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Remote Sens., Volume 9, Issue 8 (August 2017)

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Editorial

Jump to: Research, Review, Other

Open AccessEditorial Water Optics and Water Colour Remote Sensing
Remote Sens. 2017, 9(8), 818; doi:10.3390/rs9080818
Received: 4 August 2017 / Revised: 4 August 2017 / Accepted: 7 August 2017 / Published: 9 August 2017
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Abstract
The editorial paper aims to highlight the main topics investigated in the Special Issue (SI) “Water Optics and Water Colour Remote Sensing”. The outcomes of the 21 papers published in the SI are presented, along with a bibliometric analysis in the same field,
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The editorial paper aims to highlight the main topics investigated in the Special Issue (SI) “Water Optics and Water Colour Remote Sensing”. The outcomes of the 21 papers published in the SI are presented, along with a bibliometric analysis in the same field, namely, water optics and water colour remote sensing. This editorial summarises how the research articles of the SI approach the study of bio-optical properties of aquatic systems, the development of remote sensing algorithms, and the application of time-series satellite data for assessing long-term and temporal-spatial dynamics in inland, coastal, and oceanic waters. The SI shows the progress with a focus on: (1) bio-optical properties (three papers); (2) atmospheric correction and data uncertainties (five papers); (3) remote sensing estimation of chlorophyll-a (Chl-a) (eight papers); (4) remote sensing estimation of suspended matter and chromophoric dissolved organic matter (CDOM) (four papers); and (5) water quality and water ecology remote sensing (four papers). Overall, the SI presents a variety of applications at the global scale (with case studies in Europe, Asia, South and North America, and the Antarctic), achieved with different remote sensing instruments, such as hyperspectral field and airborne sensors, ocean colour radiometry, geostationary platforms, and the multispectral Landsat and Sentinel-2 satellites. The bibliometric analysis, carried out to include research articles published from 1900 to 2016, indicates that “chlorophyll-a”, “ocean colour”, “phytoplankton”, “SeaWiFS” (Sea-Viewing Wide Field-of-View Sensor), and “chromophoric dissolved organic matter” were the five most frequently used keywords in the field. The SI contents, along with the bibliometric analysis, clearly suggest that remote sensing of Chl-a is one of the topmost investigated subjects in the field. Full article
(This article belongs to the Special Issue Water Optics and Water Colour Remote Sensing)
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Open AccessEditorial Preface: Land Surface Processes and Interactions—From HCMM to Sentinel Missions and Beyond
Remote Sens. 2017, 9(8), 788; doi:10.3390/rs9080788
Received: 27 July 2017 / Revised: 27 July 2017 / Accepted: 27 July 2017 / Published: 31 July 2017
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Abstract
The scientific understanding of the energy and water fluxes between land and atmosphere primarily predicates our capacity to describe, model, and predict the highly complex Earth system, which is formed by mutually interlinked components (land, atmosphere, and ocean) [...] Full article

Research

Jump to: Editorial, Review, Other

Open AccessArticle How Do Aerosol Properties Affect the Temporal Variation of MODIS AOD Bias in Eastern China?
Remote Sens. 2017, 9(8), 800; doi:10.3390/rs9080800
Received: 21 June 2017 / Revised: 27 July 2017 / Accepted: 1 August 2017 / Published: 3 August 2017
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Abstract
The rapid changes of aerosol sources in eastern China during recent decades could bring considerable uncertainties for satellite retrieval algorithms that assume little spatiotemporal variation in aerosol single scattering properties (such as single scattering albedo (SSA) and the size distribution for fine-mode and
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The rapid changes of aerosol sources in eastern China during recent decades could bring considerable uncertainties for satellite retrieval algorithms that assume little spatiotemporal variation in aerosol single scattering properties (such as single scattering albedo (SSA) and the size distribution for fine-mode and coarse mode aerosols) in East Asia. Here, using ground-based observations in six AERONET sites, we characterize typical aerosol optical properties (including their spatiotemporal variation) in eastern China, and evaluate their impacts on Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 aerosol retrieval bias. Both the SSA and fine-mode particle sizes increase from northern to southern China in winter, reflecting the effect of relative humidity on particle size. The SSA is ~0.95 in summer regardless of the AEROENT stations in eastern China, but decreases to 0.85 in polluted winter in northern China. The dominance of larger and highly scattering fine-mode particles in summer also leads to the weakest phase function in the backscattering direction. By focusing on the analysis of high aerosol optical depth (AOD) (>0.4) conditions, we find that the overestimation of the AOD in Dark Target (DT) retrieval is prevalent throughout the whole year, with the bias decreasing from northern China, characterized by a mixture of fine and coarse (dust) particles, to southern China, which is dominated by fine particles. In contrast, Deep Blue (DB) retrieval tends to overestimate the AOD only in fall and winter, and underestimates it in spring and summer. While the retrievals from both the DT and DB algorithms show a reasonable estimation of the fine-mode fraction of AOD, the retrieval bias cannot be attributed to the bias in the prescribed SSA alone, and is more due to the bias in the prescribed scattering phase function (or aerosol size distribution) in both algorithms. In addition, a large yearly change in aerosol single scattering properties leads to correspondingly obvious variations in the time series of MODIS AOD bias. Our results reveal that the aerosol single scattering properties in the MODIS algorithm are insufficient to describe a large variation of aerosol properties in eastern China (especially change of particle size), and can be further improved by using newer AERONET data. Full article
(This article belongs to the Special Issue Remote Sensing of Atmospheric Pollution)
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Open AccessArticle Regional-Scale High Spatial Resolution Mapping of Aboveground Net Primary Productivity (ANPP) from Field Survey and Landsat Data: A Case Study for the Country of Wales
Remote Sens. 2017, 9(8), 801; doi:10.3390/rs9080801
Received: 25 May 2017 / Revised: 28 July 2017 / Accepted: 1 August 2017 / Published: 4 August 2017
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Abstract
This paper presents an alternative approach for high spatial resolution vegetation productivity mapping at a regional scale, using a combination of Normalised Difference Vegetation Index (NDVI) imagery and widely distributed ground-based Above-ground Net Primary Production (ANPP) estimates. Our method searches through all available
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This paper presents an alternative approach for high spatial resolution vegetation productivity mapping at a regional scale, using a combination of Normalised Difference Vegetation Index (NDVI) imagery and widely distributed ground-based Above-ground Net Primary Production (ANPP) estimates. Our method searches through all available single-date NDVI imagery to identify the images which give the best NDVI–ANPP relationship. The derived relationships are then used to predict ANPP values outside of field survey plots. This approach enables the use of the high spatial resolution (30 m) Landsat 8 sensor, despite its low revisit frequency that is further reduced by cloud cover. This is one of few studies to investigate the NDVI–ANPP relationship across a wide range of temperate habitats and strong relationships were observed (R2 = 0.706), which increased when only grasslands were considered (R2 = 0.833). The strongest NDVI–ANPP relationships occurred during the spring “green-up” period. A reserved subset of 20% of ground-based ANPP estimates was used for validation and results showed that our method was able to estimate ANPP with a RMSE of 15–21%. This work is important because we demonstrate a general methodological framework for mapping of ANPP from local to regional scales, with the potential to be applied to any temperate ecosystems with a pronounced green up period. Our approach allows spatial extrapolation outside of field survey plots to produce a continuous surface product, useful for capturing spatial patterns and representing small-scale heterogeneity, and well-suited for modelling applications. The data requirements for implementing this approach are also discussed. Full article
(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)
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Open AccessArticle Effect of Label Noise on the Machine-Learned Classification of Earthquake Damage
Remote Sens. 2017, 9(8), 803; doi:10.3390/rs9080803
Received: 30 May 2017 / Revised: 17 July 2017 / Accepted: 28 July 2017 / Published: 4 August 2017
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Abstract
Automated classification of earthquake damage in remotely-sensed imagery using machine learning techniques depends on training data, or data examples that are labeled correctly by a human expert as containing damage or not. Mislabeled training data are a major source of classifier error due
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Automated classification of earthquake damage in remotely-sensed imagery using machine learning techniques depends on training data, or data examples that are labeled correctly by a human expert as containing damage or not. Mislabeled training data are a major source of classifier error due to the use of imprecise digital labeling tools and crowdsourced volunteers who are not adequately trained on or invested in the task. The spatial nature of remote sensing classification leads to the consistent mislabeling of classes that occur in close proximity to rubble, which is a major byproduct of earthquake damage in urban areas. In this study, we look at how mislabeled training data, or label noise, impact the quality of rubble classifiers operating on high-resolution remotely-sensed images. We first study how label noise dependent on geospatial proximity, or geospatial label noise, compares to standard random noise. Our study shows that classifiers that are robust to random noise are more susceptible to geospatial label noise. We then compare the effects of label noise on both pixel- and object-based remote sensing classification paradigms. While object-based classifiers are known to outperform their pixel-based counterparts, this study demonstrates that they are more susceptible to geospatial label noise. We also introduce a new labeling tool to enhance precision and image coverage. This work has important implications for the Sendai framework as autonomous damage classification will ensure rapid disaster assessment and contribute to the minimization of disaster risk. Full article
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Open AccessArticle Image Fusion-Based Land Cover Change Detection Using Multi-Temporal High-Resolution Satellite Images
Remote Sens. 2017, 9(8), 804; doi:10.3390/rs9080804
Received: 17 June 2017 / Revised: 19 July 2017 / Accepted: 4 August 2017 / Published: 5 August 2017
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Abstract
Change detection is usually treated as a problem of explicitly detecting land cover transitions in satellite images obtained at different times, and helps with emergency response and government management. This study presents an unsupervised change detection method based on the image fusion of
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Change detection is usually treated as a problem of explicitly detecting land cover transitions in satellite images obtained at different times, and helps with emergency response and government management. This study presents an unsupervised change detection method based on the image fusion of multi-temporal images. The main objective of this study is to improve the accuracy of unsupervised change detection from high-resolution multi-temporal images. Our method effectively reduces change detection errors, since spatial displacement and spectral differences between multi-temporal images are evaluated. To this end, a total of four cross-fused images are generated with multi-temporal images, and the iteratively reweighted multivariate alteration detection (IR-MAD) method—a measure for the spectral distortion of change information—is applied to the fused images. In this experiment, the land cover change maps were extracted using multi-temporal IKONOS-2, WorldView-3, and GF-1 satellite images. The effectiveness of the proposed method compared with other unsupervised change detection methods is demonstrated through experimentation. The proposed method achieved an overall accuracy of 80.51% and 97.87% for cases 1 and 2, respectively. Moreover, the proposed method performed better when differentiating the water area from the vegetation area compared to the existing change detection methods. Although the water area beneath moderate and sparse vegetation canopy was captured, vegetation cover and paved regions of the water body were the main sources of omission error, and commission errors occurred primarily in pixels of mixed land use and along the water body edge. Nevertheless, the proposed method, in conjunction with high-resolution satellite imagery, offers a robust and flexible approach to land cover change mapping that requires no ancillary data for rapid implementation. Full article
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Open AccessArticle Automatic Cloud and Shadow Detection in Optical Satellite Imagery Without Using Thermal Bands—Application to Suomi NPP VIIRS Images over Fennoscandia
Remote Sens. 2017, 9(8), 806; doi:10.3390/rs9080806
Received: 15 May 2017 / Revised: 1 August 2017 / Accepted: 2 August 2017 / Published: 5 August 2017
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Abstract
In land monitoring applications, clouds and shadows are considered noise that should be removed as automatically and quickly as possible, before further analysis. This paper presents a method to detect clouds and shadows in Suomi NPP satellite’s VIIRS (Visible Infrared Imaging Radiometer Suite)
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In land monitoring applications, clouds and shadows are considered noise that should be removed as automatically and quickly as possible, before further analysis. This paper presents a method to detect clouds and shadows in Suomi NPP satellite’s VIIRS (Visible Infrared Imaging Radiometer Suite) satellite images. The proposed cloud and shadow detection method has two distinct features when compared to many other methods. First, the method does not use the thermal bands and can thus be applied to other sensors which do not contain thermal channels, such as Sentinel-2 data. Secondly, the method uses the ratio between blue and green reflectance to detect shadows. Seven hundred and forty-seven VIIRS images over Fennoscandia from August 2014 to April 2016 were processed to train and develop the method. Twenty four points from every tenth of the images were used in accuracy assessment. These 1752 points were interpreted visually to cloud, cloud shadow and clear classes, then compared to the output of the cloud and shadow detection. The comparison on VIIRS images showed 94.2% correct detection rates and 11.1% false alarms for clouds, and respectively 36.1% and 82.7% for shadows. The results on cloud detection were similar to state-of-the-art methods. Shadows showed correctly on the northern edge of the clouds, but many shadows were wrongly assigned to other classes in some cases (e.g., to water class on lake and forest boundary, or with shadows over cloud). This may be due to the low spatial resolution of VIIRS images, where shadows are only a few pixels wide and contain lots of mixed pixels. Full article
(This article belongs to the Special Issue Atmospheric Correction of Remote Sensing Data)
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Open AccessArticle Automated Quantification of Surface Water Inundation in Wetlands Using Optical Satellite Imagery
Remote Sens. 2017, 9(8), 807; doi:10.3390/rs9080807
Received: 17 May 2017 / Revised: 18 July 2017 / Accepted: 26 July 2017 / Published: 7 August 2017
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Abstract
We present a fully automated and scalable algorithm for quantifying surface water inundation in wetlands. Requiring no external training data, our algorithm estimates sub-pixel water fraction (SWF) over large areas and long time periods using Landsat data. We tested our SWF algorithm over
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We present a fully automated and scalable algorithm for quantifying surface water inundation in wetlands. Requiring no external training data, our algorithm estimates sub-pixel water fraction (SWF) over large areas and long time periods using Landsat data. We tested our SWF algorithm over three wetland sites across North America, including the Prairie Pothole Region, the Delmarva Peninsula and the Everglades, representing a gradient of inundation and vegetation conditions. We estimated SWF at 30-m resolution with accuracies ranging from a normalized root-mean-square-error of 0.11 to 0.19 when compared with various high-resolution ground and airborne datasets. SWF estimates were more sensitive to subtle inundated features compared to previously published surface water datasets, accurately depicting water bodies, large heterogeneously inundated surfaces, narrow water courses and canopy-covered water features. Despite this enhanced sensitivity, several sources of errors affected SWF estimates, including emergent or floating vegetation and forest canopies, shadows from topographic features, urban structures and unmasked clouds. The automated algorithm described in this article allows for the production of high temporal resolution wetland inundation data products to support a broad range of applications. Full article
(This article belongs to the Special Issue Remote Sensing for Flood Mapping and Monitoring of Flood Dynamics)
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Open AccessArticle Modeling the Effect of the Spatial Pattern of Airborne Lidar Returns on the Prediction and the Uncertainty of Timber Merchantable Volume
Remote Sens. 2017, 9(8), 808; doi:10.3390/rs9080808
Received: 1 June 2017 / Revised: 24 July 2017 / Accepted: 1 August 2017 / Published: 6 August 2017
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Abstract
Lidar data are regularly used to characterize forest structures. In this study, we determine the effects of three lidar attributes (density, spacing, scanning angle) on the accuracy and the uncertainty of timber merchantable volume estimates of balsam fir stands (Abies balsamea (L.)
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Lidar data are regularly used to characterize forest structures. In this study, we determine the effects of three lidar attributes (density, spacing, scanning angle) on the accuracy and the uncertainty of timber merchantable volume estimates of balsam fir stands (Abies balsamea (L.) Mill.) in eastern Canada. We used lidar point clouds to compute predictor variables of the merchantable volume in a nonlinear model. The best model included the mean height of first returns, the proportion of first returns below 2 m and the canopy surface roughness index. Our analysis shows a high correlation between lidar and field data of 119 plots (pseudo-R2 = 0.91), however, residuals were heteroscedastic. More precise parameter estimates were obtained by adding to the model a variance function of variables describing the mean height of returns and the skewness of the area distribution of triangulated lidar returns. The residual standard deviation was better estimated (3.7 m3 ha−1 multiplied by the variance function versus 28.0 m3 ha−1). We found no effect of density on the predictions (p-value = 0.74). This suggests that the height and the spatial pattern of returns, rather than the density, should be considered to better assess the uncertainty of merchantable volume estimates. Full article
(This article belongs to the Section Forest Remote Sensing)
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Open AccessArticle Towards an Operational Use of Geophysics for Archaeology in Henan (China): Methodological Approach and Results in Kaifeng
Remote Sens. 2017, 9(8), 809; doi:10.3390/rs9080809
Received: 30 June 2017 / Revised: 21 July 2017 / Accepted: 4 August 2017 / Published: 6 August 2017
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Abstract
One of the major issues in buried archeological sites especially if characterized by intense human activity, complex structures, and several constructive phases, is: to what depth conduct the excavation? The answer depends on a number of factors, among these one of the most
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One of the major issues in buried archeological sites especially if characterized by intense human activity, complex structures, and several constructive phases, is: to what depth conduct the excavation? The answer depends on a number of factors, among these one of the most important is the a priori and reliable knowledge of what the subsoil can preserve. To this end, geophysics (if used in strong synergy with archaeological research) can help in the planning of time, depth, and modes of excavation also when the physical characteristics of the remains and their matrix are not ideal for archaeo-geophysical applications. This is the case of a great part of the archaeological sites in Henan, the cradle of the most important cultures in China and the seat of several capitals for more than two millennia. There, the high depth of buried remains covered by alluvial deposits and the building materials, mainly made by rammed earth, did not favor the use of geophysics. In this paper, we present and discuss the GPR and ERT prospection we conducted in Kaifeng (Henan, China), nearby a gate of the city walls dated to the Northern Song Dynasty. The integration of GPR and ERT provided useful information for the identification and characterization of archaeological remains buried at different depths. Actually, each geophysical technique, GPR frequency (used for the data acquisition) as well as each way to analyze and visualize the results (from radargrams to time slice) only provided partial information of little use if alone. The integration of the diverse techniques, data processing and visualization enabled us to optimize the penetration capability, the resolution for the detection of archaeological features and their interpretation. Finally, the results obtained from the GPR and ERT surveys were correlated with archaeological stratigraphy, available nearby the investigated area. This enabled us to further improve the interpretation of results from GPR and ERT survey and also to date the anthropogenic layers from Qing to Yuan Dynasty. Full article
(This article belongs to the Special Issue Radar Systems for the Societal Challenges)
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Open AccessArticle Precise Orbit Determination of BeiDou Satellites with Contributions from Chinese National Continuous Operating Reference Stations
Remote Sens. 2017, 9(8), 810; doi:10.3390/rs9080810
Received: 30 June 2017 / Revised: 24 July 2017 / Accepted: 4 August 2017 / Published: 6 August 2017
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Abstract
The precise orbit determination (POD) for BeiDou satellites is usually limited by the insufficient quantity and poor distribution of ground tracking stations. To cope with this problem, this study used the GPS and BeiDou joint POD method based on Chinese national continuous operating
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The precise orbit determination (POD) for BeiDou satellites is usually limited by the insufficient quantity and poor distribution of ground tracking stations. To cope with this problem, this study used the GPS and BeiDou joint POD method based on Chinese national continuous operating reference stations (CNCORS) and IGS/MGEX stations. The results show that the 3D RMS of the differences of overlapping arcs is better than 22 cm for geostationary orbit (GEO) satellites and better than 10 cm for inclined geosynchronous orbit (IGSO) and medium earth orbit (MEO) satellites. The radial RMS is better than 2 cm for all three types of BeiDou satellites. The results of satellite laser ranging (SLR) residuals show that the RMS of the IGSO and MEO satellites is better than 5 cm, whereas the GEO satellite has a systematic bias. This study investigates the contributions of CNCORS to the POD of BeiDou satellites. The results show that after the incorporation of CNCORS, the precision of overlapping arcs of the GEO, IGSO, and MEO satellites is improved by 15.5%, 57.5%, and 5.3%, respectively. In accordance with the improvement in the precision of overlapping arcs, the accuracy of the IGSO and MEO satellites assessed by the SLR is improved by 30.1% and 4.8%, respectively. The computation results and analysis demonstrate that the inclusion of CNCORS yields the biggest contribution in the improvement of orbit accuracy for IGSO satellites, when compared to GEO satellites, while the orbit improvement for MEO satellites is the lowest due to their global coverage. Full article
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Open AccessArticle Radiometric Cross-Calibration of GF-4 PMS Sensor Based on Assimilation of Landsat-8 OLI Images
Remote Sens. 2017, 9(8), 811; doi:10.3390/rs9080811
Received: 23 April 2017 / Revised: 29 July 2017 / Accepted: 4 August 2017 / Published: 9 August 2017
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Abstract
Earth observation data obtained from remote sensors must undergo radiometric calibration before use in quantitative applications. However, the large view angles of the panchromatic multispectral sensor (PMS) aboard the GF-4 satellite pose challenges for cross-calibration due to the effects of atmospheric radiation transfer
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Earth observation data obtained from remote sensors must undergo radiometric calibration before use in quantitative applications. However, the large view angles of the panchromatic multispectral sensor (PMS) aboard the GF-4 satellite pose challenges for cross-calibration due to the effects of atmospheric radiation transfer and the bidirectional reflectance distribution function (BRDF). To address this problem, this paper introduces a novel cross-calibration method based on data assimilation considering cross-calibration as an optimal approximation problem. The GF-4 PMS was cross-calibrated with the well-calibrated Landsat-8 Operational Land Imager (OLI) as the reference sensor. In order to correct unequal bidirectional reflection effects, an adjustment factor for the BRDF was established, making complex models unnecessary. The proposed method employed the Shuffled Complex Evolution-University of Arizona (SCE-UA) algorithm to find the optimal calibration coefficients and BRDF adjustment factor through an iterative process. The validation results revealed a surface reflectance error of <5% for the new cross-calibration coefficients. The accuracy of calibration coefficients were significantly improved when compared to the officially published coefficients as well as those derived using conventional methods. The uncertainty produced by the proposed method was less than 7%, meeting the demands for future quantitative applications and research. This method is also applicable to other sensors with large view angles. Full article
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Open AccessArticle Novel Decomposition Scheme for Characterizing Urban Air Quality with MODIS
Remote Sens. 2017, 9(8), 812; doi:10.3390/rs9080812
Received: 30 May 2017 / Revised: 31 July 2017 / Accepted: 2 August 2017 / Published: 7 August 2017
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Abstract
Urban air pollution is one of the most widespread global sustainability problems. Previous research has studied growth or fall of particulate matter (PM) levels using on-ground monitoring stations in urban regions. However, studying this worldwide is difficult because most cities do not have
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Urban air pollution is one of the most widespread global sustainability problems. Previous research has studied growth or fall of particulate matter (PM) levels using on-ground monitoring stations in urban regions. However, studying this worldwide is difficult because most cities do not have sufficient infrastructure to monitor air quality. Thus, satellite data is increasingly being employed to solve this limitation. In this paper, we use 16 years (2001–2016) of aerosol optical depth (AOD) and Angstrom exponent ( α ) datasets, retrieved from MODIS (Moderate Resolution Imaging Spectroradiometer) sensors on the National Aeronautics and Space Administration’s (NASA) Terra satellite to study air quality over 60 locations globally. We propose a novel technique, called AirRGB decomposition, to characterize urban air quality by decomposing AOD and α retrievals into ‘components’ of three distinct scenarios. In the AirRGB decomposition method, using AOD and α dataset three scenarios were investigated: ‘R’—high α and high AOD, ‘G’—high α and low AOD, and ‘B’—low α and low AOD values. These scenarios were mapped and quantified over a triangular red, green and blue color scale. This visualization easily segregates regions having a high concentration of industrial aerosol from only natural aerosols. Our analysis indicates that a sharp divide exists between North American and European cities and Asian cities in terms of baseline pollution and slopes of R and G trends. We found that while pollution in cities in China has started to decrease (e.g., since 2011 for Beijing), it continues to increase in South Asia and Southeast Asia. e.g., R offset of Beijing and New Delhi was 54.98 and 50.43 respectively but R slope was −0.04 and 0.08 respectively. High offset (≥45) and slope (≥0.025) of B for New York, Tokyo, Sydney and Sao Paolo shows that they have clean air, which is still getting better. Full article
(This article belongs to the Section Atmosphere Remote Sensing)
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Open AccessArticle On-Board GNSS/IMU Assisted Feature Extraction and Matching for Oblique UAV Images
Remote Sens. 2017, 9(8), 813; doi:10.3390/rs9080813
Received: 23 June 2017 / Revised: 31 July 2017 / Accepted: 4 August 2017 / Published: 7 August 2017
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Abstract
Feature extraction and matching is a crucial task in the fields of computer vision and photogrammetry. Even though wide researches have been reported, some issues are still existing for oblique images. This paper exploits the use of on-board GNSS/IMU (Global Navigation Satellite System/Inertial
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Feature extraction and matching is a crucial task in the fields of computer vision and photogrammetry. Even though wide researches have been reported, some issues are still existing for oblique images. This paper exploits the use of on-board GNSS/IMU (Global Navigation Satellite System/Inertial Measurement Unit) data to achieve efficient and reliable feature extraction and matching for oblique unmanned aerial vehicle (UAV) images. Firstly, rough POS (Positioning and Orientation System) is calculated for each image with cooperation of on-board GNSS/IMU data and camera installation angles, which enables image rectification and footprint calculation. Secondly, two robust strategies, including the geometric rectification and tile strategy, are considered to address the issues caused by perspective deformations and to relieve the side-effects of image down-sampling. According to the results of individual performance evaluation, four combinations of these two strategies are designed and comprehensively compared in BA (Bundle Adjustment) experiments by using a real oblique UAV dataset. The results reported in this paper demonstrate that the solution with the tiling strategy is superior to the other solutions in terms of efficiency, completeness and accuracy. For feature extraction and matching of oblique UAV images, it is proposed to combine the tiling strategy with existing workflows to achieve an efficient and reliable solution. Full article
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Open AccessArticle Atmospheric Correction of Multi-Spectral Littoral Images Using a PHOTONS/AERONET-Based Regional Aerosol Model
Remote Sens. 2017, 9(8), 814; doi:10.3390/rs9080814
Received: 29 June 2017 / Revised: 1 August 2017 / Accepted: 4 August 2017 / Published: 8 August 2017
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Abstract
Spatial resolution is the main instrumental requirement for the multi-spectral optical space missions that address the scientific issues of marine coastal systems. This spatial resolution should be at least decametric. Aquatic color data processing associated with these environments requires specific atmospheric corrections (AC)
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Spatial resolution is the main instrumental requirement for the multi-spectral optical space missions that address the scientific issues of marine coastal systems. This spatial resolution should be at least decametric. Aquatic color data processing associated with these environments requires specific atmospheric corrections (AC) suitable for the spectral characteristics of high spatial resolution sensors (HRS) as well as the high range of atmospheric and marine optical properties. The objective of the present study is to develop and demonstrate the potential of a ground-based AC approach adaptable to any HRS for regional monitoring and security of littoral systems. The in Situ-based Atmospheric CORrection (SACOR) algorithm is based on simulations provided by a Successive Order of Scattering code (SOS), which is constrained by a simple regional aerosol particle model (RAM). This RAM is defined from the mixture of a standard tropospheric and maritime aerosol type. The RAM is derived from the following two processes. The first process involved the analysis of a 6-year data set composed of aerosol optical and microphysical properties acquired through the ground-based PHOTONS/AERONET network located at Arcachon (France). The second process was related to aerosol climatology using the NOAA hybrid single-particle Lagrangian integrated trajectory (HYSPLIT) model. Results show that aerosols have a bimodal particle size distribution regardless of the season and are mainly represented by a mixed coastal continental type. Furthermore, the results indicate that aerosols originate from both the Atlantic Ocean (53.6%) and Continental Europe (46.4%). Based on these results, absorbing biomass burning, urban-industrial and desert dust particles have not been considered although they represent on average 19% of the occurrences. This represents the main current limitation of the RAM. An assessment of the performances of SACOR is then performed by inter-comparing the water-leaving reflectance ( ρ w ) retrievals with three different AC methods (ACOLITE, MACCS and 6SV using three different standard aerosol types) using match-ups (N = 8) composed of Landsat-8/Operational Land Imager (OLI) scenes and field radiometric measurements. Results indicate consistency with the SWIR-based ACOLITE method, which shows the best performance, except in the green channel where SACOR matches well with the in-situ data (relative error of 7%). In conclusion, the study demonstrates the high potential of the SACOR approach for the retrieval of ρ w . In the future, the method could be improved by using an adaptive aerosol model, which may select the most relevant local aerosol model following the origin of the atmospheric air mass, and could be applied to the latest HRS (Sentinel-2/MSI, SPOT6-7, Pleiades 1A-1B). Full article
(This article belongs to the Special Issue Atmospheric Correction of Remote Sensing Data)
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Open AccessArticle Validation of Automatically Generated Global and Regional Cropland Data Sets: The Case of Tanzania
Remote Sens. 2017, 9(8), 815; doi:10.3390/rs9080815
Received: 20 April 2017 / Revised: 20 July 2017 / Accepted: 7 August 2017 / Published: 9 August 2017
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Abstract
There is a need to validate existing global cropland maps since they are used for different purposes including agricultural monitoring and assessment. In this paper we validate three recent global products (ESA-CCI, GlobeLand30, FROM-GC) and one regional product (Tanzania Land Cover 2010 Scheme
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There is a need to validate existing global cropland maps since they are used for different purposes including agricultural monitoring and assessment. In this paper we validate three recent global products (ESA-CCI, GlobeLand30, FROM-GC) and one regional product (Tanzania Land Cover 2010 Scheme II) using a validation data set that was collected by students through the Geo-Wiki tool. The ultimate aim was to understand the usefulness of these products for agricultural monitoring. Data were collected wall-to-wall for Kilosa district and for a sample across Tanzania. The results show that the amount of and spatial extent of cropland in the different products differs considerably from 8% to 42% for Tanzania, with similar values for Kilosa district. The agreement of the validation data with the four different products varied between 36% and 54% and highlighted that cropland is overestimated by the ESA-CCI and underestimated by FROM-GC. The validation data were also analyzed for consistency between the student interpreters and also compared with a sample interpreted by five experts for quality assurance. Regarding consistency between the students, there was more than 80% agreement if one difference in cropland category was considered (e.g., between low and medium cropland) while most of the confusion with the experts was also within one category difference. In addition to the validation of current cropland products, the data set collected by the students also has potential value as a training set for improving future cropland products. Full article
(This article belongs to the Special Issue Validation on Global Land Cover Datasets)
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Open AccessArticle Mapping Aboveground Carbon in Oil Palm Plantations Using LiDAR: A Comparison of Tree-Centric versus Area-Based Approaches
Remote Sens. 2017, 9(8), 816; doi:10.3390/rs9080816
Received: 19 June 2017 / Revised: 23 July 2017 / Accepted: 4 August 2017 / Published: 9 August 2017
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Abstract
Southeast Asia is the epicentre of world palm oil production. Plantations in Malaysia have increased 150% in area within the last decade, mostly at the expense of tropical forests. Maps of the aboveground carbon density (ACD) of vegetation generated by remote sensing technologies,
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Southeast Asia is the epicentre of world palm oil production. Plantations in Malaysia have increased 150% in area within the last decade, mostly at the expense of tropical forests. Maps of the aboveground carbon density (ACD) of vegetation generated by remote sensing technologies, such as airborne LiDAR, are vital for quantifying the effects of land use change for greenhouse gas emissions, and many papers have developed methods for mapping forests. However, nobody has yet mapped oil palm ACD from LiDAR. The development of carbon prediction models would open doors to remote monitoring of plantations as part of efforts to make the industry more environmentally sustainable. This paper compares the performance of tree-centric and area-based approaches to mapping ACD in oil palm plantations. We find that an area-based approach gave more accurate estimates of carbon density than tree-centric methods and that the most accurate estimation model includes LiDAR measurements of top-of-canopy height and canopy cover. We show that tree crown segmentation is sensitive to crown density, resulting in less accurate tree density and ACD predictions, but argue that tree-centric approach can nevertheless be useful for monitoring purposes, providing a method to detect, extract and count oil palm trees automatically from images. Full article
(This article belongs to the Section Forest Remote Sensing)
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Open AccessArticle Estimation of Satellite-Based SO42 and NH4+ Composition of Ambient Fine Particulate Matter over China Using Chemical Transport Model
Remote Sens. 2017, 9(8), 817; doi:10.3390/rs9080817
Received: 30 June 2017 / Revised: 4 August 2017 / Accepted: 7 August 2017 / Published: 9 August 2017
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Abstract
Epidemiologic and health impact studies have examined the chemical composition of ambient PM2.5 in China but have been constrained by the paucity of long-term ground measurements. Using the GEOS-Chem chemical transport model and satellite-derived PM2.5 data, sulfate and ammonium levels were
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Epidemiologic and health impact studies have examined the chemical composition of ambient PM2.5 in China but have been constrained by the paucity of long-term ground measurements. Using the GEOS-Chem chemical transport model and satellite-derived PM2.5 data, sulfate and ammonium levels were estimated over China from 2004 to 2014. A comparison of the satellite-estimated dataset with model simulations based on ground measurements obtained from the literature indicated our results are more accurate. Using satellite-derived PM2.5 data with a spatial resolution of 0.1 × 0.1°, we further presented finer satellite-estimated sulfate and ammonium concentrations in anthropogenic polluted regions, including the NCP (the North China Plain), the SCB (the Sichuan Basin) and the PRD (the Pearl River Delta). Linear regression results obtained on a national scale yielded an r value of 0.62, NMB of −35.9%, NME of 48.2%, ARB_50% of 53.68% for sulfate and an r value of 0.63, slope of 0.67, and intercept of 5.14 for ammonium. In typical regions, the satellite-derived dataset was significantly robust. Based on the satellite-derived dataset, the spatial-temporal variation of 11-year annual average satellite-derived SO42 and NH4+ concentrations and time series of monthly average concentrations were also investigated. On a national scale, both exhibited a downward trend each year between 2004 and 2014 (SO42: −0.61%; NH4+: −0.21%), large values were mainly concentrated in the NCP and SCB. For regions captured at a finer resolution, the inter-annual variation trends presented a positive trend over the periods 2004–2007 and 2008–2011, followed by a negative trend over the period 2012–2014, and sulfate concentrations varied appreciably. Moreover, the seasonal distributions of the 11-year satellite-derived dataset over China were presented. The distribution of both sulfate and ammonium concentrations exhibited seasonal characteristics, with the seasonal concentrations ranking as follows: winter > summer > autumn > spring. High concentrations of these species were concentrated in the NCP and SCB, originating from coal-fired power plants and agricultural activities, respectively. Efforts to reduce sulfur dioxide (SO2) emissions have yielded remarkable results since the government has adopted stricter control measures in recent years. Moreover, ammonia emissions should be controlled while reducing the concentration of sulfur, nitrogen and particulate matter. This study provides an assessment of the population’s exposure to certain chemical components. Full article
(This article belongs to the Special Issue Aerosol Remote Sensing)
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Open AccessArticle A Modified Dual-Baseline PolInSAR Method for Forest Height Estimation
Remote Sens. 2017, 9(8), 819; doi:10.3390/rs9080819
Received: 26 May 2017 / Revised: 21 July 2017 / Accepted: 4 August 2017 / Published: 9 August 2017
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Abstract
This paper investigates the potentials and limitations of a simple dual-baseline PolInSAR (DBPI) method for forest height inversion. This DBPI method follows the classical three-stage inversion method’s idea used in single baseline PolInSAR (SBPI) inversion, but it avoids the assumption of the smallest
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This paper investigates the potentials and limitations of a simple dual-baseline PolInSAR (DBPI) method for forest height inversion. This DBPI method follows the classical three-stage inversion method’s idea used in single baseline PolInSAR (SBPI) inversion, but it avoids the assumption of the smallest ground-to-volume amplitude ratio (GVR) by employing an additional baseline to constrain the inversion procedure. In this paper, we present for the first time an assessment of such a method on real PolInSAR data over boreal forest. Additionally, we propose an improvement on the original DBPI method by incorporating the sloped random volume over ground (S-RVoG) model in order to reduce the range terrain slope effect. Therefore, a digital elevation model (DEM) is needed to provide the slope information in the proposed method. Three scenes of P-band airborne PolInSAR data acquired by E-SAR and light detection and ranging (LIDAR) data available in the BioSAR2008 campaign are employed for testing purposes. The performance of the SBPI, DBPI, and modified DBPI methods is compared. The results show that the DBPI method extracts forest heights with an average root mean square error (RMSE) of 4.72 m against LIDAR heights for trees of 18 m height on average. It presents a significant improvement of forest height accuracy over the SBPI method (with a stand-level mean improvement of 42.86%). Concerning the modified DBPI method, it consistently improves the accuracy of forest height inversion over sloped areas. This improvement reaches a stand-level mean of 21.72% improvement (with a mean RMSE of 4.63 m) for slopes greater than 10°. Full article
(This article belongs to the Special Issue Recent Advances in Polarimetric SAR Interferometry)
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Open AccessArticle Soil Moisture Data Assimilation in a Hydrological Model: A Case Study in Belgium Using Large-Scale Satellite Data
Remote Sens. 2017, 9(8), 820; doi:10.3390/rs9080820
Received: 16 June 2017 / Revised: 4 August 2017 / Accepted: 7 August 2017 / Published: 10 August 2017
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Abstract
In the present study, we focus on the assimilation of satellite observations for Surface Soil Moisture (SSM) in a hydrological model. The satellite data are produced in the framework of the EUMETSAT project H-SAF and are based on measurements with the Advanced radar
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In the present study, we focus on the assimilation of satellite observations for Surface Soil Moisture (SSM) in a hydrological model. The satellite data are produced in the framework of the EUMETSAT project H-SAF and are based on measurements with the Advanced radar Scatterometer (ASCAT), embarked on the Meteorological Operational satellites (MetOp). The product generated with these measurements has a horizontal resolution of 25 km and represents the upper few centimeters of soil. Our approach is based on the Ensemble Kalman Filter technique (EnKF), where observation and model uncertainties are taken into account, implemented in a conceptual hydrological model. The analysis is carried out in the Demer catchment of the Scheldt River Basin in Belgium, for the period from June 2013–May 2016. In this context, two methodological advances are being proposed. First, the generation of stochastic terms, necessary for the EnKF, of bounded variables like SSM is addressed with the aid of specially-designed probability distributions, so that the bounds are never exceeded. Second, bias due to the assimilation procedure itself is removed using a post-processing technique. Subsequently, the impact of SSM assimilation on the simulated streamflow is estimated using a series of statistical measures based on the ensemble average. The differences from the control simulation are then assessed using a two-dimensional bootstrap sampling on the ensemble generated by the assimilation procedure. Our analysis shows that data assimilation combined with bias correction can improve the streamflow estimations or, at a minimum, produce results statistically indistinguishable from the control run of the hydrological model. Full article
(This article belongs to the Special Issue Retrieval, Validation and Application of Satellite Soil Moisture Data)
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Open AccessArticle Estimation of Fugacity of Carbon Dioxide in the East Sea Using In Situ Measurements and Geostationary Ocean Color Imager Satellite Data
Remote Sens. 2017, 9(8), 821; doi:10.3390/rs9080821
Received: 24 May 2017 / Revised: 1 August 2017 / Accepted: 8 August 2017 / Published: 10 August 2017
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Abstract
The ocean is closely related to global warming and on-going climate change by regulating amounts of carbon dioxide through its interaction with the atmosphere. The monitoring of ocean carbon dioxide is important for a better understanding of the role of the ocean as
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The ocean is closely related to global warming and on-going climate change by regulating amounts of carbon dioxide through its interaction with the atmosphere. The monitoring of ocean carbon dioxide is important for a better understanding of the role of the ocean as a carbon sink, and regional and global carbon cycles. This study estimated the fugacity of carbon dioxide (ƒCO2) over the East Sea located between Korea and Japan. In situ measurements, satellite data and products from the Geostationary Ocean Color Imager (GOCI) and the Hybrid Coordinate Ocean Model (HYCOM) reanalysis data were used through stepwise multi-variate nonlinear regression (MNR) and two machine learning approaches (i.e., support vector regression (SVR) and random forest (RF)). We used five ocean parameters—colored dissolved organic matter (CDOM; <0.3 m−1), chlorophyll-a concentration (Chl-a; <21 mg/m3), mixed layer depth (MLD; <160 m), sea surface salinity (SSS; 32–35), and sea surface temperature (SST; 8–28 °C)—and four band reflectance (Rrs) data (400 nm–565 nm) and their ratios as input parameters to estimate surface seawater ƒCO2 (270–430 μatm). Results show that RF generally performed better than stepwise MNR and SVR. The root mean square error (RMSE) of validation results by RF was 5.49 μatm (1.7%), while those of stepwise MNR and SVR were 10.59 μatm (3.2%) and 6.82 μatm (2.1%), respectively. Ocean parameters (i.e., sea surface salinity (SSS), sea surface temperature (SST), and mixed layer depth (MLD)) appeared to contribute more than the individual bands or band ratios from the satellite data. Spatial and seasonal distributions of monthly ƒCO2 produced from the RF model and sea-air CO2 flux were also examined. Full article
(This article belongs to the Section Ocean Remote Sensing)
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Open AccessArticle Evaluation of Satellite-Altimetry-Derived Pycnocline Depth Products in the South China Sea
Remote Sens. 2017, 9(8), 822; doi:10.3390/rs9080822
Received: 27 June 2017 / Revised: 8 August 2017 / Accepted: 8 August 2017 / Published: 12 August 2017
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Abstract
The climatological monthly gridded World Ocean Atlas 2013 temperature and salinity data and satellite altimeter sea level anomaly data are used to build two altimeter-derived high-resolution real-time upper layer thickness products based on a highly simplified two-layer ocean model of the South China
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The climatological monthly gridded World Ocean Atlas 2013 temperature and salinity data and satellite altimeter sea level anomaly data are used to build two altimeter-derived high-resolution real-time upper layer thickness products based on a highly simplified two-layer ocean model of the South China Sea. One product uses the proportional relationship between the sea level anomaly and upper layer thickness anomaly. The other one adds a modified component ( η M ) to account for the barotropic and thermodynamic processes that are neglected in the former product. The upper layer thickness, in this work, represents the depth of the main pycnocline, which is defined as the thickness from the sea surface to the 25 kg/m3 isopycnal depth. The mean upper layer thickness in the semi-closed South China Sea is ~120 m and the mean reduced gravity is ~0.073 m/s2, which is about one order of magnitude larger than the value obtained in the open deep ocean. The long-term temperature observations from three moored buoys, the conductivity-temperature-depth profiles from three joint cruises, and the Argo measurements from 2006 to 2015 are used to compare and evaluate these two upper layer thickness products. It shows that adding the η M component is necessary to simulate the upper layer thickness in some situations, especially in summer and fall in the northern South China Sea. Full article
(This article belongs to the Special Issue Satellite Altimetry for Earth Sciences)
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Open AccessArticle Low-Altitude Aerial Methane Concentration Mapping
Remote Sens. 2017, 9(8), 823; doi:10.3390/rs9080823
Received: 17 May 2017 / Revised: 28 July 2017 / Accepted: 2 August 2017 / Published: 10 August 2017
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Abstract
Detection of leaks of fugitive greenhouse gases (GHGs) from landfills and natural gas infrastructure is critical for not only their safe operation but also for protecting the environment. Current inspection practices involve moving a methane detector within the target area by a person
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Detection of leaks of fugitive greenhouse gases (GHGs) from landfills and natural gas infrastructure is critical for not only their safe operation but also for protecting the environment. Current inspection practices involve moving a methane detector within the target area by a person or vehicle. This procedure is dangerous, time consuming, labor intensive and above all unavailable when access to the desired area is limited. Remote sensing by an unmanned aerial vehicle (UAV) equipped with a methane detector is a cost-effective and fast method for methane detection and monitoring, especially for vast and remote areas. This paper describes the integration of an off-the-shelf laser-based methane detector into a multi-rotor UAV and demonstrates its efficacy in generating an aerial methane concentration map of a landfill. The UAV flies a preset flight path measuring methane concentrations in a vertical air column between the UAV and the ground surface. Measurements were taken at 10 Hz giving a typical distance between measurements of 0.2 m when flying at 2 m/s. The UAV was set to fly at 25 to 30 m above the ground. We conclude that besides its utility in landfill monitoring, the proposed method is ready for other environmental applications as well as the inspection of natural gas infrastructure that can release methane with much higher concentrations. Full article
(This article belongs to the Special Issue Remote Sensing of Greenhouse Gases)
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Open AccessArticle Automatic Power Line Inspection Using UAV Images
Remote Sens. 2017, 9(8), 824; doi:10.3390/rs9080824
Received: 26 June 2017 / Revised: 21 July 2017 / Accepted: 9 August 2017 / Published: 10 August 2017
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Abstract
Power line inspection ensures the safe operation of a power transmission grid. Using unmanned aerial vehicle (UAV) images of power line corridors is an effective way to carry out these vital inspections. In this paper, we propose an automatic inspection method for power
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Power line inspection ensures the safe operation of a power transmission grid. Using unmanned aerial vehicle (UAV) images of power line corridors is an effective way to carry out these vital inspections. In this paper, we propose an automatic inspection method for power lines using UAV images. This method, known as the power line automatic measurement method based on epipolar constraints (PLAMEC), acquires the spatial position of the power lines. Then, the semi patch matching based on epipolar constraints (SPMEC) dense matching method is applied to automatically extract dense point clouds within the power line corridor. Obstacles can then be automatically detected by calculating the spatial distance between a power line and the point cloud representing the ground. Experimental results show that the PLAMEC automatically measures power lines effectively with a measurement accuracy consistent with that of manual stereo measurements. The height root mean square (RMS) error of the point cloud was 0.233 m, and the RMS error of the power line was 0.205 m. In addition, we verified the detected obstacles in the field and measured the distance between the canopy and power line using a laser range finder. The results show that the difference of these two distances was within ±0.5 m. Full article
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Open AccessArticle Semi-Analytical Retrieval of the Diffuse Attenuation Coefficient in Large and Shallow Lakes from GOCI, a High Temporal-Resolution Satellite
Remote Sens. 2017, 9(8), 825; doi:10.3390/rs9080825
Received: 20 May 2017 / Revised: 24 July 2017 / Accepted: 7 August 2017 / Published: 11 August 2017
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Abstract
Monitoring the dynamic characteristics of the diffuse attenuation coefficient (Kd(490)) on the basis of the high temporal-resolution satellite data is critical for regulating the ecological environment of lake. By measuring the in-situ Kd(490) and the remote-sensing reflectance, a
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Monitoring the dynamic characteristics of the diffuse attenuation coefficient (Kd(490)) on the basis of the high temporal-resolution satellite data is critical for regulating the ecological environment of lake. By measuring the in-situ Kd(490) and the remote-sensing reflectance, a semi-analytical algorithm for Kd(490) was developed to determine the short-term variation of Kd(490). From 2006 to 2014, the data about 412 samples (among which 60 were used as match-up points, 282 for calibrating dataset and the remaining 70 for validating dataset) were gathered from nine expeditions to calibrate and validate the aforesaid semi-analytical algorithm. The root mean square percentage error (RMSP) and the mean absolute relative error (MAPE) of validation datasets were respectively 27.44% and 22.60 ± 15.57%, while that of the match-up datasets were respectively 34.29% and 27.57 ± 20.56%. These percentages indicate that the semi-analytical algorithm and Geostationary Ocean Color Imager (GOCI) data are applicable to obtain the short-term variation of Kd(490) in the turbid shallow inland waters. The short-term GOCI-observed Kd(490) shows a significant seasonal and spatial variation and a similar distribution to the matching Moderate Resolution Imaging Spectroradiometer (MODIS) which derived Kd(490). A comparative analysis on wind (observed by buoys) and GOCI-derived Kd(490) suggests that wind is a primary driving factor of Kd(490) variation, but the lacustrine morphometry affects the wind force that is contributing to Kd(490) variation. Full article
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Open AccessArticle LiDAR Validation of a Video-Derived Beachface Topography on a Tidal Flat
Remote Sens. 2017, 9(8), 826; doi:10.3390/rs9080826
Received: 26 June 2017 / Revised: 1 August 2017 / Accepted: 7 August 2017 / Published: 11 August 2017
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Abstract
Increasingly used shore-based video stations enable a high spatiotemporal frequency analysis of shoreline migration. Shoreline detection techniques combined with hydrodynamic conditions enable the creation of digital elevation models (DEMs). However, shoreline elevations are often estimated based on nearshore process empirical equations leading to
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Increasingly used shore-based video stations enable a high spatiotemporal frequency analysis of shoreline migration. Shoreline detection techniques combined with hydrodynamic conditions enable the creation of digital elevation models (DEMs). However, shoreline elevations are often estimated based on nearshore process empirical equations leading to uncertainties in video-based topography. To achieve high DEM correspondence between both techniques, we assessed video-derived DEMs against LiDAR surveys during low energy conditions. A newly installed video system on a tidal flat in the St. Lawrence Estuary, Atlantic Canada, served as a test case. Shorelines were automatically detected from time-averaged (TIMEX) images using color ratios in low energy conditions synchronously with mobile terrestrial LiDAR during two different surveys. Hydrodynamic (waves and tides) data were recorded in-situ, and established two different cases of water elevation models as a basis for shoreline elevations. DEMs were created and tested against LiDAR. Statistical analysis of shoreline elevations and migrations were made, and morphological variability was assessed between both surveys. Results indicate that the best shoreline elevation model includes both the significant wave height and the mean water level. Low energy conditions and in-situ hydrodynamic measurements made it possible to produce video-derived DEMs virtually as accurate as a LiDAR product, and therefore make an effective tool for coastal managers. Full article
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Open AccessFeature PaperArticle Determinants of Aboveground Biomass across an Afromontane Landscape Mosaic in Kenya
Remote Sens. 2017, 9(8), 827; doi:10.3390/rs9080827
Received: 21 June 2017 / Revised: 4 August 2017 / Accepted: 7 August 2017 / Published: 11 August 2017
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Abstract
Afromontane tropical forests maintain high biodiversity and provide valuable ecosystem services, such as carbon sequestration. The spatial distribution of aboveground biomass (AGB) in forest-agriculture landscape mosaics is highly variable and controlled both by physical and human factors. In this study, the objectives were
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Afromontane tropical forests maintain high biodiversity and provide valuable ecosystem services, such as carbon sequestration. The spatial distribution of aboveground biomass (AGB) in forest-agriculture landscape mosaics is highly variable and controlled both by physical and human factors. In this study, the objectives were (1) to generate a map of AGB for the Taita Hills, in Kenya, based on field measurements and airborne laser scanning (ALS), and (2) to examine determinants of AGB using geospatial data and statistical modelling. The study area is located in the northernmost part of the Eastern Arc Mountains, with an elevation range of approximately 600–2200 m. The field measurements were carried out in 215 plots in 2013–2015 and ALS flights conducted in 2014–2015. Multiple linear regression was used for predicting AGB at a 30 m × 30 m resolution based on canopy cover and the 25th percentile height derived from ALS returns (R2 = 0.88, RMSE = 52.9 Mg ha−1). Boosted regression trees (BRT) were used for examining the relationship between AGB and explanatory variables at a 250 m × 250 m resolution. According to the results, AGB patterns were controlled mainly by mean annual precipitation (MAP), the distribution of croplands and slope, which explained together 69.8% of the AGB variation. The highest AGB densities have been retained in the semi-natural vegetation in the higher elevations receiving more rainfall and in the steep slope, which is less suitable for agriculture. AGB was also relatively high in the eastern slopes as indicated by the strong interaction between slope and aspect. Furthermore, plantation forests, topographic position and the density of buildings had a minor influence on AGB. The findings demonstrate the utility of ALS-based AGB maps and BRT for describing AGB distributions across Afromontane landscapes, which is important for making sustainable land management decisions in the region. Full article
(This article belongs to the Special Issue Biomass Remote Sensing in Forest Landscapes)
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Open AccessArticle Adaptive Estimation of Crop Water Stress in Nectarine and Peach Orchards Using High-Resolution Imagery from an Unmanned Aerial Vehicle (UAV)
Remote Sens. 2017, 9(8), 828; doi:10.3390/rs9080828
Received: 30 June 2017 / Revised: 4 August 2017 / Accepted: 7 August 2017 / Published: 11 August 2017
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Abstract
The capability to monitor water status from crops on a regular basis can enhance productivity and water use efficiency. In this paper, high-resolution thermal imagery acquired by an unmanned aerial vehicle (UAV) was used to map plant water stress and its spatial variability,
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The capability to monitor water status from crops on a regular basis can enhance productivity and water use efficiency. In this paper, high-resolution thermal imagery acquired by an unmanned aerial vehicle (UAV) was used to map plant water stress and its spatial variability, including sectors under full irrigation and deficit irrigation over nectarine and peach orchards at 6.12 cm ground sample distance. The study site was classified into sub-regions based on crop properties, such as cultivars and tree training systems. In order to enhance the accuracy of the mapping, edge extraction and filtering were conducted prior to the probability modelling employed to obtain crop-property-specific (‘adaptive’ hereafter) lower and higher temperature references (Twet and Tdry respectively). Direct measurements of stem water potential (SWP, ψstem) and stomatal conductance (gs) were collected concurrently with UAV remote sensing and used to validate the thermal index as crop biophysical parameters. The adaptive crop water stress index (CWSI) presented a better agreement with both ψstem and gs with determination coefficients (R2) of 0.72 and 0.82, respectively, while the conventional CWSI applied by a single set of hot and cold references resulted in biased estimates with R2 of 0.27 and 0.34, respectively. Using a small number of ground-based measurements of SWP, CWSI was converted to a high-resolution SWP map to visualize spatial distribution of the water status at field scale. The results have important implications for the optimal management of irrigation for crops. Full article
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Open AccessArticle An Improved Vegetation Adjusted Nighttime Light Urban Index and Its Application in Quantifying Spatiotemporal Dynamics of Carbon Emissions in China
Remote Sens. 2017, 9(8), 829; doi:10.3390/rs9080829
Received: 30 May 2017 / Revised: 27 July 2017 / Accepted: 9 August 2017 / Published: 11 August 2017
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Abstract
Nighttime Light (NTL) has been widely used as a proxy of many socio-environmental issues. However, the limited range of sensor radiance of NTL prevents its further application and estimation accuracy. To improve the performance, we developed an improved Vegetation Adjusted Nighttime light Urban
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Nighttime Light (NTL) has been widely used as a proxy of many socio-environmental issues. However, the limited range of sensor radiance of NTL prevents its further application and estimation accuracy. To improve the performance, we developed an improved Vegetation Adjusted Nighttime light Urban Index (VANUI) through fusing multi-year NTL with population density, the Normalized Difference Vegetation Index and water body data and applied it to fine-scaled carbon emission analysis in China. The results proved that our proposed index could reflect more spatial variation of human activities. It is also prominent in reducing the carbon modeling error at the inter-city level and distinguishing the emission heterogeneity at the intra-city level. Between 1995 and 2013, CO2 emissions increased significantly in China, but were distributed unevenly in space with high density emissions mainly located in metropolitan areas and provincial capitals. In addition to Beijing-Tianjin-Hebei, the Yangzi River Delta and the Pearl River Delta, the Shandong Peninsula has become a new emission hotspot that needs special attention in carbon mitigation. The improved VANUI and its application to the carbon emission issue not only broadened our understanding of the spatiotemporal dynamics of fine-scaled CO2 emission, but also provided implications for low-carbon and sustainable development plans. Full article
(This article belongs to the Special Issue Recent Advances in Remote Sensing with Nighttime Lights)
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Open AccessArticle An Adaptive Offset Tracking Method with SAR Images for Landslide Displacement Monitoring
Remote Sens. 2017, 9(8), 830; doi:10.3390/rs9080830
Received: 15 July 2017 / Revised: 29 July 2017 / Accepted: 9 August 2017 / Published: 11 August 2017
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Abstract
With the development of high-resolution Synthetic Aperture Radar (SAR) systems, researchers are increasingly paying attention to the application of SAR offset tracking methods in ground deformation estimation. The traditional normalized cross correlation (NCC) tracking method is based on regular matching windows. For areas
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With the development of high-resolution Synthetic Aperture Radar (SAR) systems, researchers are increasingly paying attention to the application of SAR offset tracking methods in ground deformation estimation. The traditional normalized cross correlation (NCC) tracking method is based on regular matching windows. For areas with different moving characteristics, especially the landslide boundary areas, the NCC method will produce incorrect results. This is because in landslide boundary areas, the pixels of the regular matching window include two or more types of moving characteristics: some pixels with large displacement, and others with small or no displacement. These two kinds of pixels are uncorrelated, which result in inaccurate estimations. This paper proposes a new offset tracking method with SAR images based on the adaptive matching window to improve the accuracy of landslide displacement estimation. The proposed method generates an adaptive matching window that only contains pixels with similar moving characteristics. Three SAR images acquired by the Jet Propulsion Laboratory’s Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) system are selected to estimate the surface deformation of the Slumgullion landslide located in the southwestern Colorado, USA. The results show that the proposed method has higher accuracy than the traditional NCC method, especially in landslide boundary areas. Furthermore, it can obtain more detailed displacement information in landslide boundary areas. Full article
(This article belongs to the Special Issue Radar Interferometry for Geohazards)
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Open AccessArticle Identifying Droughts Affecting Agriculture in Africa Based on Remote Sensing Time Series between 2000–2016: Rainfall Anomalies and Vegetation Condition in the Context of ENSO
Remote Sens. 2017, 9(8), 831; doi:10.3390/rs9080831
Received: 13 July 2017 / Revised: 27 July 2017 / Accepted: 29 July 2017 / Published: 11 August 2017
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Abstract
Droughts are amongst the most destructive natural disasters in the world. In large regions of Africa, where water is a limiting factor and people strongly rely on rain-fed agriculture, droughts have frequently led to crop failure, food shortages and even humanitarian crises. In
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Droughts are amongst the most destructive natural disasters in the world. In large regions of Africa, where water is a limiting factor and people strongly rely on rain-fed agriculture, droughts have frequently led to crop failure, food shortages and even humanitarian crises. In eastern and southern Africa, major drought episodes have been linked to El Niño-Southern Oscillation (ENSO) events. In this context and with limited in-situ data available, remote sensing provides valuable opportunities for continent-wide assessment of droughts with high spatial and temporal resolutions. This study aimed to monitor agriculturally relevant droughts over Africa between 2000–2016 with a specific focus on growing seasons using remote sensing-based drought indices. Special attention was paid to the observation of drought dynamics during major ENSO episodes to illuminate the connection between ENSO and droughts in eastern and southern Africa. We utilized Tropical Rainfall Measuring Mission (TRMM)-based Standardized Precipitation Index (SPI) with 0 . 25 resolution and Moderate-resolution Imaging Spectroradiometer (MODIS)-derived Vegetation Condition Index (VCI) with 500 m resolution as indices for analysing the spatio-temporal patterns of droughts. We combined the drought indices with information on the timing of site-specific growing seasons derived from MODIS-based multi-annual average of Normalized Difference Vegetation Index (NDVI). We proved the applicability of SPI-3 and VCI as indices for a comprehensive continental-scale monitoring of agriculturally relevant droughts. The years 2009 and 2011 could be revealed as major drought years in eastern Africa, whereas southern Africa was affected by severe droughts in 2003 and 2015/2016. Drought episodes occurred over large parts of southern Africa during strong El Niño events. We observed a mixed drought pattern in eastern Africa, where areas with two growing seasons were frequently affected by droughts during La Niña and zones of unimodal rainfall regimes showed droughts during the onset of El Niño. During La Niña 2010/2011, large parts of cropland areas in Somalia (88%), Sudan (64%) and South Sudan (51%) were affected by severe to extreme droughts during the growing seasons. However, no universal El Niño- or La Niña-related response pattern of droughts could be deduced for the observation period of 16 years. In this regard, we discussed multi-year atmospheric fluctuations and characteristics of ENSO variants as further influences on the interconnection between ENSO and droughts. By utilizing remote sensing-based drought indices focussed on agricultural zones and periods, this study attempts to contribute to a better understanding of spatio-temporal patterns of droughts affecting agriculture in Africa, which can be essential for implementing strategies of drought hazard mitigation. Full article
(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)
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Open AccessArticle Mapping Annual Riparian Water Use Based on the Single-Satellite-Scene Approach
Remote Sens. 2017, 9(8), 832; doi:10.3390/rs9080832
Received: 13 June 2017 / Revised: 4 August 2017 / Accepted: 9 August 2017 / Published: 12 August 2017
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Abstract
The accurate estimation of water use by groundwater-dependent riparian vegetation is of great importance to sustainable water resource management in arid/semi-arid regions. Remote sensing methods can be effective in this regard, as they capture the inherent spatial variability in riparian ecosystems. The single-satellite-scene
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The accurate estimation of water use by groundwater-dependent riparian vegetation is of great importance to sustainable water resource management in arid/semi-arid regions. Remote sensing methods can be effective in this regard, as they capture the inherent spatial variability in riparian ecosystems. The single-satellite-scene (SSS) method uses a derivation of the Normalized Difference Vegetation Index (NDVI) from a single space-borne image during the peak growing season and minimal ground-based meteorological data to estimate the annual riparian water use on a distributed basis. This method was applied to a riparian ecosystem dominated by tamarisk along a section of the lower Colorado River in southern California. The results were compared against the estimates of a previously validated remotely sensed energy balance model for the year 2008 at two different spatial scales. A pixel-wide comparison showed good correlation (R2 = 0.86), with a mean residual error of less than 104 mm∙year−1 (18%). This error reduced to less than 95 mm∙year−1 (15%) when larger areas were used in comparisons. In addition, the accuracy improved significantly when areas with no and low vegetation cover were excluded from the analysis. The SSS method was then applied to estimate the riparian water use for a 23-year period (1988–2010). The average annual water use over this period was 748 mm∙year−1 for the entire study area, with large spatial variability depending on vegetation density. Comparisons with two independent water use estimates showed significant differences. The MODIS evapotranspiration product (MOD16) was 82% smaller, and the crop-coefficient approach employed by the US Bureau of Reclamation was 96% larger, than that from the SSS method on average. Full article
(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)
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Open AccessArticle A Study of Landfast Ice with Sentinel-1 Repeat-Pass Interferometry over the Baltic Sea
Remote Sens. 2017, 9(8), 833; doi:10.3390/rs9080833
Received: 13 July 2017 / Revised: 5 August 2017 / Accepted: 10 August 2017 / Published: 12 August 2017
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Abstract
Mapping of fast ice displacement and investigating sea ice rheological behavior is a major open topic in coastal ice engineering and sea ice modeling. This study presents first results on Sentinel-1 repeat-pass space borne synthetic aperture radar interferometry (InSAR) in the Gulf of
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Mapping of fast ice displacement and investigating sea ice rheological behavior is a major open topic in coastal ice engineering and sea ice modeling. This study presents first results on Sentinel-1 repeat-pass space borne synthetic aperture radar interferometry (InSAR) in the Gulf of Bothnia over the fast ice areas. An InSAR pair acquired in February 2015 with a temporal baseline of 12 days has been studied here in detail. According to our results, the surface of landfast ice in the study area was stable enough to preserve coherence over the 12-day baseline, while previous InSAR studies over the fast ice used much shorter temporal baselines. The advantage of longer temporal baseline is in separating the fast ice from drift ice and detecting long term trends in deformation maps. The interferogram showed displacement of fast ice on the order of 40 cm in the study area. Parts of the displacements were attributed to forces caused by sea level tilt, currents, and thermal expansion, but the main factor of the displacement seemed to be due to compression of the drift ice driven by southwest winds with high speed. Further interferometric phase and the coherence measurements over the fast ice are needed in the future for understanding sea ice mechanism and establishing sustainability of the presented InSAR approach for monitoring dynamics of the landfast ice with Sentinel-1 data. Full article
(This article belongs to the Section Ocean Remote Sensing)
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Open AccessArticle Removal of Thin Cirrus Scattering Effects in Landsat 8 OLI Images Using the Cirrus Detecting Channel
Remote Sens. 2017, 9(8), 834; doi:10.3390/rs9080834
Received: 12 July 2017 / Revised: 24 July 2017 / Accepted: 10 August 2017 / Published: 12 August 2017
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Abstract
Thin cirrus clouds frequently contaminate images acquired with either Landsat 7 ETM+ or the earlier generation of Landsat series satellite instruments. The situation has changed since the launch of the Landsat 8 Operational Land Imager (OLI) into space in 2013. OLI implemented a
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Thin cirrus clouds frequently contaminate images acquired with either Landsat 7 ETM+ or the earlier generation of Landsat series satellite instruments. The situation has changed since the launch of the Landsat 8 Operational Land Imager (OLI) into space in 2013. OLI implemented a cirrus detecting channel (Band 9) centered within a strong atmospheric water vapor absorption band near 1.375 μm with a width of 30 nm. The specifications for this channel were the same as those specified for the NASA Moderate Resolution Imaging Spectroradiometer (MODIS) in the early 1990s. The OLI Band 9 has been proven to be very effective in detecting and masking thin cirrus-contaminated pixels at the high spatial resolution of 30 m. However, this channel has not yet been routinely used for the correction of thin cirrus effects in other OLI band images. In this article, we describe an empirical technique for the removal of thin cirrus scattering effects in OLI visible near infrared (IR) and shortwave IR (SWIR) spectral regions. We present results from applications of the technique to three OLI data sets. We also discuss issues associated with parallax anomalies in OLI data. Full article
(This article belongs to the Special Issue Atmospheric Correction of Remote Sensing Data)
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Open AccessArticle Challenges in Methane Column Retrievals from AVIRIS-NG Imagery over Spectrally Cluttered Surfaces: A Sensitivity Analysis
Remote Sens. 2017, 9(8), 835; doi:10.3390/rs9080835
Received: 12 May 2017 / Revised: 2 August 2017 / Accepted: 8 August 2017 / Published: 12 August 2017
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Abstract
A comparison between efforts to detect methane anomalies by a simple band ratio approach from the Airborne Visual Infrared Imaging Spectrometer-Classic (AVIRIS-C) data for the Kern Front oil field, Central California, and the Coal Oil Point marine hydrocarbon seep field, offshore southern California,
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A comparison between efforts to detect methane anomalies by a simple band ratio approach from the Airborne Visual Infrared Imaging Spectrometer-Classic (AVIRIS-C) data for the Kern Front oil field, Central California, and the Coal Oil Point marine hydrocarbon seep field, offshore southern California, was conducted. The detection succeeded for the marine source and failed for the terrestrial source, despite these sources being of comparable strength. Scene differences were investigated in higher spectral and spatial resolution collected by the AVIRIS-C successor instrument, AVIRIS Next Generation (AVIRIS-NG), by a sensitivity study. Sensitivity to factors including water vapor, aerosol, planetary boundary layer (PBL) structure, illumination and viewing angle, and surface albedo clutter were explored. The study used the residual radiance method, with sensitivity derived from MODTRAN (MODerate resolution atmospheric correction TRANsmission) simulations of column methane (XCH4). Simulations used the spectral specifications and geometries of AVIRIS-NG and were based on a uniform or an in situ vertical CH4 profile, which was measured concurrent with the AVIRIS-NG data. Small but significant sensitivity was found for PBL structure and water vapor; however, highly non-linear, extremely strong sensitivity was found for surface albedo error. For example, a 10% decrease in the surface albedo corresponded to a 300% XCH4 increase over background XCH4 to compensate for the total signal, less so for stronger plumes. This strong non-linear sensitivity resulted from the high percentage of surface-reflected radiance in the airborne at-sensor total radiance. Coarse spectral resolution and feedback from interferents like water vapor underlay this sensitivity. Imaging spectrometry like AVIRIS and the Hyperspectral InfraRed Imager (HyspIRI) candidate satellite mission, have the advantages of contextual spatial information and greater at-sensor total radiance. However, they also face challenges due to their relatively broad spectral resolution compared to trace gas specific orbital sensors, e.g., the Greenhouse gases Observing SATellite (GOSAT), which is especially applicable to trace gas retrievals over scenes with high spectral albedo variability. Results of the sensitivity analysis are applicable for the residual radiance method and CH4 profiles used in the analysis, but they illustrate potential significant challenges in CH4 retrievals using other approaches. Full article
(This article belongs to the Special Issue Remote Sensing of Greenhouse Gases)
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Open AccessArticle Estimating Subpixel Surface Heat Fluxes through Applying Temperature-Sharpening Methods to MODIS Data
Remote Sens. 2017, 9(8), 836; doi:10.3390/rs9080836
Received: 26 May 2017 / Revised: 9 August 2017 / Accepted: 11 August 2017 / Published: 12 August 2017
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Abstract
Using high-resolution satellite data to perform routine (i.e., daily to weekly) monitoring of surface evapotranspiration, evapotranspiration (ET) (or LE, i.e., latent heat flux) has not been feasible because of the low frequency of satellite coverage over regions of interest (i.e., approximately every two
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Using high-resolution satellite data to perform routine (i.e., daily to weekly) monitoring of surface evapotranspiration, evapotranspiration (ET) (or LE, i.e., latent heat flux) has not been feasible because of the low frequency of satellite coverage over regions of interest (i.e., approximately every two weeks). Cloud cover further reduces the number of useable observations, and the utility of these data for routine ET or LE monitoring is limited. Moderate-resolution satellite imagery is available multiple times per day; however, the spatial resolution of these data is too coarse to enable the estimation of ET from individual agricultural fields or variations in ET or LE. The objective of this study is to combine high-resolution satellite data collected in the visible and near-infrared (VNIR) bands with data from the MODIS thermal-infrared (TIR) bands to estimate subpixel surface LE. Two temperature-sharpening methods, the disaggregation procedure for radiometric surface temperature (DisTrad) and the geographically-weighted regression (GWR)-based downscaling algorithm, were used to obtain accurate subpixel land surface temperature (LST) within the Zhangye oasis in China, where the surface is heterogeneous. The downscaled LSTs were validated using observations collected during the HiWATER-MUSOEXE (Multi-Scale Observation Experiment on Evapotranspiration) project. In addition, a remote sensing-based energy balance model was used to compare subpixel MODIS LST-based turbulent heat fluxes estimates with those obtained using the two LST downscaling approaches. The footprint validation results showed that the direct use of the MODIS LST approach does not consider LST heterogeneity at all, leading to significant errors (i.e., the root mean square error is 73.15 W·m−2) in LE, whereas the errors in the LE estimates obtained using DisTrad and GWR were 45.84 W·m−2 and 47.38 W·m−2, respectively. Furthermore, additional analysis showed that the ability of DisTrad and GWR to capture subpixel LST variations depends on the value of Shannon’s diversity index (SHDI) and the surface type within the flux contribution source area. Full article
(This article belongs to the Section Remote Sensing in Geology, Geomorphology and Hydrology)
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Open AccessArticle National BDS Augmentation Service System (NBASS) of China: Progress and Assessment
Remote Sens. 2017, 9(8), 837; doi:10.3390/rs9080837
Received: 6 June 2017 / Revised: 31 July 2017 / Accepted: 9 August 2017 / Published: 12 August 2017
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Abstract
Abstract: In this contribution, the processing strategies of real-time BeiDou System (BDS) precise orbits, clocks, and ionospheric corrections in the National BDS Augmentation Service System (NBASS) are briefly introduced. The Root Mean Square (RMS) of BDS predicted orbits are better than 10 cm
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Abstract: In this contribution, the processing strategies of real-time BeiDou System (BDS) precise orbits, clocks, and ionospheric corrections in the National BDS Augmentation Service System (NBASS) are briefly introduced. The Root Mean Square (RMS) of BDS predicted orbits are better than 10 cm in radial and cross-track components, and the accuracy of the BDS real-time clock is better than 0.5 ns for Inclined Geosynchronous Orbit (IGSO) and Mid Earth Orbit (MEO) satellites. The accuracy of BDS Geostationary Earth Orbit (GEO) orbits and clocks are worse than the IGSO and MEO satellites due to its poor geometry conditions. The real-time ionospheric correction is evaluated by cross-validation, and the average accuracy in the vertical direction is about 4 TECU. With these real-time corrections, the overall single and dual-frequency kinematic precise point positioning (PPP) performance in China are evaluated in terms of positioning accuracy at the 95% confidence level and convergence time. The BDS PPP positioning accuracy shows significant regional characteristics due to the geometry distribution of BDS satellites and the accuracy of ionospheric model in different regions. The BDS dual-frequency PPP positioning accuracy in high-latitude and western fringe region is about 0.5 m and 1.0 m in the horizontal and vertical component, respectively, while the horizontal accuracy is better than 0.2 m and the vertical accuracy is better than 0.3 m in the midlands. The convergence time of the BDS PPP is much longer than the GPS PPP and it needs more than 60 min to achieve the accuracy better than 10 cm in both horizontal and vertical directions for dual-frequency PPP. Similar with dual-frequency PPP, the positioning accuracy of the BDS single-frequency PPP in the fringe region is worse than other regions. The positioning in the midlands can achieve 0.5 m in horizontal component and 1.0 m in the vertical component. In addition, when GPS and BDS are combined, the positioning performance of both single-frequency and dual-frequency PPP can be greatly improved. Full article
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Open AccessArticle Evaluating Sentinel-2 and Landsat-8 Data to Map Sucessional Forest Stages in a Subtropical Forest in Southern Brazil
Remote Sens. 2017, 9(8), 838; doi:10.3390/rs9080838
Received: 7 June 2017 / Revised: 3 August 2017 / Accepted: 10 August 2017 / Published: 13 August 2017
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Abstract
Studies designed to discriminate different successional forest stages play a strategic role in forest management, forest policy and environmental conservation in tropical environments. The discrimination of different successional forest stages is still a challenge due to the spectral similarity among the concerned classes.
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Studies designed to discriminate different successional forest stages play a strategic role in forest management, forest policy and environmental conservation in tropical environments. The discrimination of different successional forest stages is still a challenge due to the spectral similarity among the concerned classes. Considering this, the objective of this paper was to investigate the performance of Sentinel-2 and Landsat-8 data for discriminating different successional forest stages of a patch located in a subtropical portion of the Atlantic Rain Forest in Southern Brazil with the aid of two machine learning algorithms and relying on the use of spectral reflectance data selected over two seasons and attributes thereof derived. Random Forest (RF) and Support Vector Machine (SVM) were used as classifiers with different subsets of predictor variables (multitemporal spectral reflectance, textural metrics and vegetation indices). All the experiments reached satisfactory results, with Kappa indices varying between 0.9, with Landsat-8 spectral reflectance alone and the SVM algorithm, and 0.98, with Sentinel-2 spectral reflectance alone also associated with the SVM algorithm. The Landsat-8 data had a significant increase in accuracy with the inclusion of other predictor variables in the classification process besides the pure spectral reflectance bands. The classification methods SVM and RF had similar performances in general. As to the RF method, the texture mean of the red-edge and SWIR bands were considered the most important ranked attributes for the classification of Sentinel-2 data, while attributes resulting from multitemporal bands, textural metrics of SWIR bands and vegetation indices were the most important ones in the Landsat-8 data classification. Full article
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Open AccessFeature PaperArticle Multiscale Remote Sensing to Map the Spatial Distribution and Extent of Cropland in the Sudanian Savanna of West Africa
Remote Sens. 2017, 9(8), 839; doi:10.3390/rs9080839
Received: 11 June 2017 / Revised: 8 August 2017 / Accepted: 10 August 2017 / Published: 14 August 2017
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Abstract
Food security is the topmost priority on the global agenda. Accurate agricultural statistics (i.e., cropped area) are essential for decision making and developing appropriate programs to achieve food security. However, derivation of these essential agricultural statistics, especially in developing countries, is fraught with
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Food security is the topmost priority on the global agenda. Accurate agricultural statistics (i.e., cropped area) are essential for decision making and developing appropriate programs to achieve food security. However, derivation of these essential agricultural statistics, especially in developing countries, is fraught with many challenges including financial, logistical and human capacity limitations. This study investigated the use of fractional cover approaches in mapping cropland area in the heterogeneous landscape of West Africa. Discrete cropland areas identified from multi-temporal Landsat data were upscaled to MODIS resolution using random forest regression. Producer’s accuracy and user’s accuracy of the cropland class in the Landsat scale analysis averaged 95% and 94%, respectively, indicating good separability between crop and non-crop land. Validation of the fractional cropland cover map at MODIS resolution (MODIS_FCM) revealed an overall mean absolute error of 19%. Comparison of MODIS_FCM with the MODIS land cover product (e.g., MODIS_LCP) demonstrate the suitability of the proposed approach to cropped area estimation in smallholder dominant heterogeneous landscapes over existing global solutions. Comparison with official government statistics (i.e., cropped area) revealed variable levels of agreement and partly enormous disagreements, which clearly indicate the need to integrate remote sensing approaches and ground based surveys conducted by agricultural ministries in improving cropped area estimation. The recent availability of a wide range of open access remote sensing data is expected to expedite this integration and contribute missing information urgently required for regional assessments of food security in West Africa and beyond. Full article
(This article belongs to the Special Issue Monitoring Agricultural Land-Use Change and Land-Use Intensity)
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Open AccessArticle Topic Modelling for Object-Based Unsupervised Classification of VHR Panchromatic Satellite Images Based on Multiscale Image Segmentation
Remote Sens. 2017, 9(8), 840; doi:10.3390/rs9080840
Received: 31 May 2017 / Revised: 10 August 2017 / Accepted: 10 August 2017 / Published: 14 August 2017
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Abstract
Image segmentation is a key prerequisite for object-based classification. However, it is often difficult, or even impossible, to determine a unique optimal segmentation scale due to the fact that various geo-objects, and even an identical geo-object, present at multiple scales in very high
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Image segmentation is a key prerequisite for object-based classification. However, it is often difficult, or even impossible, to determine a unique optimal segmentation scale due to the fact that various geo-objects, and even an identical geo-object, present at multiple scales in very high resolution (VHR) satellite images. To address this problem, this paper presents a novel unsupervised object-based classification for VHR panchromatic satellite images using multiple segmentations via the latent Dirichlet allocation (LDA) model. Firstly, multiple segmentation maps of the original satellite image are produced by means of a common multiscale segmentation technique. Then, the LDA model is utilized to learn the grayscale histogram distribution for each geo-object and the mixture distribution of geo-objects within each segment. Thirdly, the histogram distribution of each segment is compared with that of each geo-object using the Kullback-Leibler (KL) divergence measure, which is weighted with a constraint specified by the mixture distribution of geo-objects. Each segment is allocated a geo-object category label with the minimum KL divergence. Finally, the final classification map is achieved by integrating the multiple classification results at different scales. Extensive experimental evaluations are designed to compare the performance of our method with those of some state-of-the-art methods for three different types of images. The experimental results over three different types of VHR panchromatic satellite images demonstrate the proposed method is able to achieve scale-adaptive classification results, and improve the ability to differentiate the geo-objects with spectral overlap, such as water and grass, and water and shadow, in terms of both spatial consistency and semantic consistency. Full article
(This article belongs to the Special Issue Learning to Understand Remote Sensing Images)
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Open AccessArticle A Probabilistic Weighted Archetypal Analysis Method with Earth Mover’s Distance for Endmember Extraction from Hyperspectral Imagery
Remote Sens. 2017, 9(8), 841; doi:10.3390/rs9080841
Received: 1 April 2017 / Revised: 6 August 2017 / Accepted: 9 August 2017 / Published: 14 August 2017
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Abstract
A Probabilistic Weighted Archetypal Analysis method with Earth Mover’s Distance (PWAA-EMD) is proposed to extract endmembers from hyperspectral imagery (HSI). The PWAA-EMD first utilizes the EMD dissimilarity matrix to weight the coefficient matrix in the regular Archetypal Analysis (AA). The EMD metric considers
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A Probabilistic Weighted Archetypal Analysis method with Earth Mover’s Distance (PWAA-EMD) is proposed to extract endmembers from hyperspectral imagery (HSI). The PWAA-EMD first utilizes the EMD dissimilarity matrix to weight the coefficient matrix in the regular Archetypal Analysis (AA). The EMD metric considers manifold structures of spectral signatures in the HSI data and could better quantify the dissimilarity features among pairwise pixels. Second, the PWAA-EMD adopts the Bayesian framework and formulates the improved AA into a probabilistic inference problem by maximizing a joint posterior density. Third, the optimization problem is solved by the iterative multiplicative update scheme, with a careful initialization from the two-stage algorithm and the proper endmembers are finally obtained. The synthetic and real Cuprite Hyperspectral datasets are utilized to verify the performance of PWAA-EMD and five popular methods are implemented to make comparisons. The results show that PWAA-EMD surpasses all the five methods in the average results of spectral angle distance (SAD) and root-mean-square-error (RMSE). Especially, the PWAA-EMD obtains more accurate estimation than AA in almost all the classes of endmembers including two similar ones. Therefore, the PWAA-EMD could be an alternative choice for endmember extraction on the hyperspectral data. Full article
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Open AccessArticle Long-Term Water Storage Changes of Lake Volta from GRACE and Satellite Altimetry and Connections with Regional Climate
Remote Sens. 2017, 9(8), 842; doi:10.3390/rs9080842
Received: 8 July 2017 / Revised: 2 August 2017 / Accepted: 9 August 2017 / Published: 14 August 2017
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Abstract
Satellite gravity data from the Gravity Recovery and Climate Experiment (GRACE) provides a quantitative measure of terrestrial water storage (TWS) change at different temporal and spatial scales. In this study, we investigate the ability of GRACE to quantitatively monitor long-term hydrological characteristics over
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Satellite gravity data from the Gravity Recovery and Climate Experiment (GRACE) provides a quantitative measure of terrestrial water storage (TWS) change at different temporal and spatial scales. In this study, we investigate the ability of GRACE to quantitatively monitor long-term hydrological characteristics over the Lake Volta region. Principal component analysis (PCA) is employed to study temporal and spatial variability of long-term TWS changes. Long-term Lake Volta water storage change appears to be the dominant long-term TWS change signal in the Volta basin. GRACE-derived TWS changes and precipitation variations compiled by the Global Precipitation Climatology Centre (GPCC) are related both temporally and spatially, but spatial leakage attenuates the magnitude of GRACE estimates, especially at small regional scales. Using constrained forward modeling, we successfully remove leakage error in GRACE estimates. After this leakage correction, GRACE-derived Lake Volta water storage changes agree remarkably well with independent estimates from satellite altimetry at interannual and longer time scales. This demonstrates the value of GRACE estimates to monitor and quantify water storage changes in lakes, especially in relatively small regions with complicated topography. Full article
(This article belongs to the Special Issue Remote Sensing of Climate Change and Water Resources)
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Open AccessArticle An Accuracy Assessment of Derived Digital Elevation Models from Terrestrial Laser Scanning in a Sub-Tropical Forested Environment
Remote Sens. 2017, 9(8), 843; doi:10.3390/rs9080843
Received: 15 June 2017 / Revised: 28 July 2017 / Accepted: 9 August 2017 / Published: 14 August 2017
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Abstract
Forest structure attributes produced from terrestrial laser scanning (TLS) rely on normalisation of the point cloud values from sensor coordinates to height above ground. One method to do this is through the derivation of an accurate and repeatable digital elevation model (DEM) from
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Forest structure attributes produced from terrestrial laser scanning (TLS) rely on normalisation of the point cloud values from sensor coordinates to height above ground. One method to do this is through the derivation of an accurate and repeatable digital elevation model (DEM) from the TLS point cloud that is used to adjust the height. The primary aim of this paper was to test a number of TLS scan configurations, filtering options and output DEM grid resolutions (from 0.02 m to 1.0 m) to define a best practice method for DEM generation in sub-tropical forest environments. The generated DEMs were compared to both total station (TS) spot heights and a 1-m DEM generated from airborne laser scanning (ALS) to assess accuracy. The comparison to TS spot heights found that a DEM produced using the minimum elevation (minimum Z value) from a point cloud derived from a single scan had mean errors >1 m for DEM grid resolutions <0.2 m at a 25-m plot radius. At a 1-m grid resolution, the mean error was 0.19 m. The addition of a filtering approach that combined a median filter with a progressive morphological filter and a global percentile filter was able to reduce mean error of the 0.02-m grid resolution DEM to 0.31 m at a 25-m plot radius using all returns. Using multiple scan positions to derive the DEM reduced the mean error for all DEM methods. Our results suggest that a simple minimum Z filtering DEM method using a single scan at the grid resolution of 1 m can produce mean errors <0.2 m, but for a small grid resolution, such as 0.02 m, a more complex filtering approach and multiple scan positions are required to reduced mean errors. The additional validation data provided by the 1-m ALS DEM showed that when using the combined filtering method on a point cloud derived from a single scan at the plot centre, errors between 0.1 and 0.5 m occurred in the TLS DEM for all tested grid resolutions at a plot radius of 25 m. These findings present a protocol for DEM production from TLS data at a range of grid resolutions and provide an overview of factors affecting DEMs produced from single and multiple TLS scan positions. Full article
(This article belongs to the Section Forest Remote Sensing)
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Open AccessArticle Extension of a Fast GLRT Algorithm to 5D SAR Tomography of Urban Areas
Remote Sens. 2017, 9(8), 844; doi:10.3390/rs9080844
Received: 12 June 2017 / Revised: 26 July 2017 / Accepted: 4 August 2017 / Published: 14 August 2017
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Abstract
This paper analyzes a method for Synthetic Aperture Radar (SAR) Tomographic (TomoSAR) imaging, allowing the detection of multiple scatterers that can exhibit time deformation and thermal dilation by using a CFAR (Constant False Alarm Rate) approach. In the last decade, several methods for
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This paper analyzes a method for Synthetic Aperture Radar (SAR) Tomographic (TomoSAR) imaging, allowing the detection of multiple scatterers that can exhibit time deformation and thermal dilation by using a CFAR (Constant False Alarm Rate) approach. In the last decade, several methods for TomoSAR have been proposed. The objective of this paper is to present the results obtained on high resolution tomographic SAR data of urban areas, by using a statistical test for detecting multiple scatterers that takes into account phase variations due to possible deformations and/or thermal dilation. The test can be evaluated in terms of probability of detection (PD) and probability of false alarm (PFA), and is based on an approximation of a Generalized Likelihood Ratio Test (GLRT), denoted as Fast-Sup-GLRT. It was already applied and validated by the authors in the 3D case, while here it is extended and experimented in the 5D case. Numerical experiments on simulated and on StripMap TerraSAR-X (TSX) data have been carried out. The presented results show that the adopted method allows the detection of a large number of scatterers and the estimation of their position with a good accuracy, and that the consideration of the thermal dilation and surface deformation helps in recovering more single and double scatterers, with respect to the case in which these contributions are not taken into account. Moreover, the capability of method to provide reliable estimates of the deformations in urban structure suggests its use in structure stress monitoring. Full article
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Open AccessArticle Assimilation of Sentinel-1 Derived Sea Surface Winds for Typhoon Forecasting
Remote Sens. 2017, 9(8), 845; doi:10.3390/rs9080845 (registering DOI)
Received: 16 June 2017 / Revised: 9 August 2017 / Accepted: 10 August 2017 / Published: 14 August 2017
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Abstract
High-resolution synthetic aperture radar (SAR) wind observations provide fine structural information for tropical cycles and could be assimilated into numerical weather prediction (NWP) models. However, in the conventional method assimilating the u and v components for SAR wind observations (SAR_uv), the wind direction
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High-resolution synthetic aperture radar (SAR) wind observations provide fine structural information for tropical cycles and could be assimilated into numerical weather prediction (NWP) models. However, in the conventional method assimilating the u and v components for SAR wind observations (SAR_uv), the wind direction is not a state vector and its observational error is not considered during the assimilation calculation. In this paper, an improved method for wind observation directly assimilates the SAR wind observations in the form of speed and direction (SAR_sd). This method was implemented to assimilate the sea surface wind retrieved from Sentinel-1 synthetic aperture radar (SAR) in the basic three-dimensional variational system for the Weather Research and Forecasting Model (WRF 3DVAR). Furthermore, a new quality control scheme for wind observations is also presented. Typhoon Lionrock in August 2016 is chosen as a case study to investigate and compare both assimilation methods. The experimental results show that the SAR wind observations can increase the number of the effective observations in the area of a typhoon and have a positive impact on the assimilation analysis. The numerical forecast results for this case show better results for the SAR_sd method than for the SAR_uv method. The SAR_sd method looks very promising for winds assimilation under typhoon conditions, but more cases need to be considered to draw final conclusions. Full article
(This article belongs to the Special Issue Ocean Remote Sensing with Synthetic Aperture Radar)
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Open AccessArticle A Novel Method of Change Detection in Bi-Temporal PolSAR Data Using a Joint-Classification Classifier Based on a Similarity Measure
Remote Sens. 2017, 9(8), 846; doi:10.3390/rs9080846
Received: 29 May 2017 / Revised: 4 August 2017 / Accepted: 9 August 2017 / Published: 15 August 2017
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Abstract
Accurate and timely change detection of the Earth’s surface features is extremely important for understanding the relationships and interactions between people and natural phenomena. Owing to the all-weather response capability, polarimetric synthetic aperture radar (PolSAR) has become a key tool for change detection.
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Accurate and timely change detection of the Earth’s surface features is extremely important for understanding the relationships and interactions between people and natural phenomena. Owing to the all-weather response capability, polarimetric synthetic aperture radar (PolSAR) has become a key tool for change detection. Change detection includes both unsupervised and supervised methods. Unsupervised change detection is simple and effective, but cannot detect the type of land cover change. Supervised change detection can detect the type of land cover change, but is easily affected and depended by the human interventions. To solve these problems, a novel method of change detection using a joint-classification classifier (JCC) based on a similarity measure is introduced. The similarity measure is obtained by a test statistic and the Kittler and Illingworth (TSKI) minimum-error thresholding algorithm, which is used to automatically control the JCC. The efficiency of the proposed method is demonstrated by the use of bi-temporal PolSAR images acquired by RADARSAT-2 over Wuhan, China. The experimental results show that the proposed method can identify the different types of land cover change and can reduce both the false detection rate and false alarm rate in the change detection. Full article
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Open AccessArticle Pre-Trained AlexNet Architecture with Pyramid Pooling and Supervision for High Spatial Resolution Remote Sensing Image Scene Classification
Remote Sens. 2017, 9(8), 848; doi:10.3390/rs9080848 (registering DOI)
Received: 11 July 2017 / Revised: 2 August 2017 / Accepted: 7 August 2017 / Published: 16 August 2017
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Abstract
The rapid development of high spatial resolution (HSR) remote sensing imagery techniques not only provide a considerable amount of datasets for scene classification tasks but also request an appropriate scene classification choice when facing with finite labeled samples. AlexNet, as a relatively simple
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The rapid development of high spatial resolution (HSR) remote sensing imagery techniques not only provide a considerable amount of datasets for scene classification tasks but also request an appropriate scene classification choice when facing with finite labeled samples. AlexNet, as a relatively simple convolutional neural network (CNN) architecture, has obtained great success in scene classification tasks and has been proven to be an excellent foundational hierarchical and automatic scene classification technique. However, current HSR remote sensing imagery scene classification datasets always have the characteristics of small quantities and simple categories, where the limited annotated labeling samples easily cause non-convergence. For HSR remote sensing imagery, multi-scale information of the same scenes can represent the scene semantics to a certain extent but lacks an efficient fusion expression manner. Meanwhile, the current pre-trained AlexNet architecture lacks a kind of appropriate supervision for enhancing the performance of this model, which easily causes overfitting. In this paper, an improved pre-trained AlexNet architecture named pre-trained AlexNet-SPP-SS has been proposed, which incorporates the scale pooling—spatial pyramid pooling (SPP) and side supervision (SS) to improve the above two situations. Extensive experimental results conducted on the UC Merced dataset and the Google Image dataset of SIRI-WHU have demonstrated that the proposed pre-trained AlexNet-SPP-SS model is superior to the original AlexNet architecture as well as the traditional scene classification methods. Full article
(This article belongs to the Special Issue Remote Sensing Big Data: Theory, Methods and Applications)
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Open AccessArticle Reflectance Intensity Assisted Automatic and Accurate Extrinsic Calibration of 3D LiDAR and Panoramic Camera Using a Printed Chessboard
Remote Sens. 2017, 9(8), 851; doi:10.3390/rs9080851 (registering DOI)
Received: 27 April 2017 / Revised: 28 July 2017 / Accepted: 13 August 2017 / Published: 16 August 2017
PDF Full-text (34336 KB)
Abstract
This paper presents a novel method for fully automatic and convenient extrinsic calibration of a 3D LiDAR and a panoramic camera with a normally printed chessboard. The proposed method is based on the 3D corner estimation of the chessboard from the sparse point
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This paper presents a novel method for fully automatic and convenient extrinsic calibration of a 3D LiDAR and a panoramic camera with a normally printed chessboard. The proposed method is based on the 3D corner estimation of the chessboard from the sparse point cloud generated by one frame scan of the LiDAR. To estimate the corners, we formulate a full-scale model of the chessboard and fit it to the segmented 3D points of the chessboard. The model is fitted by optimizing the cost function under constraints of correlation between the reflectance intensity of laser and the color of the chessboard’s patterns. Powell’s method is introduced for resolving the discontinuity problem in optimization. The corners of the fitted model are considered as the 3D corners of the chessboard. Once the corners of the chessboard in the 3D point cloud are estimated, the extrinsic calibration of the two sensors is converted to a 3D-2D matching problem. The corresponding 3D-2D points are used to calculate the absolute pose of the two sensors with Unified Perspective-n-Point (UPnP). Further, the calculated parameters are regarded as initial values and are refined using the Levenberg-Marquardt method. The performance of the proposed corner detection method from the 3D point cloud is evaluated using simulations. The results of experiments, conducted on a Velodyne HDL-32e LiDAR and a Ladybug3 camera under the proposed re-projection error metric, qualitatively and quantitatively demonstrate the accuracy and stability of the final extrinsic calibration parameters. Full article
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Open AccessArticle A Method to Obtain Orange Crop Geometry Information Using a Mobile Terrestrial Laser Scanner and 3D Modeling
Remote Sens. 2017, 9(8), 763; doi:10.3390/rs9080763
Received: 14 June 2017 / Revised: 13 July 2017 / Accepted: 19 July 2017 / Published: 25 July 2017
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Abstract
LiDAR (Light Detection and Ranging) technology has been used to obtain geometrical attributes of tree crops in small field plots, sometimes using manual steps in data processing. The objective of this study was to develop a method for estimating canopy volume and height
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LiDAR (Light Detection and Ranging) technology has been used to obtain geometrical attributes of tree crops in small field plots, sometimes using manual steps in data processing. The objective of this study was to develop a method for estimating canopy volume and height based on a mobile terrestrial laser scanner suited for large commercial orange groves. A 2D LiDAR sensor and a GNSS (Global Navigation Satellite System) receiver were mounted on a vehicle for data acquisition. A georeferenced point cloud representing the laser beam impacts on the crop was created and later classified into transversal sections along the row or into individual trees. The convex-hull and the alpha-shape reconstruction algorithms were used to reproduce the shape of the tree crowns. Maps of canopy volume and height were generated for a 25 ha orange grove. The different options of data processing resulted in different values of canopy volume. The alpha-shape algorithm was considered a good option to represent individual trees whereas the convex-hull was better when representing transversal sections of the row. Nevertheless, the canopy volume and height maps produced by those two methods were similar. The proposed system is useful for site-specific management in orange groves. Full article
(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)
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Open AccessArticle A Simple Normalized Difference Approach to Burnt Area Mapping Using Multi-Polarisation C-Band SAR
Remote Sens. 2017, 9(8), 764; doi:10.3390/rs9080764
Received: 12 June 2017 / Revised: 7 July 2017 / Accepted: 19 July 2017 / Published: 31 July 2017
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Abstract
In fire-prone ecosystems, periodic fires are vital for ecosystem functioning. Fire managers seek to promote the optimal fire regime by managing fire season and frequency requiring detailed information on the extent and date of previous burns. This paper investigates a Normalised Difference α-Angle
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In fire-prone ecosystems, periodic fires are vital for ecosystem functioning. Fire managers seek to promote the optimal fire regime by managing fire season and frequency requiring detailed information on the extent and date of previous burns. This paper investigates a Normalised Difference α-Angle (NDαI) approach to burn-scar mapping using C-band data. Polarimetric decompositions are used to derive α-angles from pre-burn and post-burn scenes and NDαI is calculated to identify decreases in vegetation between the scenes. The technique was tested in an area affected by a wildfire in January 2016 in the Western Cape, South Africa. The quad-pol H-A-α decomposition was applied to RADARSAT-2 data and the dual-pol H-α decomposition was applied to Sentinel-1A data. The NDαI results were compared to a burn scar extracted from Sentinel-2A data. High overall accuracies of 97.4% (Kappa = 0.72) and 94.8% (Kappa = 0.57) were obtained for RADARSAT-2 and Sentinel-1A, respectively. However, large omission errors were found and correlated strongly with areas of high local incidence angle for both datasets. The combined use of data from different orbits will likely reduce these errors. Furthermore, commission errors were observed, most notably on Sentinel-1A results. These errors may be due to the inability of the dual-pol H-α decomposition to effectively distinguish between scattering mechanisms. Despite these errors, the results revealed that burnt areas could be extracted and were in good agreement with the results from Sentinel-2A. Therefore, the approach can be considered in areas where persistent cloud cover or smoke prevents the extraction of burnt area information using conventional multispectral approaches. Full article
(This article belongs to the Special Issue Remote Sensing of Wildfire)
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Open AccessArticle Correcting InSAR Topographically Correlated Tropospheric Delays Using a Power Law Model Based on ERA-Interim Reanalysis
Remote Sens. 2017, 9(8), 765; doi:10.3390/rs9080765
Received: 20 June 2017 / Revised: 20 July 2017 / Accepted: 20 July 2017 / Published: 26 July 2017
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Abstract
Tropospheric delay caused by spatiotemporal variations in pressure, temperature, and humidity in the lower troposphere remains one of the major challenges in Interferometric Synthetic Aperture Radar (InSAR) deformation monitoring applications. Acquiring an acceptable level of accuracy (millimeter-level) for small amplitude surface displacement is
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Tropospheric delay caused by spatiotemporal variations in pressure, temperature, and humidity in the lower troposphere remains one of the major challenges in Interferometric Synthetic Aperture Radar (InSAR) deformation monitoring applications. Acquiring an acceptable level of accuracy (millimeter-level) for small amplitude surface displacement is difficult without proper delay estimation. Tropospheric delay can be estimated from the InSAR phase itself using the spatiotemporal relationship between the phase and topography, but separating the deformation signal from the tropospheric delay is difficult when the deformation is topographically related. Approaches using external data such as ground GPS networks, space-borne spectrometers, and meteorological observations have been exploited with mixed success in the past. These methods are plagued, however, by low spatiotemporal resolution, unfavorable weather conditions or limited coverage. A phase-based power law method recently proposed by Bekaert et al. estimates the tropospheric delay by assuming the phase and topography following a power law relationship. This method can account for the spatial variation of the atmospheric properties and can be applied to interferograms containing topographically correlated deformation. However, the parameter estimates of this method are characterized by two limitations: one is that the power law coefficients are estimated using the sounding data, which are not always available in a study region; the other is that the scaled factor between band-filtered topography and phase is inverted by least squares regression, which is not outlier-resistant. To address these problems, we develop and test a power law model based on ERA-Interim (PLE). Our version estimates the power law coefficients by using ERA-Interim (ERA-I) reanalysis. A robust estimation technique was introduced in the PLE method to estimate the scaled factor, which is insensitive to outliers. We applied the PLE method to ENVISAT ASAR images collected over Southern California, US, and Tianshan, China. We compared tropospheric corrections estimated from using our PLE method with the corrections estimated using the linear method and ERA-I method. Accuracy was evaluated by using delay measurements from the Medium Resolution Imaging Spectrometer (MERIS) onboard the ENVISAT satellite. The PLE method consistently delivered greater standard deviation (STD) reduction after tropospheric corrections than both the linear method and ERA-I method and agreed well with the MERIS measurements. Full article
(This article belongs to the Section Atmosphere Remote Sensing)
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Open AccessArticle Wall-to-Wall Tree Type Mapping from Countrywide Airborne Remote Sensing Surveys
Remote Sens. 2017, 9(8), 766; doi:10.3390/rs9080766
Received: 14 June 2017 / Revised: 13 July 2017 / Accepted: 24 July 2017 / Published: 27 July 2017
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Abstract
Although wall-to-wall, accurate, and up-to-date forest composition maps at the stand level are a fundamental input for many applications, ranging from global environmental issues to local forest management planning, countrywide mapping approaches on the tree type level remain rare. This paper presents and
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Although wall-to-wall, accurate, and up-to-date forest composition maps at the stand level are a fundamental input for many applications, ranging from global environmental issues to local forest management planning, countrywide mapping approaches on the tree type level remain rare. This paper presents and validates an innovative remote sensing based approach for a countrywide mapping of broadleaved and coniferous trees in Switzerland with a spatial resolution of 3 m. The classification approach incorporates a random forest classifier, explanatory variables from multispectral aerial imagery and a Digital Terrain Model (DTM) from Airborne Laser Scanning (ALS) data, digitized training polygons and independent validation data from the National Forest Inventory (NFI). The methodological workflow was optimized for an area of 41,285 km2 that is characterized by temperate forests within a complex topography. Whereas high model overall accuracies (0.99) and kappa (0.98) were achieved, the comparison of the tree type map with independent NFI data revealed significant deviations that are related to underestimations of broadleaved trees (median of −3.17%). Constraints of the tree type mapping approach are mostly related to the acquisition date and time of the imagery and the topographic (negative) effects on the prediction. A comparison with the most recent High Resolution Layers (HRL) forest 2012 from the European Environmental Agency revealed that the tree type map is superior regarding spatial resolution, level of detail and accuracy. The high-quality map achieved with the approach presented here is of great value for optimizing forest management and planning activities and is also an important information source for applications outside the forestry sector. Full article
(This article belongs to the Section Forest Remote Sensing)
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Open AccessArticle Gauging the Severity of the 2012 Midwestern U.S. Drought for Agriculture
Remote Sens. 2017, 9(8), 767; doi:10.3390/rs9080767
Received: 7 June 2017 / Revised: 21 July 2017 / Accepted: 22 July 2017 / Published: 26 July 2017
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Abstract
Different drought indices often provide different diagnoses of drought severity, making it difficult to determine the best way to evaluate these different drought monitoring results. Additionally, the ability of a newly proposed drought index, the Process-based Accumulated Drought Index (PADI) has not yet
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Different drought indices often provide different diagnoses of drought severity, making it difficult to determine the best way to evaluate these different drought monitoring results. Additionally, the ability of a newly proposed drought index, the Process-based Accumulated Drought Index (PADI) has not yet been tested in United States. In this study, we quantified the severity of 2012 drought which affected the agricultural output for much of the Midwestern US. We used several popular drought indices, including the Standardized Precipitation Index and Standardized Precipitation Evapotranspiration Index with multiple time scales, Palmer Drought Severity Index, Palmer Z-index, VegDRI, and PADI by comparing the spatial distribution, temporal evolution, and crop impacts produced by each of these indices with the United States Drought Monitor. Results suggested this drought incubated around June 2011 and ended in May 2013. While different drought indices depicted drought severity variously. SPI outperformed SPEI and has decent correlation with yield loss especially at a 6 months scale and in the middle growth season, while VegDRI and PADI demonstrated the highest correlation especially in late growth season, indicating they are complementary and should be used together. These results are valuable for comparing and understanding the different performances of drought indices in the Midwestern US. Full article
(This article belongs to the Special Issue Remote Sensing of Land-Atmosphere Interactions)
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Open AccessArticle Performance Evaluation for China’s Planned CO2-IPDA
Remote Sens. 2017, 9(8), 768; doi:10.3390/rs9080768
Received: 15 June 2017 / Revised: 21 July 2017 / Accepted: 21 July 2017 / Published: 26 July 2017
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Abstract
Active remote sensing of atmospheric XCO2 has several advantages over existing passive remote sensors, including global coverage, a smaller footprint, improved penetration of aerosols, and night observation capabilities. China is planning to launch a multi-functional atmospheric observation satellite equipped with a CO
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Active remote sensing of atmospheric XCO2 has several advantages over existing passive remote sensors, including global coverage, a smaller footprint, improved penetration of aerosols, and night observation capabilities. China is planning to launch a multi-functional atmospheric observation satellite equipped with a CO2-IPDA (integrated path differential absorption Lidar) to measure columnar concentrations of atmospheric CO2 globally. As space and power are limited on the satellite, compromises have been made to accommodate other passive sensors. In this study, we evaluated the sensitivity of the system’s retrieval accuracy and precision to some critical parameters to determine whether the current configuration is adequate to obtain the desired results and whether any further compromises are possible. We then mapped the distribution of random errors across China and surrounding regions using pseudo-observations to explore the performance of the planned CO2-IPDA over these regions. We found that random errors of less than 0.3% can be expected for most regions of our study area, which will allow the provision of valuable data that will help researchers gain a deeper insight into carbon cycle processes and accurately estimate carbon uptake and emissions. However, in the areas where major anthropogenic carbon sources are located, and in coastal seas, random errors as high as 0.5% are predicted. This is predominantly due to the high concentrations of aerosols, which cause serious attenuation of returned signals. Novel retrieving methods must, therefore, be developed in the future to suppress interference from low surface reflectance and high aerosol loading. Full article
(This article belongs to the Special Issue Remote Sensing of Greenhouse Gases)
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Open AccessArticle Diurnal Cycle Relationships between Passive Fluorescence, PRI and NPQ of Vegetation in a Controlled Stress Experiment
Remote Sens. 2017, 9(8), 770; doi:10.3390/rs9080770
Received: 8 May 2017 / Revised: 7 July 2017 / Accepted: 21 July 2017 / Published: 28 July 2017
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Abstract
In order to estimate vegetation photosynthesis from remote sensing observations; some critical parameters need to be quantified. From all absorbed light; the plant needs to release any excess that is not used for photosynthesis; by non-photochemical quenching; by fluorescence emission and unregulated thermal
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In order to estimate vegetation photosynthesis from remote sensing observations; some critical parameters need to be quantified. From all absorbed light; the plant needs to release any excess that is not used for photosynthesis; by non-photochemical quenching; by fluorescence emission and unregulated thermal dissipation. Non-photochemical quenching (NPQ) processes are controlled photoprotective mechanisms which; once activated; strongly control the dynamics of photochemical efficiency. With illumination conditions increasing and decreasing during a diurnal cycle; photoprotection mechanisms needs to change accordingly. The goal of this work is to quantify dynamic NPQ; measured from active fluorescence measurements; based on passive proximal sensing leaf measurements. During a 22-day controlled light and water stress experiment on a tobacco (Nicotiana tabacum L.) leaf we measured the diurnal dynamics of passive fluorescence (Chl F); the Photochemical Reflectance Index (PRI); the Absorbed Photosynthetically Active Radiation (APAR) and leaf temperature in combination with the actively retrieved non-photochemical quenching (NPQ) parameter. Based on a bi-linear combination of diurnal APAR and PRI (plane fit model) we succeeded to estimate NPQ with a RMSE of 0.08. The simple plane fit model estimation represents well the diurnal NPQ dynamics; except for the high light stress phase; when additional reversible photoinhibition processes took place. The present works presents a way of determining NPQ from passive remote sensing measurements; as a necessary step towards estimating photosynthetic rate. Full article
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Open AccessArticle Supervised Classification of Power Lines from Airborne LiDAR Data in Urban Areas
Remote Sens. 2017, 9(8), 771; doi:10.3390/rs9080771
Received: 10 May 2017 / Revised: 12 July 2017 / Accepted: 21 July 2017 / Published: 28 July 2017
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Abstract
Automatic extraction of power lines using airborne LiDAR (Light Detection and Ranging) data has been one of the most important topics for electric power management. However, this is very challenging over complex urban areas, where power lines are in close proximity to buildings
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Automatic extraction of power lines using airborne LiDAR (Light Detection and Ranging) data has been one of the most important topics for electric power management. However, this is very challenging over complex urban areas, where power lines are in close proximity to buildings and trees. In this paper, we presented a new, semi-automated and versatile framework that consists of four steps: (i) power line candidate point filtering, (ii) local neighborhood selection, (iii) spatial structural feature extraction, and (iv) SVM classification. We introduced the power line corridor direction for candidate point filtering and multi-scale slant cylindrical neighborhood for spatial structural features extraction. In a detailed evaluation involving seven scales and four types for local neighborhood selection, 26 structural features, and two datasets, we demonstrated that the use of multi-scale slant cylindrical neighborhood for individual 3D points significantly improved the power line classification. The experiments indicated that precision, recall and quality rate of power line classification is more than 98%, 98% and 97%, respectively. Additionally, we showed that our approach can reduce the whole processing time while achieving high accuracy. Full article
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Open AccessArticle Continental Shelf-Scale Passive Acoustic Detection and Characterization of Diesel-Electric Ships Using a Coherent Hydrophone Array
Remote Sens. 2017, 9(8), 772; doi:10.3390/rs9080772
Received: 11 June 2017 / Revised: 21 July 2017 / Accepted: 24 July 2017 / Published: 28 July 2017
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Abstract
The passive ocean acoustic waveguide remote sensing (POAWRS) technique is employed to detect and characterize the underwater sound radiated from three scientific research and fishing vessels received at long ranges on a large-aperture densely-sampled horizontal coherent hydrophone array. The sounds radiated from the
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The passive ocean acoustic waveguide remote sensing (POAWRS) technique is employed to detect and characterize the underwater sound radiated from three scientific research and fishing vessels received at long ranges on a large-aperture densely-sampled horizontal coherent hydrophone array. The sounds radiated from the research vessel (RV) Delaware II in the Gulf of Maine, and the RV Johan Hjort and the fishing vessel (FV) Artus in the Norwegian Sea are found to be dominated by distinct narrowband tonals and cyclostationary signals in the 150 Hz to 2000 Hz frequency range. The source levels of these signals are estimated by correcting the received pressure levels for transmission losses modeled using a calibrated parabolic equation-based acoustic propagation model for random range-dependent ocean waveguides. The probability of the detection region for the most prominent signal radiated by each ship is estimated and shown to extend over areas spanning roughly 200 km in diameter when employing a coherent hydrophone array. The current standard procedure for quantifying ship-radiated sound source levels via one-third octave bandwidth intensity averaging smoothes over the prominent tonals radiated by a ship that can stand 10 to 30 dB above the local broadband level, which may lead to inaccurate or incorrect assessments of the impact of ship-radiated sound. Full article
(This article belongs to the Section Ocean Remote Sensing)
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Open AccessArticle Improving Super-Resolution Mapping by Combining Multiple Realizations Obtained Using the Indicator-Geostatistics Based Method
Remote Sens. 2017, 9(8), 773; doi:10.3390/rs9080773
Received: 5 June 2017 / Revised: 9 July 2017 / Accepted: 25 July 2017 / Published: 28 July 2017
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Abstract
Indicator-geostatistics based super-resolution mapping (IGSRM) is a popular super-resolution mapping (SRM) method. Unlike most existing SRM methods that produce only one SRM result each, IGSRM generates multiple equally plausible super-resolution realizations (i.e., SRM results). However, multiple super-resolution realizations are not desirable in many
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Indicator-geostatistics based super-resolution mapping (IGSRM) is a popular super-resolution mapping (SRM) method. Unlike most existing SRM methods that produce only one SRM result each, IGSRM generates multiple equally plausible super-resolution realizations (i.e., SRM results). However, multiple super-resolution realizations are not desirable in many applications, where only one SRM result is usually required. These super-resolution realizations may have different strengths and weaknesses. This paper proposes a novel two-step combination method of generating a single SRM result from multiple super-resolution realizations obtained by IGSRM. In the first step of the method, a constrained majority rule is proposed to combine multiple super-resolution realizations generated by IGSRM into a single SRM result under the class proportion constraint. In the second step, partial pixel swapping is proposed to further improve the SRM result obtained in the previous step. The proposed combination method was evaluated for two study areas. The proposed method was quantitatively compared with IGSRM and Multiple SRM (M-SRM), an existing multiple SRM result combination method, in terms of thematic accuracy and geometric accuracy. Experimental results show that the proposed method produces SRM results that are better than those of IGSRM and M-SRM. For example, in the first example, the overall accuracy of the proposed method is 7.43–10.96% higher than that of the IGSRM method for different scale factors, and 1.09–3.44% higher than that of the M-SRM, while, in the second example, the improvement in overall accuracy is 2.42–4.92%, and 0.08–0.90%, respectively. The proposed method provides a general framework for combining multiple results from different SRM methods. Full article
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Open AccessArticle Impact Analysis of Climate Change on Snow over a Complex Mountainous Region Using Weather Research and Forecast Model (WRF) Simulation and Moderate Resolution Imaging Spectroradiometer Data (MODIS)-Terra Fractional Snow Cover Products
Remote Sens. 2017, 9(8), 774; doi:10.3390/rs9080774
Received: 17 May 2017 / Revised: 18 July 2017 / Accepted: 26 July 2017 / Published: 29 July 2017
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Abstract
Climate change has a complex effect on snow at the regional scale. The change in snow patterns under climate change remains unknown for certain regions. Here, we used high spatiotemporal resolution snow-related variables simulated by a weather research and forecast model (WRF) including
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Climate change has a complex effect on snow at the regional scale. The change in snow patterns under climate change remains unknown for certain regions. Here, we used high spatiotemporal resolution snow-related variables simulated by a weather research and forecast model (WRF) including snowfall, snow water equivalent and snow depth along with fractional snow cover (FSC) data extracted from Moderate Resolution Imaging Spectroradiometer Data (MODIS)-Terra to evaluate the effects of climate change on snow over the Heihe River Basin (HRB), a typical inland river basin in arid northwestern China from 2000 to 2013. We utilized Empirical Orthogonal Function (EOF) analysis and Mann-Kendall/Theil-Sen trend analysis to evaluate the results. The results are as follows: (1) FSC, snow water equivalent, and snow depth across the entire HRB region decreased, especially at elevations over 4500 m; however, snowfall increased at mid-altitude ranges in the upstream area of the HRB. (2) Total snowfall also increased in the upstream area of the HRB; however, the number of snowfall days decreased. Therefore, the number of extreme snow events in the upstream area of the HRB may have increased. (3) Snowfall over the downstream area of the HRB decreased. Thus, ground stations, WRF simulations and remote sensing products can be used to effectively explore the effect of climate change on snow at the watershed scale. Full article
(This article belongs to the Special Issue Snow Remote Sensing)
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Open AccessArticle Hybrid Spectral Unmixing: Using Artificial Neural Networks for Linear/Non-Linear Switching
Remote Sens. 2017, 9(8), 775; doi:10.3390/rs9080775
Received: 1 June 2017 / Revised: 7 July 2017 / Accepted: 26 July 2017 / Published: 29 July 2017
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Abstract
Spectral unmixing is a key process in identifying spectral signature of materials and quantifying their spatial distribution over an image. The linear model is expected to provide acceptable results when two assumptions are satisfied: (1) The mixing process should occur at macroscopic level
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Spectral unmixing is a key process in identifying spectral signature of materials and quantifying their spatial distribution over an image. The linear model is expected to provide acceptable results when two assumptions are satisfied: (1) The mixing process should occur at macroscopic level and (2) Photons must interact with single material before reaching the sensor. However, these assumptions do not always hold and more complex nonlinear models are required. This study proposes a new hybrid method for switching between linear and nonlinear spectral unmixing of hyperspectral data based on artificial neural networks. The neural networks was trained with parameters within a window of the pixel under consideration. These parameters are computed to represent the diversity of the neighboring pixels and are based on the Spectral Angular Distance, Covariance and a non linearity parameter. The endmembers were extracted using Vertex Component Analysis while the abundances were estimated using the method identified by the neural networks (Vertex Component Analysis, Fully Constraint Least Square Method, Polynomial Post Nonlinear Mixing Model or Generalized Bilinear Model). Results show that the hybrid method performs better than each of the individual techniques with high overall accuracy, while the abundance estimation error is significantly lower than that obtained using the individual methods. Experiments on both synthetic dataset and real hyperspectral images demonstrated that the proposed hybrid switch method is efficient for solving spectral unmixing of hyperspectral images as compared to individual algorithms. Full article
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Open AccessArticle An Improved Spectrum Model for Sea Surface Radar Backscattering at L-Band
Remote Sens. 2017, 9(8), 776; doi:10.3390/rs9080776
Received: 22 June 2017 / Revised: 24 July 2017 / Accepted: 27 July 2017 / Published: 29 July 2017
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Abstract
L-band active microwave remote sensing is one of the most important technical methods of ocean environmental monitoring and dynamic parameter retrieval. Recently, a unique negative upwind-crosswind (NUC) asymmetry of L-band ocean backscatter over a low wind speed range was observed. To study the
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L-band active microwave remote sensing is one of the most important technical methods of ocean environmental monitoring and dynamic parameter retrieval. Recently, a unique negative upwind-crosswind (NUC) asymmetry of L-band ocean backscatter over a low wind speed range was observed. To study the directional features of L-band ocean surface backscattering, a new directional spectrum model is proposed and built into the advanced integral equation method (AIEM). This spectrum combines Apel’s omnidirectional spectrum and an improved empirical angular spreading function (ASF). The coefficients in the ASF were determined by the fitting of radar observations so that it provides a better description of wave directionality, especially over wavenumber ranges from short-gravity waves to capillary waves. Based on the improved spectrum and the AIEM scattering model, L-band NUC asymmetry at low wind speeds and positive upwind-crosswind (PUC) asymmetry at higher wind speeds are simulated successfully. The model outputs are validated against Aquarius/SAC-D observations under different incidence angles, azimuth angles and wind speed conditions. Full article
(This article belongs to the Special Issue Ocean Radar)
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Open AccessArticle A New Urban Index for Expressing Inner-City Patterns Based on MODIS LST and EVI Regulated DMSP/OLS NTL
Remote Sens. 2017, 9(8), 777; doi:10.3390/rs9080777
Received: 25 May 2017 / Revised: 18 July 2017 / Accepted: 27 July 2017 / Published: 29 July 2017
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Abstract
With the rapid pace of urban expansion, comprehensively understanding urban spatial patterns, built environments, green-spaces distributions, demographic distributions, and economic activities becomes more meaningful. Night Time Lights (NTL) images acquired through the Operational Linescan System of the US Defense Meteorological Satellite Program (DMSP/OLS
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With the rapid pace of urban expansion, comprehensively understanding urban spatial patterns, built environments, green-spaces distributions, demographic distributions, and economic activities becomes more meaningful. Night Time Lights (NTL) images acquired through the Operational Linescan System of the US Defense Meteorological Satellite Program (DMSP/OLS NTL) have long been utilized to monitor urban areas and their expansion characteristics since this system detects variation in NTL emissions. However, the pixel saturation phenomenon leads to a serious limitation in mapping luminance variations in urban zones with nighttime illumination levels that approach or exceed the pixel saturation limits of OLS sensors. Consequently, we propose an NTL-based city index that utilizes the Moderate-resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST) and Enhanced Vegetation Index (EVI) images to regulate and compensate for desaturation on NTL images acquired from corresponding urban areas. The regulated results achieve good performance in differentiating central business districts (CBDs), airports, and urban green spaces. Consequently, these derived imageries could effectively convey the structural details of urban cores. In addition, compared with the Vegetation Adjusted NTL Urban Index (VANUI), LST-and-EVI-regulated-NTL-city index (LERNCI) reveals superior capability in delineating the spatial structures of selected metropolis areas across the world, especially in the large cities of developing countries. The currently available results indicate that LERNCI corresponds better to city spatial patterns. Moreover, LERNCI displays a remarkably better “goodness-of-fit” correspondence with both the Version 1 Nighttime Visible Infrared Imaging Radiometer Suite Day/Night Band Composite (NPP/VIIRS DNB) data and the WorldPop population-density data compared with the VANUI imageries. Thus, LERNCI can act as a helpful indicator for differentiating and classifying regional economic activities, population aggregations, and energy-consumption and city-expansion patterns. LERNCI can also serve as a valuable auxiliary reference for decision-making processes that concern subjects such as urban planning and easing the central functions of metropolis. Full article
(This article belongs to the Special Issue Recent Advances in Remote Sensing with Nighttime Lights)
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Open AccessArticle Synergistic Use of Remote Sensing and Modeling to Assess an Anomalously High Chlorophyll-a Event during Summer 2015 in the South Central Red Sea
Remote Sens. 2017, 9(8), 778; doi:10.3390/rs9080778
Received: 19 May 2017 / Revised: 24 July 2017 / Accepted: 27 July 2017 / Published: 29 July 2017
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Abstract
An anomalously high chlorophyll-a (Chl-a) event (>2 mg/m3) during June 2015 in the South Central Red Sea (17.5° to 22°N, 37° to 42°E) was observed using Moderate Resolution Imaging Spectroradiometer (MODIS) data from the Terra and Aqua satellite
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An anomalously high chlorophyll-a (Chl-a) event (>2 mg/m3) during June 2015 in the South Central Red Sea (17.5° to 22°N, 37° to 42°E) was observed using Moderate Resolution Imaging Spectroradiometer (MODIS) data from the Terra and Aqua satellite platforms. This differs from the low Chl-a values (<0.5 mg/m3) usually encountered over the same region during summertime. To assess this anomaly and possible causes, we used a wide range of oceanographical and meteorological datasets, including Chl-a concentrations, sea surface temperature (SST), sea surface height (SSH), mixed layer depth (MLD), ocean current velocity and aerosol optical depth (AOD) obtained from different sensors and models. Findings confirmed this anomalous behavior in the spatial domain using Hovmöller data analysis techniques, while a time series analysis addressed monthly and daily variability. Our analysis suggests that a combination of factors controlling nutrient supply contributed to the anomalous phytoplankton growth. These factors include horizontal transfer of upwelling water through eddy circulation and possible mineral fertilization from atmospheric dust deposition. Coral reefs might have provided extra nutrient supply, yet this is out of the scope of our analysis. We thought that dust deposition from a coastal dust jet event in late June, coinciding with the phytoplankton blooms in the area under investigation, might have also contributed as shown by our AOD findings. However, a lag cross correlation showed a two- month lag between strong dust outbreak and the high Chl-a anomaly. The high Chl-a concentration at the edge of the eddy emphasizes the importance of horizontal advection in fertilizing oligotrophic (nutrient poor) Red Sea waters. Full article
(This article belongs to the Section Ocean Remote Sensing)
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Open AccessArticle Estimating Daily Reference Evapotranspiration in a Semi-Arid Region Using Remote Sensing Data
Remote Sens. 2017, 9(8), 779; doi:10.3390/rs9080779
Received: 19 May 2017 / Revised: 26 July 2017 / Accepted: 27 July 2017 / Published: 29 July 2017
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Abstract
Estimating daily evapotranspiration is challenging when ground observation data are not available or scarce. Remote sensing can be used to estimate the meteorological data necessary for calculating reference evapotranspiration ETₒ. Here, we assessed the accuracy of daily ETₒ estimates derived from remote
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Estimating daily evapotranspiration is challenging when ground observation data are not available or scarce. Remote sensing can be used to estimate the meteorological data necessary for calculating reference evapotranspiration ETₒ. Here, we assessed the accuracy of daily ETₒ estimates derived from remote sensing (ETₒ-RS) compared with those derived from four ground-based stations (ETₒ-G) in Kurdistan (Iraq) over the period 2010–2014. Near surface air temperature, relative humidity and cloud cover fraction were derived from the Atmospheric Infrared Sounder/Advanced Microwave Sounding Unit (AIRS/AMSU), and wind speed at 10 m height from MERRA (Modern-Era Retrospective Analysis for Research and Application). Four methods were used to estimate ETₒ: Hargreaves–Samani (HS), Jensen–Haise (JH), McGuinness–Bordne (MB) and the FAO Penman Monteith equation (PM). ETₒ-G (PM) was adopted as the main benchmark. HS underestimated ETₒ by 2%–3% (R2 = 0.86 to 0.90; RMSE = 0.95 to 1.2 mm day−1 at different stations). JH and MB overestimated ETₒ by 8% to 40% (R2= 0.85 to 0.92; RMSE from 1.18 to 2.18 mm day−1). The annual average values of ETₒ estimated using RS data and ground-based data were similar to one another reflecting low bias in daily estimates. They ranged between 1153 and 1893 mm year−1 for ETₒ-G and between 1176 and 1859 mm year−1 for ETₒ-RS for the different stations. Our results suggest that ETₒ-RS (HS) can yield accurate and unbiased ETₒ estimates for semi-arid regions which can be usefully employed in water resources management. Full article
(This article belongs to the Special Issue Remote Sensing of Arid/Semiarid Lands)
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Open AccessArticle A Robust Inversion Algorithm for Surface Leaf and Soil Temperatures Using the Vegetation Clumping Index
Remote Sens. 2017, 9(8), 780; doi:10.3390/rs9080780
Received: 12 June 2017 / Revised: 13 July 2017 / Accepted: 28 July 2017 / Published: 30 July 2017
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Abstract
The inversion of land surface component temperatures is an essential source of information for mapping heat fluxes and the angular normalization of thermal infrared (TIR) observations. Leaf and soil temperatures can be retrieved using multiple-view-angle TIR observations. In a satellite-scale pixel, the clumping
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The inversion of land surface component temperatures is an essential source of information for mapping heat fluxes and the angular normalization of thermal infrared (TIR) observations. Leaf and soil temperatures can be retrieved using multiple-view-angle TIR observations. In a satellite-scale pixel, the clumping effect of vegetation is usually present, but it is not completely considered during the inversion process. Therefore, we introduced a simple inversion procedure that uses gap frequency with a clumping index (GCI) for leaf and soil temperatures over both crop and forest canopies. Simulated datasets corresponding to turbid vegetation, regularly planted crops and randomly distributed forest were generated using a radiosity model and were used to test the proposed inversion algorithm. The results indicated that the GCI algorithm performed well for both crop and forest canopies, with root mean squared errors of less than 1.0 °C against simulated values. The proposed inversion algorithm was also validated using measured datasets over orchard, maize and wheat canopies. Similar results were achieved, demonstrating that using the clumping index can improve inversion results. In all evaluations, we recommend using the GCI algorithm as a foundation for future satellite-based applications due to its straightforward form and robust performance for both crop and forest canopies using the vegetation clumping index. Full article
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Open AccessArticle Reconstructing Satellite-Based Monthly Precipitation over Northeast China Using Machine Learning Algorithms
Remote Sens. 2017, 9(8), 781; doi:10.3390/rs9080781
Received: 31 May 2017 / Revised: 12 July 2017 / Accepted: 26 July 2017 / Published: 30 July 2017
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Abstract
Attaining accurate precipitation data is critical to understanding land surface processes and global climate change. The development of satellite sensors and remote sensing technology has resulted in multi-source precipitation datasets that provide reliable estimates of precipitation over un-gauged areas. However, gaps exist over
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Attaining accurate precipitation data is critical to understanding land surface processes and global climate change. The development of satellite sensors and remote sensing technology has resulted in multi-source precipitation datasets that provide reliable estimates of precipitation over un-gauged areas. However, gaps exist over high latitude areas due to the limited spatial extent of several satellite-based precipitation products. In this study, we propose an approach for the reconstruction of the Tropical Rainfall Measuring Mission (TRMM) 3B43 monthly precipitation data over Northeast China based on the interaction between precipitation and surface environment. Two machine learning algorithms, support vector machine (SVM) and random forests (RF), are implemented to detect possible relationships between precipitation and normalized difference vegetation index (NDVI), land surface temperature (LST), and digital elevation model (DEM). The relationships between precipitation and geographical location variations based on longitude and latitude are also considered in the reconstruction model. The reconstruction of monthly precipitation in the study area is conducted in two spatial resolutions (25 km and 1 km). The validation is performed using in-situ observations from eight meteorological stations within the study area. The results show that the RF algorithm is robust and not sensitive to the choice of parameters, while the training accuracy of the SVM algorithm has relatively large fluctuations depending on the parameter settings and month. The precipitation data reconstructed with RF show strong correlation with in situ observations at each station and are more accurate than that obtained using the SVM algorithm. In general, the accuracy of the estimated precipitation at 1 km resolution is slightly lower than that of data at 25 km resolution. The estimation errors are positively related to the average precipitation. Full article
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Open AccessArticle Discriminative Feature Metric Learning in the Affinity Propagation Model for Band Selection in Hyperspectral Images
Remote Sens. 2017, 9(8), 782; doi:10.3390/rs9080782
Received: 26 May 2017 / Revised: 25 July 2017 / Accepted: 27 July 2017 / Published: 30 July 2017
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Abstract
Traditional supervised band selection (BS) methods mainly consider reducing the spectral redundancy to improve hyperspectral imagery (HSI) classification with class labels and pairwise constraints. A key observation is that pixels spatially close to each other in HSI have probably the same signature, while
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Traditional supervised band selection (BS) methods mainly consider reducing the spectral redundancy to improve hyperspectral imagery (HSI) classification with class labels and pairwise constraints. A key observation is that pixels spatially close to each other in HSI have probably the same signature, while pixels further away from each other in the space have a high probability of belonging to different classes. In this paper, we propose a novel discriminative feature metric-based affinity propagation (DFM-AP) technique where the spectral and the spatial relationships among pixels are constructed by a new type of discriminative constraint. This discriminative constraint involves chunklet and discriminative information, which are introduced into the BS process. The chunklet information allows for grouping of spectrally-close and spatially-close pixels together without requiring explicit knowledge of their class labels, while discriminative information provides important separability information. A discriminative feature metric (DFM) is proposed with the discriminative constraints modeled in terms of an optimal criterion for identifying an efficient distance metric learning method, which involves discriminative component analysis (DCA). Following this, the representative subset of bands can be identified by means of an exemplar-based clustering algorithm, which is also known as the process of affinity propagation. Experimental results show that the proposed approach yields a better performance in comparison with several representative class label and pairwise constraint-based BS algorithms. The proposed DFM-AP improves the classification performance with discriminative constraints by selecting highly discriminative bands with low redundancy. Full article
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Open AccessArticle A Novel Spaceborne Sliding Spotlight Range Sweep Synthetic Aperture Radar: System and Imaging
Remote Sens. 2017, 9(8), 783; doi:10.3390/rs9080783
Received: 10 May 2017 / Revised: 18 July 2017 / Accepted: 26 July 2017 / Published: 31 July 2017
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Abstract
In this paper, a new Spaceborne Sliding Spotlight Range Sweep Synthetic Aperture Radar (SSS-RSSAR) is proposed to generate a high-resolution image of a Region of Interest (ROI) tilted with respect to the satellite track. Comparing to the traditional Spaceborne Sliding Spotlight Synthetic Aperture
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In this paper, a new Spaceborne Sliding Spotlight Range Sweep Synthetic Aperture Radar (SSS-RSSAR) is proposed to generate a high-resolution image of a Region of Interest (ROI) tilted with respect to the satellite track. Comparing to the traditional Spaceborne Sliding Spotlight Synthetic Aperture Radar (SSS-SAR), the SSS-RSSAR is superior in contributing to less data amount, lighter computational load and hence higher observation efficiency. Unlike the Spaceborne Stripmap Range Sweep Synthetic Aperture Radar (SS-RSSAR) proposed in a previous paper, the SSS-RSSAR not only continuously sweeps the beam in range for the ROI tracking, but also in azimuth to enlarge the synthetic aperture for an improved azimuth resolution. Two aspects of the SSS-RSSAR are focused: system and imaging. For the system part, a Continuous Varying Pulse Interval (CVPI) technique is proposed to avoid the transmission blockage problem by non-uniformly adjusting the pulse intervals based on the geometry. For the imaging part, a Modified Polar Format Algorithm (MPFA) is proposed to accommodate the original polar format algorithm to the echo received with the CVPI technique. Moreover, an integrate system parameter design flow for the SSS-RSSAR is also suggested. The presented approach is evaluated by exploiting the point target simulations. Full article
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Open AccessArticle Agricultural Expansion and Intensification in the Foothills of Mount Kenya: A Landscape Perspective
Remote Sens. 2017, 9(8), 784; doi:10.3390/rs9080784
Received: 3 July 2017 / Revised: 27 July 2017 / Accepted: 28 July 2017 / Published: 31 July 2017
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Abstract
This study spatially assesses, quantifies, and visualizes the agricultural expansion and land use intensification in the northwestern foothills of Mount Kenya over the last 30 years: processes triggered by population growth, and, more recently, by large-scale commercial investments. We made use of Google
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This study spatially assesses, quantifies, and visualizes the agricultural expansion and land use intensification in the northwestern foothills of Mount Kenya over the last 30 years: processes triggered by population growth, and, more recently, by large-scale commercial investments. We made use of Google Earth Engine to access the USGS Landsat data archive and to generate cloud-free seasonal composites. These enabled us to accurately differentiate between rainfed and irrigated cropland, which was important for assessing agricultural intensification. We developed three land cover and land use classifications using the random forest classifier, and assessed land cover and land use change by creating cross-tabulation matrices for the intervals from 1987 to 2002, 2002 to 2016, and 1987 to 2016 and calculating the net change. We then applied a landscape mosaic approach to each classification to identify landscape types categorized by land use intensity. We discuss the impacts of landscape changes on natural habitats, biodiversity, and water. Kappa accuracies for the three classifications lay between 78.3% and 82.1%. Our study confirms that rainfed and irrigated cropland expanded at the expense of natural habitats, including protected areas. Agricultural expansion took place mainly in the 1980s and 1990s, whereas agricultural intensification largely happened after 2000. Since then, not only large-scale producers, but also many smallholders have begun to practice irrigated farming. The spatial pattern of agricultural expansion and intensification in the study area is defined by water availability. Agricultural intensification and the expansion of horticulture agribusinesses increase pressure on water. Furthermore, the observed changes have heightened pressure on pasture and idle land due to the decrease in natural and agropastoral landscapes. Conflicts between pastoralists, smallholder farmers, large-scale ranches, and wildlife might further increase, particularly during the dry seasons and in years of extreme drought. Full article
(This article belongs to the Special Issue Monitoring Agricultural Land-Use Change and Land-Use Intensity)
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Open AccessArticle Autonomous Collection of Forest Field Reference—The Outlook and a First Step with UAV Laser Scanning
Remote Sens. 2017, 9(8), 785; doi:10.3390/rs9080785
Received: 15 June 2017 / Revised: 14 July 2017 / Accepted: 19 July 2017 / Published: 31 July 2017
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Abstract
A compact solution for the accurate and automated collection of field data in forests has long been anticipated, and tremendous efforts have been made by applying various remote sensing technologies. The employment of advanced techniques, such as the smartphone-based relascope, terrestrial and mobile
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A compact solution for the accurate and automated collection of field data in forests has long been anticipated, and tremendous efforts have been made by applying various remote sensing technologies. The employment of advanced techniques, such as the smartphone-based relascope, terrestrial and mobile photogrammetry, and laser scanning, have led to steady progress, thus steering their applications to a practical stage. However, all recent strategies require human operation for data acquisition, either to place the instrument on site (e.g., terrestrial laser scanning, TLS) or to carry the instrument by an operator (e.g., personal laser scanning, PLS), which remained laborious and expensive. In this paper, a new concept of autonomous forest field investigation is proposed, which includes data collection above and inside the forest canopy by integrating an unmanned aircraft vehicle (UAV) with autonomous driving. As a first step towards realizing this concept, the feasibility of automated tree-level field measurements from a mini-UAV laser scanning system is evaluated. A “low-cost” Velodyne Puck LITE laser scanner is applied for the test. It is revealed that, with the above canopy flight data, the detection rate was 100% for isolated and dominant trees. The accuracy of direct measurements on the diameter at breast height (DBH) from the point cloud is between 5.5 and 6.8 cm due to the system and the methodological error propagation. The estimation of DBH from point cloud metrics, on the other hand, showed an accuracy of 2.6 cm, which is comparable to the accuracies obtained with terrestrial surveys using mobile laser scanning (MLS), TLS or photogrammetric point clouds. The estimation of basal area, stem volume and biomass of individual trees could be obtained with less than 20% RMSE, which is adequate for field reference measurements at tree level. Such results indicate that the concept of UAV laser scanning-based automated tree-level field reference collection can be feasible, even though the whole topic requires further research. Full article
(This article belongs to the Section Forest Remote Sensing)
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Open AccessArticle Transferability of Economy Estimation Based on DMSP/OLS Night-Time Light
Remote Sens. 2017, 9(8), 786; doi:10.3390/rs9080786
Received: 29 May 2017 / Revised: 14 July 2017 / Accepted: 28 July 2017 / Published: 31 July 2017
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Abstract
Despite the fact that economic data are of great significance in the assessment of human socioeconomic development, the application of this data has been hindered partly due to the unreliable and inefficient economic censuses conducted in developing countries. The night-time light (NTL) imagery
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Despite the fact that economic data are of great significance in the assessment of human socioeconomic development, the application of this data has been hindered partly due to the unreliable and inefficient economic censuses conducted in developing countries. The night-time light (NTL) imagery from the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP/OLS) provides one of the most important ways to evaluate an economy with low cost and high efficiency. However, little research has addressed the transferability of the estimation across years. Based on the entire DN series from 0 to 63 of NTL dat