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Remote Sens., Volume 8, Issue 2 (February 2016)

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Editorial

Jump to: Research, Review, Other

Open AccessEditorial Preface: Remote Sensing of Water Resources
Remote Sens. 2016, 8(2), 115; doi:10.3390/rs8020115
Received: 1 February 2016 / Accepted: 2 February 2016 / Published: 4 February 2016
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Abstract
The Special Issue (SI) on “Remote Sensing of Water Resources” presents a diverse range of papers studying remote sensing tools, methods, and models to better monitor water resources which include inland, coastal, and open ocean waters. The SI is comprised of fifteen articles
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The Special Issue (SI) on “Remote Sensing of Water Resources” presents a diverse range of papers studying remote sensing tools, methods, and models to better monitor water resources which include inland, coastal, and open ocean waters. The SI is comprised of fifteen articles on widely ranging research topics related to water bodies. This preface summarizes each article published in the SI. Full article
(This article belongs to the Special Issue Remote Sensing of Water Resources)
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Open AccessEditorial Preface: Recent Advances in Remote Sensing for Crop Growth Monitoring
Remote Sens. 2016, 8(2), 116; doi:10.3390/rs8020116
Received: 1 February 2016 / Accepted: 2 February 2016 / Published: 4 February 2016
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Abstract
This Special Issue gathers sixteen papers focusing on applying various remote sensing techniques to crop growth monitoring. The studies span observations from multiple scales, a combination of model simulations and experimental measurements, and a range of topics on crop monitoring and mapping. This
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This Special Issue gathers sixteen papers focusing on applying various remote sensing techniques to crop growth monitoring. The studies span observations from multiple scales, a combination of model simulations and experimental measurements, and a range of topics on crop monitoring and mapping. This preface provides a brief overview of the contributed papers. Full article
Open AccessEditorial “Use of 3D Point Clouds in Geohazards” Special Issue: Current Challenges and Future Trends
Remote Sens. 2016, 8(2), 130; doi:10.3390/rs8020130
Received: 28 January 2016 / Accepted: 29 January 2016 / Published: 6 February 2016
Cited by 8 | PDF Full-text (471 KB) | HTML Full-text | XML Full-text
Abstract
The fast proliferation of new satellite, aerial and terrestrial remote sensing techniques has undoubtedly provided new technological and scientific opportunities to society during the last few decades. [...] Full article
(This article belongs to the Special Issue Use of LiDAR and 3D point clouds in Geohazards)
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Research

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Open AccessArticle Monitoring and Assessing the 2012 Drought in the Great Plains: Analyzing Satellite-Retrieved Solar-Induced Chlorophyll Fluorescence, Drought Indices, and Gross Primary Production
Remote Sens. 2016, 8(2), 61; doi:10.3390/rs8020061
Received: 19 November 2015 / Revised: 5 January 2016 / Accepted: 8 January 2016 / Published: 27 January 2016
Cited by 4 | PDF Full-text (2754 KB) | HTML Full-text | XML Full-text
Abstract
We examined the relationship between satellite measurements of solar-induced chlorophyll fluorescence (SIF) and several meteorological drought indices, including the multi-time-scale standard precipitation index (SPI) and the Palmer drought severity index (PDSI), to evaluate the potential of using SIF to monitor and assess drought.
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We examined the relationship between satellite measurements of solar-induced chlorophyll fluorescence (SIF) and several meteorological drought indices, including the multi-time-scale standard precipitation index (SPI) and the Palmer drought severity index (PDSI), to evaluate the potential of using SIF to monitor and assess drought. We found significant positive relationships between SIF and drought indices during the growing season (from June to September). SIF was found to be more sensitive to short-term SPIs (one or two months) and less sensitive to long-term SPI (three months) than were the normalized difference vegetation index (NDVI) or the normalized difference water index (NDWI). Significant correlations were found between SIF and PDSI during the growing season for the Great Plains. We found good consistency between SIF and flux-estimated gross primary production (GPP) for the years studied, and synchronous declines of SIF and GPP in an extreme drought year (2012). We used SIF to monitor and assess the drought that occurred in the Great Plains during the summer of 2012, and found that although a meteorological drought was experienced throughout the Great Plains from June to September, the western area experienced more agricultural drought than the eastern area. Meanwhile, SIF declined more significantly than NDVI during the peak growing season. Yet for senescence, during which time the reduction of NDVI still went on, the reduction of SIF was eased. Our work provides an alternative to traditional reflectance-based vegetation or drought indices for monitoring and assessing agricultural drought. Full article
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Open AccessArticle A Linear Feature-Based Approach for the Registration of Unmanned Aerial Vehicle Remotely-Sensed Images and Airborne LiDAR Data
Remote Sens. 2016, 8(2), 82; doi:10.3390/rs8020082
Received: 6 October 2015 / Revised: 9 December 2015 / Accepted: 11 January 2016 / Published: 25 January 2016
Cited by 5 | PDF Full-text (2632 KB) | HTML Full-text | XML Full-text
Abstract
Compared with traditional manned airborne photogrammetry, unmanned aerial vehicle remote sensing (UAVRS) has the advantages of lower cost and higher flexibility in data acquisition. It has, therefore, found various applications in fields such as three-dimensional (3D) mapping, emergency management, and so on. However,
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Compared with traditional manned airborne photogrammetry, unmanned aerial vehicle remote sensing (UAVRS) has the advantages of lower cost and higher flexibility in data acquisition. It has, therefore, found various applications in fields such as three-dimensional (3D) mapping, emergency management, and so on. However, due to the instability of the UAVRS platforms and the low accuracy of the onboard exterior orientation (EO) observations, the use of direct georeferencing image data leads to large location errors. Light detection and ranging (LiDAR) data, which is highly accurate 3D information, is treated as a complementary data source to the optical images. This paper presents a semi-automatic approach for the registration of UAVRS images and airborne LiDAR data based on linear control features. The presented approach consists of three main components, as follows. (1) Buildings are first separated from the point cloud by the integrated use of height and size filtering and RANdom SAmple Consensus (RANSAC) plane fitting, and the 3D line segments of the building ridges and boundaries are semi-automatically extracted through plane intersection and boundary regularization with manual selections; (2) the 3D line segments are projected to the image space using the initial EO parameters to obtain the approximate locations, and all the corresponding 2D line segments are semi-automatically extracted from the UAVRS images. Meanwhile, the tie points of the UAVRS images are generated using a Förstner operator and least-squares image matching; and (3) by use of the equations derived from the coplanarity constraints of the linear control features and the colinear constraints of the tie points, block bundle adjustment is carried out to update the EO parameters of the UAVRS images in the coordinate framework of the LiDAR data, achieving the co-registration of the two datasets. Experiments were performed to demonstrate the validity and effectiveness of the presented method, and a comparison with the traditional registration method based on LiDAR intensity images showed that the presented method is more accurate, and a sub-pixel accuracy level can be achieved. Full article
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Open AccessArticle Improved Pansharpening with Un-Mixing of Mixed MS Sub-Pixels near Boundaries between Vegetation and Non-Vegetation Objects
Remote Sens. 2016, 8(2), 83; doi:10.3390/rs8020083
Received: 13 October 2015 / Revised: 14 January 2016 / Accepted: 16 January 2016 / Published: 22 January 2016
Cited by 1 | PDF Full-text (7009 KB) | HTML Full-text | XML Full-text
Abstract
Pansharpening is an important technique that produces high spatial resolution multispectral (MS) images by fusing low spatial resolution MS images and high spatial resolution panchromatic (PAN) images of the same area. Although numerous successful image fusion algorithms have been proposed in the last
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Pansharpening is an important technique that produces high spatial resolution multispectral (MS) images by fusing low spatial resolution MS images and high spatial resolution panchromatic (PAN) images of the same area. Although numerous successful image fusion algorithms have been proposed in the last few decades to reduce the spectral distortions in fused images, few of these take into account the spectral distortions caused by mixed MS sub-pixels (MSPs). Typically, the fused versions of MSPs remain mixed, although some of the MSPs correspond to pure PAN pixels. Due to the significant spectral differences between vegetation and non-vegetation (VNV) objects, the fused versions of MSPs near VNV boundaries cause blurred VNV boundaries and significant spectral distortions in the fused images. In order to reduce the spectral distortions, an improved version of the haze- and ratio-based fusion method is proposed to realize the spectral un-mixing of MSPs near VNV boundaries. In this method, the MSPs near VNV boundaries are identified first. The identified MSPs are then defined as either pure vegetation or non-vegetation pixels according to the categories of the corresponding PAN pixels. Experiments on WorldView-2 and IKONOS images of urban areas using the proposed method yielded fused images with significantly clearer VNV boundaries and smaller spectral distortions than several other currently-used image fusion methods. Full article
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Open AccessArticle Assessment of S-NPP VIIRS On-Orbit Radiometric Calibration and Performance
Remote Sens. 2016, 8(2), 84; doi:10.3390/rs8020084
Received: 25 November 2015 / Revised: 6 January 2016 / Accepted: 16 January 2016 / Published: 23 January 2016
Cited by 4 | PDF Full-text (5835 KB) | HTML Full-text | XML Full-text
Abstract
The VIIRS instrument on board the S-NPP spacecraft has successfully operated for more than four years since its launch in October 2011. Many VIIRS environmental data records (EDR) have been continuously generated from its sensor data records (SDR) with improved quality, enabling a
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The VIIRS instrument on board the S-NPP spacecraft has successfully operated for more than four years since its launch in October 2011. Many VIIRS environmental data records (EDR) have been continuously generated from its sensor data records (SDR) with improved quality, enabling a wide range of applications in support of users in both the operational and research communities. This paper provides a brief review of sensor on-orbit calibration methodologies for both the reflective solar bands (RSB) and the thermal emissive bands (TEB) and an overall assessment of their on-orbit radiometric performance using measurements from instrument on-board calibrators (OBC), as well as regularly scheduled lunar observations. It describes and illustrates changes made and to be made for calibration and data quality improvements. Throughout the mission, all of the OBC have continued to operate and function normally, allowing critical calibration parameters used in the data production systems to be derived and updated. The temperatures of the on-board blackbody (BB) and the cold focal plane assemblies are controlled with excellent stability. Despite large optical throughput degradation discovered shortly after launch in several near- and short-wave infrared spectral bands and strong wavelength-dependent solar diffuser degradation, the VIIRS overall performance has continued to meet its design requirements. Also discussed in this paper are challenging issues identified and efforts to be made to further enhance the sensor calibration and characterization, thereby maintaining or improving data quality. Full article
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Open AccessArticle Characterization of Available Light for Seagrass and Patch Reef Productivity in Sugarloaf Key, Lower Florida Keys
Remote Sens. 2016, 8(2), 86; doi:10.3390/rs8020086
Received: 16 September 2015 / Revised: 4 January 2016 / Accepted: 18 January 2016 / Published: 23 January 2016
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Abstract
Light availability is an important factor driving primary productivity in benthic ecosystems, but in situ and remote sensing measurements of light quality are limited for coral reefs and seagrass beds. We evaluated the productivity responses of a patch reef and a seagrass site
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Light availability is an important factor driving primary productivity in benthic ecosystems, but in situ and remote sensing measurements of light quality are limited for coral reefs and seagrass beds. We evaluated the productivity responses of a patch reef and a seagrass site in the Lower Florida Keys to ambient light availability and spectral quality. In situ optical properties were characterized utilizing moored and water column bio-optical and hydrographic measurements. Net ecosystem productivity (NEP) was also estimated for these study sites using benthic productivity chambers. Our results show higher spectral light attenuation and absorption, and lower irradiance during low tide in the patch reef, tracking the influx of materials from shallower coastal areas. In contrast, the intrusion of clearer surface Atlantic Ocean water caused lower values of spectral attenuation and absorption, and higher irradiance in the patch reef during high tide. Storms during the studied period, with winds >10 m·s−1, caused higher spectral attenuation values. A spatial gradient of NEP was observed, from high productivity in the shallow seagrass area, to lower productivity in deeper patch reefs. The highest daytime NEP was observed in the seagrass, with values of almost 0.4 g·O2·m−2·h−1. Productivity at the patch reef area was lower in May than during October 2012 (mean = 0.137 and 0.177 g·O2·m−2·h−1, respectively). Higher photosynthetic active radiation (PAR) levels measured above water and lower light attenuation in the red region of the visible spectrum (~666 to ~699 nm) had a positive correlation with NEP. Our results indicate that changes in light availability and quality by suspended or resuspended particles limit benthic productivity in the Florida Keys. Full article
(This article belongs to the Special Issue Remote Sensing for Coral Reef Monitoring)
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Open AccessArticle Organismic-Scale Remote Sensing of Canopy Foliar Traits in Lowland Tropical Forests
Remote Sens. 2016, 8(2), 87; doi:10.3390/rs8020087
Received: 5 December 2015 / Revised: 9 January 2016 / Accepted: 14 January 2016 / Published: 23 January 2016
Cited by 7 | PDF Full-text (5255 KB) | HTML Full-text | XML Full-text
Abstract
Airborne high fidelity imaging spectroscopy (HiFIS) holds great promise for bridging the gap between field studies of functional diversity, which are spatially limited, and satellite detection of ecosystem properties, which lacks resolution to understand within landscape dynamics. We use Carnegie Airborne Observatory HiFIS
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Airborne high fidelity imaging spectroscopy (HiFIS) holds great promise for bridging the gap between field studies of functional diversity, which are spatially limited, and satellite detection of ecosystem properties, which lacks resolution to understand within landscape dynamics. We use Carnegie Airborne Observatory HiFIS data combined with field collected foliar trait data to develop quantitative prediction models of foliar traits at the tree-crown level across over 1000 ha of humid tropical forest. We predicted foliar leaf mass per area (LMA) as well as foliar concentrations of nitrogen, phosphorus, calcium, magnesium and potassium for canopy emergent trees (R2: 0.45–0.67, relative RMSE: 11%–14%). Correlations between remotely sensed model coefficients for these foliar traits are similar to those found in laboratory studies, suggesting that the detection of these mineral nutrients is possible through their biochemical stoichiometry. Maps derived from HiFIS provide quantitative foliar trait information across a tropical forest landscape at fine spatial resolution, and along environmental gradients. Multi-nutrient maps implemented at the fine organismic scale will subsequently provide new insight to the functional biogeography and biological diversity of tropical forest ecosystems. Full article
(This article belongs to the Special Issue Remote Sensing of Biodiversity)
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Open AccessFeature PaperArticle Mapping Complex Urban Land Cover from Spaceborne Imagery: The Influence of Spatial Resolution, Spectral Band Set and Classification Approach
Remote Sens. 2016, 8(2), 88; doi:10.3390/rs8020088
Received: 30 September 2015 / Revised: 23 December 2015 / Accepted: 19 January 2016 / Published: 23 January 2016
Cited by 7 | PDF Full-text (3298 KB) | HTML Full-text | XML Full-text
Abstract
Detailed land cover information is valuable for mapping complex urban environments. Recent enhancements to satellite sensor technology promise fit-for-purpose data, particularly when processed using contemporary classification approaches. We evaluate this promise by comparing the influence of spatial resolution, spectral band set and classification
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Detailed land cover information is valuable for mapping complex urban environments. Recent enhancements to satellite sensor technology promise fit-for-purpose data, particularly when processed using contemporary classification approaches. We evaluate this promise by comparing the influence of spatial resolution, spectral band set and classification approach for mapping detailed urban land cover in Nottingham, UK. A WorldView-2 image provides the basis for a set of 12 images with varying spatial and spectral characteristics, and these are classified using three different approaches (maximum likelihood (ML), support vector machine (SVM) and object-based image analysis (OBIA)) to yield 36 output land cover maps. Classification accuracy is evaluated independently and McNemar tests are conducted between all paired outputs (630 pairs in total) to determine which classifications are significantly different. Overall accuracy varied between 35% for ML classification of 30 m spatial resolution, 4-band imagery and 91% for OBIA classification of 2 m spatial resolution, 8-band imagery. The results demonstrate that spatial resolution is clearly the most influential factor when mapping complex urban environments, and modern “very high resolution” or VHR sensors offer great advantage here. However, the advanced spectral capabilities provided by some recent sensors, coupled with contemporary classification approaches (especially SVMs and OBIA), can also lead to significant gains in mapping accuracy. Ongoing development in instrumentation and methodology offer huge potential here and imply that urban mapping opportunities will continue to grow. Full article
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Open AccessArticle Multi-View Stereo Matching Based on Self-Adaptive Patch and Image Grouping for Multiple Unmanned Aerial Vehicle Imagery
Remote Sens. 2016, 8(2), 89; doi:10.3390/rs8020089
Received: 26 November 2015 / Revised: 13 January 2016 / Accepted: 18 January 2016 / Published: 23 January 2016
Cited by 3 | PDF Full-text (19213 KB) | HTML Full-text | XML Full-text
Abstract
Robust and rapid image dense matching is the key to large-scale three-dimensional (3D) reconstruction for multiple Unmanned Aerial Vehicle (UAV) images. However, the following problems must be addressed: (1) the amount of UAV image data is very large, but ordinary computer memory is
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Robust and rapid image dense matching is the key to large-scale three-dimensional (3D) reconstruction for multiple Unmanned Aerial Vehicle (UAV) images. However, the following problems must be addressed: (1) the amount of UAV image data is very large, but ordinary computer memory is limited; (2) the patch-based multi-view stereo-matching algorithm (PMVS) does not work well for narrow-baseline cases, and its computing efficiency is relatively low, and thus, it is difficult to meet the UAV photogrammetry’s requirements of convenience and speed. This paper proposes an Image-grouping and Self-Adaptive Patch-based Multi-View Stereo-matching algorithm (IG-SAPMVS) for multiple UAV imagery. First, multiple UAV images were grouped reasonably by a certain grouping strategy. Second, image dense matching was performed in each group and included three processes. (1) Initial feature-matching consists of two steps: The first was feature point detection and matching, which made some improvements to PMVS, according to the characteristics of UAV imagery. The second was edge point detection and matching, which aimed to control matching propagation during the expansion process; (2) The second process was matching propagation based on the self-adaptive patch. Initial patches were built that were centered by the obtained 3D seed points, and these were repeatedly expanded. The patches were prevented from crossing the discontinuous terrain by using the edge constraint, and the extent size and shape of the patches could automatically adapt to the terrain relief; (3) The third process was filtering the erroneous matching points. Taken the overlap problem between each group of 3D dense point clouds into account, the matching results were merged into a whole. Experiments conducted on three sets of typical UAV images with different texture features demonstrate that the proposed algorithm can address a large amount of UAV image data almost without computer memory restrictions, and the processing efficiency is significantly better than that of the PMVS algorithm and the matching accuracy is equal to that of the state-of-the-art PMVS algorithm. Full article
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Open AccessArticle Validation and Spatiotemporal Analysis of CERES Surface Net Radiation Product
Remote Sens. 2016, 8(2), 90; doi:10.3390/rs8020090
Received: 12 October 2015 / Revised: 19 December 2015 / Accepted: 18 January 2016 / Published: 23 January 2016
Cited by 1 | PDF Full-text (9009 KB) | HTML Full-text | XML Full-text
Abstract
The Clouds and the Earth’s Radiant Energy System (CERES) generates one of the few global satellite radiation products. The CERES ARM Validation Experiment (CAVE) has been providing long-term in situ observations for the validation of the CERES products. However, the number of these
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The Clouds and the Earth’s Radiant Energy System (CERES) generates one of the few global satellite radiation products. The CERES ARM Validation Experiment (CAVE) has been providing long-term in situ observations for the validation of the CERES products. However, the number of these sites is low and their distribution is globally sparse, and particularly the surface net radiation product has not been rigorously validated yet. Therefore, additional validation efforts are highly required to determine the accuracy of the CERES radiation products. In this study, global land surface measurements were comprehensively collected for use in the validation of the CERES net radiation (Rn) product on a daily (340 sites) and a monthly (260 sites) basis, respectively. The validation results demonstrated that the CERES Rn product was, overall, highly accurate. The daily validations had a Mean Bias Error (MBE) of 3.43 W·m−2, Root Mean Square Error (RMSE) of 33.56 W·m−2, and R2 of 0.79, and the monthly validations had an MBE of 3.40 W·m−2, RMSE of 25.57 W·m−2, and R2 of 0.84. The accuracy was slightly lower for the high latitudes. Following the validation, the monthly CERES Rn product, from March 2000 to July 2014, was used for a further analysis. The global spatiotemporal variation of the Rn, which occurred during the measurement period, was analyzed. In addition, two hot spot regions, the southern Great Plains and south-central Africa, were then selected for use in determining the driving factors or attribution of the Rn variation. We determined that Rn over the southern Great Plains decreased by −0.33 W·m−2 per year, which was mainly driven by changes in surface green vegetation and precipitation. In south-central Africa, Rn decreased at a rate of −0.63 W·m−2 per year, the major driving factor of which was surface green vegetation. Full article
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Open AccessArticle Treating the Hooking Effect in Satellite Altimetry Data: A Case Study along the Mekong River and Its Tributaries
Remote Sens. 2016, 8(2), 91; doi:10.3390/rs8020091
Received: 5 November 2015 / Revised: 12 January 2016 / Accepted: 18 January 2016 / Published: 23 January 2016
Cited by 6 | PDF Full-text (3290 KB) | HTML Full-text | XML Full-text
Abstract
This study investigates the potential of satellite altimetry for water level time series estimation of smaller inland waters where only very few measurements above the water surface are available. A new method was developed using off-nadir measurements to estimate the parabola generated by
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This study investigates the potential of satellite altimetry for water level time series estimation of smaller inland waters where only very few measurements above the water surface are available. A new method was developed using off-nadir measurements to estimate the parabola generated by the hooking effect. For this purpose, a new waveform retracker was used as well as an adopted version of the RANdom SAmple Consensus (RANSAC) algorithm. The method is applied to compute time series of the water levels height of the Mekong River and some of its tributaries from Envisat high-frequency data. Reliable time series can be obtained from river crossings with widths of less than 500 m and without direct nadir measurements over the water. The expected annual variations are clearly depicted and the time series well agree with available in situ gauging data. The mean RMS value is 1.22 m between the resulting time series and in situ data, the best result is 0.34 m, the worst 2.26 m, and 80% of the time series have an RMS below 1.5 m. Full article
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Open AccessArticle Detection of Spatio-Temporal Changes of Norway Spruce Forest Stands in Ore Mountains Using Landsat Time Series and Airborne Hyperspectral Imagery
Remote Sens. 2016, 8(2), 92; doi:10.3390/rs8020092
Received: 24 November 2015 / Revised: 10 January 2016 / Accepted: 16 January 2016 / Published: 26 January 2016
Cited by 1 | PDF Full-text (4013 KB) | HTML Full-text | XML Full-text
Abstract
The study focuses on spatio-temporal changes in the physiological status of the Norway spruce forests located at the central and western parts of the Ore Mountains (northwestern part of the Czech Republic), which suffered from severe environmental pollution from the 1970s to the
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The study focuses on spatio-temporal changes in the physiological status of the Norway spruce forests located at the central and western parts of the Ore Mountains (northwestern part of the Czech Republic), which suffered from severe environmental pollution from the 1970s to the 1990s. The situation started improving after the pollution loads decreased significantly at the end of the 1990s. The general trends in forest recovery were studied using the tasseled cap transformation and disturbance index (DI) extracted from the 1985–2015 time series of Landsat data. In addition, 16 vegetation indices (VIs) extracted from airborne hyperspectral (HS) data acquired in 1998 using the Advanced Solid-State Array Spectroradiometer (ASAS) and in 2013 using the Airborne Prism Experiment (APEX) were used to study changes in forest health. The forest health status analysis of HS image data was performed at two levels of spatial resolution; at a tree level (original 2.0 m spatial resolution), as well as at a forest stand level (generalized to 6.0 m spatial resolution). The temporal changes were studied primarily using the VOG1 vegetation index (VI) as it was showing high and stable sensitivity to forest damage for both spatial resolutions considered. In 1998, significant differences between the moderately to heavily damaged (central Ore Mountains) and initially damaged (western Ore Mountains) stands were detected for all the VIs tested. In 2013, the stands in the central Ore Mountains exhibited VI values much closer to the global mean, indicating an improvement in their health status. This result fully confirms the finding of the Landsat time series analysis. The greatest difference in Disturbance Index (DI) values between the central (1998: 0.37) and western Ore Mountains stands (1998: −1.21) could be seen at the end of the 1990s. Nonetheless, levelling of the physiological status of Norway spruce was observed for the central and western parts of the Ore Mountains in 2013 (mean DI values −1.04 (western) and −0.66 (central)). Although the differences between originally moderately-to-heavily damaged, and initially damaged stands generally levelled out by 2013, it is still possible to detect signs of the previous damage in some cases. Full article
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Open AccessArticle Satellite SST-Based Coral Disease Outbreak Predictions for the Hawaiian Archipelago
Remote Sens. 2016, 8(2), 93; doi:10.3390/rs8020093
Received: 15 September 2015 / Revised: 15 December 2015 / Accepted: 20 January 2016 / Published: 26 January 2016
Cited by 1 | PDF Full-text (1261 KB) | HTML Full-text | XML Full-text
Abstract
Predicting wildlife disease risk is essential for effective monitoring and management, especially for geographically expansive ecosystems such as coral reefs in the Hawaiian archipelago. Warming ocean temperature has increased coral disease outbreaks contributing to declines in coral cover worldwide. In this study we
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Predicting wildlife disease risk is essential for effective monitoring and management, especially for geographically expansive ecosystems such as coral reefs in the Hawaiian archipelago. Warming ocean temperature has increased coral disease outbreaks contributing to declines in coral cover worldwide. In this study we investigated seasonal effects of thermal stress on the prevalence of the three most widespread coral diseases in Hawai’i: Montipora white syndrome, Porites growth anomalies and Porites tissue loss syndrome. To predict outbreak likelihood we compared disease prevalence from surveys conducted between 2004 and 2015 from 18 Hawaiian Islands and atolls with biotic (e.g., coral density) and abiotic (satellite-derived sea surface temperature metrics) variables using boosted regression trees. To date, the only coral disease forecast models available were developed for Acropora white syndrome on the Great Barrier Reef (GBR). Given the complexities of disease etiology, differences in host demography and environmental conditions across reef regions, it is important to refine and adapt such models for different diseases and geographic regions of interest. Similar to the Acropora white syndrome models, anomalously warm conditions were important for predicting Montipora white syndrome, possibly due to a relationship between thermal stress and a compromised host immune system. However, coral density and winter conditions were the most important predictors of all three coral diseases in this study, enabling development of a forecasting system that can predict regions of elevated disease risk up to six months before an expected outbreak. Our research indicates satellite-derived systems for forecasting disease outbreaks can be appropriately adapted from the GBR tools and applied for a variety of diseases in a new region. These models can be used to enhance management capacity to prepare for and respond to emerging coral diseases throughout Hawai’i and can be modified for other diseases and regions around the world. Full article
(This article belongs to the Special Issue Remote Sensing for Coral Reef Monitoring)
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Open AccessArticle Quantitative Estimation of the Velocity of Urbanization in China Using Nighttime Luminosity Data
Remote Sens. 2016, 8(2), 94; doi:10.3390/rs8020094
Received: 9 November 2015 / Revised: 7 January 2016 / Accepted: 18 January 2016 / Published: 26 January 2016
Cited by 3 | PDF Full-text (4788 KB) | HTML Full-text | XML Full-text
Abstract
Rapid urbanization with sizeable enhancements of urban population and built-up land in China creates challenging planning and management issues due to the complexity of both the urban development and the socioeconomic drivers of environmental change. Improved understanding of spatio-temporal characteristics of urbanization processes
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Rapid urbanization with sizeable enhancements of urban population and built-up land in China creates challenging planning and management issues due to the complexity of both the urban development and the socioeconomic drivers of environmental change. Improved understanding of spatio-temporal characteristics of urbanization processes are increasingly important for investigating urban expansion and environmental responses to corresponding socioeconomic and landscape dynamics. In this study, we present an artificial luminosity-derived index of the velocity of urbanization, defined as the ratio of temporal trend and spatial gradient of mean annual stable nighttime brightness, to estimate the pace of urbanization and consequent changes in land cover in China for the period of 2000–2010. Using the Defense Meteorological Satellite Program–derived time series of nighttime light data and corresponding satellite-based land cover maps, our results show that the geometric mean velocity of urban dispersal at the country level was 0.21 km·yr−1 across 88.58 × 103 km2 urbanizing areas, in which ~23% of areas originally made of natural and cultivated lands were converted to artificial surfaces between 2000 and 2010. The speed of urbanization varies among urban agglomerations and cities with different development stages and urban forms. Particularly, the Yangtze River Delta conurbation shows the fastest (0.39 km·yr−1) and most extensive (16.12 × 103 km2) urban growth in China over the 10-year period. Moreover, if the current velocity holds, our estimates suggest that an additional 13.29 × 103 km2 in land area will be converted to human-built features while high density socioeconomic activities across the current urbanizing regions and urbanized areas will greatly increase from 52.44 × 103 km2 in 2010 to 62.73 × 103 km2 in China’s mainland during the next several decades. Our findings may provide potential insights into the pace of urbanization in China, its impacts on land changes, and accompanying alterations in environment and ecosystems in a spatially and temporally explicit manner. Full article
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Open AccessArticle Using an Unmanned Aerial Vehicle-Based Digital Imaging System to Derive a 3D Point Cloud for Landslide Scarp Recognition
Remote Sens. 2016, 8(2), 95; doi:10.3390/rs8020095
Received: 21 October 2015 / Revised: 9 January 2016 / Accepted: 18 January 2016 / Published: 27 January 2016
Cited by 8 | PDF Full-text (16990 KB) | HTML Full-text | XML Full-text
Abstract
Landslides often cause economic losses, property damage, and loss of lives. Monitoring landslides using high spatial and temporal resolution imagery and the ability to quickly identify landslide regions are the basis for emergency disaster management. This study presents a comprehensive system that uses
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Landslides often cause economic losses, property damage, and loss of lives. Monitoring landslides using high spatial and temporal resolution imagery and the ability to quickly identify landslide regions are the basis for emergency disaster management. This study presents a comprehensive system that uses unmanned aerial vehicles (UAVs) and Semi-Global dense Matching (SGM) techniques to identify and extract landslide scarp data. The selected study area is located along a major highway in a mountainous region in Jordan, and contains creeping landslides induced by heavy rainfall. Field observations across the slope body and a deformation analysis along the highway and existing gabions indicate that the slope is active and that scarp features across the slope will continue to open and develop new tension crack features, leading to the downward movement of rocks. The identification of landslide scarps in this study was performed via a dense 3D point cloud of topographic information generated from high-resolution images captured using a low-cost UAV and a target-based camera calibration procedure for a low-cost large-field-of-view camera. An automated approach was used to accurately detect and extract the landslide head scarps based on geomorphological factors: the ratio of normalized Eigenvalues (i.e., λ1/λ2 ≥ λ3) derived using principal component analysis, topographic surface roughness index values, and local-neighborhood slope measurements from the 3D image-based point cloud. Validation of the results was performed using root mean square error analysis and a confusion (error) matrix between manually digitized landslide scarps and the automated approaches. The experimental results using the fully automated 3D point-based analysis algorithms show that these approaches can effectively distinguish landslide scarps. The proposed algorithms can accurately identify and extract landslide scarps with centimeter-scale accuracy. In addition, the combination of UAV-based imagery, 3D scene reconstruction, and landslide scarp recognition/extraction algorithms can provide flexible and effective tool for monitoring landslide scarps and is acceptable for landslide mapping purposes. Full article
(This article belongs to the Special Issue Earth Observations for Geohazards) Printed Edition available
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Open AccessArticle Real-Time Classification of Seagrass Meadows on Flat Bottom with Bathymetric Data Measured by a Narrow Multibeam Sonar System
Remote Sens. 2016, 8(2), 96; doi:10.3390/rs8020096
Received: 19 October 2015 / Revised: 23 December 2015 / Accepted: 18 January 2016 / Published: 27 January 2016
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Abstract
Seagrass meadows, one of the most important habitats for many marine species, provide essential ecological services. Thus, society must conserve seagrass beds as part of their sustainable development efforts. Conserving these ecosystems requires information on seagrass distribution and relative abundance, and an efficient,
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Seagrass meadows, one of the most important habitats for many marine species, provide essential ecological services. Thus, society must conserve seagrass beds as part of their sustainable development efforts. Conserving these ecosystems requires information on seagrass distribution and relative abundance, and an efficient, accurate monitoring system. Although narrow multibeam sonar systems (NMBSs) are highly effective in resolving seagrass beds, post-processing methods are required to extract key data. The purpose of this study was to develop a simple method capable of detecting seagrass meadows and estimating their relative abundance in real time using an NMBS. Because most seagrass meadows grow on sandy seafloors, we proposed a way of discriminating seagrass meadows from the sand bed. We classify meadows into three categories of relative seagrass abundance using the 95% confidence level of beam depths and the depth range of the beam depth. These are respectively two times the standard deviation of beam depths, and the difference between the shallowest and the deepest depths in a 0.5 × 0.5 m grid cell sampled with several narrow beams. We examined Zostera caulescens Miki, but this simple NMBS method of seagrass classification can potentially be used to map seagrass meadows with longer shoots of other species, such as Posidonia, as both have gas filled cavities. Full article
(This article belongs to the Special Issue Underwater Acoustic Remote Sensing)
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Open AccessArticle Hierarchical Object-Based Mapping of Riverscape Units and in-Stream Mesohabitats Using LiDAR and VHR Imagery
Remote Sens. 2016, 8(2), 97; doi:10.3390/rs8020097
Received: 27 October 2015 / Revised: 13 January 2016 / Accepted: 18 January 2016 / Published: 27 January 2016
Cited by 13 | PDF Full-text (11818 KB) | HTML Full-text | XML Full-text
Abstract
In this paper, we present a new, semi-automated methodology for mapping hydromorphological indicators of rivers at a regional scale using multisource remote sensing (RS) data. This novel approach is based on the integration of spectral and topographic information within a multilevel, geographic, object-based
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In this paper, we present a new, semi-automated methodology for mapping hydromorphological indicators of rivers at a regional scale using multisource remote sensing (RS) data. This novel approach is based on the integration of spectral and topographic information within a multilevel, geographic, object-based image analysis (GEOBIA). Different segmentation levels were generated based on the two sources of Remote Sensing (RS) data, namely very-high spatial resolution, near-infrared imagery (VHR) and high-resolution LiDAR topography. At each level, different input object features were tested with Machine Learning classifiers for mapping riverscape units and in-stream mesohabitats. The GEOBIA approach proved to be a powerful tool for analyzing the river system at different levels of detail and for coupling spectral and topographic datasets, allowing for the delineation of the natural fluvial corridor with its primary riverscape units (e.g., water channel, unvegetated sediment bars, riparian densely-vegetated units, etc.) and in-stream mesohabitats with a high level of accuracy, respectively of K = 0.91 and K = 0.83. This method is flexible and can be adapted to different sources of data, with the potential to be implemented at regional scales in the future. The analyzed dataset, composed of VHR imagery and LiDAR data, is nowadays increasingly available at larger scales, notably through European Member States. At the same time, this methodology provides a tool for monitoring and characterizing the hydromorphological status of river systems continuously along the entire channel network and coherently through time, opening novel and significant perspectives to river science and management, notably for planning and targeting actions. Full article
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Open AccessArticle Study of the Effect of Temporal Sampling Frequency on DSCOVR Observations Using the GEOS-5 Nature Run Results (Part I): Earth’s Radiation Budget
Remote Sens. 2016, 8(2), 98; doi:10.3390/rs8020098
Received: 30 November 2015 / Revised: 6 January 2016 / Accepted: 20 January 2016 / Published: 27 January 2016
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Abstract
Satellites always sample the Earth-atmosphere system in a finite temporal resolution. This study investigates the effect of sampling frequency on the satellite-derived Earth radiation budget, with the Deep Space Climate Observatory (DSCOVR) as an example. The output from NASA’s Goddard Earth Observing System
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Satellites always sample the Earth-atmosphere system in a finite temporal resolution. This study investigates the effect of sampling frequency on the satellite-derived Earth radiation budget, with the Deep Space Climate Observatory (DSCOVR) as an example. The output from NASA’s Goddard Earth Observing System Version 5 (GEOS-5) Nature Run is used as the truth. The Nature Run is a high spatial and temporal resolution atmospheric simulation spanning a two-year period. The effect of temporal resolution on potential DSCOVR observations is assessed by sampling the full Nature Run data with 1-h to 24-h frequencies. The uncertainty associated with a given sampling frequency is measured by computing means over daily, monthly, seasonal and annual intervals and determining the spread across different possible starting points. The skill with which a particular sampling frequency captures the structure of the full time series is measured using correlations and normalized errors. Results show that higher sampling frequency gives more information and less uncertainty in the derived radiation budget. A sampling frequency coarser than every 4 h results in significant error. Correlations between true and sampled time series also decrease more rapidly for a sampling frequency less than 4 h. Full article
(This article belongs to the Special Issue Uncertainties in Remote Sensing)
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Open AccessArticle Hyperspectral Imagery Classification Using Sparse Representations of Convolutional Neural Network Features
Remote Sens. 2016, 8(2), 99; doi:10.3390/rs8020099
Received: 19 October 2015 / Revised: 20 December 2015 / Accepted: 30 December 2015 / Published: 27 January 2016
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Abstract
In recent years, deep learning has been widely studied for remote sensing image analysis. In this paper, we propose a method for remotely-sensed image classification by using sparse representation of deep learning features. Specifically, we use convolutional neural networks (CNN) to extract deep
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In recent years, deep learning has been widely studied for remote sensing image analysis. In this paper, we propose a method for remotely-sensed image classification by using sparse representation of deep learning features. Specifically, we use convolutional neural networks (CNN) to extract deep features from high levels of the image data. Deep features provide high level spatial information created by hierarchical structures. Although the deep features may have high dimensionality, they lie in class-dependent sub-spaces or sub-manifolds. We investigate the characteristics of deep features by using a sparse representation classification framework. The experimental results reveal that the proposed method exploits the inherent low-dimensional structure of the deep features to provide better classification results as compared to the results obtained by widely-used feature exploration algorithms, such as the extended morphological attribute profiles (EMAPs) and sparse coding (SC). Full article
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Open AccessArticle Characterization of Black Sand Mining Activities and Their Environmental Impacts in the Philippines Using Remote Sensing
Remote Sens. 2016, 8(2), 100; doi:10.3390/rs8020100
Received: 20 October 2015 / Revised: 31 December 2015 / Accepted: 20 January 2016 / Published: 28 January 2016
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Abstract
Magnetite is a type of iron ore and a valuable commodity that occurs naturally in black sand beaches in the Philippines. However, black sand mining often takes place illegally and increases the likelihood and magnitude of geohazards, such as land subsidence, which augments
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Magnetite is a type of iron ore and a valuable commodity that occurs naturally in black sand beaches in the Philippines. However, black sand mining often takes place illegally and increases the likelihood and magnitude of geohazards, such as land subsidence, which augments the exposure of local communities to sea level rise and to typhoon-related threats. Detection of black sand mining activities traditionally relies on word of mouth, while measurement of their environmental effects requires on-the-ground geological surveys, which are precise, but costly and limited in scope. Here we show that systematic analysis of remote sensing data provides an objective, reliable, safe, and cost-effective way to monitor black sand mining activities and their impacts. First, we show that optical satellite data can be used to identify legal and illegal mining sites and characterize the direct effect of mining on the landscape. Second, we demonstrate that Interferometric Synthetic Aperture Radar (InSAR) can be used to evaluate the environmental impacts of black sand mining despite the small spatial extent of the activities. We detected a total of twenty black sand mining sites on Luzon Island and InSAR ALOS data reveal that out of the thirteen sites with coherence, nine experienced land subsidence at rates ranging from 1.5 to 5.7 cm/year during 2007–2011. The mean ground velocity map also highlights that the spatial extent of the subsiding areas is 10 to 100 times larger than the mining sites, likely associated with groundwater use or sediment redistribution. As a result of this subsidence, several coastal areas will be lowered to sea level elevation in a few decades and exposed to permanent flooding. This work demonstrates that remote sensing data are critical in monitoring the development of such activities and their environmental and societal impacts. Full article
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Open AccessArticle An Assessment of the Cultivated Cropland Class of NLCD 2006 Using a Multi-Source and Multi-Criteria Approach
Remote Sens. 2016, 8(2), 101; doi:10.3390/rs8020101
Received: 29 July 2015 / Accepted: 13 January 2016 / Published: 28 January 2016
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Abstract
We developed a method that analyzes the quality of the cultivated cropland class mapped in the USA National Land Cover Database (NLCD) 2006. The method integrates multiple geospatial datasets and a Multi Index Integrated Change Analysis (MIICA) change detection method that captures spectral
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We developed a method that analyzes the quality of the cultivated cropland class mapped in the USA National Land Cover Database (NLCD) 2006. The method integrates multiple geospatial datasets and a Multi Index Integrated Change Analysis (MIICA) change detection method that captures spectral changes to identify the spatial distribution and magnitude of potential commission and omission errors for the cultivated cropland class in NLCD 2006. The majority of the commission and omission errors in NLCD 2006 are in areas where cultivated cropland is not the most dominant land cover type. The errors are primarily attributed to the less accurate training dataset derived from the National Agricultural Statistics Service Cropland Data Layer dataset. In contrast, error rates are low in areas where cultivated cropland is the dominant land cover. Agreement between model-identified commission errors and independently interpreted reference data was high (79%). Agreement was low (40%) for omission error comparison. The majority of the commission errors in the NLCD 2006 cultivated crops were confused with low-intensity developed classes, while the majority of omission errors were from herbaceous and shrub classes. Some errors were caused by inaccurate land cover change from misclassification in NLCD 2001 and the subsequent land cover post-classification process. Full article
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Open AccessArticle Spatio-Temporal Variation and Impact Factors for Vegetation Carbon Sequestration and Oxygen Production Based on Rocky Desertification Control in the Karst Region of Southwest China
Remote Sens. 2016, 8(2), 102; doi:10.3390/rs8020102
Received: 30 September 2015 / Revised: 19 January 2016 / Accepted: 20 January 2016 / Published: 28 January 2016
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Abstract
The Grain to Green Program (GTGP) and eco-environmental emigration have been employed to alleviate poverty and control rocky desertification in the Southwest China Karst region. Carbon sequestration and oxygen production (CSOP) is used to indicate major ecological changes, because they involve complex processes
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The Grain to Green Program (GTGP) and eco-environmental emigration have been employed to alleviate poverty and control rocky desertification in the Southwest China Karst region. Carbon sequestration and oxygen production (CSOP) is used to indicate major ecological changes, because they involve complex processes of material circulation and energy flow. Using remote sensing images and weather records, the spatiotemporal variation of CSOP was analyzed in a typical karst region of northwest Guangxi, China, during 2000–2010 to determine the effects of the Chinese government’s ecological rehabilitation initiatives implemented in 1999. An increase with substantial annual change and a significant increase (20.94%, p < 0.05) in variation were found from 2000 to 2010. CSOP had a highly clustered distribution in 2010 and was correlated with precipitation and temperature (9.18% and 8.96%, respectively, p < 0.05). CSOP was significantly suppressed by human activities (p < 0.01, r = −0.102) but was consistent with the intensity of GTGP (43.80% positive). The power spectrum of CSOP was consistent with that of the gross domestic product. These results indicate that ecological services were improved by rocky desertification control in a typical karst region. The results may provide information to evaluate the efficiency of ecological reconstruction projects. Full article
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Open AccessArticle Vertical Profiling of Volcanic Ash from the 2011 Puyehue Cordón Caulle Eruption Using IASI
Remote Sens. 2016, 8(2), 103; doi:10.3390/rs8020103
Received: 18 September 2015 / Revised: 8 January 2016 / Accepted: 20 January 2016 / Published: 29 January 2016
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Abstract
Volcanic ash is emitted by most eruptions, sometimes reaching the stratosphere. In addition to its climate effect, ash may have a significant impact on civilian flights. Currently, the horizontal distribution of ash aerosols is quite extensively studied, but not its vertical profile, while
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Volcanic ash is emitted by most eruptions, sometimes reaching the stratosphere. In addition to its climate effect, ash may have a significant impact on civilian flights. Currently, the horizontal distribution of ash aerosols is quite extensively studied, but not its vertical profile, while of high importance for both applications mentioned. Here, we study the sensitivity of the thermal infrared spectral range to the altitude distribution of volcanic ash, based on similar work that was undertaken on mineral dust. We use measurements by the Infrared Atmospheric Sounding Interferometer (IASI) instruments onboard the MetOp satellite series. The retrieval method that we develop for the ash vertical profile is based on the optimal estimation formalism. This method is applied to study the eruption of the Chilean volcano Puyehue, which started on the 4th of June 2011. The retrieved profiles agree reasonably well with Cloud-Aerosol LiDAR with Orthogonal Polarization (CALIOP) measurements, and our results generally agree with literature studies of the same eruption. The retrieval strategy presented here therefore is very promising for improving our knowledge of the vertical distribution of volcanic ash and obtaining a global 3D ash distribution twice a day. Future improvements of our retrieval strategy are also discussed. Full article
(This article belongs to the Special Issue Aerosol and Cloud Remote Sensing)
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Open AccessArticle Multi-Class Simultaneous Adaptive Segmentation and Quality Control of Point Cloud Data
Remote Sens. 2016, 8(2), 104; doi:10.3390/rs8020104
Received: 30 November 2015 / Revised: 6 January 2016 / Accepted: 21 January 2016 / Published: 29 January 2016
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Abstract
3D modeling of a given site is an important activity for a wide range of applications including urban planning, as-built mapping of industrial sites, heritage documentation, military simulation, and outdoor/indoor analysis of airflow. Point clouds, which could be either derived from passive or
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3D modeling of a given site is an important activity for a wide range of applications including urban planning, as-built mapping of industrial sites, heritage documentation, military simulation, and outdoor/indoor analysis of airflow. Point clouds, which could be either derived from passive or active imaging systems, are an important source for 3D modeling. Such point clouds need to undergo a sequence of data processing steps to derive the necessary information for the 3D modeling process. Segmentation is usually the first step in the data processing chain. This paper presents a region-growing multi-class simultaneous segmentation procedure, where planar, pole-like, and rough regions are identified while considering the internal characteristics (i.e., local point density/spacing and noise level) of the point cloud in question. The segmentation starts with point cloud organization into a kd-tree data structure and characterization process to estimate the local point density/spacing. Then, proceeding from randomly-distributed seed points, a set of seed regions is derived through distance-based region growing, which is followed by modeling of such seed regions into planar and pole-like features. Starting from optimally-selected seed regions, planar and pole-like features are then segmented. The paper also introduces a list of hypothesized artifacts/problems that might take place during the region-growing process. Finally, a quality control process is devised to detect, quantify, and mitigate instances of partially/fully misclassified planar and pole-like features. Experimental results from airborne and terrestrial laser scanning as well as image-based point clouds are presented to illustrate the performance of the proposed segmentation and quality control framework. Full article
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Open AccessArticle Estimation of Land Surface Temperature through Blending MODIS and AMSR-E Data with the Bayesian Maximum Entropy Method
Remote Sens. 2016, 8(2), 105; doi:10.3390/rs8020105
Received: 17 November 2015 / Revised: 21 January 2016 / Accepted: 25 January 2016 / Published: 29 January 2016
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Abstract
Land surface temperature (LST) plays a major role in the study of surface energy balances. Remote sensing techniques provide ways to monitor LST at large scales. However, due to atmospheric influences, significant missing data exist in LST products retrieved from satellite thermal infrared
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Land surface temperature (LST) plays a major role in the study of surface energy balances. Remote sensing techniques provide ways to monitor LST at large scales. However, due to atmospheric influences, significant missing data exist in LST products retrieved from satellite thermal infrared (TIR) remotely sensed data. Although passive microwaves (PMWs) are able to overcome these atmospheric influences while estimating LST, the data are constrained by low spatial resolution. In this study, to obtain complete and high-quality LST data, the Bayesian Maximum Entropy (BME) method was introduced to merge 0.01° and 0.25° LSTs inversed from MODIS and AMSR-E data, respectively. The result showed that the missing LSTs in cloudy pixels were filled completely, and the availability of merged LSTs reaches 100%. Because the depths of LST and soil temperature measurements are different, before validating the merged LST, the station measurements were calibrated with an empirical equation between MODIS LST and 0~5 cm soil temperatures. The results showed that the accuracy of merged LSTs increased with the increasing quantity of utilized data, and as the availability of utilized data increased from 25.2% to 91.4%, the RMSEs of the merged data decreased from 4.53 °C to 2.31 °C. In addition, compared with the filling gap method in which MODIS LST gaps were filled with AMSR-E LST directly, the merged LSTs from the BME method showed better spatial continuity. The different penetration depths of TIR and PMWs may influence fusion performance and still require further studies. Full article
(This article belongs to the Special Issue Satellite Climate Data Records and Applications)
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Open AccessArticle Madagascar’s Mangroves: Quantifying Nation-Wide and Ecosystem Specific Dynamics, and Detailed Contemporary Mapping of Distinct Ecosystems
Remote Sens. 2016, 8(2), 106; doi:10.3390/rs8020106
Received: 31 August 2015 / Revised: 21 November 2015 / Accepted: 8 January 2016 / Published: 30 January 2016
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Abstract
Mangrove ecosystems help mitigate climate change, are highly biodiverse, and provide critical goods and services to coastal communities. Despite their importance, anthropogenic activities are rapidly degrading and deforesting mangroves world-wide. Madagascar contains 2% of the world’s mangroves, many of which have undergone or
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Mangrove ecosystems help mitigate climate change, are highly biodiverse, and provide critical goods and services to coastal communities. Despite their importance, anthropogenic activities are rapidly degrading and deforesting mangroves world-wide. Madagascar contains 2% of the world’s mangroves, many of which have undergone or are starting to exhibit signs of widespread degradation and deforestation. Remotely sensed data can be used to quantify mangrove loss and characterize remaining distributions, providing detailed, accurate, timely and updateable information. We use USGS maps produced from Landsat data to calculate nation-wide dynamics for Madagascar’s mangroves from 1990 to 2010, and examine change more closely by partitioning the national distribution in to primary (i.e., >1000 ha) ecosystems; with focus on four Areas of Interest (AOIs): Ambaro-Ambanja Bays (AAB), Mahajamba Bay (MHJ), Tsiribihina Manombolo Delta (TMD) and Bay des Assassins (BdA). Results indicate a nation–wide net-loss of 21% (i.e., 57,359 ha) from 1990 to 2010, with dynamics varying considerably among primary mangrove ecosystems. Given the limitations of national-level maps for certain localized applications (e.g., carbon stock inventories), building on two previous studies for AAB and MHJ, we employ Landsat data to produce detailed, contemporary mangrove maps for TMD and BdA. These contemporary, AOI-specific maps provide improved detail and accuracy over the USGS national-level maps, and are being applied to conservation and restoration initiatives through the Blue Ventures’ Blue Forests programme and WWF Madagascar West Indian Ocean Programme Office’s work in the region. Full article
(This article belongs to the Special Issue Remote Sensing of Mangroves: Observation and Monitoring)
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Open AccessArticle Rank-Based Methods for Selection of Landscape Metrics for Land Cover Pattern Change Detection
Remote Sens. 2016, 8(2), 107; doi:10.3390/rs8020107
Received: 12 November 2015 / Revised: 10 January 2016 / Accepted: 20 January 2016 / Published: 1 February 2016
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Abstract
Often landscape metrics are not thoroughly evaluated with respect to remote sensing data characteristics, such as their behavior in relation to variation in spatial and temporal resolution, number of land cover classes or dominant land cover categories. In such circumstances, it may be
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Often landscape metrics are not thoroughly evaluated with respect to remote sensing data characteristics, such as their behavior in relation to variation in spatial and temporal resolution, number of land cover classes or dominant land cover categories. In such circumstances, it may be difficult to ascertain whether a change in a metric is due to landscape pattern change or due to the inherent variability in multi-temporal data. This study builds on this important consideration and proposes a rank-based metric selection process through computation of four difference-based indices (β, γ, ξ and θ) using a Max–Min/Max normalization approach. Land cover classification was carried out for two contrasting provinces, the Liverpool Range (LR) and Liverpool Plains (LP), of the Brigalow Belt South Bioregion (BBSB) of NSW, Australia. Landsat images, Multi Spectral Scanner (MSS) of 1972–1973 and TM of 1987–1988, 1993–1994, 1999–2000 and 2009–2010 were classified using object-based image analysis methods. A total of 30 landscape metrics were computed and their sensitivities towards variation in spatial and temporal resolutions, number of land cover classes and dominant land cover categories were evaluated by computing a score based on Max–Min/Max normalization. The landscape metrics selected on the basis of the proposed methods (Diversity index (MSIDI), Area weighted mean patch fractal dimension (SHAPE_AM), Mean core area (CORE_MN), Total edge (TE), No. of patches (NP), Contagion index (CONTAG), Mean nearest neighbor index (ENN_MN) and Mean patch fractal dimension (FRAC_MN)) were successful and effective in identifying changes over five different change periods. Major changes in land cover pattern after 1993 were observed, and though the trends were similar in both cases, the LP region became more fragmented than the LR. The proposed method was straightforward to apply, and can deal with multiple metrics when selection of an appropriate set can become difficult. Full article
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Open AccessArticle Daytime Thermal Anisotropy of Urban Neighbourhoods: Morphological Causation
Remote Sens. 2016, 8(2), 108; doi:10.3390/rs8020108
Received: 30 October 2015 / Revised: 6 January 2016 / Accepted: 8 January 2016 / Published: 30 January 2016
Cited by 7 | PDF Full-text (4782 KB) | HTML Full-text | XML Full-text
Abstract
Surface temperature is a key variable in boundary-layer meteorology and is typically acquired by remote observation of emitted thermal radiation. However, the three-dimensional structure of cities complicates matters: uneven solar heating of urban facets produces an “effective anisotropy” of surface thermal emission at
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Surface temperature is a key variable in boundary-layer meteorology and is typically acquired by remote observation of emitted thermal radiation. However, the three-dimensional structure of cities complicates matters: uneven solar heating of urban facets produces an “effective anisotropy” of surface thermal emission at the neighbourhood scale. Remotely-sensed urban surface temperature varies with sensor view angle as a consequence. The authors combine a microscale urban surface temperature model with a thermal remote sensing model to predict the effective anisotropy of simplified neighbourhood configurations. The former model provides detailed surface temperature distributions for a range of “urban” forms, and the remote sensing model computes aggregate temperatures for multiple view angles. The combined model’s ability to reproduce observed anisotropy is evaluated against measurements from a neighbourhood in Vancouver, Canada. As in previous modeling studies, anisotropy is underestimated. Addition of moderate coverages of small (sub-facet scale) structure can account for much of the missing anisotropy. Subsequently, over 1900 sensitivity simulations are performed with the model combination, and the dependence of daytime effective thermal anisotropy on diurnal solar path (i.e., latitude and time of day) and blunt neighbourhood form is assessed. The range of effective anisotropy, as well as the maximum difference from nadir-observed brightness temperature, peak for moderate building-height-to-spacing ratios (H/W), and scale with canyon (between-building) area; dispersed high-rise urban forms generate maximum anisotropy. Maximum anisotropy increases with solar elevation and scales with shortwave irradiance. Moreover, it depends linearly on H/W for H/W < 1.25, with a slope that depends on maximum off-nadir sensor angle. Decreasing minimum brightness temperature is primarily responsible for this linear growth of maximum anisotropy. These results allow first order estimation of the minimum effective anisotropy magnitude of urban neighbourhoods as a function of building-height-to-spacing ratio, building plan area density, and shortwave irradiance. Finally, four “local climate zones” are simulated at two latitudes. Removal of neighbourhood street orientation regularity for these zones decreases maximum anisotropy by 3%–31%. Furthermore, thermal and radiative material properties are a weaker predictor of anisotropy than neighbourhood morphology. This study is the first systematic evaluation of effective anisotropy magnitude and causation for urban landscapes. Full article
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Open AccessArticle Analysis of Aerosol Properties in Beijing Based on Ground-Based Sun Photometer and Air Quality Monitoring Observations from 2005 to 2014
Remote Sens. 2016, 8(2), 110; doi:10.3390/rs8020110
Received: 27 October 2015 / Revised: 25 January 2016 / Accepted: 28 January 2016 / Published: 3 February 2016
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Abstract
Aerosol particles are the major contributor to the deterioration of air quality in China’s capital, Beijing. Using ground-based sun photometer observations from 2005 to 2014, the long-term variations in optical properties and microphysical properties of aerosol in and around Beijing were investigated in
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Aerosol particles are the major contributor to the deterioration of air quality in China’s capital, Beijing. Using ground-based sun photometer observations from 2005 to 2014, the long-term variations in optical properties and microphysical properties of aerosol in and around Beijing were investigated in this study. The results indicated little inter-annual variations in aerosol optic depth (AOD) but an increase in the fine mode AODs both in and outside Beijing. Furthermore, the single scattering albedo in urban Beijing is larger, while observations at the site that is southeast of Beijing suggested that the aerosol there has become more absorbing. The intra-annual aspects were as follow: The largest AOD and high amount of fine mode aerosols are observed in the summer. However, the result of air pollution index (API) that mainly affected by the dry density of near-surface aerosol indicated that the air quality has been improving since 2006. Winter and spring were the most polluted seasons considering only the API values. The inconsistency between AOD and API suggested that fine aerosol particles may have a more important role in the deterioration of air quality and that neglecting particulate matter with aerodynamic diameter less than 2.5 μm (PM2.5) in the calculation of API might not be appropriate in air quality evaluation. Through analysis of the aerosol properties in high API days, the results suggested that the fine mode aerosol, especially PM2.5 has become a major contributor to the aerosol pollution in Beijing. Full article
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Open AccessArticle Validation of MODIS Aerosol Optical Depth Retrieval over Mountains in Central China Based on a Sun-Sky Radiometer Site of SONET
Remote Sens. 2016, 8(2), 111; doi:10.3390/rs8020111
Received: 4 December 2015 / Revised: 27 January 2016 / Accepted: 29 January 2016 / Published: 3 February 2016
Cited by 4 | PDF Full-text (1944 KB) | HTML Full-text | XML Full-text
Abstract
The 3 km Dark Target (DT) aerosol optical depth (AOD) products, 10 km DT and Deep Blue (DB) AOD products from the Collection 6 (C6) product data of Moderate Resolution Imaging Spectroradiometer (MODIS) are compared with Sun-sky Radiometer Network (SONET) measurements at Song
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The 3 km Dark Target (DT) aerosol optical depth (AOD) products, 10 km DT and Deep Blue (DB) AOD products from the Collection 6 (C6) product data of Moderate Resolution Imaging Spectroradiometer (MODIS) are compared with Sun-sky Radiometer Network (SONET) measurements at Song Mountain in central China, where ground-based remote sensing measurements of aerosol properties are still very limited. The seasonal variations of AODs are significant in the Song Mountain region, with higher AODs in spring and summer and lower AODs in autumn and winter. Annual mean AODs (0.55 µm) vary in the range of 0.5–0.7, which indicates particle matter (PM) pollutions in this mountain region. Validation against one-year ground-based measurements shows that AOD retrievals from the MODIS onboard Aqua satellite are better than those from the Terra satellite in Song Mountain. The 3 km and 10 km AODs from DT algorithms are comparable over this region, while the AOD accuracy of DB algorithm is relatively lower. However, the spatial coverage of DB products is higher than that of 10 km DT products. Moreover, the optical and microphysical characteristics of aerosols at Song Mountain are analyzed on the basis of SONET observations. It suggests that coarse-mode aerosol particles dominate in spring, and fine-mode particles dominate in summer. The aerosol property models are also established and compared to aerosol types used by MODIS algorithm. Full article
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Open AccessArticle Characterization of a Highly Biodiverse Floodplain Meadow Using Hyperspectral Remote Sensing within a Plant Functional Trait Framework
Remote Sens. 2016, 8(2), 112; doi:10.3390/rs8020112
Received: 5 December 2015 / Revised: 26 January 2016 / Accepted: 28 January 2016 / Published: 3 February 2016
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Abstract
We assessed the potential for using optical functional types as effective markers to monitor changes in vegetation in floodplain meadows associated with changes in their local environment. Floodplain meadows are challenging ecosystems for monitoring and conservation because of their highly biodiverse nature. Our
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We assessed the potential for using optical functional types as effective markers to monitor changes in vegetation in floodplain meadows associated with changes in their local environment. Floodplain meadows are challenging ecosystems for monitoring and conservation because of their highly biodiverse nature. Our aim was to understand and explain spectral differences among key members of floodplain meadows and also characterize differences with respect to functional traits. The study was conducted on a typical floodplain meadow in UK (MG4-type, mesotrophic grassland type 4, according to British National Vegetation Classification). We compared two approaches to characterize floodplain communities using field spectroscopy. The first approach was sub-community based, in which we collected spectral signatures for species groupings indicating two distinct eco-hydrological conditions (dry and wet soil indicator species). The other approach was “species-specific”, in which we focused on the spectral reflectance of three key species found on the meadow. One herb species is a typical member of the MG4 floodplain meadow community, while the other two species, sedge and rush, represent wetland vegetation. We also monitored vegetation biophysical and functional properties as well as soil nutrients and ground water levels. We found that the vegetation classes representing meadow sub-communities could not be spectrally distinguished from each other, whereas the individual herb species was found to have a distinctly different spectral signature from the sedge and rush species. The spectral differences between these three species could be explained by their observed differences in plant biophysical parameters, as corroborated through radiative transfer model simulations. These parameters, such as leaf area index, leaf dry matter content, leaf water content, and specific leaf area, along with other functional parameters, such as maximum carboxylation capacity and leaf nitrogen content, also helped explain the species’ differences in functional dynamics. Groundwater level and soil nitrogen availability, which are important factors governing plant nutrient status, were also found to be significantly different for the herb/wetland species’ locations. The study concludes that spectrally distinguishable species, typical for a highly biodiverse site such as a floodplain meadow, could potentially be used as target species to monitor vegetation dynamics under changing environmental conditions. Full article
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Open AccessArticle Quantifying Multiscale Habitat Structural Complexity: A Cost-Effective Framework for Underwater 3D Modelling
Remote Sens. 2016, 8(2), 113; doi:10.3390/rs8020113
Received: 19 September 2015 / Revised: 7 January 2016 / Accepted: 25 January 2016 / Published: 4 February 2016
Cited by 8 | PDF Full-text (4189 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Coral reef habitat structural complexity influences key ecological processes, ecosystem biodiversity, and resilience. Measuring structural complexity underwater is not trivial and researchers have been searching for accurate and cost-effective methods that can be applied across spatial extents for over 50 years. This study
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Coral reef habitat structural complexity influences key ecological processes, ecosystem biodiversity, and resilience. Measuring structural complexity underwater is not trivial and researchers have been searching for accurate and cost-effective methods that can be applied across spatial extents for over 50 years. This study integrated a set of existing multi-view, image-processing algorithms, to accurately compute metrics of structural complexity (e.g., ratio of surface to planar area) underwater solely from images. This framework resulted in accurate, high-speed 3D habitat reconstructions at scales ranging from small corals to reef-scapes (10s km2). Structural complexity was accurately quantified from both contemporary and historical image datasets across three spatial scales: (i) branching coral colony (Acropora spp.); (ii) reef area (400 m2); and (iii) reef transect (2 km). At small scales, our method delivered models with <1 mm error over 90% of the surface area, while the accuracy at transect scale was 85.3% ± 6% (CI). Advantages are: no need for an a priori requirement for image size or resolution, no invasive techniques, cost-effectiveness, and utilization of existing imagery taken from off-the-shelf cameras (both monocular or stereo). This remote sensing method can be integrated to reef monitoring and improve our knowledge of key aspects of coral reef dynamics, from reef accretion to habitat provisioning and productivity, by measuring and up-scaling estimates of structural complexity. Full article
(This article belongs to the Special Issue Remote Sensing for Coral Reef Monitoring)
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Open AccessArticle Evaluating Multi-Sensor Nighttime Earth Observation Data for Identification of Mixed vs. Residential Use in Urban Areas
Remote Sens. 2016, 8(2), 114; doi:10.3390/rs8020114
Received: 28 November 2015 / Revised: 13 January 2016 / Accepted: 25 January 2016 / Published: 4 February 2016
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Abstract
This paper introduces a novel top-down approach to geospatially identify and distinguish areas of mixed use from predominantly residential areas within urban agglomerations. Under the framework of the World Bank’s Central American Country Disaster Risk Profiles (CDRP) initiative, a disaggregated property stock exposure
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This paper introduces a novel top-down approach to geospatially identify and distinguish areas of mixed use from predominantly residential areas within urban agglomerations. Under the framework of the World Bank’s Central American Country Disaster Risk Profiles (CDRP) initiative, a disaggregated property stock exposure model has been developed as one of the key elements for disaster risk and loss estimation. Global spatial datasets are therefore used consistently to ensure wide-scale applicability and transferability. Residential and mixed use areas need to be identified in order to spatially link accordingly compiled property stock information. In the presented study, multi-sensor nighttime Earth Observation data and derivative products are evaluated as proxies to identify areas of peak human activity. Intense artificial night lighting in that context is associated with a high likelihood of commercial and/or industrial presence. Areas of low light intensity, in turn, can be considered more likely residential. Iterative intensity thresholding is tested for Cuenca City, Ecuador, in order to best match a given reference situation based on cadastral land use data. The results and findings are considered highly relevant for the CDRP initiative, but more generally underline the relevance of remote sensing data for top-down modeling approaches at a wide spatial scale. Full article
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Open AccessArticle Classification of Small-Scale Eucalyptus Plantations Based on NDVI Time Series Obtained from Multiple High-Resolution Datasets
Remote Sens. 2016, 8(2), 117; doi:10.3390/rs8020117
Received: 9 October 2015 / Revised: 20 January 2016 / Accepted: 25 January 2016 / Published: 5 February 2016
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Abstract
Eucalyptus, a short-rotation plantation, has been expanding rapidly in southeast China in recent years owing to its short growth cycle and high yield of wood. Effective identification of eucalyptus, therefore, is important for monitoring land use changes and investigating environmental quality. For this
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Eucalyptus, a short-rotation plantation, has been expanding rapidly in southeast China in recent years owing to its short growth cycle and high yield of wood. Effective identification of eucalyptus, therefore, is important for monitoring land use changes and investigating environmental quality. For this article, we used remote sensing images over 15 years (one per year) with a 30-m spatial resolution, including Landsat 5 thematic mapper images, Landsat 7-enhanced thematic mapper images, and HJ 1A/1B images. These data were used to construct a 15-year Normalized Difference Vegetation Index (NDVI) time series for several cities in Guangdong Province, China. Eucalyptus reference NDVI time series sub-sequences were acquired, including one-year-long and two-year-long growing periods, using invested eucalyptus samples in the study region. In order to compensate for the discontinuity of the NDVI time series that is a consequence of the relatively coarse temporal resolution, we developed an inverted triangle area methodology. Using this methodology, the images were classified on the basis of the matching degree of the NDVI time series and two reference NDVI time series sub-sequences during the growing period of the eucalyptus rotations. Three additional methodologies (Bounding Envelope, City Block, and Standardized Euclidian Distance) were also tested and used as a comparison group. Threshold coefficients for the algorithms were adjusted using commission–omission error criteria. The results show that the triangle area methodology out-performed the other methodologies in classifying eucalyptus plantations. Threshold coefficients and an optimal discriminant function were determined using a mosaic photograph that had been taken by an unmanned aerial vehicle platform. Good stability was found as we performed further validation using multiple-year data from the high-resolution Gaofen Satellite 1 (GF-1) observations of larger regions. Eucalyptus planting dates were also estimated using invested eucalyptus samples and the Root Mean Square Error (RMSE) of the estimation was 84 days. This novel and reliable method for classifying short-rotation plantations at small scales is the focus of this study. Full article
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Open AccessArticle Efficient Emulation of Radiative Transfer Codes Using Gaussian Processes and Application to Land Surface Parameter Inferences
Remote Sens. 2016, 8(2), 119; doi:10.3390/rs8020119
Received: 23 December 2015 / Revised: 20 January 2016 / Accepted: 27 January 2016 / Published: 5 February 2016
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Abstract
There is an increasing need to consistently combine observations from different sensors to monitor the state of the land surface. In order to achieve this, robust methods based on the inversion of radiative transfer (RT) models can be used to interpret the satellite
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There is an increasing need to consistently combine observations from different sensors to monitor the state of the land surface. In order to achieve this, robust methods based on the inversion of radiative transfer (RT) models can be used to interpret the satellite observations. This typically results in an inverse problem, but a major drawback of these methods is the computational complexity. We introduce the concept of Gaussian Process (GP) emulators: surrogate functions that accurately approximate RT models using a small set of input (e.g., leaf area index, leaf chlorophyll, etc.) and output (e.g., top-of-canopy reflectances or at sensor radiances) pairs. The emulators quantify the uncertainty of their approximation, and provide a fast and easy route to estimating the Jacobian of the original model, enabling the use of e.g., efficient gradient descent methods. We demonstrate the emulation of widely used RT models (PROSAIL and SEMIDISCRETE) and the coupling of vegetation and atmospheric (6S) RT models targetting particular sensor bands. A comparison with the full original model outputs shows that the emulators are a viable option to replace the original model, with negligible bias and discrepancies which are much smaller than the typical uncertainty in the observations. We also extend the theory of GP to cope with models with multivariate outputs (e.g., over the full solar reflective domain), and apply this to the emulation of PROSAIL, coupled 6S and PROSAIL and to the emulation of individual spectral components of 6S. In all cases, emulators successfully predict the full model output as well as accurately predict the gradient of the model calculated by finite differences, and produce speed ups between 10,000 and 50,000 times that of the original model. Finally, we use emulators to invert leaf area index ( L A I ), leaf chlorophyll content ( C a b ) and equivalent leaf water thickness ( C w ) from a time series of observations from Sentinel-2/MSI, Sentinel-3/SLSTR and Proba-V observations. We use sophisticated Hamiltonian Markov Chain Monte Carlo (MCMC) methods that exploit the speed of the emulators as well as the gradient estimation, a variational data assimilation (DA) method that extends the problem with temporal regularisation, and a particle filter using a regularisation model. The variational and particle filter approach appear more successful (meaning parameters closer to the truth, and smaller uncertainties) than the MCMC approach as a result of using the temporal regularisation mode. These work therefore suggests that GP emulators are a practical way to implement sophisticated parameter retrieval schemes in an era of increasing data volumes. Full article
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Open AccessArticle PSInSAR Analysis in the Pisa Urban Area (Italy): A Case Study of Subsidence Related to Stratigraphical Factors and Urbanization
Remote Sens. 2016, 8(2), 120; doi:10.3390/rs8020120
Received: 17 November 2015 / Revised: 15 January 2016 / Accepted: 25 January 2016 / Published: 5 February 2016
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Abstract
Permanent Scatterer Interferometry (PSI) has been used to detect and characterize the subsidence of the Pisa urban area, which extends for 33 km2 within the Arno coastal plain (Tuscany, Italy). Two SAR (Synthetic Aperture Radar) datasets, covering the time period from 1992
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Permanent Scatterer Interferometry (PSI) has been used to detect and characterize the subsidence of the Pisa urban area, which extends for 33 km2 within the Arno coastal plain (Tuscany, Italy). Two SAR (Synthetic Aperture Radar) datasets, covering the time period from 1992 to 2010, were used to quantify the ground subsidence and its temporal evolution. A geotechnical borehole database was also used to make a correspondence with the detected displacements. Finally, the results of the SAR data analysis were contrasted with the urban development of the eastern part of the city in the time period from 1978 to 2013. ERS 1/2 (European Remote-Sensing Satellite) and Envisat SAR data, processed with the PSInSAR (Permanent Scatterer InSAR) algorithm, show that the investigated area is divided in two main sectors: the southwestern part, with null or very small subsidence rates (<2 mm/year), and the eastern portion which shows a general lowering with maximum deformation rates of 5 mm/year. This second area includes deformation rates higher than 15 mm/year, corresponding to small groups of buildings. The case studies in the eastern sector of the urban area have demonstrated the direct correlation between the age of construction of buildings and the registered subsidence rates, showing the importance of urbanization as an accelerating factor for the ground consolidation process. Full article
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Open AccessArticle The Greenness of Major Shrublands in China Increased from 2001 to 2013
Remote Sens. 2016, 8(2), 121; doi:10.3390/rs8020121
Received: 14 November 2015 / Revised: 30 December 2015 / Accepted: 18 January 2016 / Published: 5 February 2016
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Abstract
Shrubs have been reported to expand into grassland and polar regions in the world, which causes complex changes in ecosystem carbon, nutrients, and resilience. Given the projected global drying trend, shrubs with their superior drought resistance and tolerance may play more important roles
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Shrubs have been reported to expand into grassland and polar regions in the world, which causes complex changes in ecosystem carbon, nutrients, and resilience. Given the projected global drying trend, shrubs with their superior drought resistance and tolerance may play more important roles in global ecosystem function. Shrubland exists in all of the climate zones in China, from subtropical to temperate and high cold regions, and they occupy more than 20% of the land area. In this paper, we analyzed the spatiotemporal trend of MODIS (Moderate Resolution Imaging Spectroradiometer) EVI (Enhanced Vegetation Index) for six shrubland types in China from 2001 to 2013 and its relationship to intra- and inter-annual regional climate dynamics. Existing literature reported that the vegetation index did not change significantly in China during 2000–2012. However, we found that the shrubland EVI in China increased significantly at a rate of 1.01 × 10−3 EVI·a−1 from 2001 to 2013. Two major shrubland types (subtropical evergreen and temperate deciduous) and two desert types (high-cold desert and temperate desert) increased significantly, whereas subalpine evergreen shrubland decreased at a rate of −0.64 × 10−3 EVI·a−1. We also detected a significantly lengthened growing season of temperate deciduous shrubland. The growing season length contributed significantly to the annual averaged EVI for temperate deciduous, subalpine deciduous and subtropical evergreen shrublands. Furthermore, the precipitation variation contributed more to the annual averaged EVI than the temperature. The year-round decrease in rainfall and the increase in temperature led to a significant reduction in the subalpine evergreen shrubland EVI. The enhancement of countrywide shrubland EVI may promote its contribution to the regional ecosystem function and its potential to invade grasslands. Full article
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Open AccessArticle Comparison of Sun-Induced Chlorophyll Fluorescence Estimates Obtained from Four Portable Field Spectroradiometers
Remote Sens. 2016, 8(2), 122; doi:10.3390/rs8020122
Received: 25 September 2015 / Revised: 26 January 2016 / Accepted: 29 January 2016 / Published: 5 February 2016
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Abstract
Remote Sensing of Sun-Induced Chlorophyll Fluorescence (SIF) is a research field of growing interest because it offers the potential to quantify actual photosynthesis and to monitor plant status. New satellite missions from the European Space Agency, such as the Earth Explorer 8 FLuorescence
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Remote Sensing of Sun-Induced Chlorophyll Fluorescence (SIF) is a research field of growing interest because it offers the potential to quantify actual photosynthesis and to monitor plant status. New satellite missions from the European Space Agency, such as the Earth Explorer 8 FLuorescence EXplorer (FLEX) mission—scheduled to launch in 2022 and aiming at SIF mapping—and from the National Aeronautics and Space Administration (NASA) such as the Orbiting Carbon Observatory-2 (OCO-2) sampling mission launched in July 2014, provide the capability to estimate SIF from space. The detection of the SIF signal from airborne and satellite platform is difficult and reliable ground level data are needed for calibration/validation. Several commercially available spectroradiometers are currently used to retrieve SIF in the field. This study presents a comparison exercise for evaluating the capability of four spectroradiometers to retrieve SIF. The results show that an accurate far-red SIF estimation can be achieved using spectroradiometers with an ultrafine resolution (less than 1 nm), while the red SIF estimation requires even higher spectral resolution (less than 0.5 nm). Moreover, it is shown that the Signal to Noise Ratio (SNR) plays a significant role in the precision of the far-red SIF measurements. Full article
(This article belongs to the Special Issue Field Spectroscopy and Radiometry)
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Open AccessArticle Accuracy of Reconstruction of the Tree Stem Surface Using Terrestrial Close-Range Photogrammetry
Remote Sens. 2016, 8(2), 123; doi:10.3390/rs8020123
Received: 8 September 2015 / Revised: 20 January 2016 / Accepted: 25 January 2016 / Published: 5 February 2016
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Abstract
Airborne laser scanning (ALS) allows for extensive coverage, but the accuracy of tree detection and form can be limited. Although terrestrial laser scanning (TLS) can improve on ALS accuracy, it is rather expensive and area coverage is limited. Multi-view stereopsis (MVS) techniques combining
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Airborne laser scanning (ALS) allows for extensive coverage, but the accuracy of tree detection and form can be limited. Although terrestrial laser scanning (TLS) can improve on ALS accuracy, it is rather expensive and area coverage is limited. Multi-view stereopsis (MVS) techniques combining computer vision and photogrammetry may offer some of the coverage benefits of ALS and the improved accuracy of TLS; MVS combines computer vision research and automatic analysis of digital images from common commercial digital cameras with various algorithms to reconstruct three-dimensional (3D) objects with realistic shape and appearance. Despite the relative accuracy (relative geometrical distortion) of the reconstructions available in the processing software, the absolute accuracy is uncertain and difficult to evaluate. We evaluated the data collected by a common digital camera through the processing software (Agisoft PhotoScan ©) for photogrammetry by comparing those by direct measurement of the 3D magnetic motion tracker. Our analyses indicated that the error is mostly concentrated in the portions of the tree where visibility is lower, i.e., the bottom and upper parts of the stem. For each reference point from the digitizer we determined how many cameras could view this point. With a greater number of cameras we found increasing accuracy of the measured object space point positions (as expected), with a significant positive change in the trend beyond five cameras; when more than five cameras could view this point, the accuracy began to increase more abruptly, but eight cameras or more provided no increases in accuracy. This method allows for the retrieval of larger datasets from the measurements, which could improve the accuracy of estimates of 3D structure of trees at potentially reduced costs. Full article
(This article belongs to the Special Issue Digital Forest Resource Monitoring and Uncertainty Analysis)
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Open AccessArticle Spectral-Spatial Clustering with a Local Weight Parameter Determination Method for Remote Sensing Imagery
Remote Sens. 2016, 8(2), 124; doi:10.3390/rs8020124
Received: 27 September 2015 / Revised: 13 January 2016 / Accepted: 1 February 2016 / Published: 5 February 2016
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Abstract
Remote sensing image clustering is a challenging task considering its intrinsic complexity. Recently, by combining the spectral and spatial information of the remote sensing data, the clustering performance can be dramatically enhanced, termed as Spectral-Spatial Clustering (SSC). However, it has always been difficult
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Remote sensing image clustering is a challenging task considering its intrinsic complexity. Recently, by combining the spectral and spatial information of the remote sensing data, the clustering performance can be dramatically enhanced, termed as Spectral-Spatial Clustering (SSC). However, it has always been difficult to determine the weight parameter for balancing the spectral term and spatial term of the clustering objective function. In this paper, spectral-spatial clustering with a local weight parameter determination method for remote sensing image was proposed, i.e., L-SSC. In L-SSC, considering the large scale of remote sensing images, the weight parameter can be determined locally in a patch image instead of the whole image. Afterwards, the local weight parameter was used in constructing the objective function of L-SSC. Thus, the remote sensing image clustering problem was transformed into an optimization problem. Finally, in order to achieve a better optimization performance, a variant of differential evolution (i.e., jDE) was used as the optimizer due to its powerful optimization capability. Experimental results on three remote sensing images, including a Wuhan TM image, a Fancun Quickbird image, and an Indian Pine AVIRIS image, demonstrated that the proposed L-SSC can acquire higher clustering accuracy in comparison to other spectral-spatial clustering methods. Full article
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Open AccessArticle Estimation of Forest Structural Diversity Using the Spectral and Textural Information Derived from SPOT-5 Satellite Images
Remote Sens. 2016, 8(2), 125; doi:10.3390/rs8020125
Received: 20 October 2015 / Revised: 14 January 2016 / Accepted: 25 January 2016 / Published: 5 February 2016
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Abstract
Uneven-aged forest management has received increasing attention in the past few years. Compared with even-aged plantations, the complex structure of uneven-aged forests complicates the formulation of management strategies. Forest structural diversity is expected to provide considerable significant information for uneven-aged forest management planning.
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Uneven-aged forest management has received increasing attention in the past few years. Compared with even-aged plantations, the complex structure of uneven-aged forests complicates the formulation of management strategies. Forest structural diversity is expected to provide considerable significant information for uneven-aged forest management planning. In the present study, we investigated the potential of using SPOT-5 satellite images for extracting forest structural diversity. Forest stand variables were calculated from the field plots, whereas spectral and textural measures were derived from the corresponding satellite images. We firstly employed Pearson’s correlation analysis to examine the relationship between the forest stand variables and the image-derived measures. Secondly, we performed all possible subsets multiple linear regression to produce models by including the image-derived measures, which showed significant correlations with the forest stand variables, used as independent variables. The produced models were evaluated with the adjusted coefficient of determination ( R a d j 2 ) and the root mean square error (RMSE). Furthermore, a ten-fold cross-validation approach was used to validate the best-fitting models ( R a d j 2 > 0.5). The results indicated that basal area, stand volume, the Shannon index, Simpson index, Pielou index, standard deviation of DBHs, diameter differentiation index and species intermingling index could be reliably predicted using the spectral or textural measures extracted from SPOT-5 satellite images. Full article
(This article belongs to the Special Issue Remote Sensing of Vegetation Structure and Dynamics)
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Open AccessArticle EnGeoMAP 2.0—Automated Hyperspectral Mineral Identification for the German EnMAP Space Mission
Remote Sens. 2016, 8(2), 127; doi:10.3390/rs8020127
Received: 2 November 2015 / Revised: 25 January 2016 / Accepted: 1 February 2016 / Published: 5 February 2016
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Abstract
Algorithms for a rapid analysis of hyperspectral data are becoming more and more important with planned next generation spaceborne hyperspectral missions such as the Environmental Mapping and Analysis Program (EnMAP) and the Japanese Hyperspectral Imager Suite (HISUI), together with an ever growing pool
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Algorithms for a rapid analysis of hyperspectral data are becoming more and more important with planned next generation spaceborne hyperspectral missions such as the Environmental Mapping and Analysis Program (EnMAP) and the Japanese Hyperspectral Imager Suite (HISUI), together with an ever growing pool of hyperspectral airborne data. The here presented EnGeoMAP 2.0 algorithm is an automated system for material characterization from imaging spectroscopy data, which builds on the theoretical framework of the Tetracorder and MICA (Material Identification and Characterization Algorithm) of the United States Geological Survey and of EnGeoMAP 1.0 from 2013. EnGeoMAP 2.0 includes automated absorption feature extraction, spatio-spectral gradient calculation and mineral anomaly detection. The usage of EnGeoMAP 2.0 is demonstrated at the mineral deposit sites of Rodalquilar (SE-Spain) and Haib River (S-Namibia) using HyMAP and simulated EnMAP data. Results from Hyperion data are presented as supplementary information. Full article
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Open AccessArticle Seasonal Variation in the NDVI–Species Richness Relationship in a Prairie Grassland Experiment (Cedar Creek)
Remote Sens. 2016, 8(2), 128; doi:10.3390/rs8020128
Received: 2 December 2015 / Revised: 28 January 2016 / Accepted: 1 February 2016 / Published: 5 February 2016
Cited by 4 | PDF Full-text (2168 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Species richness generally promotes ecosystem productivity, although the shape of the relationship varies and remains the subject of debate. One reason for this uncertainty lies in the multitude of methodological approaches to sampling biodiversity and productivity, some of which can be subjective. Remote
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Species richness generally promotes ecosystem productivity, although the shape of the relationship varies and remains the subject of debate. One reason for this uncertainty lies in the multitude of methodological approaches to sampling biodiversity and productivity, some of which can be subjective. Remote sensing offers new, objective ways of assessing productivity and biodiversity. In this study, we tested the species richness–productivity relationship using a common remote sensing index, the Normalized Difference Vegetation Index (NDVI), as a measure of productivity in experimental prairie grassland plots (Cedar Creek). Our study spanned a growing season (May to October, 2014) to evaluate dynamic changes in the NDVI–species richness relationship through time and in relation to environmental variables and phenology. We show that NDVI, which is strongly associated with vegetation percent cover and biomass, is related to biodiversity for this prairie site, but it is also strongly influenced by other factors, including canopy growth stage, short-term water stress and shifting flowering patterns. Remarkably, the NDVI-biodiversity correlation peaked at mid-season, a period of warm, dry conditions and anthesis, when NDVI reached a local minimum. These findings confirm a positive, but dynamic, productivity–diversity relationship and highlight the benefit of optical remote sensing as an objective and non-invasive tool for assessing diversity–productivity relationships. Full article
(This article belongs to the Special Issue Remote Sensing of Biodiversity)
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Open AccessArticle Understanding the Spatial Temporal Vegetation Dynamics in Rwanda
Remote Sens. 2016, 8(2), 129; doi:10.3390/rs8020129
Received: 20 November 2015 / Revised: 26 January 2016 / Accepted: 1 February 2016 / Published: 5 February 2016
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Abstract
Knowledge of current vegetation dynamics and an ability to make accurate predictions of ecological changes are essential for minimizing food scarcity in developing countries. Vegetation trends are also closely related to sustainability issues, such as management of conservation areas and wildlife habitats. In
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Knowledge of current vegetation dynamics and an ability to make accurate predictions of ecological changes are essential for minimizing food scarcity in developing countries. Vegetation trends are also closely related to sustainability issues, such as management of conservation areas and wildlife habitats. In this study, AVHRR and MODIS NDVI datasets have been used to assess the spatial temporal dynamics of vegetation greenness in Rwanda under the contrasting trends of precipitation, for the period starting from 1990 to 2014, and for the first growing season (season A). Based on regression analysis and the Hurst exponent index methods, we have investigated the spatial temporal characteristics and the interrelationships between vegetation greenness and precipitation in light of NDVI and gridded meteorological datasets. The findings revealed that the vegetation cover was characterized by an increasing trend of a maximum annual change rate of 0.043. The results also suggest that 81.3% of the country’s vegetation has improved throughout the study period, while 14.1% of the country’s vegetation degraded, from slight (7.5%) to substantial (6.6%) deterioration. Most pixels with severe degradation were found in Kigali city and the Eastern Province. The analysis of changes per vegetation type highlighted that five types of vegetation are seriously endangered: The “mosaic grassland/forest or shrubland” was severely degraded, followed by “sparse vegetation,” “grassland or woody vegetation regularly flooded on water logged soil,” “artificial surfaces” and “broadleaved forest regularly flooded.” The Hurst exponent results indicated that the vegetation trend was consistent, with a sustainable area percentage of 40.16%, unsustainable area of 1.67% and an unpredictable area of 58.17%. This study will provide government and local authorities with valuable information for improving efficiency in the recently targeted countrywide efforts of environmental protection and regeneration. Full article
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Open AccessArticle The Added Value of Stratified Topographic Correction of Multispectral Images
Remote Sens. 2016, 8(2), 131; doi:10.3390/rs8020131
Received: 20 November 2015 / Revised: 26 January 2016 / Accepted: 29 January 2016 / Published: 15 February 2016
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Abstract
Satellite images in mountainous areas are strongly affected by topography. Different studies demonstrated that the results of semi-empirical topographic correction algorithms improved when a stratification of land covers was carried out first. However, differences in the stratification strategies proposed and also in the
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Satellite images in mountainous areas are strongly affected by topography. Different studies demonstrated that the results of semi-empirical topographic correction algorithms improved when a stratification of land covers was carried out first. However, differences in the stratification strategies proposed and also in the evaluation of the results obtained make it unclear how to implement them. The objective of this study was to compare different stratification strategies with a non-stratified approach using several evaluation criteria. For that purpose, Statistic-Empirical and Sun-Canopy-Sensor + C algorithms were applied and six different stratification approaches, based on vegetation indices and land cover maps, were implemented and compared with the non-stratified traditional option. Overall, this study demonstrates that for this particular case study the six stratification approaches can give results similar to applying a traditional topographic correction with no previous stratification. Therefore, the non-stratified correction approach could potentially aid in removing the topographic effect, because it does not require any ancillary information and it is easier to implement in automatic image processing chains. The findings also suggest that the Statistic-Empirical method performs slightly better than the Sun-Canopy-Sensor + C correction, regardless of the stratification approach. In any case, further research is necessary to evaluate other stratification strategies and confirm these results. Full article
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Open AccessArticle Airborne Hyperspectral Data Predict Fine-Scale Plant Species Diversity in Grazed Dry Grasslands
Remote Sens. 2016, 8(2), 133; doi:10.3390/rs8020133
Received: 16 September 2015 / Revised: 3 December 2015 / Accepted: 25 January 2016 / Published: 8 February 2016
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Abstract
Semi-natural grasslands with grazing management are characterized by high fine-scale species richness and have a high conservation value. The fact that fine-scale surveys of grassland plant communities are time-consuming may limit the spatial extent of ground-based diversity surveys. Remote sensing tools have the
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Semi-natural grasslands with grazing management are characterized by high fine-scale species richness and have a high conservation value. The fact that fine-scale surveys of grassland plant communities are time-consuming may limit the spatial extent of ground-based diversity surveys. Remote sensing tools have the potential to support field-based sampling and, if remote sensing data are able to identify grassland sites that are likely to support relatively higher or lower levels of species diversity, then field sampling efforts could be directed towards sites that are of potential conservation interest. In the present study, we examined whether aerial hyperspectral (414–2501 nm) remote sensing can be used to predict fine-scale plant species diversity (characterized as species richness and Simpson’s diversity) in dry grazed grasslands. Vascular plant species were recorded within 104 (4 m × 4 m) plots on the island of Öland (Sweden) and each plot was characterized by a 245-waveband hyperspectral data set. We used two different modeling approaches to evaluate the ability of the airborne spectral measurements to predict within-plot species diversity: (1) a spectral response approach, based on reflectance information from (i) all wavebands, and (ii) a subset of wavebands, analyzed with a partial least squares regression model, and (2) a spectral heterogeneity approach, based on the mean distance to the spectral centroid in an ordinary least squares regression model. Species diversity was successfully predicted by the spectral response approach (with an error of ca. 20%) but not by the spectral heterogeneity approach. When using the spectral response approach, iterative selection of important wavebands for the prediction of the diversity measures simplified the model but did not improve its predictive quality (prediction error). Wavebands sensitive to plant pigment content (400–700 nm) and to vegetation structural properties, such as above-ground biomass (700–1300 nm), were identified as being the most important predictors of plant species diversity. We conclude that hyperspectral remote sensing technology is able to identify fine-scale variation in grassland diversity and has a potential use as a tool in surveys of grassland plant diversity. Full article
(This article belongs to the Special Issue Remote Sensing of Biodiversity)
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Open AccessArticle Deformation and Source Parameters of the 2015 Mw 6.5 Earthquake in Pishan, Western China, from Sentinel-1A and ALOS-2 Data
Remote Sens. 2016, 8(2), 134; doi:10.3390/rs8020134
Received: 19 December 2015 / Revised: 20 January 2016 / Accepted: 4 February 2016 / Published: 8 February 2016
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Abstract
In this study, Interferometric Synthetic Aperture Radar (InSAR) was used to determine the seismogenic fault and slip distribution of the 3 July 2015 Pishan earthquake in the Tarim Basin, western China. We obtained a coseismic deformation map from the ascending and descending Sentinel-1A
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In this study, Interferometric Synthetic Aperture Radar (InSAR) was used to determine the seismogenic fault and slip distribution of the 3 July 2015 Pishan earthquake in the Tarim Basin, western China. We obtained a coseismic deformation map from the ascending and descending Sentinel-1A satellite Terrain Observation with Progressive Scans (TOPS) mode and the ascending Advanced Land Observation Satellite-2 (ALOS-2) satellite Fine mode InSAR data. The maximum ground uplift and subsidence were approximately 13.6 cm and 3.2 cm, respectively. Our InSAR observations associated with focal mechanics indicate that the source fault dips to southwest (SW). Further nonlinear inversions show that the dip angle of the seimogenic fault is approximate 24°, with a strike of 114°, which is similar with the strike of the southeastern Pishan fault. However, this fault segment responsible for the Pishan event has not been mapped before. Our finite fault model reveals that the peak slip of 0.89 m occurred at a depth of 11.6 km, with substantial slip at a depth of 9–14 km and a near-uniform slip of 0.2 m at a depth of 0–7 km. The estimated moment magnitude was approximately Mw 6.5, consistent with seismological results. Full article
(This article belongs to the Special Issue Remote Sensing in Tibet and Siberia)
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Open AccessArticle Assessment of GPM-IMERG and Other Precipitation Products against Gauge Data under Different Topographic and Climatic Conditions in Iran: Preliminary Results
Remote Sens. 2016, 8(2), 135; doi:10.3390/rs8020135
Received: 2 November 2015 / Accepted: 2 February 2016 / Published: 6 February 2016
Cited by 14 | PDF Full-text (4499 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
The new generation of weather observatory satellites, namely Global Precipitation Measurement (GPM) constellation satellites, is the lead observatory of the 10 highly advanced earth orbiting weather research satellites. Indeed, GPM is the first satellite that has been designed to measure light rain and
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The new generation of weather observatory satellites, namely Global Precipitation Measurement (GPM) constellation satellites, is the lead observatory of the 10 highly advanced earth orbiting weather research satellites. Indeed, GPM is the first satellite that has been designed to measure light rain and snowfall, in addition to heavy tropical rainfall. This work compares the final run of the Integrated Multi-satellitE Retrievals for GPM (IMERG) product, the post real time of TRMM and Multi-satellite Precipitation Analysis (TMPA-3B42) and the Era-Interim product from the European Centre for Medium Range Weather Forecasts (ECMWF) against the Iran Meteorological Organization (IMO) daily precipitation measured by the synoptic rain-gauges over four regions with different topography and climate conditions in Iran. Assessment is implemented for a one-year period from March 2014 to February 2015. Overall, in daily scale the results reveal that all three products lead to underestimation but IMERG performs better than other products and underestimates precipitation slightly in all four regions. Based on monthly and seasonal scale, in Guilan all products, in Bushehr and Kermanshah ERA-Interim and in Tehran IMERG and ERA-Interim tend to underestimate. The correlation coefficient between IMERG and the rain-gauge data in daily scale is far superior to that of Era-Interim and TMPA-3B42. On the basis of daily timescale of bias in comparison with the ground data, the IMERG product far outperforms ERA-Interim and 3B42 products. According to the categorical verification technique in this study, IMERG yields better results for detection of precipitation events on the basis of Probability of Detection (POD), Critical Success Index (CSI) and False Alarm Ratio (FAR) in those areas with stratiform and orographic precipitation, such as Tehran and Kermanshah, compared with other satellite/model data sets. In particular, for heavy precipitation (>15 mm/day), IMERG is superior to the other products in all study areas and could be used in future for meteorological and hydrological models, etc. Full article
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Open AccessArticle Wide-Area Landslide Deformation Mapping with Multi-Path ALOS PALSAR Data Stacks: A Case Study of Three Gorges Area, China
Remote Sens. 2016, 8(2), 136; doi:10.3390/rs8020136
Received: 30 November 2015 / Accepted: 29 January 2016 / Published: 6 February 2016
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Abstract
In recent years, satellite synthetic aperture radar interferometry (InSAR) has been adopted as a spaceborne geodetic tool to successfully measure surface deformation of a few well-known landslides in the Three Gorges area. In consideration of the fact that most events of slope failure
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In recent years, satellite synthetic aperture radar interferometry (InSAR) has been adopted as a spaceborne geodetic tool to successfully measure surface deformation of a few well-known landslides in the Three Gorges area. In consideration of the fact that most events of slope failure happened at places other than those famous landslides since the reservoir impoundment in 2003, focusing on a limited number of slopes is insufficient to meet the requirements of regional-scale landslide disaster prevention and early warning. As a result, it has become a vital task to evaluate the overall stability of slopes across the vast area of Three Gorges using wide-coverage InSAR datasets. In this study, we explored the approach of carrying out joint analysis of multi-path InSAR data stacks for wide-area landslide deformation mapping. As an example, three ALOS (Advanced Land Observing Satellite) PALSAR (Phased Array type L-band Synthetic Aperture Radar) data stacks of neighboring ascending paths covering the area along the Yangtze River from Fengjie to Zigui were analyzed. A key problem to be solved is the separation of the tropospheric signal from the interferometric phase, for which we employed a hybrid description model of the atmospheric phase screen (APS) to improve APS estimation from time series interferograms. The estimated atmospheric phase was largely correlated with the seasonal rainfall in the temporal dimension. The experimental results show that about 30 slopes covering total areas of 48 km2 were identified to be landslides in active deformation and should be kept under routine surveillance. Analyses of time series displacement measurements revealed that most landslides in the mountainous area far away from Yangtze River suffered from linear deformation, whereas landslides located on the river bank were destabilized predominantly by the influences of reservoir water level fluctuation and rainfall. Full article
(This article belongs to the Special Issue Remote Sensing in Geology)
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Open AccessArticle Assessment of the Suomi NPP VIIRS Land Surface Albedo Data Using Station Measurements and High-Resolution Albedo Maps
Remote Sens. 2016, 8(2), 137; doi:10.3390/rs8020137
Received: 6 December 2015 / Revised: 16 January 2016 / Accepted: 26 January 2016 / Published: 8 February 2016
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Abstract
Land surface albedo (LSA), one of the Visible Infrared Imaging Radiometer Suite (VIIRS) environmental data records (EDRs), is a fundamental component for linking the land surface and the climate system by regulating shortwave energy exchange between the land and the atmosphere. Currently, the
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Land surface albedo (LSA), one of the Visible Infrared Imaging Radiometer Suite (VIIRS) environmental data records (EDRs), is a fundamental component for linking the land surface and the climate system by regulating shortwave energy exchange between the land and the atmosphere. Currently, the improved bright pixel sub-algorithm (BPSA) is a unique algorithm employed by VIIRS to routinely generate LSA EDR from VIIRS top-of-atmosphere (TOA) observations. As a product validation procedure, LSA EDR reached validated (V1 stage) maturity in December 2014. This study summarizes recent progress in algorithm refinement, and presents comprehensive validation and evaluation results of VIIRS LSA by using extensive field measurements, Moderate Resolution Imaging Spectroradiometer (MODIS) albedo product, and Landsat-retrieved albedo maps. Results indicate that: (1) by testing the updated desert-specific look-up-table (LUT) that uses a stricter standard to select the training data specific for desert aerosol type in our local environment, it is found that the VIIRS LSA retrieval accuracy is improved over a desert surface and the absolute root mean square error (RMSE) is reduced from 0.036 to 0.023, suggesting the potential of the updated desert LUT to the improve the VIIRS LSA product accuracy; (2) LSA retrieval on snow-covered surfaces is more accurate if the newly developed snow-specific LUT (RMSE = 0.082) replaces the generic LUT (RMSE = 0.093) that is employed in the current operational LSA EDR production; (3) VIIRS LSA is also comparable to high-resolution Landsat albedo retrieval (RMSE < 0.04), although Landsat albedo has a slightly higher accuracy, probably owing to higher spatial resolution with less impacts of mixed pixel; (4) VIIRS LSA retrievals agree well with the MODIS albedo product over various land surface types, with overall RMSE of lower than 0.05 and the overall bias as low as 0.025, demonstrating the comparable data quality between VIIRS and the MODIS LSA product. Full article
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Open AccessArticle Soumi NPP VIIRS Day/Night Band Stray Light Characterization and Correction Using Calibration View Data
Remote Sens. 2016, 8(2), 138; doi:10.3390/rs8020138
Received: 2 November 2015 / Revised: 27 January 2016 / Accepted: 29 January 2016 / Published: 8 February 2016
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Abstract
The Soumi NPP VIIRS Day/Night Band (DNB) nighttime imagery quality is affected by stray light contamination. In this study, we examined the relationship between the Earth scene stray light and the signals in VIIRS’s calibrators to better understand stray light characteristics and to
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The Soumi NPP VIIRS Day/Night Band (DNB) nighttime imagery quality is affected by stray light contamination. In this study, we examined the relationship between the Earth scene stray light and the signals in VIIRS’s calibrators to better understand stray light characteristics and to improve upon the current correction method. Our analyses showed the calibrator signal to be highly predictive of Earth scene stray light and can provide additional stray light characteristics that are difficult to obtain from Earth scene data alone. In the current stray light correction regions (mid-to-high latitude), the stray light onset angles can be tracked by calibration view data to reduce correction biases. In the southern hemisphere, it is possible to identify the angular extent of the additional stray light feature in the calibration view data and develop a revised correction method to remove the additional stray light occurring during the southern hemisphere springtime. Outside of current stray light correction region, the analysis of calibration view data indicated occasional stray light contamination at low latitude and possible background biases caused by Moon illumination. As stray light affects a significant portion of nighttime scenes, further refinement in characterization and correction is important to ensure VIIRS DNB imagery quality for Soumi NPP and future missions. Full article
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Open AccessArticle An Overview of the Joint Polar Satellite System (JPSS) Science Data Product Calibration and Validation
Remote Sens. 2016, 8(2), 139; doi:10.3390/rs8020139
Received: 22 December 2015 / Revised: 20 January 2016 / Accepted: 25 January 2016 / Published: 8 February 2016
Cited by 2 | PDF Full-text (2828 KB) | HTML Full-text | XML Full-text
Abstract
The Joint Polar Satellite System (JPSS) will launch its first JPSS-1 satellite in early 2017. The JPSS-1 and follow-on satellites will carry aboard an array of instruments including the Visible Infrared Imaging Radiometer Suite (VIIRS), the Cross-track Infrared Sounder (CrIS), the Advanced Technology
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The Joint Polar Satellite System (JPSS) will launch its first JPSS-1 satellite in early 2017. The JPSS-1 and follow-on satellites will carry aboard an array of instruments including the Visible Infrared Imaging Radiometer Suite (VIIRS), the Cross-track Infrared Sounder (CrIS), the Advanced Technology Microwave Sounder (ATMS), and the Ozone Mapping and Profiler Suite (OMPS). These instruments are similar to the instruments currently operating on the Suomi National Polar-orbiting Partnership (S-NPP) satellite. In preparation for the JPSS-1 launch, the JPSS program at the Center for Satellite Applications and Research (JSTAR) Calibration/Validation (Cal/Val) teams, have laid out the Cal/Val plans to oversee JPSS-1 science products’ algorithm development efforts, verification and characterization of these algorithms during the pre-launch period, calibration and validation of the products during post-launch, and long-term science maintenance (LTSM). In addition, the team has developed the necessary schedules, deliverables and infrastructure for routing JPSS-1 science product algorithms for operational implementation. This paper presents an overview of these efforts. In addition, this paper will provide insight into the processes of both adapting S-NPP science products for JPSS-1 and performing upgrades for enterprise solutions, and will discuss Cal/Val processes and quality assurance procedures. Full article
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Open AccessArticle L-Band Polarimetric Target Decomposition of Mangroves of the Rufiji Delta, Tanzania
Remote Sens. 2016, 8(2), 140; doi:10.3390/rs8020140
Received: 27 August 2015 / Revised: 15 January 2016 / Accepted: 1 February 2016 / Published: 9 February 2016
Cited by 1 | PDF Full-text (2735 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
The mangroves of the Rufiji Delta are an important habitat and resource. The mangrove forest reserve is home to an indigenous population and has been under pressure from an influx of migrants from the landward side of the delta. Timely and effective forest
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The mangroves of the Rufiji Delta are an important habitat and resource. The mangrove forest reserve is home to an indigenous population and has been under pressure from an influx of migrants from the landward side of the delta. Timely and effective forest management is needed to preserve the delta and mangrove forest. Here, we investigate the potential of polarimetric target decomposition for mangrove forest monitoring and analysis. Using three ALOS PALSAR images, we show that L-band polarimetry is capable of mapping mangrove dynamics and is sensitive to stand structure and the hydro-geomorphology of stands. Entropy-alpha-anisotropy and incoherent target decompositions provided valuable measures of scattering behavior related to forest structure. Little difference was found between Yamaguchi and Arii decompositions, despite the conceptual differences between these models. Using these models, we were able to differentiate the scattering behavior of the four main species found in the delta, though classification was impractical due to the lack of pure stands. Scattering differences related to season were attributed primarily to differences in ground moisture or inundation. This is the first time mangrove species have been identified by their scattering behavior in L-band polarimetric data. These results suggest higher resolution L-band quad-polarized imagery, such as from PALSAR-2, may be a powerful tool for mangrove species mapping. Full article
(This article belongs to the Special Issue Remote Sensing of Mangroves: Observation and Monitoring)
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Open AccessArticle JPSS-1 VIIRS Pre-Launch Response Versus Scan Angle Testing and Performance
Remote Sens. 2016, 8(2), 141; doi:10.3390/rs8020141
Received: 28 November 2015 / Revised: 28 January 2016 / Accepted: 4 February 2016 / Published: 12 February 2016
Cited by 4 | PDF Full-text (6639 KB) | HTML Full-text | XML Full-text
Abstract
The Visible Infrared Imaging Radiometer Suite (VIIRS) instruments on-board both the Suomi National Polar-orbiting Partnership (S-NPP) and the first Joint Polar Satellite System (JPSS-1) spacecraft, with launch dates of October 2011 and December 2016 respectively, are cross-track scanners with an angular swath of
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The Visible Infrared Imaging Radiometer Suite (VIIRS) instruments on-board both the Suomi National Polar-orbiting Partnership (S-NPP) and the first Joint Polar Satellite System (JPSS-1) spacecraft, with launch dates of October 2011 and December 2016 respectively, are cross-track scanners with an angular swath of ±56.06°. A four-mirror Rotating Telescope Assembly (RTA) is used for scanning combined with a Half Angle Mirror (HAM) that directs light exiting from the RTA into the aft-optics. It has 14 Reflective Solar Bands (RSBs), seven Thermal Emissive Bands (TEBs) and a panchromatic Day Night Band (DNB). There are three internal calibration targets, the Solar Diffuser, the BlackBody and the Space View, that have fixed scan angles within the internal cavity of VIIRS. VIIRS has calibration requirements of 2% on RSB reflectance and as tight as 0.4% on TEB radiance that requires the sensor’s gain change across the scan or Response Versus Scan angle (RVS) to be well quantified. A flow down of the top level calibration requirements put constraints on the characterization of the RVS to 0.2%–0.3% but there are no specified limitations on the magnitude of response change across scan. The RVS change across scan angle can vary significantly between bands with the RSBs having smaller changes of ~2% and some TEBs having ~10% variation. Within a band, the RVS has both detector and HAM side dependencies that vary across scan. Errors in the RVS characterization will contribute to image banding and striping artifacts if their magnitudes are above the noise level of the detectors. The RVS was characterized pre-launch for both S-NPP and JPSS-1 VIIRS and a comparison of the RVS curves between these two sensors will be discussed. Full article
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Open AccessArticle Evaluation of Simplified Polarimetric Decomposition for Soil Moisture Retrieval over Vegetated Agricultural Fields
Remote Sens. 2016, 8(2), 142; doi:10.3390/rs8020142
Received: 17 December 2015 / Revised: 26 January 2016 / Accepted: 4 February 2016 / Published: 10 February 2016
Cited by 4 | PDF Full-text (7512 KB) | HTML Full-text | XML Full-text
Abstract
This paper investigates a simplified polarimetric decomposition for soil moisture retrieval over agricultural fields. In order to overcome the coherent superposition of the backscattering contributions from vegetation and underlying soils, a simplification of an existing polarimetric decomposition is proposed in this study. It
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This paper investigates a simplified polarimetric decomposition for soil moisture retrieval over agricultural fields. In order to overcome the coherent superposition of the backscattering contributions from vegetation and underlying soils, a simplification of an existing polarimetric decomposition is proposed in this study. It aims to retrieve the soil moisture by using only the surface scattering component, once the volume scattering contribution is removed. Evaluation of the proposed simplified algorithm is performed using extensive ground measurements of soil and vegetation characteristics and the time series of UAVSAR (Uninhabited Aerial Vehicle Synthetic Aperture Radar) data collected in the framework of SMAP (Soil Moisture Active Passive) Validation Experiment 2012 (SMAPVEX12). The retrieval process is tested and analyzed in detail for a variety of crops during the phenological stages considered in this study. The results show that the performance of soil moisture retrieval depends on both the crop types and the crop phenological stage. Soybean and pasture fields present the higher inversion rate during the considered phenological stage, while over canola and wheat fields, the soil moisture can be retrieved only partially during the crop developing stage. RMSE of 0.06–0.12 m3/m3 and an inversion rate of 26%–38% are obtained for the soil moisture retrieval based on the simplified polarimetric decomposition. Full article
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Open AccessArticle Sub-Pixel Classification of MODIS EVI for Annual Mappings of Impervious Surface Areas
Remote Sens. 2016, 8(2), 143; doi:10.3390/rs8020143
Received: 27 November 2015 / Revised: 28 January 2016 / Accepted: 4 February 2016 / Published: 15 February 2016
Cited by 2 | PDF Full-text (2845 KB) | HTML Full-text | XML Full-text
Abstract
Regular monitoring of expanding impervious surfaces areas (ISAs) in urban areas is highly desirable. MODIS data can meet this demand in terms of frequent observations but are lacking in spatial detail, leading to the mixed land cover problem when per-pixel classifications are applied.
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Regular monitoring of expanding impervious surfaces areas (ISAs) in urban areas is highly desirable. MODIS data can meet this demand in terms of frequent observations but are lacking in spatial detail, leading to the mixed land cover problem when per-pixel classifications are applied. To overcome this issue, this research develops and applies a spatio-temporal sub-pixel model to estimate ISAs on an annual basis during 2001–2013 in the Jakarta Metropolitan Area, Indonesia. A Random Forest (RF) regression inferred the ISA proportion from annual 23 values of MODIS MOD13Q1 EVI and reference data in which such proportion was visually allocated from very high-resolution images in Google Earth over time at randomly selected locations. Annual maps of ISA proportion were generated and showed an average increase of 30.65 km2/year over 13 years. For comparison, a series of RF per-pixel classifications were also developed from the same reference data using a Boolean class constructed from different thresholds of ISA proportion. Results from per-pixel models varied when such thresholds change, suggesting difficulty of estimation of actual ISAs. This research demonstrated the advantages of spatio-temporal sub-pixel analysis for annual ISAs mapping and addresses the problem associated with definitions of thresholds in per-pixel approaches. Full article
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Open AccessArticle Examining the Spectral Separability of Prosopis glandulosa from Co-Existent Species Using Field Spectral Measurement and Guided Regularized Random Forest
Remote Sens. 2016, 8(2), 144; doi:10.3390/rs8020144
Received: 10 December 2015 / Revised: 23 January 2016 / Accepted: 4 February 2016 / Published: 15 February 2016
PDF Full-text (2622 KB) | HTML Full-text | XML Full-text
Abstract
The invasive taxa of Prosopis is rated the world’s top 100 unwanted species, and a lack of spatial data about the invasion dynamics has made the current control and monitoring methods unsuccessful. This study thus tests the use of in situ spectroscopy data
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The invasive taxa of Prosopis is rated the world’s top 100 unwanted species, and a lack of spatial data about the invasion dynamics has made the current control and monitoring methods unsuccessful. This study thus tests the use of in situ spectroscopy data with a newly-developed algorithm, guided regularized random forest (GRRF), to spectrally discriminate Prosopis from coexistent acacia species (Acacia karroo, Acacia mellifera and Ziziphus mucronata) in the arid environment of South Africa. Results show that GRRF was able to reduce the high dimensionality of the spectroscopy data and select key wavelengths (n = 11) for discriminating amongst the species. These wavelengths are located at 356.3 nm, 468.5 nm, 531.1 nm, 665.2 nm, 1262.3 nm, 1354.1 nm, 1361.7 nm, 1376.9 nm, 1407.1 nm, 1410.9 nm and 1414.6 nm. The use of these selected wavelengths increases the overall classification accuracy from 79.19% and a Kappa value of 0.7201 when using all wavelengths to 88.59% and a Kappa of 0.8524 when the selected wavelengths were used. Based on our relatively high accuracies and ease of use, it is worth considering the GRRF method for reducing the high dimensionality of spectroscopy data. However, this assertion should receive considerable additional testing and comparison before it is accepted as a substitute for reliable high dimensionality reduction. Full article
(This article belongs to the Special Issue Field Spectroscopy and Radiometry)
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Open AccessArticle Assessing the Effects of Suomi NPP VIIRS M15/M16 Detector Radiometric Stability and Relative Spectral Response Variation on Striping
Remote Sens. 2016, 8(2), 145; doi:10.3390/rs8020145
Received: 20 October 2015 / Revised: 1 February 2016 / Accepted: 2 February 2016 / Published: 15 February 2016
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Abstract
Modern satellite radiometers have many detectors with different relative spectral response (RSR). Effect of RSR differences on striping and the root cause of striping in sensor data record (SDR) radiance and brightness temperature products have not been well studied. A previous study used
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Modern satellite radiometers have many detectors with different relative spectral response (RSR). Effect of RSR differences on striping and the root cause of striping in sensor data record (SDR) radiance and brightness temperature products have not been well studied. A previous study used MODTRAN radiative transfer model (RTM) to analyze striping. In this study, we make efforts to find the possible root causes of striping. Line-by-Line RTM (LBLRTM) is used to evaluate the effect of RSR difference on striping and the atmospheric dependency for VIIRS bands M15 and M16. The results show that previous study using MODTRAN is repeatable: the striping is related to the difference between band-averaged and detector-level RSR, and the BT difference has some atmospheric dependency. We also analyzed VIIRS earth view (EV) data with several striping index methods. Since the EV data is complex, we further analyze the onboard calibration data. Analysis of Variance (ANOVA) test shows that the noise along track direction is the major reason for striping. We also found evidence of correlation between solar diffuser (SD) and blackbody (BB) for detector 1 in M15. Digital Count Restoration (DCR) and detector instability are possibly related to the striping in SD and EV data, but further analysis is needed. These findings can potentially lead to further SDR processing improvements. Full article
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Open AccessArticle The Combined Use of Airborne Remote Sensing Techniques within a GIS Environment for the Seismic Vulnerability Assessment of Urban Areas: An Operational Application
Remote Sens. 2016, 8(2), 146; doi:10.3390/rs8020146
Received: 20 November 2015 / Revised: 28 January 2016 / Accepted: 4 February 2016 / Published: 16 February 2016
Cited by 2 | PDF Full-text (9452 KB) | HTML Full-text | XML Full-text
Abstract
The knowledge of the topographic features, the building properties, and the road infrastructure settings are relevant operational tasks for managing post-crisis events, restoration activities, and for supporting search and rescue operations. Within such a framework, airborne remote sensing tools have demonstrated to be
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The knowledge of the topographic features, the building properties, and the road infrastructure settings are relevant operational tasks for managing post-crisis events, restoration activities, and for supporting search and rescue operations. Within such a framework, airborne remote sensing tools have demonstrated to be powerful instruments, whose joint use can provide meaningful analyses to support the risk assessment of urban environments. Based on this rationale, in this study, the operational benefits obtained by combining airborne LiDAR and hyperspectral measurements are shown. Terrain and surface digital models are gathered by using LiDAR data. Information about roads and roof materials are provided through the supervised classification of hyperspectral images. The objective is to combine such products within a geographic information system (GIS) providing value-added maps to be used for the seismic vulnerability assessment of urban environments. Experimental results are gathered for the city of Cosenza, Italy. Full article
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Open AccessArticle Diurnal Variability of Turbidity Fronts Observed by Geostationary Satellite Ocean Color Remote Sensing
Remote Sens. 2016, 8(2), 147; doi:10.3390/rs8020147
Received: 18 November 2015 / Revised: 23 January 2016 / Accepted: 4 February 2016 / Published: 16 February 2016
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Abstract
Monitoring front dynamics is essential for studying the ocean’s physical and biogeochemical processes. However, the diurnal displacement of fronts remains unclear because of limited in situ observations. Using the hourly satellite imageries from the Geostationary Ocean Color Imager (GOCI) with a spatial resolution
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Monitoring front dynamics is essential for studying the ocean’s physical and biogeochemical processes. However, the diurnal displacement of fronts remains unclear because of limited in situ observations. Using the hourly satellite imageries from the Geostationary Ocean Color Imager (GOCI) with a spatial resolution of 500 m, we investigated the diurnal displacement of turbidity fronts in both the northern Jiangsu shoal water (NJSW) and the southwestern Korean coastal water (SKCW) in the Yellow Sea (YS). The hourly turbidity fronts were retrieved from the GOCI-derived total suspended matter using the entropy-based algorithm. The results showed that the entropy-based algorithm could provide fine structure and clearly temporal evolution of turbidity fronts. Moreover, the diurnal displacement of turbidity fronts in NJSW can be up to 10.3 km in response to the onshore-offshore movements of tidal currents, much larger than it is in SKCW (around 4.7 km). The discrepancy between NJSW and SKCW are mainly caused by tidal current direction relative to the coastlines. Our results revealed the significant diurnal displacement of turbidity fronts, and highlighted the feasibility of using geostationary ocean color remote sensing technique to monitor the short-term frontal variability, which may contribute to understanding of the sediment dynamics and the coupling physical-biogeochemical processes. Full article
(This article belongs to the Special Issue Remote Sensing of Biogeochemical Cycles)
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Open AccessArticle IKONOS Image-Based Extraction of the Distribution Area of Stellera chamaejasme L. in Qilian County of Qinghai Province, China
Remote Sens. 2016, 8(2), 148; doi:10.3390/rs8020148
Received: 28 November 2015 / Revised: 2 February 2016 / Accepted: 4 February 2016 / Published: 16 February 2016
Cited by 1 | PDF Full-text (5485 KB) | HTML Full-text | XML Full-text
Abstract
Stellera chamaejasme L. (S. chamaejasme) is one of the primary toxic grass species (poisonous plants) distributed in the alpine meadows of Qinghai Province, China. In this study, according to the distinctive phenological characteristics of S. chamaejasme, the spectral differences between S. chamaejasme in
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Stellera chamaejasme L. (S. chamaejasme) is one of the primary toxic grass species (poisonous plants) distributed in the alpine meadows of Qinghai Province, China. In this study, according to the distinctive phenological characteristics of S. chamaejasme, the spectral differences between S. chamaejasme in the full-bloom stage and other pasture grasses were analyzed and the red, blue, and near-infrared bands of IKONOS image were determined as the diagnostic bands of S. chamaejasme recognition. Feature indexes related to S. chamaejasme were established using the diagnostic bands, and \(NDVI_{blue} = (\rho_{nir} − \rho_{blue})/(\rho_{nir} + \rho_{blue})\) obtained as S. chamaejasme sensitive index based on the linear regression analysis between the indexes derived from field spectra and the actual cover fraction of S. chamaejasme communities. The distribution area of S. chamaejasme was extracted by using the index \(NDVI_{blue}\) derived from IKONOS multispectral image in Qilian County of Qinghai Province, China and the verified result reached an overall accuracy of 90.71%. The study indicated that high resolution multispectral satellite images (such as IKONOS images) had significant potential in remote sensing recognition of toxic grass species. Full article
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Open AccessArticle Moving Towards Dynamic Ocean Management: How Well Do Modeled Ocean Products Predict Species Distributions?
Remote Sens. 2016, 8(2), 149; doi:10.3390/rs8020149
Received: 12 November 2015 / Revised: 16 January 2016 / Accepted: 1 February 2016 / Published: 16 February 2016
Cited by 10 | PDF Full-text (4160 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Species distribution models are now widely used in conservation and management to predict suitable habitat for protected marine species. The primary sources of dynamic habitat data have been in situ and remotely sensed oceanic variables (both are considered “measured data”), but now ocean
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Species distribution models are now widely used in conservation and management to predict suitable habitat for protected marine species. The primary sources of dynamic habitat data have been in situ and remotely sensed oceanic variables (both are considered “measured data”), but now ocean models can provide historical estimates and forecast predictions of relevant habitat variables such as temperature, salinity, and mixed layer depth. To assess the performance of modeled ocean data in species distribution models, we present a case study for cetaceans that compares models based on output from a data assimilative implementation of the Regional Ocean Modeling System (ROMS) to those based on measured data. Specifically, we used seven years of cetacean line-transect survey data collected between 1991 and 2009 to develop predictive habitat-based models of cetacean density for 11 species in the California Current Ecosystem. Two different generalized additive models were compared: one built with a full suite of ROMS output and another built with a full suite of measured data. Model performance was assessed using the percentage of explained deviance, root mean squared error (RMSE), observed to predicted density ratios, and visual inspection of predicted and observed distributions. Predicted distribution patterns were similar for models using ROMS output and measured data, and showed good concordance between observed sightings and model predictions. Quantitative measures of predictive ability were also similar between model types, and RMSE values were almost identical. The overall demonstrated success of the ROMS-based models opens new opportunities for dynamic species management and biodiversity monitoring because ROMS output is available in near real time and can be forecast. Full article
(This article belongs to the Special Issue Remote Sensing of Biodiversity)
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Open AccessArticle The Potential of Autonomous Ship-Borne Hyperspectral Radiometers for the Validation of Ocean Color Radiometry Data
Remote Sens. 2016, 8(2), 150; doi:10.3390/rs8020150
Received: 29 November 2015 / Revised: 1 February 2016 / Accepted: 4 February 2016 / Published: 16 February 2016
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Abstract
Calibration and validation of satellite observations are essential and on-going tasks to ensure compliance with mission accuracy requirements. An automated above water hyperspectral radiometer significantly augmented Australia’s ability to contribute to global and regional ocean color validation and algorithm design activities. The hyperspectral
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Calibration and validation of satellite observations are essential and on-going tasks to ensure compliance with mission accuracy requirements. An automated above water hyperspectral radiometer significantly augmented Australia’s ability to contribute to global and regional ocean color validation and algorithm design activities. The hyperspectral data can be re-sampled for comparison with current and future sensor wavebands. The continuous spectral acquisition along the ship track enables spatial resampling to match satellite footprint. This study reports spectral comparisons of the radiometer data with Visible Infrared Imaging Radiometer Suite (VIIRS) and Moderate Resolution Imaging Spectroradiometer (MODIS)-Aqua for contrasting water types in tropical waters off northern Australia based on the standard NIR atmospheric correction implemented in SeaDAS. Consistent match-ups are shown for transects of up to 50 km over a range of reflectance values. The MODIS and VIIRS satellite reflectance data consistently underestimated the in situ spectra in the blue with a bias relative to the “dynamic above water radiance and irradiance collector” (DALEC) at 443 nm ranging from 9.8 × 10−4 to 3.1 × 10−3 sr−1. Automated acquisition has produced good quality data under standard operating and maintenance procedures. A sensitivity analysis explored the effects of some assumptions in the data reduction methods, indicating the need for a comprehensive investigation and quantification of each source of uncertainty in the estimate of the DALEC reflectances. Deployment on a Research Vessel provides the potential for the radiometric data to be combined with other sampling and observational activities to contribute to algorithm development in the wider bio-optical research community. Full article
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Open AccessArticle Mapping Urban Land Use by Using Landsat Images and Open Social Data
Remote Sens. 2016, 8(2), 151; doi:10.3390/rs8020151
Received: 23 October 2015 / Revised: 31 January 2016 / Accepted: 4 February 2016 / Published: 17 February 2016
Cited by 17 | PDF Full-text (3683 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
High-resolution urban land use maps have important applications in urban planning and management, but the availability of these maps is low in countries such as China. To address this issue, we have developed a protocol to identify urban land use functions over large
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High-resolution urban land use maps have important applications in urban planning and management, but the availability of these maps is low in countries such as China. To address this issue, we have developed a protocol to identify urban land use functions over large areas using satellite images and open social data. We first derived parcels from road networks contained in Open Street Map (OSM) and used the parcels as the basic mapping unit. We then used 10 features derived from Points of Interest (POI) data and two indices obtained from Landsat 8 Operational Land Imager (OLI) images to classify parcels into eight Level I classes and sixteen Level II classes of land use. Similarity measures and threshold methods were used to identify land use types in the classification process. This protocol was tested in Beijing, China. The results showed that the generated land use map had an overall accuracy of 81.04% and 69.89% for Level I and Level II classes, respectively. The map revealed significantly more details of the spatial pattern of land uses in Beijing than the land use map released by the government. Full article
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Open AccessArticle Satellite-Based Thermophysical Analysis of Volcaniclastic Deposits: A Terrestrial Analog for Mantled Lava Flows on Mars
Remote Sens. 2016, 8(2), 152; doi:10.3390/rs8020152
Received: 7 October 2015 / Revised: 21 January 2016 / Accepted: 25 January 2016 / Published: 17 February 2016
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Abstract
Orbital thermal infrared (TIR) remote sensing is an important tool for characterizing geologic surfaces on Earth and Mars. However, deposition of material from volcanic or eolian activity results in bedrock surfaces becoming significantly mantled over time, hindering the accuracy of TIR compositional analysis.
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Orbital thermal infrared (TIR) remote sensing is an important tool for characterizing geologic surfaces on Earth and Mars. However, deposition of material from volcanic or eolian activity results in bedrock surfaces becoming significantly mantled over time, hindering the accuracy of TIR compositional analysis. Moreover, interplay between particle size, albedo, composition and surface roughness add complexity to these interpretations. Apparent Thermal Inertia (ATI) is the measure of the resistance to temperature change and has been used to determine parameters such as grain/block size, density/mantling, and the presence of subsurface soil moisture/ice. Our objective is to document the quantitative relationship between ATI derived from orbital visible/near infrared (VNIR) and thermal infrared (TIR) data and tephra fall mantling of the Mono Craters and Domes (MCD) in California, which were chosen as an analog for partially mantled flows observed at Arsia Mons volcano on Mars. The ATI data were created from two images collected ~12 h apart by the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) instrument. The results were validated with a quantitative framework developed using fieldwork that was conducted at 13 pre-chosen sites. These sites ranged in grain size from ash-sized to meter-scale blocks and were all rhyolitic in composition. Block size and mantling were directly correlated with ATI. Areas with ATI under 2.3 × 10−2 were well-mantled with average grain size below 4 cm; whereas values greater than 3.0 × 10−2 corresponded to mantle-free surfaces. Correlation was less accurate where checkerboard-style mixing between mantled and non-mantled surfaces occurred below the pixel scale as well as in locations where strong shadowing occurred. However, the results validate that the approach is viable for a large majority of mantled surfaces on Earth and Mars. This is relevant for determining the volcanic history of Mars, for example. Accurate identification of non-mantled lava surfaces within an apparently well-mantled flow field on either planet provides locations to extract important mineralogical constraints on the individual flows using TIR data. Full article
(This article belongs to the Special Issue Volcano Remote Sensing)
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Open AccessArticle Quantifying the Daytime and Night-Time Urban Heat Island in Birmingham, UK: A Comparison of Satellite Derived Land Surface Temperature and High Resolution Air Temperature Observations
Remote Sens. 2016, 8(2), 153; doi:10.3390/rs8020153
Received: 13 October 2015 / Revised: 27 January 2016 / Accepted: 1 February 2016 / Published: 17 February 2016
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Abstract
The Urban Heat Island (UHI) is one of the most well documented phenomena in urban climatology. Although a range of measurements and modelling techniques can be used to assess the UHI, the paucity of traditional meteorological observations in urban areas has been an
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The Urban Heat Island (UHI) is one of the most well documented phenomena in urban climatology. Although a range of measurements and modelling techniques can be used to assess the UHI, the paucity of traditional meteorological observations in urban areas has been an ongoing limitation for studies. The availability of remote sensing data has therefore helped fill a scientific need by providing high resolution temperature data of our cities. However, satellite-mounted sensors measure land surface temperatures (LST) and not canopy air temperatures with the latter being the key parameter in UHI investigations. Fortunately, such data is becoming increasingly available via urban meteorological networks, which now provide an opportunity to quantify and compare surface and canopy UHI on an unprecedented scale. For the first time, this study uses high resolution air temperature data from the Birmingham Urban Climate Laboratory urban meteorological network and MODIS LST to quantify and identify the spatial pattern of the daytime and night-time UHI in Birmingham, UK (a city with an approximate population of 1 million). This analysis is performed under a range of atmospheric stability classes and investigates the relationship between surface and canopy UHI in the city. A significant finding of this work is that it demonstrates, using observations, that the distribution of the surface UHI appears to be clearly linked to landuse, whereas for canopy UHI, advective processes appear to play an increasingly important role. Strong relationships were found between air temperatures and LST during both the day and night at a neighbourhood scale, but even with the use of higher resolution urban meteorological datasets, relationships at the city scale are still limited. Full article
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Open AccessArticle Extracting Soil Water Holding Capacity Parameters of a Distributed Agro-Hydrological Model from High Resolution Optical Satellite Observations Series
Remote Sens. 2016, 8(2), 154; doi:10.3390/rs8020154
Received: 12 October 2015 / Revised: 21 January 2016 / Accepted: 25 January 2016 / Published: 17 February 2016
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Abstract
Sentinel-2 (S2) earth observation satellite mission, launched in 2015, is foreseen to promote within-field decisions in Precision Agriculture (PA) for both: (1) optimizing crop production; and (2) regulating environmental impacts. In this second scope, a set of Leaf Area Index (LAI) derived from
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Sentinel-2 (S2) earth observation satellite mission, launched in 2015, is foreseen to promote within-field decisions in Precision Agriculture (PA) for both: (1) optimizing crop production; and (2) regulating environmental impacts. In this second scope, a set of Leaf Area Index (LAI) derived from S2 type time-series (2006–2010, using Formosat-2 satellite) is used to spatially constrain the within-field crop growth and the related nitrogen contamination of surface water simulated at a small experimental catchment scale with the distributed agro-hydrological model Topography Nitrogen Transfer and Transformation (TNT2). The Soil Water Holding Capacity (SWHC), represented by two parameters, soil depth and retention porosity, is used to fit the yearly maximum of LAI (LAX) at each pixel of the satellite image. Possible combinations of soil parameters, defining 154 realistic SWHC found on the study site are used to force spatially homogeneous SWHC. LAX simulated at the pixel level for the 154 SWHC, for each of the five years of the study period, are recorded and hereafter referred to as synthetic LAX. Optimal SWHCyear_I,pixel_j, corresponding to minimal difference between observed and synthetic LAXyear_I,pixel_j, is selected for each pixel, independent of the value at neighboring pixels. Each re-estimated soil maps are used to re-simulate LAXyear_I. Results show that simulated and synthetic LAXyear_I,allpixels obtained from SWHCyear_I,allpixels are close and accurately fit the observed LAXyear_I,allpixels (RMSE = 0.05 m2/m2 to 0.2 and R2 = 0.99 to 0.94), except for the year 2008 (RMSE = 0.8 m2/m2 and R2 = 0.8). These results show that optimal SWHC can be derived from remote sensing series for one year. Unique SWHC solutions for each pixel that limit the LAX error for the five years to less than 0.2 m2/m2 are found for only 10% of the pixels. Selection of unique soil parameters using multi-year LAX and neighborhood solution is expected to deliver more robust soil parameters solutions and need to be assessed further. The use of optical remote sensing series is then a promising calibration step to represent crop growth within crop field at catchment level. Nevertheless, this study discusses the model and data improvements that are needed to get realistic spatial representation of agro-hydrological processes simulated within catchments. Full article
(This article belongs to the Special Issue Remote Sensing in Precision Agriculture)
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Open AccessArticle Representation of Block-Based Image Features in a Multi-Scale Framework for Built-Up Area Detection
Remote Sens. 2016, 8(2), 155; doi:10.3390/rs8020155
Received: 24 August 2015 / Revised: 17 January 2016 / Accepted: 2 February 2016 / Published: 18 February 2016
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Abstract
The accurate extraction and mapping of built-up areas play an important role in many social, economic, and environmental studies. In this paper, we propose a novel approach for built-up area detection from high spatial resolution remote sensing images, using a block-based multi-scale feature
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The accurate extraction and mapping of built-up areas play an important role in many social, economic, and environmental studies. In this paper, we propose a novel approach for built-up area detection from high spatial resolution remote sensing images, using a block-based multi-scale feature representation framework. First, an image is divided into small blocks, in which the spectral, textural, and structural features are extracted and represented using a multi-scale framework; a set of refined Harris corner points is then used to select blocks as training samples; finally, a built-up index image is obtained by minimizing the normalized spectral, textural, and structural distances to the training samples, and a built-up area map is obtained by thresholding the index image. Experiments confirm that the proposed approach is effective for high-resolution optical and synthetic aperture radar images, with different scenes and different spatial resolutions. Full article
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Open AccessArticle Impacts of Re-Vegetation on Surface Soil Moisture over the Chinese Loess Plateau Based on Remote Sensing Datasets
Remote Sens. 2016, 8(2), 156; doi:10.3390/rs8020156
Received: 4 November 2015 / Revised: 31 January 2016 / Accepted: 15 February 2016 / Published: 19 February 2016
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Abstract
A large-scale re-vegetation supported by the Grain for Green Project (GGP) has greatly changed local eco-hydrological systems, with an impact on soil moisture conditions for the Chinese Loess Plateau. It is important to know how, exactly, re-vegetation influences soil moisture conditions, which not
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A large-scale re-vegetation supported by the Grain for Green Project (GGP) has greatly changed local eco-hydrological systems, with an impact on soil moisture conditions for the Chinese Loess Plateau. It is important to know how, exactly, re-vegetation influences soil moisture conditions, which not only crucially constrain growth and distribution of vegetation, and hence, further re-vegetation, but also determine the degree of soil desiccation and, thus, erosion risk in the region. In this study, three eco-environmental factors, which are Soil Water Index (SWI), the Normalized Difference Vegetation Index (NDVI), and precipitation, were used to investigate the response of soil moisture in the one-meter layer of top soil to the re-vegetation during the GGP. SWI was estimated based on the backscatter coefficient produced by the European Remote Sensing Satellite (ERS-1/2) and Meteorological Operational satellite program (MetOp), while NDVI was derived from SPOT imageries. Two separate periods, which are 1998–2000 and 2008–2010, were selected to examine the spatiotemporal pattern of the chosen eco-environmental factors. It has been shown that the amount of precipitation in 1998–2000 was close to that of 2008–2010 (the difference being 13.10 mm). From 1998–2000 to 2008–2010, the average annual NDVI increased for 80.99%, while the SWI decreased for 72.64% of the area on the Loess Plateau. The average NDVI over the Loess Plateau increased rapidly by 17.76% after the 10-year GGP project. However, the average SWI decreased by 4.37% for two-thirds of the area. More specifically, 57.65% of the area on the Loess Plateau experienced an increased NDVI and decreased SWI, 23.34% of the area had an increased NDVI and SWI. NDVI and SWI decreased simultaneously for 14.99% of the area, and the decreased NDVI and increased SWI occurred at the same time for 4.02% of the area. These results indicate that re-vegetation, human activities, and climate change have impacts on soil moisture. However, re-vegetation, which consumes a large quantity of soil water, may be the major factor for soil moisture change in most areas of the Loess Plateau. It is, therefore, suggested that Soil Moisture Content (SMC) should be kept in mind when carrying out re-vegetation in China’s arid and semi-arid regions. Full article
(This article belongs to the Special Issue Remote Sensing of Forest Health)
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Open AccessArticle The Fisher Kernel Coding Framework for High Spatial Resolution Scene Classification
Remote Sens. 2016, 8(2), 157; doi:10.3390/rs8020157
Received: 8 December 2015 / Revised: 20 January 2016 / Accepted: 14 February 2016 / Published: 19 February 2016
Cited by 7 | PDF Full-text (4238 KB) | HTML Full-text | XML Full-text
Abstract
High spatial resolution (HSR) image scene classification is aimed at bridging the semantic gap between low-level features and high-level semantic concepts, which is a challenging task due to the complex distribution of ground objects in HSR images. Scene classification based on the bag-of-visual-words
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High spatial resolution (HSR) image scene classification is aimed at bridging the semantic gap between low-level features and high-level semantic concepts, which is a challenging task due to the complex distribution of ground objects in HSR images. Scene classification based on the bag-of-visual-words (BOVW) model is one of the most successful ways to acquire the high-level semantic concepts. However, the BOVW model assigns local low-level features to their closest visual words in the “visual vocabulary” (the codebook obtained by k-means clustering), which discards too many useful details of the low-level features in HSR images. In this paper, a feature coding method under the Fisher kernel (FK) coding framework is introduced to extend the BOVW model by characterizing the low-level features with a gradient vector instead of the count statistics in the BOVW model, which results in a significant decrease in the codebook size and an acceleration of the codebook learning process. By considering the differences in the distributions of the ground objects in different regions of the images, local FK (LFK) is proposed for the HSR image scene classification method. The experimental results show that the proposed scene classification methods under the FK coding framework can greatly reduce the computational cost, and can obtain a better scene classification accuracy than the methods based on the traditional BOVW model. Full article
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Open AccessArticle Evaluation of VIIRS and MODIS Thermal Emissive Band Calibration Stability Using Ground Target
Remote Sens. 2016, 8(2), 158; doi:10.3390/rs8020158
Received: 2 November 2015 / Revised: 2 February 2016 / Accepted: 4 February 2016 / Published: 19 February 2016
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Abstract
The S-NPP Visible Infrared Imaging Radiometer Suite (VIIRS) instrument, a polar orbiting Earth remote sensing instrument built using a strong MODIS background, employs a similarly designed on-board calibrating source—a V-grooved blackbody for the Thermal Emissive Bands (TEB). The central wavelengths of most VIIRS
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The S-NPP Visible Infrared Imaging Radiometer Suite (VIIRS) instrument, a polar orbiting Earth remote sensing instrument built using a strong MODIS background, employs a similarly designed on-board calibrating source—a V-grooved blackbody for the Thermal Emissive Bands (TEB). The central wavelengths of most VIIRS TEBs are very close to those of MODIS with the exception of the 10.7 µm channel. To ensure the long term continuity of climate data records derived using VIIRS and MODIS TEB, it is necessary to assess any systematic differences between the two instruments, including scenes with temperatures significantly lower than blackbody operating temperatures at approximately 290 K. Previous work performed by the MODIS Characterization Support Team (MCST) at NASA/GSFC used the frequent observations of the Dome Concordia site located in Antarctica to evaluate the calibration stability and consistency of Terra and Aqua MODIS over the mission lifetime. The near-surface temperature measurements from an automatic weather station (AWS) provide a direct reference useful for tracking the stability and determining the relative bias between the two MODIS instruments. In this study, the same technique is applied to the VIIRS TEB and the results are compared with those from the matched MODIS TEB. The results of this study show a small negative bias when comparing the matching VIIRS and Aqua MODIS TEB, implying a higher brightness temperature for S-VIIRS at the cold end. Statistically no significant drift is observed for VIIRS TEB performance over the first 3.5 years of the mission. Full article
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Open AccessArticle A Comparative Study of Cross-Product NDVI Dynamics in the Kilimanjaro Region—A Matter of Sensor, Degradation Calibration, and Significance
Remote Sens. 2016, 8(2), 159; doi:10.3390/rs8020159
Received: 7 December 2015 / Revised: 20 January 2016 / Accepted: 14 February 2016 / Published: 19 February 2016
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Abstract
While satellite-based monitoring of vegetation activity at the earth’s surface is of vital importance for many eco-climatological applications, the degree of agreement among certain sensors and products providing estimates of the Normalized Difference Vegetation Index (NDVI) has been found to vary considerably. In
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While satellite-based monitoring of vegetation activity at the earth’s surface is of vital importance for many eco-climatological applications, the degree of agreement among certain sensors and products providing estimates of the Normalized Difference Vegetation Index (NDVI) has been found to vary considerably. In order to assess the extent of such differences in highly heterogeneous terrain, we analyze and compare intra-annual seasonal fluctuations and long-term monotonic trends (2003–2012) in the Kilimanjaro region, Tanzania. The considered NDVI datasets include the Moderate Resolution Imaging Spectroradiometer (MODIS) products from Terra and Aqua, Collections 5 and 6, and the 3rd Generation Global Inventory Modeling and Mapping Studies (GIMMS) product. The degree of agreement in seasonal fluctuations is assessed by calculating a pairwise Index of Association (IOAs), whereas long-term trends are derived from the trend-free pre-whitened Mann–Kendall test. On the seasonal scale, the two Terra-MODIS products (and, accordingly, the two Aqua-MODIS products) are best associated with each other, indicating that the seasonal signal remained largely unaffected by the new Collection 6 calibration approach. On the long-term scale, we find that the negative impacts of band ageing on Terra-MODIS NDVI have been accounted for in Collection 6, which now distinctly outweighs Aqua-MODIS in terms of greening trends. GIMMS NDVI, by contrast, fails to capture small-scale seasonal and trend patterns that are characteristic for the highly fragmented landscape which is likely owing to the coarse spatial resolution. As a short digression, we also demonstrate that the amount of false discoveries in the determined trend fraction is distinctly higher for p < 0.05 ( 52.6 % ) than for p < 0.001 ( 2.2 % ) which should point the way for any future studies focusing on the reliable deduction of long-term monotonic trends. Full article
(This article belongs to the Special Issue Multi-Sensor and Multi-Data Integration in Remote Sensing)
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Open AccessArticle Dynamic Mapping of Evapotranspiration Using an Energy Balance-Based Model over an Andean Páramo Catchment of Southern Ecuador
Remote Sens. 2016, 8(2), 160; doi:10.3390/rs8020160
Received: 9 December 2015 / Revised: 1 February 2016 / Accepted: 14 February 2016 / Published: 19 February 2016
Cited by 3 | PDF Full-text (10795 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Understanding of evapotranspiration (ET) processes over Andean mountain environments is crucial, particularly due to the importance of these regions to deliver water-related ecosystem services. In this context, the detection of spatio-temporal changes in ET remains poorly investigated for specific Andean ecosystems, like the
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Understanding of evapotranspiration (ET) processes over Andean mountain environments is crucial, particularly due to the importance of these regions to deliver water-related ecosystem services. In this context, the detection of spatio-temporal changes in ET remains poorly investigated for specific Andean ecosystems, like the páramo. To overcome this lack of knowledge, we implemented the energy-balance model METRIC with Landsat 7 ETM+ and MODIS-Terra imagery for a páramo catchment. The implementation contemplated adjustments for complex terrain in order to obtain daily, monthly and annual ET maps (between 2013 and 2014). In addition, we compared our results to the global ET product MOD16. Finally, a rigorous validation of the outputs was conducted with residual ET from the water balance. ET retrievals from METRIC (Landsat-based) showed good agreement with the validation-related ET at monthly and annual steps (mean bias error <8 mm·month−1 and annual deviation <17%). However, METRIC (MODIS-based) outputs and the MOD16 product were revealed to be unsuitable for our study due to the low spatial resolution. At last, the plausibility of METRIC to obtain spatial ET retrievals using higher resolution satellite data is demonstrated, which constitutes the first contribution to the understanding of spatially-explicit ET over an alpine catchment in the neo-tropical Andes. Full article
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Open AccessArticle Tree Species Abundance Predictions in a Tropical Agricultural Landscape with a Supervised Classification Model and Imbalanced Data
Remote Sens. 2016, 8(2), 161; doi:10.3390/rs8020161
Received: 2 December 2015 / Revised: 3 February 2016 / Accepted: 14 February 2016 / Published: 19 February 2016
Cited by 11 | PDF Full-text (5505 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Mapping species through classification of imaging spectroscopy data is facilitating research to understand tree species distributions at increasingly greater spatial scales. Classification requires a dataset of field observations matched to the image, which will often reflect natural species distributions, resulting in an imbalanced
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Mapping species through classification of imaging spectroscopy data is facilitating research to understand tree species distributions at increasingly greater spatial scales. Classification requires a dataset of field observations matched to the image, which will often reflect natural species distributions, resulting in an imbalanced dataset with many samples for common species and few samples for less common species. Despite the high prevalence of imbalanced datasets in multiclass species predictions, the effect on species prediction accuracy and landscape species abundance has not yet been quantified. First, we trained and assessed the accuracy of a support vector machine (SVM) model with a highly imbalanced dataset of 20 tropical species and one mixed-species class of 24 species identified in a hyperspectral image mosaic (350–2500 nm) of Panamanian farmland and secondary forest fragments. The model, with an overall accuracy of 62% ± 2.3% and F-score of 59% ± 2.7%, was applied to the full image mosaic (23,000 ha at a 2-m resolution) to produce a species prediction map, which suggested that this tropical agricultural landscape is more diverse than what has been presented in field-based studies. Second, we quantified the effect of class imbalance on model accuracy. Model assessment showed a trend where species with more samples were consistently over predicted while species with fewer samples were under predicted. Standardizing sample size reduced model accuracy, but also reduced the level of species over- and under-prediction. This study advances operational species mapping of diverse tropical landscapes by detailing the effect of imbalanced data on classification accuracy and providing estimates of tree species abundance in an agricultural landscape. Species maps using data and methods presented here can be used in landscape analyses of species distributions to understand human or environmental effects, in addition to focusing conservation efforts in areas with high tree cover and diversity. Full article
(This article belongs to the Special Issue Remote Sensing of Biodiversity)
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Open AccessArticle Comparing and Combining Remotely Sensed Land Surface Temperature Products for Improved Hydrological Applications
Remote Sens. 2016, 8(2), 162; doi:10.3390/rs8020162
Received: 26 November 2015 / Revised: 11 February 2016 / Accepted: 15 February 2016 / Published: 20 February 2016
Cited by 2 | PDF Full-text (3525 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Land surface temperature (LST) is an important variable that provides a valuable connection between the energy and water budget and is strongly linked to land surface hydrology. Space-borne remote sensing provides a consistent means for regularly observing LST using thermal infrared (TIR) and
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Land surface temperature (LST) is an important variable that provides a valuable connection between the energy and water budget and is strongly linked to land surface hydrology. Space-borne remote sensing provides a consistent means for regularly observing LST using thermal infrared (TIR) and passive microwave observations each with unique strengths and weaknesses. The spatial resolution of TIR based LST observations is around 1 km, a major advantage when compared to passive microwave observations (around 10 km). However, a major advantage of passive microwaves is their cloud penetrating capability making them all-weather sensors whereas TIR observations are routinely masked under the presence of clouds and aerosols. In this study, a relatively simple combination approach that benefits from the cloud penetrating capacity of passive microwave sensors was proposed. In the first step, TIR and passive microwave LST products were compared over Australia for both anomalies and raw timeseries. A very high agreement was shown over the vast majority of the country with R2 typically ranging from 0.50 to 0.75 for the anomalies and from 0.80 to 1.00 for the raw timeseries. Then, the scalability of the passive microwave based LST product was examined and a pixel based merging approach through linear scaling was proposed. The individual and merged LST products were further compared against independent LST from the re-analysis model outputs. This comparison revealed that the TIR based LST product agrees best with the re-analysis data (R2 0.26 for anomalies and R2 0.76 for raw data), followed by the passive microwave LST product (R2 0.16 for anomalies and R2 0.66 for raw data) and the combined LST product (R2 0.18 for anomalies and R2 0.62 for raw data). It should be noted that the drop in performance comes with an increased revisit frequency of approximately 20% compared to the revised frequency of the TIR alone. Additionally, this comparison against re-analysis data was subdivided over Australia’s major climate zones and revealed that the relative agreement between the individual and combined LST products against the re-analysis data is consistent over these climate zones. These results are also consistent for both the anomalies and the raw time series. Finally, two examples were provided that demonstrate the proposed merging approach including an example for the Hunter Valley floods along Australia’s central coast that experienced significant flooding in April 2015. Full article
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Open AccessArticle Remote Sensing of Soil Alkalinity and Salinity in the Wuyu’er-Shuangyang River Basin, Northeast China
Remote Sens. 2016, 8(2), 163; doi:10.3390/rs8020163
Received: 2 December 2015 / Revised: 21 January 2016 / Accepted: 29 January 2016 / Published: 20 February 2016
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Abstract
The Songnen Plain of the Northeast China is one of the three largest soda saline-alkali regions worldwide. To better understand soil alkalinization and salinization in this important agricultural region, it is vital to explore the distribution and variation of soil alkalinity and salinity
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The Songnen Plain of the Northeast China is one of the three largest soda saline-alkali regions worldwide. To better understand soil alkalinization and salinization in this important agricultural region, it is vital to explore the distribution and variation of soil alkalinity and salinity in space and time. This study examined soil properties and identified the variables to extract soil alkalinity and salinity via physico-chemical, statistical, spectral, and image analysis. The physico-chemical and statistical results suggested that alkaline soils, coming from the main solute Na2CO3 and NaHCO3 in parent rocks, characterized the study area. The pH and electric conductivity (EC ) were correlated with both narrow band and broad band reflectance. For soil pH, the sensitive bands were in short wavelength (VIS) and the band with the highest correlation was 475 nm (r = 0.84). For soil EC, the sensitive bands were also in VIS and the band with the highest correlation was 354 nm (r = 0.84). With the stepwise regression, it was found that the pH was sensitive to reflectance of OLI band 2 and band 6, while the EC was only sensitive to band 1. The R2Adj (0.73 and 0.72) and root mean square error (RMSE) (0.98 and 1.07 dS/m) indicated that, the two stepwise regression models could estimate soil alkalinity and salinity with a considerable accuracy. Spatial distributions of soil alkalinity and salinity were mapped from the OLI image with the RMSE of 1.01 and 0.64 dS/m, respectively. Soil alkalinity was related to salinity but most soils in the study area were non-saline soils. The area of alkaline soils was 44.46% of the basin. Highly alkaline soils were close to the Zhalong wetland and downstream of rivers, which could become a severe concern for crop productivity in this area. Full article
(This article belongs to the Special Issue Remote Sensing Applied to Soils: From Ground to Space)
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Review

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Open AccessReview Remote Sensing of Coral Reefs for Monitoring and Management: A Review
Remote Sens. 2016, 8(2), 118; doi:10.3390/rs8020118
Received: 19 October 2015 / Accepted: 21 December 2015 / Published: 6 February 2016
Cited by 17 | PDF Full-text (7471 KB) | HTML Full-text | XML Full-text
Abstract
Coral reefs are in decline worldwide and monitoring activities are important for assessing the impact of disturbance on reefs and tracking subsequent recovery or decline. Monitoring by field surveys provides accurate data but at highly localised scales and so is not cost-effective for
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Coral reefs are in decline worldwide and monitoring activities are important for assessing the impact of disturbance on reefs and tracking subsequent recovery or decline. Monitoring by field surveys provides accurate data but at highly localised scales and so is not cost-effective for reef scale monitoring at frequent time points. Remote sensing from satellites is an alternative and complementary approach. While remote sensing cannot provide the level of detail and accuracy at a single point than a field survey, the statistical power for inferring large scale patterns benefits in having complete areal coverage. This review considers the state of the art of coral reef remote sensing for the diverse range of objectives relevant for management, ranging from the composition of the reef: physical extent, benthic cover, bathymetry, rugosity; to environmental parameters: sea surface temperature, exposure, light, carbonate chemistry. In addition to updating previous reviews, here we also consider the capability to go beyond basic maps of habitats or environmental variables, to discuss concepts highly relevant to stakeholders, policy makers and public communication: such as biodiversity, environmental threat and ecosystem services. A clear conclusion of the review is that advances in both sensor technology and processing algorithms continue to drive forward remote sensing capability for coral reef mapping, particularly with respect to spatial resolution of maps, and synthesis across multiple data products. Both trends can be expected to continue. Full article
(This article belongs to the Special Issue Remote Sensing for Coral Reef Monitoring)
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Open AccessReview Comparison of the Calibration Algorithms and SI Traceability of MODIS, VIIRS, GOES, and GOES-R ABI Sensors
Remote Sens. 2016, 8(2), 126; doi:10.3390/rs8020126
Received: 20 November 2015 / Accepted: 27 January 2016 / Published: 6 February 2016
Cited by 2 | PDF Full-text (307 KB) | HTML Full-text | XML Full-text
Abstract
The radiometric calibration equations for the thermal emissive bands (TEB) and the reflective solar bands (RSB) measurements of the earth scenes by the polar satellite sensors, (Terra and Aqua) MODIS and Suomi NPP (VIIRS), and geostationary sensors, GOES Imager and the GOES-R Advanced
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The radiometric calibration equations for the thermal emissive bands (TEB) and the reflective solar bands (RSB) measurements of the earth scenes by the polar satellite sensors, (Terra and Aqua) MODIS and Suomi NPP (VIIRS), and geostationary sensors, GOES Imager and the GOES-R Advanced Baseline Imager (ABI) are analyzed towards calibration algorithm harmonization on the basis of SI traceability which is one of the goals of the NOAA National Calibration Center (NCC). One of the overarching goals of NCC is to provide knowledge base on the NOAA operational satellite sensors and recommend best practices for achieving SI traceability for the radiance measurements on-orbit. As such, the calibration methodologies of these satellite optical sensors are reviewed in light of the recommended practice for radiometric calibration at the National Institute of Standards and Technology (NIST). The equivalence of some of the spectral bands in these sensors for their end products is presented. The operational and calibration features of the sensors for on-orbit observation of radiance are also compared in tabular form. This review is also to serve as a quick cross reference to researchers and analysts on how the observed signals from these sensors in space are converted to radiances. Full article
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Other

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Open AccessLetter Radiometric Resolution Analysis and a Simulation Model
Remote Sens. 2016, 8(2), 85; doi:10.3390/rs8020085
Received: 13 October 2015 / Revised: 12 January 2016 / Accepted: 18 January 2016 / Published: 22 January 2016
Cited by 1 | PDF Full-text (366 KB) | HTML Full-text | XML Full-text
Abstract
Total power radiometer has a simple configuration and the best theoretical resolution. Gain fluctuations and calibration errors, however, can induce severe errors in the solved scene brightness temperature. To estimate the overall radiometer performance we present a numerical simulation tool that can be
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Total power radiometer has a simple configuration and the best theoretical resolution. Gain fluctuations and calibration errors, however, can induce severe errors in the solved scene brightness temperature. To estimate the overall radiometer performance we present a numerical simulation tool that can be used to determine the radiometric resolution. Our model considers three main components that degrade the radiometric resolution: thermal noise, 1/f noise and calibration errors. These error sources have long been known to exist, but comprehensive models able to account all these effects quantitatively and accurately in a practical manner have been missing. We have developed a radiometer simulation model that is able to produce radiometer signals that incorporate realistic radiometer effects that show up as noise and other errors in the radiometer video signal. Our simulation tool integrates the fundamental radiometer theories numerically and allows the investigation of different calibration schemes and receiver topologies. The model can be used as a guide for design and optimization as well as for verification of the radiometer performance. Moreover, it can be extended to a much larger and more complex radiometer systems allowing better system level performance estimation and optimization with minimal bread-board implementations. The model mimics real radiometer video data and thus the complete data analysis pipeline can be developed and verified before the real video data is available. In this paper, the model has been applied to a total power radiometer operating in the 52 GHz frequency range. Full article
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Open AccessTechnical Note A Combination of Plant NDVI and LiDAR Measurements Improve the Estimation of Pasture Biomass in Tall Fescue (Festuca arundinacea var. Fletcher)
Remote Sens. 2016, 8(2), 109; doi:10.3390/rs8020109
Received: 7 October 2015 / Revised: 19 January 2016 / Accepted: 25 January 2016 / Published: 1 February 2016
Cited by 5 | PDF Full-text (2477 KB) | HTML Full-text | XML Full-text
Abstract
The total biomass of a tall fescue (Festuca arundinacea var. Fletcher) pasture was assessed by using a vehicle mounted light detection and ranging (LiDAR) unit to derive canopy height and an active optical reflectance sensor to determine the spectro-optical reflectance index, normalized
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The total biomass of a tall fescue (Festuca arundinacea var. Fletcher) pasture was assessed by using a vehicle mounted light detection and ranging (LiDAR) unit to derive canopy height and an active optical reflectance sensor to determine the spectro-optical reflectance index, normalized difference vegetation index (NDVI). In a random plot design, measurements of NDVI and pasture height were combined to estimate biomass with a root mean square error of prediction (RMSEP) equal to ±455.28 kg green dry matter (GDM)/ha, over a range of 286 kg to 3933 kg GDM/ha. The combination of NDVI and height measurements were observed to be more accurate in assessing total biomass than just the NDVI (RMSEP ± 846.51 kg/ha) and height (RMSEP ± 708.13 kg/ha). Based on the results of the study it was concluded the use of combined LiDAR and active optical reflectance sensors can help unlock the complex interrelationship between green fraction and biomass in swards containing both green and senescent material. Full article
(This article belongs to the Special Issue Remote Sensing in Precision Agriculture)
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Open AccessTechnical Note Validation of the Calibration Coefficient of the GaoFen-1 PMS Sensor Using the Landsat 8 OLI
Remote Sens. 2016, 8(2), 132; doi:10.3390/rs8020132
Received: 18 November 2015 / Revised: 26 January 2016 / Accepted: 29 January 2016 / Published: 8 February 2016
Cited by 4 | PDF Full-text (2026 KB) | HTML Full-text | XML Full-text
Abstract
The panchromatic and multispectral (PMS) sensor is an optical imaging sensor aboard the Gao Fen-1 (GF-1) satellite. This work describes an approach to validate the calibration coefficients of the PMS sensors based on the image data of the Landsat 8 Operational Land Imager
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The panchromatic and multispectral (PMS) sensor is an optical imaging sensor aboard the Gao Fen-1 (GF-1) satellite. This work describes an approach to validate the calibration coefficients of the PMS sensors based on the image data of the Landsat 8 Operational Land Imager (OLI). Two image pairs, one obtained over the Dunhuang test site and the other over the Golmud test site, were used in this paper. Two spectral band adjustment factors (SBAF), given as the radiance SBAF and reflectance SBAF, were applied to correct the differences in the respective images caused by the relative spectral responses of the PMS sensor and OLI. Uncertainties in the SBAF values arising from atmospheric parameters and the absence of a measured ground spectrum were analyzed in this paper. The results show that the average relative differences of top-of-atmosphere radiance and reflectance values for each band between the PMS sensor and OLI images are 2%–5% for most bands, after SBAF correction with a measured ground spectrum or fitted spectrum. It is demonstrated that the OLI image can be used to validate the calibration coefficient of the PMS sensor, even if the image pairs are not imaged on the same day. Full article
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