrogression tendency exposed the villages of Capriglio and Pianestolla, located in the upper watershed area of the Bardea Creek, to great danger. Furthermore, the advancing toe seriously threatened the Antria bridge, representing the “Massese” provincial roadway S.P. 665R transect over the Bardea Creek, the only strategic roadway left able to connect the above-mentioned villages. With the final aim of supporting local authorities in the hazard assessment and risk management during the emergency phase, on 4 May 2013 aerial optical surveys were conducted to accurately map the landslide extension and evolution. Moreover, a GB-InSAR monitoring campaign was started in order to assess displacements of the whole landslide area. The versatility and flexibility of the GB-InSAR sensors allowed acquiring data with two different configurations, designed and set up to continuously retrieve information on the landslide movement rates (both in its upper slow-moving sectors and in its fast-moving toe). The first acquisition mode revealed that the Capriglio and Pianestolla villages were affected by minor displacements (at an order of magnitude of a few millimeters per month). The second acquisition mode allowed to acquire data every 28 seconds, reaching very high temporal resolution values by applying the GB-InSAR technique. Full article
(This article belongs to the Special Issue Remote Sensing of Landslides)
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Open AccessArticle Texture-Analysis-Incorporated Wind Parameters Extraction from Rain-Contaminated X-Band Nautical Radar Images
Remote Sens. 2017, 9(2), 166; doi:10.3390/rs9020166
Received: 18 November 2016 / Revised: 10 February 2017 / Accepted: 14 February 2017 / Published: 16 February 2017
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Abstract
In this paper, a method for extracting wind parameters from rain-contaminated X-band nautical radar images is presented. The texture of the radar image is first generated based on spatial variability analysis. Through this process, the rain clutter in an image can be removed
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In this paper, a method for extracting wind parameters from rain-contaminated X-band nautical radar images is presented. The texture of the radar image is first generated based on spatial variability analysis. Through this process, the rain clutter in an image can be removed while the wave echoes are retained. The number of rain-contaminated pixels in each azimuthal direction of the texture is estimated, and this is used to determine the azimuthal directions in which the rain-contamination is negligible. Then, the original image data in these directions are selected for wind direction and speed retrieval using the modified intensity-level-selection-based wind algorithm. The proposed method is applied to shipborne radar data collected from the east Coast of Canada. The comparison of the radar results with anemometer data shows that the standard deviations of wind direction and speed using the rain mitigation technique can be reduced by about 14.5° and 1.3 m/s, respectively. Full article
(This article belongs to the Special Issue Radar Systems for the Societal Challenges)
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Open AccessArticle A Broad-Area Method for the Diurnal Characterisation of Upwelling Medium Wave Infrared Radiation
Remote Sens. 2017, 9(2), 167; doi:10.3390/rs9020167
Received: 15 December 2016 / Revised: 9 February 2017 / Accepted: 13 February 2017 / Published: 17 February 2017
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Abstract
Fire detection from satellite sensors relies on an accurate estimation of the unperturbed state of a target pixel, from which an anomaly can be isolated. Methods for estimating the radiation budget of a pixel without fire depend upon training data derived from the
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Fire detection from satellite sensors relies on an accurate estimation of the unperturbed state of a target pixel, from which an anomaly can be isolated. Methods for estimating the radiation budget of a pixel without fire depend upon training data derived from the location’s recent history of brightness temperature variation over the diurnal cycle, which can be vulnerable to cloud contamination and the effects of weather. This study proposes a new method that utilises the common solar budget found at a given latitude in conjunction with an area’s local solar time to aggregate a broad-area training dataset, which can be used to model the expected diurnal temperature cycle of a location. This training data is then used in a temperature fitting process with the measured brightness temperatures in a pixel, and compared to pixel-derived training data and contextual methods of background temperature determination. Results of this study show similar accuracy between clear-sky medium wave infrared upwelling radiation and the diurnal temperature cycle estimation compared to previous methods, with demonstrable improvements in processing time and training data availability. This method can be used in conjunction with brightness temperature thresholds to provide a baseline for upwelling radiation, from which positive thermal anomalies such as fire can be isolated. Full article
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Open AccessArticle A Soil Moisture Retrieval Method Based on Typical Polarization Decomposition Techniques for a Maize Field from Full-Polarization Radarsat-2 Data
Remote Sens. 2017, 9(2), 168; doi:10.3390/rs9020168
Received: 10 November 2016 / Accepted: 9 February 2017 / Published: 17 February 2017
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Abstract
Soil moisture (SM) estimates are important to research, but are not accurately predictable in areas with tall vegetation. Full-polarization Radarsat-2 C-band data were used to retrieve SM contents using typical polarization decomposition (Freeman–Durden, Yamaguchi and VanZly) at different growth stages of maize. Applicability
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Soil moisture (SM) estimates are important to research, but are not accurately predictable in areas with tall vegetation. Full-polarization Radarsat-2 C-band data were used to retrieve SM contents using typical polarization decomposition (Freeman–Durden, Yamaguchi and VanZly) at different growth stages of maize. Applicability analyses were conducted, including proportion, regression and surface scattering model analyses. Furthermore, the Bragg, the extended Bragg scattering model (X-Bragg) and improved surface scattering models (ISSM) were used to retrieve SM content. The results indicated that the VanZly decomposition method was the best. The proportion of surface scattering in the proportion analysis was highest (>52%), followed by that in the Yamaguchi method (>41%). The R2 (>0.6144) between surface scattering and SM was significantly higher (R2 < 0.4484) between dihedral scattering and SM in the regression analysis. The ISSM was better at different maize growth stages than the Bragg and X-Bragg models with a higher R2 (>0.6599) and lower absolute error (AE) (<5.82) and root mean square error (RMSE) (<3.73). The best algorithm was obtained at the sowing stage (R2 = 0.8843, AE = 3.13, RMSE = 1.76). In addition, the X-Bragg model provided better approximation of actual surface scattering without the measured data (better algorithm: R2 = 0.8314, AE = 4.39, RMSE = 2.81). Full article
(This article belongs to the Special Issue Earth Observations for Precision Farming in China (EO4PFiC))
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Open AccessArticle Marsh Loss Due to Cumulative Impacts of Hurricane Isaac and the Deepwater Horizon Oil Spill in Louisiana
Remote Sens. 2017, 9(2), 169; doi:10.3390/rs9020169
Received: 3 November 2016 / Revised: 12 January 2017 / Accepted: 13 February 2017 / Published: 17 February 2017
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Abstract
Coastal ecosystems are greatly endangered due to anthropogenic development and climate change. Multiple disturbances may erode the ability of a system to recover from stress if there is little time between disturbance events. We evaluated the ability of the saltmarshes in Barataria Bay,
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Coastal ecosystems are greatly endangered due to anthropogenic development and climate change. Multiple disturbances may erode the ability of a system to recover from stress if there is little time between disturbance events. We evaluated the ability of the saltmarshes in Barataria Bay, Louisiana, USA, to recover from two successive disturbances, the DeepWater Horizon oil spill in 2010 and Hurricane Isaac in 2012. We measured recovery using vegetation indices and land cover change metrics. We found that after the hurricane, land loss along oiled shorelines was 17.8%, while along oil-free shorelines, it was 13.6% within the first 7 m. At a distance of 7–14 m, land loss from oiled regions was 11.6%, but only 6.3% in oil-free regions. We found no differences in vulnerability to land loss between narrow and wide shorelines; however, vegetation in narrow sites was significantly more stressed, potentially leading to future land loss. Treated oiled regions also lost more land due to the hurricane than untreated regions. These results suggest that ecosystem recovery after the two disturbances is compromised, as the observed high rates of land loss may prevent salt marsh from establishing in the same areas where it existed prior to the oil spill. Full article
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Open AccessArticle Analyses of Recent Sediment Surface Dynamic of a Namibian Kalahari Salt Pan Based on Multitemporal Landsat and Hyperspectral Hyperion Data
Remote Sens. 2017, 9(2), 170; doi:10.3390/rs9020170
Received: 27 July 2016 / Revised: 14 February 2017 / Accepted: 15 February 2017 / Published: 18 February 2017
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Abstract
This study combines spaceborne multitemporal and hyperspectral data to analyze the spatial distribution of surface evaporite minerals and changes in a semi-arid depositional environment associated with episodic flooding events, the Omongwa salt pan (Kalahari, Namibia). The dynamic of the surface crust is evaluated
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This study combines spaceborne multitemporal and hyperspectral data to analyze the spatial distribution of surface evaporite minerals and changes in a semi-arid depositional environment associated with episodic flooding events, the Omongwa salt pan (Kalahari, Namibia). The dynamic of the surface crust is evaluated by a change-detection approach using the Iterative-reweighted Multivariate Alteration Detection (IR-MAD) based on the Landsat archive imagery from 1984 to 2015. The results show that the salt pan is a highly dynamic and heterogeneous landform. A change gradient is observed from very stable pan border to a highly dynamic central pan. On the basis of hyperspectral EO-1 Hyperion images, the current distribution of surface evaporite minerals is characterized using Spectral Mixture Analysis (SMA). Assessment of field and image endmembers revealed that the pan surface can be categorized into three major crust types based on diagnostic absorption features and mineralogical ground truth data. The mineralogical crust types are related to different zones of surface change as well as pan morphology that influences brine flow during the pan inundation and desiccation cycles. These combined information are used to spatially map depositional environments where the more dynamic halite crust concentrates in lower areas although stable gypsum and calcite/sepiolite crusts appear in higher elevated areas. Full article
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Open AccessArticle Contour Detection for UAV-Based Cadastral Mapping
Remote Sens. 2017, 9(2), 171; doi:10.3390/rs9020171
Received: 2 December 2016 / Revised: 8 February 2017 / Accepted: 15 February 2017 / Published: 18 February 2017
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Abstract
Unmanned aerial vehicles (UAVs) provide a flexible and low-cost solution for the acquisition of high-resolution data. The potential of high-resolution UAV imagery to create and update cadastral maps is being increasingly investigated. Existing procedures generally involve substantial fieldwork and many manual processes. Arguably,
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Unmanned aerial vehicles (UAVs) provide a flexible and low-cost solution for the acquisition of high-resolution data. The potential of high-resolution UAV imagery to create and update cadastral maps is being increasingly investigated. Existing procedures generally involve substantial fieldwork and many manual processes. Arguably, multiple parts of UAV-based cadastral mapping workflows could be automated. Specifically, as many cadastral boundaries coincide with visible boundaries, they could be extracted automatically using image analysis methods. This study investigates the transferability of gPb contour detection, a state-of-the-art computer vision method, to remotely sensed UAV images and UAV-based cadastral mapping. Results show that the approach is transferable to UAV data and automated cadastral mapping: object contours are comprehensively detected at completeness and correctness rates of up to 80%. The detection quality is optimal when the entire scene is covered with one orthoimage, due to the global optimization of gPb contour detection. However, a balance between high completeness and correctness is hard to achieve, so a combination with area-based segmentation and further object knowledge is proposed. The localization quality exhibits the usual dependency on ground resolution. The approach has the potential to accelerate the process of general boundary delineation during the creation and updating of cadastral maps. Full article
(This article belongs to the Special Issue Recent Trends in UAV Remote Sensing)
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Open AccessArticle Testing Accuracy and Repeatability of UAV Blocks Oriented with GNSS-Supported Aerial Triangulation
Remote Sens. 2017, 9(2), 172; doi:10.3390/rs9020172
Received: 15 December 2016 / Revised: 6 February 2017 / Accepted: 15 February 2017 / Published: 18 February 2017
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Abstract
UAV Photogrammetry today already enjoys a largely automated and efficient data processing pipeline. However, the goal of dispensing with Ground Control Points looks closer, as dual-frequency GNSS receivers are put on board. This paper reports on the accuracy in object space obtained by
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UAV Photogrammetry today already enjoys a largely automated and efficient data processing pipeline. However, the goal of dispensing with Ground Control Points looks closer, as dual-frequency GNSS receivers are put on board. This paper reports on the accuracy in object space obtained by GNSS-supported orientation of four photogrammetric blocks, acquired by a senseFly eBee RTK and all flown according to the same flight plan at 80 m above ground over a test field. Differential corrections were sent to the eBee from a nearby ground station. Block orientation has been performed with three software packages: PhotoScan, Pix4D and MicMac. The influence on the checkpoint errors of the precision given to the projection centers has been studied: in most cases, values in Z are critical. Without GCP, the RTK solution consistently achieves a RMSE of about 2–3 cm on the horizontal coordinates of checkpoints. In elevation, the RMSE varies from flight to flight, from 2 to 10 cm. Using at least one GCP, with all packages and all test flights, the geocoding accuracy of GNSS-supported orientation is almost as good as that of a traditional GCP orientation in XY and only slightly worse in Z. Full article
(This article belongs to the Special Issue Recent Trends in UAV Remote Sensing)
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Open AccessArticle Effect of Training Class Label Noise on Classification Performances for Land Cover Mapping with Satellite Image Time Series
Remote Sens. 2017, 9(2), 173; doi:10.3390/rs9020173
Received: 12 December 2016 / Revised: 25 January 2017 / Accepted: 13 February 2017 / Published: 18 February 2017
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Abstract
Supervised classification systems used for land cover mapping require accurate reference databases. These reference data come generally from different sources such as field measurements, thematic maps, or aerial photographs. Due to misregistration, update delay, or land cover complexity, they may contain class label
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Supervised classification systems used for land cover mapping require accurate reference databases. These reference data come generally from different sources such as field measurements, thematic maps, or aerial photographs. Due to misregistration, update delay, or land cover complexity, they may contain class label noise, i.e., a wrong label assignment. This study aims at evaluating the impact of mislabeled training data on classification performances for land cover mapping. Particularly, it addresses the random and systematic label noise problem for the classification of high resolution satellite image time series. Experiments are carried out on synthetic and real datasets with two traditional classifiers: Support Vector Machines (SVM) and Random Forests (RF). A synthetic dataset has been designed for this study, simulating vegetation profiles over one year. The real dataset is composed of Landsat-8 and SPOT-4 images acquired during one year in the south of France. The results show that both classifiers are little influenced for low random noise levels up to 25%–30%, but their performances drop down for higher noise levels. Different classification configurations are tested by increasing the number of classes, using different input feature vectors, and changing the number of training instances. Algorithm complexities are also analyzed. The RF classifier achieves high robustness to random and systematic label noise for all the tested configurations; whereas the SVM classifier is more sensitive to the kernel choice and to the input feature vectors. Finally, this work reveals that the cross-validation procedure is impacted by the presence of class label noise. Full article
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