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Remote Sens., Volume 9, Issue 10 (October 2017)

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Open AccessArticle Haze Removal Based on a Fully Automated and Improved Haze Optimized Transformation for Landsat Imagery over Land
Remote Sens. 2017, 9(10), 972; https://doi.org/10.3390/rs9100972
Received: 24 August 2017 / Revised: 18 September 2017 / Accepted: 19 September 2017 / Published: 21 September 2017
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Abstract
Optical satellite imagery is often contaminated by the persistent presence of clouds and atmospheric haze. Without an effective method for removing this contamination, most optical remote sensing applications are less reliable. In this research, a methodology has been developed to fully automate and
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Optical satellite imagery is often contaminated by the persistent presence of clouds and atmospheric haze. Without an effective method for removing this contamination, most optical remote sensing applications are less reliable. In this research, a methodology has been developed to fully automate and improve the Haze Optimized Transformation (HOT)-based haze removal. The method is referred to as AutoHOT and characterized with three notable features: a fully automated HOT process, a novel HOT image post-processing tool and a class-based HOT radiometric adjustment method. The performances of AutoHOT in haze detection and compensation were evaluated through three experiments with one Landsat-5 TM, one Landsat-7 ETM+ and eight Landsat-8 OLI scenes that encompass diverse landscapes and atmospheric haze conditions. The first experiment confirms that AutoHOT is robust and effective for haze detection. The average overall, user’s and producer’s accuracies of AutoHOT in haze detection can reach 96.4%, 97.6% and 97.5%, respectively. The second and third experiments demonstrate that AutoHOT can not only accurately characterize the haze intensities but also improve dehazed results, especially for brighter targets, compared to traditional HOT radiometric adjustment. Full article
(This article belongs to the Section Remote Sensing Image Processing)
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Open AccessArticle Parallel Implementation of the CCSDS 1.2.3 Standard for Hyperspectral Lossless Compression
Remote Sens. 2017, 9(10), 973; https://doi.org/10.3390/rs9100973
Received: 2 August 2017 / Revised: 7 September 2017 / Accepted: 18 September 2017 / Published: 21 September 2017
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Abstract
Hyperspectral imaging is a technology which, by sensing hundreds of wavelengths per pixel, enables fine studies of the captured objects. This produces great amounts of data that require equally big storage, and compression with algorithms such as the Consultative Committee for Space Data
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Hyperspectral imaging is a technology which, by sensing hundreds of wavelengths per pixel, enables fine studies of the captured objects. This produces great amounts of data that require equally big storage, and compression with algorithms such as the Consultative Committee for Space Data Systems (CCSDS) 1.2.3 standard is a must. However, the speed of this lossless compression algorithm is not enough in some real-time scenarios if we use a single-core processor. This is where architectures such as Field Programmable Gate Arrays (FPGAs) and Graphics Processing Units (GPUs) can shine best. In this paper, we present both FPGA and OpenCL implementations of the CCSDS 1.2.3 algorithm. The proposed paralellization method has been implemented on the Virtex-7 XC7VX690T, Virtex-5 XQR5VFX130 and Virtex-4 XC2VFX60 FPGAs, and on the GT440 and GT610 GPUs, and tested using hyperspectral data from NASA’s Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS). Both approaches fulfill our real-time requirements. This paper attempts to shed some light on the comparison between both approaches, including other works from existing literature, explaining the trade-offs of each one. Full article
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Open AccessArticle Assessment of the NOAA S-NPP VIIRS Geolocation Reprocessing Improvements
Remote Sens. 2017, 9(10), 974; https://doi.org/10.3390/rs9100974
Received: 1 August 2017 / Revised: 16 September 2017 / Accepted: 18 September 2017 / Published: 21 September 2017
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Abstract
Long-term time series analysis requires consistent data records from satellites. The Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar orbiting Partner (S-NPP) satellite launched in 2011 requires a major effort to produce consistently calibrated sensor data records (SDR). Accurate VIIRS
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Long-term time series analysis requires consistent data records from satellites. The Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar orbiting Partner (S-NPP) satellite launched in 2011 requires a major effort to produce consistently calibrated sensor data records (SDR). Accurate VIIRS geolocation products are critical to other VIIRS products and products from other instruments on the S-NPP satellite. This paper presents methods for assessing major improvements to the VIIRS geolocation products in the ongoing National Oceanic and Atmospheric Administration (NOAA)/Center for Satellite Applications and Research (STAR) reprocessing that incorporates all corrections in calibration parameters and SDR algorithms since launch to present. In this study, we analyzed the history of VIIRS geometric calibration parameter updates to identify optimal parameters to account for geolocation errors in the early days of the mission. A sample area located in North Western Africa was selected for validation purposes after analyzing global VIIRS and Landsat control point matching results. Geolocation products over the study region were reprocessed and I-bands/M-bands geolocation improvements were characterized by comparing geolocation errors before and after the reprocessing. Our results indicate that all short-term geolocation anomalies before the latest operational geometric calibration parameter update on 22 August 2013 were effectively minimized after reprocessing, with geolocation errors reduced from −47.1 ± 83.8 m to −23.3 ± 51.1 m (along scan) and from −15.6 ± 43.6 m to −5.9 ± 37.7 m (along track). Terrain correction for the VIIRS Day-Night-Band (DNB) was not implemented in the NOAA operational processing until 22 May 2015. In the reprocessing, it will be implemented to the entire DNB geolocation data record. DNB reprocessing improvement due to this implementation was evaluated using nighttime observations over point sources at sea level and over high altitude. Our results show that the implementation of terrain correction will reduce DNB geolocation errors at off-nadir high elevation locations from up to 9 km to ~0.5 pixel (0.375 km), comparable to those at sea level site. The reprocessed geolocation dataset will be distributed online for end-users to access. Full article
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Open AccessArticle Towards High-Definition 3D Urban Mapping: Road Feature-Based Registration of Mobile Mapping Systems and Aerial Imagery
Remote Sens. 2017, 9(10), 975; https://doi.org/10.3390/rs9100975
Received: 24 July 2017 / Revised: 6 September 2017 / Accepted: 18 September 2017 / Published: 21 September 2017
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Abstract
Various applications have utilized a mobile mapping system (MMS) as the main 3D urban remote sensing platform. However, the accuracy and precision of the three-dimensional data acquired by an MMS is highly dependent on the performance of the vehicle’s self-localization, which is generally
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Various applications have utilized a mobile mapping system (MMS) as the main 3D urban remote sensing platform. However, the accuracy and precision of the three-dimensional data acquired by an MMS is highly dependent on the performance of the vehicle’s self-localization, which is generally performed by high-end global navigation satellite system (GNSS)/inertial measurement unit (IMU) integration. However, GNSS/IMU positioning quality degrades significantly in dense urban areas with high-rise buildings, which block and reflect the satellite signals. Traditional landmark updating methods, which improve MMS accuracy by measuring ground control points (GCPs) and manually identifying those points in the data, are both labor-intensive and time-consuming. In this paper, we propose a novel and comprehensive framework for automatically georeferencing MMS data by capitalizing on road features extracted from high-resolution aerial surveillance data. The proposed framework has three key steps: (1) extracting road features from the MMS and aerial data; (2) obtaining Gaussian mixture models from the extracted aerial road features; and (3) performing registration of the MMS data to the aerial map using a dynamic sliding window and the normal distribution transform (NDT). The accuracy of the proposed framework is verified using field data, demonstrating that it is a reliable solution for high-precision urban mapping. Full article
(This article belongs to the Special Issue Remote Sensing for 3D Urban Morphology)
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Open AccessArticle A Hybrid Pansharpening Algorithm of VHR Satellite Images that Employs Injection Gains Based on NDVI to Reduce Computational Costs
Remote Sens. 2017, 9(10), 976; https://doi.org/10.3390/rs9100976
Received: 1 August 2017 / Revised: 12 September 2017 / Accepted: 19 September 2017 / Published: 21 September 2017
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Abstract
The objective of this work is to develop an algorithm for pansharpening of very high resolution (VHR) satellite imagery that reduces the spectral distortion of the pansharpened images and enhances their spatial clarity with minimal computational costs. In order to minimize the spectral
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The objective of this work is to develop an algorithm for pansharpening of very high resolution (VHR) satellite imagery that reduces the spectral distortion of the pansharpened images and enhances their spatial clarity with minimal computational costs. In order to minimize the spectral distortion and computational costs, the global injection gain is transformed to the local injection gains using the normalized difference vegetation index (NDVI), on the assumption that the NDVI are positively or negatively correlated with local injection gains obtained from each band of the satellite data. In addition, the local injection gains are then applied in the hybrid pansharpening algorithm to optimize the spatial clarity. In particular, in the proposed algorithm, a synthetic intensity image is determined using block-based linear regression. In experiments using imagery collected by various satellites, such as KOrea Multi-Purpose SATellite-3 (KOMPSAT-3), KOMPSAT-3A and WorldView-3, the pansharpened results obtained using the proposed Hybrid Pansharpening algorithm using NDVI and based on the spectral mode (HP-NDVIspectral) provide a better representation of the values of the Erreur Relative Globale Adimensionnelle de Synthèse (ERGAS), the spectral angle mapper (SAM) and the Q4/Q8 than those produced by existing pansharpening algorithms. In terms of spatial quality, the pansharpened images obtained using the proposed pansharpening algorithm based on the spatial mode (HP-NDVIspatial) have higher average gradient (AG) values than those obtained using existing pansharpening methods. In addition, the computational complexity of our method is similar to that of a pansharpening algorithm that is based on a global injection model, although our methodology has characteristics that are similar to those of a local injection gain-based model that has a very high computational cost. Thus, the quantitative and qualitative assessments presented here indicate that the proposed algorithm can be utilized in various applications that employ spectral information or require high spatial clarity. Full article
(This article belongs to the Section Remote Sensing Image Processing)
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Open AccessArticle Measurements of Surface-Layer Turbulence in a Wide Norwegian Fjord Using Synchronized Long-Range Doppler Wind Lidars
Remote Sens. 2017, 9(10), 977; https://doi.org/10.3390/rs9100977
Received: 8 July 2017 / Revised: 29 August 2017 / Accepted: 18 September 2017 / Published: 21 September 2017
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Abstract
Three synchronized pulsed Doppler wind lidars were deployed from May 2016 to June 2016 on the shores of a wide Norwegian fjord called Bjørnafjord to study the wind characteristics at the proposed location of a planned bridge. The purpose was to investigate the
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Three synchronized pulsed Doppler wind lidars were deployed from May 2016 to June 2016 on the shores of a wide Norwegian fjord called Bjørnafjord to study the wind characteristics at the proposed location of a planned bridge. The purpose was to investigate the potential of using lidars to gather information on turbulence characteristics in the middle of a wide fjord. The study includes the analysis of the single-point and two-point statistics of wind turbulence, which are of major interest to estimate dynamic wind loads on structures. The horizontal wind components were measured by the intersecting scanning beams, along a line located 25 m above the sea surface, at scanning distances up to 4.6 k m . For a mean wind velocity above 8 m · s - 1 , the recorded turbulence intensity was below 0.06 on average. Even though the along-beam spatial averaging leads to an underestimated turbulence intensity, such a value indicates a roughness length much lower than provided in the European standard EN 1991-1-4:2005. The normalized spectrum of the along-wind component was compared to the one provided by the Norwegian Petroleum Industry Standard and the Norwegian Handbook for bridge design N400. A good overall agreement was observed for wave-numbers below 0 . 02 / m . The along-beam spatial averaging in the adopted set-up prevented a more detailed comparison at larger wave-numbers, which challenges the study of wind turbulence at scanning distances of several kilometres. The results presented illustrate the need to complement lidar data with point-measurement to reduce the uncertainties linked to the atmospheric stability and the spatial averaging of the lidar probe volume. The measured lateral coherence was associated with a decay coefficient larger than expected for the along-wind component, with a value around 21 for a mean wind velocity bounded between 10 m · s - 1 and 14 m · s - 1 , which may be related to a stable atmospheric stratification. Full article
(This article belongs to the Section Atmosphere Remote Sensing)
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Open AccessArticle Raman Lidar Observations of Aerosol Optical Properties in 11 Cities from France to Siberia
Remote Sens. 2017, 9(10), 978; https://doi.org/10.3390/rs9100978
Received: 24 August 2017 / Revised: 12 September 2017 / Accepted: 13 September 2017 / Published: 22 September 2017
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Abstract
In June 2013, a ground-based mobile lidar performed the ~10,000 km ride from Paris to Ulan-Ude, near Lake Baikal, profiling aerosol optical properties in the cities visited along the journey and allowing the first comparison of urban aerosols optical properties across Eurasia. The
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In June 2013, a ground-based mobile lidar performed the ~10,000 km ride from Paris to Ulan-Ude, near Lake Baikal, profiling aerosol optical properties in the cities visited along the journey and allowing the first comparison of urban aerosols optical properties across Eurasia. The lidar instrument was equipped with N2-Raman and depolarization channels, enabling the retrieval of the 355-nm extinction-to-backscatter ratio (also called Lidar Ratio (LR)) and the linear Particle Depolarization Ratio (PDR) in the urban planetary boundary or residual layer over 11 cities. The optical properties of pollution particles were found to be homogeneous all along the journey: no longitude dependence was observed for the LR, with most values falling within the 67–96 sr range. There exists only a slight increase of PDR between cities in Europe and Russia, which we attribute to a higher fraction of coarse terrigenous particles lifted from bad-tarmac roads and unvegetated terrains, which resulted, for instance, in a +1.7% increase between the megalopolises of Paris and Moscow. A few lower LR values (38 to 50 sr) were encountered above two medium size Siberian cities and in an isolated plume, suggesting that the relative weight of terrigenous aerosols in the mix may increase in smaller cities. Space-borne observations from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), retrieved during summer 2013 above the same Russian cities, confirmed the prevalence of aerosols classified as “polluted dust”. Finally, we encountered one special feature in the Russian aerosol mix as we observed with good confidence an unusual aerosol layer displaying both a very high LR (96 sr) and a very high PDR (20%), even though both features make it difficult to identify the aerosol type. Full article
(This article belongs to the Special Issue Aerosol Remote Sensing)
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Open AccessArticle Remote Sensing of Aerosol Optical Depth Using an Airborne Polarimeter over North China
Remote Sens. 2017, 9(10), 979; https://doi.org/10.3390/rs9100979
Received: 28 June 2017 / Revised: 20 September 2017 / Accepted: 20 September 2017 / Published: 22 September 2017
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Abstract
The airborne Atmosphere Multi-angle Polarization Radiometer (AMPR) was employed to perform airborne measurements over North China between 2012 and 2016. Seven flights and synchronous ground-based observations were acquired. These data were used to test the sensor’s measurements and associated aerosol retrieval algorithm. According
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The airborne Atmosphere Multi-angle Polarization Radiometer (AMPR) was employed to perform airborne measurements over North China between 2012 and 2016. Seven flights and synchronous ground-based observations were acquired. These data were used to test the sensor’s measurements and associated aerosol retrieval algorithm. According to the AMPR measurements, a successive surface-atmosphere decoupling based algorithm was developed to retrieve the aerosol optical depth (AOD). It works via an iteration method, and the lookup table was employed in the aerosol inversion. Throughout the results of the AMPR retrievals, the surface polarized reflectances derived from air- and ground-based instruments were well matched; the measured and simulated reflectances at the aircraft level, which were simulated based on in situ sun photometer observed aerosol properties, were in good agreement; and the AOD measurements were validated against the automatic sun-photometer (CE318) at the nearest time and location. The AOD results were close; the average deviation was less than 0.03. The MODIS AODs were also employed to test the AMPR retrievals, and they showed the same trend. These results illustrate that (i) the successive surface-atmosphere decoupling method in the retrieved program completed its mission and (ii) the aerosol retrieval method has its rationality and potential ability in the regionally accurate remote sensing of aerosol. Full article
(This article belongs to the Special Issue Aerosol Remote Sensing)
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Open AccessFeature PaperArticle Two-Dimensional Linear Inversion of GPR Data with a Shifting Zoom along the Observation Line
Remote Sens. 2017, 9(10), 980; https://doi.org/10.3390/rs9100980
Received: 23 May 2017 / Revised: 9 September 2017 / Accepted: 13 September 2017 / Published: 22 September 2017
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Abstract
Linear inverse scattering problems can be solved by regularized inversion of a matrix, whose calculation and inversion may require significant computing resources, in particular, a significant amount of RAM memory. This effort is dependent on the extent of the investigation domain, which drives
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Linear inverse scattering problems can be solved by regularized inversion of a matrix, whose calculation and inversion may require significant computing resources, in particular, a significant amount of RAM memory. This effort is dependent on the extent of the investigation domain, which drives a large amount of data to be gathered and a large number of unknowns to be looked for, when this domain becomes electrically large. This leads, in turn, to the problem of inversion of excessively large matrices. Here, we consider the problem of a ground-penetrating radar (GPR) survey in two-dimensional (2D) geometry, with antennas at an electrically short distance from the soil. In particular, we present a strategy to afford inversion of large investigation domains, based on a shifting zoom procedure. The proposed strategy was successfully validated using experimental radar data. Full article
(This article belongs to the Special Issue Radar Systems for the Societal Challenges)
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Open AccessArticle Assessing a Multi-Platform Data Fusion Technique in Capturing Spatiotemporal Dynamics of Heterogeneous Dryland Ecosystems in Topographically Complex Terrain
Remote Sens. 2017, 9(10), 981; https://doi.org/10.3390/rs9100981
Received: 4 June 2017 / Revised: 11 September 2017 / Accepted: 19 September 2017 / Published: 22 September 2017
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Abstract
Water-limited ecosystems encompass approximately 40% of terrestrial land mass and play a critical role in modulating Earth’s climate and provisioning ecosystem services to humanity. Spaceborne remote sensing is a critical tool for characterizing ecohydrologic patterns and advancing the understanding of the interactions between
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Water-limited ecosystems encompass approximately 40% of terrestrial land mass and play a critical role in modulating Earth’s climate and provisioning ecosystem services to humanity. Spaceborne remote sensing is a critical tool for characterizing ecohydrologic patterns and advancing the understanding of the interactions between atmospheric forcings and ecohydrologic responses. Fine to medium scale spatial and temporal resolutions are needed to capture the spatial heterogeneity and the temporally intermittent response of these ecosystems to environmental forcings. Techniques combining complementary remote sensing datasets have been developed, but the heterogeneous nature of these regions present significant challenges. Here we investigate the capacity of one such approach, the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) algorithm, to map Normalized Difference Vegetation Index (NDVI) at 30 m spatial resolution and at a daily temporal resolution in an experimental watershed in southwest Idaho, USA. The Dry Creek Experimental Watershed captures an ecotone from a sagebrush steppe ecosystem to evergreen needle-leaf forests along an approximately 1000 m elevation gradient. We used STARFM to fuse NDVI retrievals from the MODerate-resolution Imaging Spectroradiometer (MODIS) and Landsat during the course of a growing season (April to September). Specifically we input to STARFM a pair of Landsat NDVI retrievals bracketing a sequence of daily MODIS NDVI retrievals to yield daily estimates of NDVI at resolutions of 30 m. In a suite of data denial experiments we compared these STARFM predictions against corresponding Landsat NDVI retrievals and characterized errors in predicted NDVI. We investigated how errors vary as a function of vegetation functional type and topographic aspect. We find that errors in predicting NDVI were highest during green-up and senescence and lowest during the middle of the growing season. Absolute errors were generally greatest in tree-covered portions of the watershed and lowest in locations characterized by grasses/bare ground. On average, relative errors in predicted average NDVI were greatest in grass/bare ground regions, on south-facing aspects, and at the height of the growing season. We present several ramifications revealed in this study for the use of multi-sensor remote sensing data for the study of spatiotemporal ecohydrologic patterns in dryland ecosystems. Full article
(This article belongs to the Special Issue Remote Sensing of Arid/Semiarid Lands)
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Open AccessArticle Wuhan Surface Subsidence Analysis in 2015–2016 Based on Sentinel-1A Data by SBAS-InSAR
Remote Sens. 2017, 9(10), 982; https://doi.org/10.3390/rs9100982
Received: 18 July 2017 / Revised: 11 September 2017 / Accepted: 18 September 2017 / Published: 22 September 2017
Cited by 3 | PDF Full-text (28660 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
The Terrain Observation with Progressive Scans (TOPS) acquisition mode of Sentinel-1A provides a wide coverage per acquisition and features a repeat cycle of 12 days, making this acquisition mode attractive for surface subsidence monitoring. A few studies have analyzed wide-coverage surface subsidence of
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The Terrain Observation with Progressive Scans (TOPS) acquisition mode of Sentinel-1A provides a wide coverage per acquisition and features a repeat cycle of 12 days, making this acquisition mode attractive for surface subsidence monitoring. A few studies have analyzed wide-coverage surface subsidence of Wuhan based on Sentinel-1A data. In this study, we investigated wide-area surface subsidence characteristics in Wuhan using 15 Sentinel-1A TOPS Synthetic Aperture Radar (SAR) images acquired from 11 April 2015 to 29 April 2016 with the Small Baseline Subset Interferometric SAR (SBAS InSAR) technique. The Sentinel-1A SBAS InSAR results were validated by 110 leveling points at an accuracy of 6 mm/year. Based on the verified SBAS InSAR results, prominent uneven subsidence patterns were identified in Wuhan. Specifically, annual average subsidence rates ranged from −82 mm/year to 18 mm/year in Wuhan, and maximum subsidence rate was detected in Houhu areas. Surface subsidence time series presented nonlinear subsidence with pronounced seasonal variations. Comparative analysis of surface subsidence and influencing factors (i.e., urban construction, precipitation, industrial development, carbonate karstification and water level changes in Yangtze River) indicated a relatively high spatial correlation between locations of subsidence bowl and those of engineering construction and industrial areas. Seasonal variations in subsidence were correlated with water level changes and precipitation. Surface subsidence in Wuhan was mainly attributed to anthropogenic activities, compressibility of soil layer, carbonate karstification, and groundwater overexploitation. Finally, the spatial-temporal characteristics of wide-area surface subsidence and the relationship between surface subsidence and influencing factors in Wuhan were determined. Full article
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Open AccessEditor’s ChoiceArticle Fractional Snow Cover Mapping from FY-2 VISSR Imagery of China
Remote Sens. 2017, 9(10), 983; https://doi.org/10.3390/rs9100983
Received: 3 August 2017 / Revised: 18 September 2017 / Accepted: 19 September 2017 / Published: 22 September 2017
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Abstract
Daily fractional snow cover (FSC) products derived from optical sensors onboard low Earth orbit (LEO) satellites are often discontinuous, primarily due to prevalent cloud cover. To map the daily cloud-reduced FSC over China, we utilized clear-sky multichannel observations from the first-generation Chinese geostationary
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Daily fractional snow cover (FSC) products derived from optical sensors onboard low Earth orbit (LEO) satellites are often discontinuous, primarily due to prevalent cloud cover. To map the daily cloud-reduced FSC over China, we utilized clear-sky multichannel observations from the first-generation Chinese geostationary orbit (GEO) satellites (namely, the FY-2 series) by taking advantage of their high temporal resolution. The method proposed in this study combines a newly developed binary snow cover detection algorithm designed for the Visible and Infrared Spin Scan Radiometer (VISSR) onboard FY-2F with a simple linear spectral mixture technique applied to the visible (VIS) band. This method relies upon full snow cover and snow-free end-members to estimate the daily FSC. The FY-2E/F VISSR FSC maps of China were compared with the Moderate Resolution Imaging Spectroradiometer (MODIS) FSC data based on the multiple end-member spectral mixture analysis (MESMA), and with Landsat-8 Operational Land Imager (OLI) FSC maps based on the SNOWMAP approach. The FY-2E/F VISSR FSC maps, which demonstrate a lower cloud coverage, exhibit the root mean squared errors (RMSEs) of 0.20/0.19 compared with the MODIS FSC data. When validated against the Landsat-8 OLI FSC data, the FY-2E/F VISSR FSC maps, which display overall accuracies that can reach 0.92, have an RMSE of 0.18~0.29 with R2 values ranging from 0.46 to 0.80. Full article
(This article belongs to the Special Issue Snow Remote Sensing)
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Open AccessArticle Modelling above Ground Biomass in Tanzanian Miombo Woodlands Using TanDEM-X WorldDEM and Field Data
Remote Sens. 2017, 9(10), 984; https://doi.org/10.3390/rs9100984
Received: 11 August 2017 / Revised: 13 September 2017 / Accepted: 19 September 2017 / Published: 22 September 2017
Cited by 3 | PDF Full-text (4165 KB) | HTML Full-text | XML Full-text
Abstract
The use of Interferometric Synthetic Aperture Radar (InSAR) data has great potential for monitoring large scale forest above ground biomass (AGB) in the tropics due to the increased ability to retrieve 3D information even under cloud cover. To date; results in
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The use of Interferometric Synthetic Aperture Radar (InSAR) data has great potential for monitoring large scale forest above ground biomass (AGB) in the tropics due to the increased ability to retrieve 3D information even under cloud cover. To date; results in tropical forests have been inconsistent and further knowledge on the accuracy of models linking AGB and InSAR height data is crucial for the development of large scale forest monitoring programs. This study provides an example of the use of TanDEM-X WorldDEM data to model AGB in Tanzanian woodlands. The primary objective was to assess the accuracy of a model linking AGB with InSAR height from WorldDEM after the subtraction of ground heights. The secondary objective was to assess the possibility of obtaining InSAR height for field plots when the terrain heights were derived from global navigation satellite systems (GNSS); i.e., as an alternative to using airborne laser scanning (ALS). The results revealed that the AGB model using InSAR height had a predictive accuracy of R M S E = 24.1 t·ha−1; or 38.8% of the mean AGB when terrain heights were derived from ALS. The results were similar when using terrain heights from GNSS. The accuracy of the predicted AGB was improved when compared to a previous study using TanDEM-X for a sub-area of the area of interest and was of similar magnitude to what was achieved in the same sub-area using ALS data. Overall; this study sheds new light on the opportunities that arise from the use of InSAR data for large scale AGB modelling in tropical woodlands. Full article
(This article belongs to the Special Issue Biomass Remote Sensing in Forest Landscapes)
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Open AccessArticle Ship Detection in Optical Remote Sensing Images Based on Wavelet Transform and Multi-Level False Alarm Identification
Remote Sens. 2017, 9(10), 985; https://doi.org/10.3390/rs9100985
Received: 10 July 2017 / Revised: 18 September 2017 / Accepted: 20 September 2017 / Published: 22 September 2017
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Abstract
Ship detection by Unmanned Airborne Vehicles (UAVs) and satellites plays an important role in a spectrum of related military and civil applications. To improve the detection efficiency, accuracy, and speed, a novel ship detection method from coarse to fine is presented. Ship targets
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Ship detection by Unmanned Airborne Vehicles (UAVs) and satellites plays an important role in a spectrum of related military and civil applications. To improve the detection efficiency, accuracy, and speed, a novel ship detection method from coarse to fine is presented. Ship targets are viewed as uncommon regions in the sea background caused by the differences in colors, textures, shapes, or other factors. Inspired by this fact, a global saliency model is constructed based on high-frequency coefficients of the multi-scale and multi-direction wavelet decomposition, which can characterize different feature information from edge to texture of the input image. To further reduce the false alarms, a new and effective multi-level discrimination method is designed based on the improved entropy and pixel distribution, which is robust against the interferences introduced by islands, coastlines, clouds, and shadows. The experimental results on optical remote sensing images validate that the presented saliency model outperforms the comparative models in terms of the area under the receiver operating characteristic curves core and the accuracy in the images with different sizes. After the target identification, the locations and the number of the ships in various sizes and colors can be detected accurately and fast with high robustness. Full article
(This article belongs to the Special Issue Instruments and Methods for Ocean Observation and Monitoring)
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Open AccessArticle Mapping and Attributing Normalized Difference Vegetation Index Trends for Nepal
Remote Sens. 2017, 9(10), 986; https://doi.org/10.3390/rs9100986
Received: 8 September 2017 / Revised: 8 September 2017 / Accepted: 21 September 2017 / Published: 23 September 2017
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Abstract
Global change affects vegetation cover and processes through multiple pathways. Long time series of surface land surface properties derived from satellite remote sensing give unique abilities to observe these changes, particularly in areas with complex topography and limited research infrastructure. Here, we focus
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Global change affects vegetation cover and processes through multiple pathways. Long time series of surface land surface properties derived from satellite remote sensing give unique abilities to observe these changes, particularly in areas with complex topography and limited research infrastructure. Here, we focus on Nepal, a biodiversity hotspot where vegetation productivity is limited by moisture availability (dominated by a summer monsoon) at lower elevations and by temperature at high elevations. We analyze the normalized difference vegetation index (NDVI) from 1981 to 2015 semimonthly, at an 8 km spatial resolution. We use a random forest (RF) of regression trees to generate a statistical model of the NDVI as a function of elevation, land use, CO 2 level, temperature, and precipitation. We find that the NDVI increased over the studied period, particularly at low and middle elevations and during the fall (post-monsoon). We infer from the fitted RF model that the NDVI linear trend is primarily due to CO 2 level (or another environmental parameter that is changing quasi-linearly), and not primarily due to temperature or precipitation trends. On the other hand, interannual fluctuation in the NDVI is more correlated with temperature and precipitation. The RF accurately fits the available data and shows promise for estimating trends and testing hypotheses about their causes. Full article
(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)
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Open AccessArticle Assimilation of Typhoon Wind Field Retrieved from Scatterometer and SAR Based on the Huber Norm Quality Control
Remote Sens. 2017, 9(10), 987; https://doi.org/10.3390/rs9100987
Received: 17 June 2017 / Revised: 7 September 2017 / Accepted: 20 September 2017 / Published: 23 September 2017
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Abstract
Observations of sea surface wind field are critical for typhoon prediction. The scatterometer observation is one of the most important sources of sea surface winds, which provides both wind speed and wind direction information. However, the spatial resolution of scatterometer wind is low.
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Observations of sea surface wind field are critical for typhoon prediction. The scatterometer observation is one of the most important sources of sea surface winds, which provides both wind speed and wind direction information. However, the spatial resolution of scatterometer wind is low. Synthetic Aperture Radar (SAR) can provide a more detailed wind structure of the tropical cyclone. In addition, the cross-polarization observation of SAR can provide more detailed information of high speed wind (>25 m·s 1 ) than the scatterometer. Nevertheless, due to the narrow swath of SAR, the number of retrieved sea surface wind data used in the data assimilation is limited, and another limitation of SAR wind observation is that it does not provide true wind direction information. In this paper, the joint assimilation of the Advanced Scatterometer (ASCAT) wind and Sentinel-1 SAR wind was investigated. Another limitation in the current operational typhoon prediction is the inefficient quality control (QC) method used in the data assimilation since a large number of high speed wind observations was rejected by the traditional Gaussian distribution QC. We introduce the Huber norm distribution quality control (QC) into the data assimilation successfully. A numerical simulation experiment of typhoon by Lionrock (2016) is conducted to test the proposed method. The experimental results showed that the new quality control scheme not only greatly increases the availability of wind data in the area of the typhoon center, but also improves the typhoon track prediction, as well as the intensity prediction. The joint assimilation of scatterometer and SAR winds does have a positive impact on the typhoon prediction. Full article
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Open AccessArticle Sensitivity of Landsat 8 Surface Temperature Estimates to Atmospheric Profile Data: A Study Using MODTRAN in Dryland Irrigated Systems
Remote Sens. 2017, 9(10), 988; https://doi.org/10.3390/rs9100988
Received: 15 June 2017 / Revised: 8 September 2017 / Accepted: 13 September 2017 / Published: 23 September 2017
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Abstract
The land surface temperature (LST) represents a critical element in efforts to characterize global surface energy and water fluxes, as well as being an essential climate variable in its own right. Current satellite platforms provide a range of spatial and temporal resolution radiance
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The land surface temperature (LST) represents a critical element in efforts to characterize global surface energy and water fluxes, as well as being an essential climate variable in its own right. Current satellite platforms provide a range of spatial and temporal resolution radiance data from which LST can be determined. One of the most complete records of data comes via the Landsat series of satellites, which provide a continuous sequence that extends back to 1982. However, for much of this time, Landsat thermal data were provided through a single broadband thermal channel, making surface temperature retrieval challenging. To fully exploit the valuable time-series of thermal information that is available from these satellites requires efforts to better describe and understand the accuracy of temperature retrievals. Here, we contribute to these efforts by examining the impact of atmospheric correction on the estimation of LST, using atmospheric profiles derived from a range of in-situ, reanalysis, and satellite data. Radiance data from the thermal infrared (TIR) sensor onboard Landsat 8 was converted to LST by using the MODTRAN version 5.2 radiative transfer model, allowing the production of an LST time series based upon 28 Landsat overpasses. LST retrievals were then evaluated against in-situ thermal measurements collected over an arid zone farmland comprising both bare soil and vegetated surface types. Atmospheric profiles derived from AIRS, MOD07, ECMWF, NCEP, and balloon-based radiosonde data were used to drive the MODTRAN simulations. In addition to examining the direct impact of using various profile data on LST retrievals, randomly distributed errors were introduced into a range of forcing variables to better understand retrieval uncertainty. Results indicated differences in LST of up to 1 K for perturbations in emissivity and profile measurements, with the analysis also highlighting the challenges in modeling aerosol optical depth (AOD) over arid lands and its impact on the TIR bands. Days with high AOD content (AOD > 0.5) in the evaluation study seem to consistently underestimate in-situ LSTs by 1–2 K, suggesting that MODTRAN is unable to accurately simulate the aerosol conditions for the TIR bands. Comparisons between available in-situ and Landsat 8 derived LST illustrate a range of seasonal and land surface dynamics and provide an assessment of retrieval accuracy throughout the nine-month long study period. In terms of the choice of atmospheric profile, when excluding the in-situ data, results show a mean absolute range of between 1.2 K to 1.8 K over bare soil and 3.3 K to 3.8 K over alfalfa for the different meteorological forcing, with the AIRS profile providing the best reproduction over the studied arid land irrigation region. Full article
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Open AccessArticle Seasonal and Spatial Characteristics of Urban Heat Islands (UHIs) in Northern West Siberian Cities
Remote Sens. 2017, 9(10), 989; https://doi.org/10.3390/rs9100989
Received: 28 July 2017 / Revised: 1 September 2017 / Accepted: 18 September 2017 / Published: 27 September 2017
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Abstract
Anthropogenic heat and modified landscapes raise air and surface temperatures in urbanized areas around the globe. This phenomenon is widely known as an urban heat island (UHI). Previous UHI studies, and specifically those based on remote sensing data, have not included cities north
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Anthropogenic heat and modified landscapes raise air and surface temperatures in urbanized areas around the globe. This phenomenon is widely known as an urban heat island (UHI). Previous UHI studies, and specifically those based on remote sensing data, have not included cities north of 60°N. A few in situ studies have indicated that even relatively small cities in high latitudes may exhibit significantly amplified UHIs. The UHI characteristics and factors controlling its intensity in high latitudes remain largely unknown. This study attempts to close this knowledge gap for 28 cities in northern West Siberia (NWS). NWS cities are convenient for urban intercomparison studies as they have relatively similar cold continental climates, and flat, rather homogeneous landscapes. We investigated the UHI in NWS cities using the moderate-resolution imaging spectroradiometer (MODIS) MOD 11A2 land surface temperature (LST) product in 8-day composites. The analysis reveals that all 28 NWS cities exhibit a persistent UHI in summer and winter. The LST analysis found differences in summer and winter regarding the UHI effect, and supports the hypothesis of seasonal differences in the causes of UHI formation. Correlation analysis found the strongest relationships between the UHI and population (log P). Regression models using log P alone could explain 65–67% of the variability of UHIs in the region. Additional explanatory power—at least in summer—is provided by the surrounding background temperatures, which themselves are strongly correlated with latitude. The performed regression analysis thus confirms the important role of the surrounding temperature in explaining spatial–temporal variation of UHI intensity. These findings suggest a climatological basis for these phenomena and, given the importance of climatic warming, an aspect that deserves future study. Full article
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Open AccessArticle A Rigorously-Weighted Spatiotemporal Fusion Model with Uncertainty Analysis
Remote Sens. 2017, 9(10), 990; https://doi.org/10.3390/rs9100990
Received: 14 August 2017 / Revised: 18 September 2017 / Accepted: 21 September 2017 / Published: 25 September 2017
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Abstract
Interest has been growing with regard to the use of remote sensing data characterized by a fine spatial resolution and frequent coverage for the monitoring of land surface dynamics. However, current satellite sensors are fundamentally limited by a trade-off between their spatial and
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Interest has been growing with regard to the use of remote sensing data characterized by a fine spatial resolution and frequent coverage for the monitoring of land surface dynamics. However, current satellite sensors are fundamentally limited by a trade-off between their spatial and temporal resolutions. Spatiotemporal fusion thus provides a feasible solution to overcome this limitation, and many blending algorithms have been developed. Among them, the popular spatial and temporal adaptive reflectance fusion model (STARFM) is based on a weighted function; however, it uses an ad hoc approach to estimate the weights of surrounding similar pixels. Additionally, an uncertainty analysis of the predicted result is not provided in the STARFM or any other fusion algorithm. This paper proposes a rigorously-weighted spatiotemporal fusion model (RWSTFM) based on geostatistics to blend the surface reflectances of Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat-5 Thematic Mapper (TM) imagery. The RWSTFM, which is based on ordinary kriging, derives the weights in terms of a fitted semivariance-distance relationship and calculates the estimation variance, which is a measure of the prediction uncertainty. The RWSTFM was tested using three datasets and compared with two commonly-used spatiotemporal reflectance fusion algorithms: the STARFM and the flexible spatiotemporal data fusion (FSDAF) method. The fusion results show that the proposed RWSTFM consistently outperformed the other algorithms both visually and quantitatively. Additionally, more than 70% of the squared error was accounted for by the estimation variance of the RWSTFM for all three of the datasets. Full article
(This article belongs to the Special Issue Remote Sensing Image Downscaling)
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Open AccessArticle Effects of Urban Expansion on Forest Loss and Fragmentation in Six Megaregions, China
Remote Sens. 2017, 9(10), 991; https://doi.org/10.3390/rs9100991
Received: 30 July 2017 / Revised: 6 September 2017 / Accepted: 22 September 2017 / Published: 26 September 2017
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Abstract
Urban expansion has significant effects on forest loss and fragmentation. Previous studies mostly focused on how the amount of developed land affected forest loss and fragmentation, but neglected the impacts of its spatial pattern. This paper examines the effects of both the amount
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Urban expansion has significant effects on forest loss and fragmentation. Previous studies mostly focused on how the amount of developed land affected forest loss and fragmentation, but neglected the impacts of its spatial pattern. This paper examines the effects of both the amount and spatial pattern of urban expansion on forest loss and fragmentation. We conducted a comparison study in the six largest urban megaregions in China—Beijing-Tianjin-Hebei (BTH), Yangtze River Delta (YRD), Pearl River Delta (PRD), Wuhan (WH), Chengdu-Chongqing (CY), and Changsha-Zhuzhou-Xiangtan (CZT) urban megaregions. We first quantified both the magnitude and speed of urban expansion, and forest loss and fragmentation from 2000 to 2010. We then examined the relationships between urban expansion and forest loss and fragmentation by Pearson correlation and partial correlation analysis using the prefecture city as the analytical unit. We found: (1) urban expansion was a major driver of forest loss in the CZT, PRD, and CY megaregions, with 34.05%, 22.58%, and 19.65% of newly-developed land converted from forests. (2) Both the proportional cover of developed land and its spatial pattern (e.g., patch density) had significant impacts on forest fragmentation at the city level. (3) Proportional cover of developed land was the major factor for forest fragmentation at the city level for the PRD and YRD megaregions, but the impact of the spatial pattern of developed land was more important for the BTH and WH megaregions. Full article
(This article belongs to the Special Issue Remote Sensing of Urban Ecology)
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Open AccessArticle Sixty-Year Changes in Residential Landscapes in Beijing: A Perspective from Both the Horizontal (2D) and Vertical (3D) Dimensions
Remote Sens. 2017, 9(10), 992; https://doi.org/10.3390/rs9100992
Received: 31 July 2017 / Revised: 17 September 2017 / Accepted: 22 September 2017 / Published: 25 September 2017
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Abstract
Landscape changes associated with urbanization can lead to many serious ecological and environmental problems. Quantifying the vertical structure of the urban landscape and its change is important to understand its social and ecological impacts, but previous studies mainly focus on urban horizontal expansion
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Landscape changes associated with urbanization can lead to many serious ecological and environmental problems. Quantifying the vertical structure of the urban landscape and its change is important to understand its social and ecological impacts, but previous studies mainly focus on urban horizontal expansion and its impacts on land cover/land use change. This papers focuses on the residential landscape to investigate how the vertical dimension of the urban landscape (i.e., building height) change through time, and how such change is related to changes in the horizontal dimension of the landscape, using Beijing, the capital of China, as a case study. We quantified the expansion of the residential neighborhoods from 1949 to 2009, and changes in vegetation coverage, building density, and building height within these neighborhoods, using 1 m spatial resolution imagery. One-way ANOVA and correlation analysis were used to examine the relationships of building height to vegetation coverage and building density. We found: (1) The residential areas expanded rapidly and were dominated by outward growth, with much less within-city infilling. The growth rate varied greatly through time, first increasing from 1949–2004 and then decreasing from 2005–2009. The expansion direction of newly built residential neighborhoods shifted from west to north in a clockwise direction. (2) The vertical structure of residential neighborhoods changed with time and varied in space, forming a “low-high” pattern from urban central areas to the urban edges within the 5th ring road of Beijing. (3) The residential neighborhoods built in different time periods had significant differences in vegetation coverage, building density, and building height. The residential neighborhoods built in more recent years tended to have taller buildings, lower building density and lower vegetation coverage. Full article
(This article belongs to the Special Issue Remote Sensing of Urban Ecology)
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Open AccessArticle Spectro-Temporal Heterogeneity Measures from Dense High Spatial Resolution Satellite Image Time Series: Application to Grassland Species Diversity Estimation
Remote Sens. 2017, 9(10), 993; https://doi.org/10.3390/rs9100993
Received: 25 July 2017 / Revised: 8 September 2017 / Accepted: 22 September 2017 / Published: 25 September 2017
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Abstract
Grasslands represent a significant source of biodiversity that is important to monitor over large extents. The Spectral Variation Hypothesis (SVH) assumes that the Spectral Heterogeneity (SH) measured from remote sensing data can be used as a proxy for species diversity. Here, we argue
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Grasslands represent a significant source of biodiversity that is important to monitor over large extents. The Spectral Variation Hypothesis (SVH) assumes that the Spectral Heterogeneity (SH) measured from remote sensing data can be used as a proxy for species diversity. Here, we argue the hypothesis that the grassland’s species differ in their phenology and, hence, that the temporal variations can be used in addition to the spectral variations. The purpose of this study is to attempt verifying the SVH in grasslands using the temporal information provided by dense Satellite Image Time Series (SITS) with a high spatial resolution. Our method to assess the spectro-temporal heterogeneity is based on a clustering of grasslands using a robust technique for high dimensional data. We propose new SH measures derived from this clustering and computed at the grassland level. We compare them to the Mean Distance to Centroid (MDC). The method is experimented on 192 grasslands from southwest France using an intra-annual multispectral SPOT5 SITS comprising 18 images and using single images from this SITS. The combination of two of the proposed SH measures—the within-class variability and the entropy—in a multivariate linear model explained the variance of the grasslands’ Shannon index more than the MDC. However, there were no significant differences between the predicted values issued from the best models using multitemporal and monotemporal imagery. We conclude that multitemporal data at a spatial resolution of 10 m do not contribute to estimating the species diversity. The temporal variations may be more related to the effect of management practices. Full article
(This article belongs to the Special Issue Dense Image Time Series Analysis for Ecosystem Monitoring)
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Open AccessCommunication Sensitivity of Common Vegetation Indices to the Canopy Structure of Field Crops
Remote Sens. 2017, 9(10), 994; https://doi.org/10.3390/rs9100994
Received: 20 August 2017 / Revised: 19 September 2017 / Accepted: 21 September 2017 / Published: 26 September 2017
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Abstract
Leaf inclination angle distribution is an important canopy structure characteristic which directly impacts the fraction of the intercepted solar radiation. Together with the leaf area index (LAI) it determines the structure and fractional cover of a homogeneous crop canopy. Unfortunately, this key canopy
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Leaf inclination angle distribution is an important canopy structure characteristic which directly impacts the fraction of the intercepted solar radiation. Together with the leaf area index (LAI) it determines the structure and fractional cover of a homogeneous crop canopy. Unfortunately, this key canopy parameter has usually been ignored when applying common multispectral vegetation indices to the mapping of LAI, although its impact is known from model simulations. Therefore, we measured leaf angles and determined their distribution (quantified using the leaf mean tilt angle, MTA) for six crop species with different structures growing on 162 plots with a broad range of LAI (1.1–5.0) and leaf chlorophyll content (26–94 μg cm−2). Next, we calculated six vegetation indices widely used for LAI monitoring—the normalized difference vegetation index (NDVI), the enhanced vegetation index (EVI), the two band enhanced vegetation index (EVI2), the modified triangular vegetation index (MTVI2), the optimized soil adjusted vegetation index (OSAVI) and the wide dynamic range vegetation index (WDRVI)—from airborne imaging spectroscopy data. We calculated the Spearman’s correlation coefficient R s , a non-parametric statistic chosen because of the non-normal distribution of canopy parameters. All studied indices depended on the LAI ( 0.50 R s 0.71 ) , but the dependence on the MTA was of similar magnitude ( 0.83 R s 0.53 ) with EVI, EVI2, OSAVI and MTVI2 depending more strongly on MTA than on LAI. All studied indices were good proxies ( 0.78 R s 0.88 ) for vegetation fractional cover (Fcover) which, for homogeneous crop canopies, is a nonlinear function of LAI and MTA. EVI2 and MTVI2 were the most strongly correlated with Fcover, although the difference to the other studied indices was small. This first study involving a large range of crop structures confirms the results from canopy reflectance simulations and emphasizes the necessity of leaf angle information for the successful mapping of LAI with Earth observation data. Full article
(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)
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Open AccessArticle Quantifying Snow Cover Distribution in Semiarid Regions Combining Satellite and Terrestrial Imagery
Remote Sens. 2017, 9(10), 995; https://doi.org/10.3390/rs9100995
Received: 29 August 2017 / Revised: 15 September 2017 / Accepted: 22 September 2017 / Published: 26 September 2017
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Abstract
Mediterranean mountainous regions constitute a climate change hotspot where snow plays a crucial role in water resources. The characteristic snow-patched distribution over these areas makes spatial resolution the limiting factor for its correct representation. This work assesses the estimation of snow cover area
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Mediterranean mountainous regions constitute a climate change hotspot where snow plays a crucial role in water resources. The characteristic snow-patched distribution over these areas makes spatial resolution the limiting factor for its correct representation. This work assesses the estimation of snow cover area and the contribution of the patchy areas to the seasonal and annual regime of the snow in a semiarid mountainous range, the Sierra Nevada Mountains in southern Spain, by means of Landsat imagery combined with terrestrial photography (TP). Two methodologies were tested: (1) difference indexes to produce binary maps; and (2) spectral mixture analysis (SMA) to obtain fractional maps; their results were validated from “ground-truth” data by means of TP in a small monitored control area. Both methods provided satisfactory results when the snow cover was above 85% of the study area; below this threshold, the use of spectral mixture analysis is clearly recommended. Mixed pixels can reach up to 40% of the area during wet and cold years, their importance being larger as altitude increases, proving the usefulness of TP for assessing the accuracy of remote data sources. Mixed pixels identification allows for determining the more vulnerable areas facing potential changes of the snow regime due to global warming and climate variability. Full article
(This article belongs to the Special Issue Snow Remote Sensing)
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Open AccessFeature PaperArticle Metrological Characterization for Vital Sign Detection by a Bioradar
Remote Sens. 2017, 9(10), 996; https://doi.org/10.3390/rs9100996
Received: 30 June 2017 / Revised: 29 August 2017 / Accepted: 22 September 2017 / Published: 26 September 2017
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Abstract
In space missions, during the long isolation at extreme conditions for human health, it is of paramount importance to monitor vital parameters. One such parameter is the breathing rate. Indeed, several factors can induce some breathing anomalies during the sleep, which may cause
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In space missions, during the long isolation at extreme conditions for human health, it is of paramount importance to monitor vital parameters. One such parameter is the breathing rate. Indeed, several factors can induce some breathing anomalies during the sleep, which may cause apnea episodes. In order to act timely with the right therapy, an early diagnosis is required. Conventional devices are usually uncomfortable since they require electrodes or probes in contact with the subject. An alternative way to perform this kind of measurement in a remote sensing modality is provided by a continuous wave bioradar operating in the microwave frequency band. This is an effective contactless tool for monitoring the respiratory activity through the measurement of chest deformation due to inhalation and exhalation. The radar emits a low power electromagnetic wave at a single frequency, which is reflected by the human chest. By measuring of the phase shift between the incident and reflected wave, it is possible to detect and monitor the respiratory rate. The main contribution of this work is concerned with a metrological characterization of the continuous wave bioradar; which is a topic not thoroughly assessed in the relevant literature. In particular, the bioradar measurements are also compared with data recorded by a spirometer, which is a standard medical device that measures the air volume inhaled and exhaled by the subject. The purpose of this study is the characterization of the measurement standard uncertainty to enable the assessment of the bioradar system performance. Full article
(This article belongs to the Special Issue Radar Systems for the Societal Challenges)
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Open AccessArticle Aerosol Optical Properties and Direct Radiative Effects over Central China
Remote Sens. 2017, 9(10), 997; https://doi.org/10.3390/rs9100997
Received: 9 August 2017 / Revised: 19 September 2017 / Accepted: 21 September 2017 / Published: 26 September 2017
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Abstract
Central China is important for aerosols and climate because it is among the worst regions for air pollution in China. However, it is understudied due to a lag in establishing an atmospheric monitoring network. So we did a comprehensive analysis using multiple techniques
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Central China is important for aerosols and climate because it is among the worst regions for air pollution in China. However, it is understudied due to a lag in establishing an atmospheric monitoring network. So we did a comprehensive analysis using multiple techniques to improve the understanding of aerosol optical properties and their radiative effect in this region. The results showed that high aerosol optical depth (AOD) was generally found in the northern and central parts, whereas low values were observed in the southern and western parts. Most regions were predominantly loaded with small aerosol particles and a significant influence of long-distance transported dust was found in springtime. A strong and significantly decreasing trend was observed with a maximum decrease rate of −0.08 per year in the northern and western parts, related to the decreasing emission of aerosols and increasing rainfall. Aerosol optical properties and radiative effects were compared between an urban site, Wuhan, and a rural site, Dengfeng. The seasonal variations of AOD and Ångström exponent (AE) are similar for Wuhan and Dengfeng, but both values are larger in Wuhan than in Dengfeng. A greater dominance of coarse-mode and absorbing aerosols was found over Dengfeng. Annual averaged aerosol radiative effect (ARE) in shortwave spectrum (ARESW) and its efficiency (REE) are −48.01 W/m2 and −51.38 W/m2, respectively, in Wuhan, −40.02 W/m2 and −53.26 W/m2, respectively, in Dengfeng. The dependence of REE on aerosol absorptive and size properties was studied; the results showed that REE was strongly influenced by the aerosol absorptivity and size of fine-mode particles, but there was not a strong correlation between REE and AE. The percentage of ARE in visible spectrum (AREVIS) in ARESW in Wuhan was 3% lower than in Dengfeng. The AREVIS percentage depended largely on aerosol particle size, but was less influenced by aerosol absorptivity. Full article
(This article belongs to the Section Atmosphere Remote Sensing)
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Open AccessArticle Applications of Satellite-Based Rainfall Estimates in Flood Inundation Modeling—A Case Study in Mundeni Aru River Basin, Sri Lanka
Remote Sens. 2017, 9(10), 998; https://doi.org/10.3390/rs9100998
Received: 19 July 2017 / Revised: 15 September 2017 / Accepted: 22 September 2017 / Published: 27 September 2017
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Abstract
The performance of Satellite Rainfall Estimate (SRE) products applied to flood inundation modelling was tested for the Mundeni Aru River Basin in eastern Sri Lanka. Three SREs (PERSIANN, TRMM, and GSMaP) were tested, with the Rainfall-Runoff-Inundation (RRI) model used as the flood inundation
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The performance of Satellite Rainfall Estimate (SRE) products applied to flood inundation modelling was tested for the Mundeni Aru River Basin in eastern Sri Lanka. Three SREs (PERSIANN, TRMM, and GSMaP) were tested, with the Rainfall-Runoff-Inundation (RRI) model used as the flood inundation model. All the SREs were found to be suitable for applying to the RRI model. The simulations created by applying the SREs were generally accurate, although there were some discrepancies in discharge due to differing precipitation volumes. The volumes of precipitation of the SREs tended to be smaller than those of the gauged data, but using a scale factor to correct this improved the simulations. In particular, the SRE, i.e., the GSMaP yielding the best simulation that correlated most closely with the flood inundation extent from the satellite data, was considered the most appropriate to apply to the model calculation. The application procedures and suggestions shown in this study could help authorities to make better-informed decisions when giving early flood warnings and making rapid flood forecasts, especially in areas where in-situ observations are limited. Full article
(This article belongs to the Special Issue Remote Sensing for Flood Mapping and Monitoring of Flood Dynamics)
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Open AccessArticle Polarimetric ALOS PALSAR Time Series in Mapping Biomass of Boreal Forests
Remote Sens. 2017, 9(10), 999; https://doi.org/10.3390/rs9100999
Received: 4 July 2017 / Revised: 19 September 2017 / Accepted: 21 September 2017 / Published: 27 September 2017
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Abstract
Here, we examined multitemporal behavior of fully polarimetric SAR (PolSAR) parameters at L-band in relation to the stem volume of boreal forests. The PolSAR parameters were evaluated in terms of their temporal consistency, inter-dependence and suitability for forest stem volume estimation across several
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Here, we examined multitemporal behavior of fully polarimetric SAR (PolSAR) parameters at L-band in relation to the stem volume of boreal forests. The PolSAR parameters were evaluated in terms of their temporal consistency, inter-dependence and suitability for forest stem volume estimation across several seasonal conditions (frozen, thaw and unfrozen). The satellite SAR data were represented by a time series of PolSAR images acquired during several seasons in the years 2006 to 2009 by the ALOS PALSAR sensor. The study area was in central Finland, and represented a managed area in typical boreal mixed forest land. Utility of different PolSAR parameters, their temporal stability and cross-correlations were studied along with reference stand-level stem volume data from forest inventory. Further, two polarimetric parameters, cross-polarization backscatter and co-polarization coherence, were chosen for further investigation and stem volume retrieval. A relationship between forest stem volume and PolSAR parameters was established using the kNN regression approach. Ways of optimally combining PolSAR images were evaluated as well. For a single scene, best results were observed with polarimetric coherence (RMSE ≈ 38.8 m3/ha) for scene acquired in frozen conditions. An RMSE of 40.8 m3/ha (42.9%, R2 = 0.66) was achieved for cross-polarization backscatter in the best case. Cross-polarization backscatter was a better predictor than polarimetric coherence for few summer scenes. Multitemporal aggregation of selected PolSAR scenes improved estimates for both studied PolSAR parameters. Stronger improvement was observed for coherence with RMSE down to 34 m3/ha (35.8%, R2 = 0.77) compared to 38.8–51.6 m3/ha (40.8–54.3%) from separate scenes. Finally, the accuracy statistics reached RMSE of 32.2 m3/ha (34%, R2 = 0.79) when multitemporal HHVV coherence was combined with multitemporal HV-backscatter. Full article
(This article belongs to the Section Forest Remote Sensing)
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Open AccessArticle Global Surface Mass Variations from Continuous GPS Observations and Satellite Altimetry Data
Remote Sens. 2017, 9(10), 1000; https://doi.org/10.3390/rs9101000
Received: 7 August 2017 / Revised: 8 September 2017 / Accepted: 22 September 2017 / Published: 27 September 2017
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Abstract
The Gravity Recovery and Climate Experiment (GRACE) mission is able to observe the global large-scale mass and water cycle for the first time with unprecedented spatial and temporal resolution. However, no other time-varying gravity fields validate GRACE. Furthermore, the C20 of GRACE
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The Gravity Recovery and Climate Experiment (GRACE) mission is able to observe the global large-scale mass and water cycle for the first time with unprecedented spatial and temporal resolution. However, no other time-varying gravity fields validate GRACE. Furthermore, the C20 of GRACE is poor, and no GRACE data are available before 2002 and there will likely be a gap between the GRACE and GRACE-FOLLOW-ON mission. To compensate for GRACE’s shortcomings, in this paper, we provide an alternative way to invert Earth’s time-varying gravity field, using a priori degree variance as a constraint on amplitudes of Stoke’s coefficients up to degree and order 60, by combining continuous GPS coordinate time series and satellite altimetry (SA) mean sea level anomaly data from January 2003 to December 2012. Analysis results show that our estimated zonal low-degree gravity coefficients agree well with those of GRACE, and large-scale mass distributions are also investigated and assessed. It was clear that our method effectively detected global large-scale mass changes, which is consistent with GRACE observations and the GLDAS model, revealing the minimums of annual water cycle in the Amazon in September and October. The global mean mass uncertainty of our solution is about two times larger than that of GRACE after applying a Gaussian spatial filter with a half wavelength at 500 km. The sensitivity analysis further shows that ground GPS observations dominate the lower-degree coefficients but fail to contribute to the higher-degree coefficients, while SA plays a complementary role at higher-degree coefficients. Consequently, a comparison in both the spherical harmonic and geographic domain confirms our global inversion for the time-varying gravity field from GPS and Satellite Altimetry. Full article
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Open AccessArticle One-Class Classification of Airborne LiDAR Data in Urban Areas Using a Presence and Background Learning Algorithm
Remote Sens. 2017, 9(10), 1001; https://doi.org/10.3390/rs9101001
Received: 10 August 2017 / Revised: 6 September 2017 / Accepted: 25 September 2017 / Published: 27 September 2017
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Abstract
Automatic classification of light detection and ranging (LiDAR) data in urban areas is of great importance for many applications such as generating three-dimensional (3D) building models and monitoring power lines. Traditional supervised classification methods require training samples of all classes to construct a
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Automatic classification of light detection and ranging (LiDAR) data in urban areas is of great importance for many applications such as generating three-dimensional (3D) building models and monitoring power lines. Traditional supervised classification methods require training samples of all classes to construct a reliable classifier. However, complete training samples are normally hard and costly to collect, and a common circumstance is that only training samples for a class of interest are available, in which traditional supervised classification methods may be inappropriate. In this study, we investigated the possibility of using a novel one-class classification algorithm, i.e., the presence and background learning (PBL) algorithm, to classify LiDAR data in an urban scenario. The results demonstrated that the PBL algorithm implemented by back propagation (BP) neural network (PBL-BP) could effectively classify a single class (e.g., building, tree, terrain, power line, and others) from airborne LiDAR point cloud with very high accuracy. The mean F-score for all of the classes from the PBL-BP classification results was 0.94, which was higher than those from one-class support vector machine (SVM), biased SVM, and maximum entropy methods (0.68, 0.82 and 0.93, respectively). Moreover, the PBL-BP algorithm yielded a comparable overall accuracy to the multi-class SVM method. Therefore, this method is very promising in the classification of the LiDAR point cloud. Full article
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Open AccessArticle A Methodology to Detect and Update Active Deformation Areas Based on Sentinel-1 SAR Images
Remote Sens. 2017, 9(10), 1002; https://doi.org/10.3390/rs9101002
Received: 4 August 2017 / Revised: 12 September 2017 / Accepted: 21 September 2017 / Published: 28 September 2017
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Abstract
This work is focused on deformation activity mapping and monitoring using Sentinel-1 (S-1) data and the DInSAR (Differential Interferometric Synthetic Aperture Radar) technique. The main goal is to present a procedure to periodically update and assess the geohazard activity (volcanic activity, landslides and
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This work is focused on deformation activity mapping and monitoring using Sentinel-1 (S-1) data and the DInSAR (Differential Interferometric Synthetic Aperture Radar) technique. The main goal is to present a procedure to periodically update and assess the geohazard activity (volcanic activity, landslides and ground-subsidence) of a given area by exploiting the wide area coverage and the high coherence and temporal sampling (revisit time up to six days) provided by the S-1 satellites. The main products of the procedure are two updatable maps: the deformation activity map and the active deformation areas map. These maps present two different levels of information aimed at different levels of geohazard risk management, from a very simplified level of information to the classical deformation map based on SAR interferometry. The methodology has been successfully applied to La Gomera, Tenerife and Gran Canaria Islands (Canary Island archipelago). The main obtained results are discussed. Full article
(This article belongs to the Special Issue Radar Interferometry for Geohazards)
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Open AccessArticle Deriving Spatio-Temporal Development of Ground Subsidence Due to Subway Construction and Operation in Delta Regions with PS-InSAR Data: A Case Study in Guangzhou, China
Remote Sens. 2017, 9(10), 1004; https://doi.org/10.3390/rs9101004
Received: 17 July 2017 / Revised: 22 September 2017 / Accepted: 23 September 2017 / Published: 28 September 2017
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Abstract
Subways have been an important method for relieving traffic pressures in urban areas, but ground subsidence, during construction and operation, can be a serious problem as it may affect the safety of its operation and that of the surrounding buildings. Thus, conducting long-term
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Subways have been an important method for relieving traffic pressures in urban areas, but ground subsidence, during construction and operation, can be a serious problem as it may affect the safety of its operation and that of the surrounding buildings. Thus, conducting long-term ground deformation monitoring and modeling for subway networks are essential. Compared with traditional geodetic methods, the Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) technique offers wider coverage and denser measurements along subway lines. In this study, we mapped the surface deformation of the Guangzhou subway network with Advanced Synthetic Aperture Radar (ASAR) and Phased Array Type L-band Synthetic Aperture Radar (PALSAR) data using the Interferometric Point Target Analysis (IPTA) technique. The results indicate that newly excavated tunnels have regional subsidence with an average rate of more than 8 mm/year, as found on Lines Two, Three, Six, and GuangFo (GF). Furthermore, we determined the spatio-temporal subsidence behavior of subways with PALSAR in delta areas using Peck’s formula and the logistic time model. We estimated the tunneling-related parameters in soft soil areas, which had not been previously explored. We examined a section of line GF, as an example, to estimate the ground settlement trough development. The results showed the maximum settlement increased from −5.2 mm to −23.6 mm and its ground loss ratio ranged from 1.5–8.7% between 13 July 2008 and 19 January 2011. In addition, we found that the tunnels in line GF will become stable after a period of about 2300 days in peak subsidence areas. The results show that the proposed approach can help explain the dynamic ground subsidence along a metro line. This study can provide references for urban subway projects in delta areas, and for the risk assessment of nearby buildings and underground pipelines along metro lines. Full article
(This article belongs to the Special Issue Radar Interferometry for Geohazards)
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Open AccessArticle Spectral Similarity and PRI Variations for a Boreal Forest Stand Using Multi-angular Airborne Imagery
Remote Sens. 2017, 9(10), 1005; https://doi.org/10.3390/rs9101005
Received: 1 August 2017 / Revised: 14 September 2017 / Accepted: 22 September 2017 / Published: 29 September 2017
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Abstract
The photochemical reflectance index (PRI) is a proxy for light use efficiency (LUE), and is used in remote sensing to measure plant stress and photosynthetic downregulation in plant canopies. It is known to depend on local light conditions within a canopy indicating non-photosynthetic
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The photochemical reflectance index (PRI) is a proxy for light use efficiency (LUE), and is used in remote sensing to measure plant stress and photosynthetic downregulation in plant canopies. It is known to depend on local light conditions within a canopy indicating non-photosynthetic quenching of incident radiation. Additionally, when measured from a distance, canopy PRI depends on shadow fraction—the fraction of shaded foliage in the instantaneous field of view of the sensor—due to observation geometry. Our aim is to quantify the extent to which sunlit fraction alone can describe variations in PRI so that it would be possible to correct for its variation and identify other possible factors affecting the PRI–sunlit fraction relationship. We used a high spatial and spectral resolution Aisa Eagle airborne imaging spectrometer above a boreal Scots pine site in Finland (Hyytiälä forest research station, 61°50′N, 24°17′E), with the sensor looking in nadir and tilted (off-nadir) directions. The spectral resolution of the data was 4.6 nm, and the spatial resolution was 0.6 m. We compared the PRI for three different scatter angles ( β = 19 ° , 55 ° and 76 °, defined as the angle between sensor and solar directions) at the forest stand level, and observed a small (0.006) but statistically significant (p < 0.01) difference in stand PRI. We found that stand mean PRI was not a direct function of sunlit fraction. However, for each scatter angle separately, we found a clear non-linear relationship between PRI and sunlit fraction. The relationship was systematic and had a similar shape for all of the scatter angles. As the PRI–sunlit fraction curves for the different scatter angles were shifted with respect to each other, no universal curve could be found causing the observed independence of canopy PRI from the average sunlit fraction of each view direction. We found the shifts of the curves to be related to a leaf structural effect on canopy scattering: the ratio of needle spectral reflectance to transmittance. We demonstrate that modeling PRI–sunlit fraction relationships using high spatial resolution imaging spectroscopy data is suitable and needed in order to quantify PRI variations over forest canopies. Full article
(This article belongs to the Special Issue Recent Progress and Developments in Imaging Spectroscopy)
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Open AccessArticle Integration of Absorption Feature Information from Visible to Longwave Infrared Spectral Ranges for Mineral Mapping
Remote Sens. 2017, 9(10), 1006; https://doi.org/10.3390/rs9101006
Received: 4 September 2017 / Revised: 22 September 2017 / Accepted: 24 September 2017 / Published: 28 September 2017
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Abstract
Merging hyperspectral data from optical and thermal ranges allows a wider variety of minerals to be mapped and thus allows lithology to be mapped in a more complex way. In contrast, in most of the studies that have taken advantage of the data
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Merging hyperspectral data from optical and thermal ranges allows a wider variety of minerals to be mapped and thus allows lithology to be mapped in a more complex way. In contrast, in most of the studies that have taken advantage of the data from the visible (VIS), near-infrared (NIR), shortwave infrared (SWIR) and longwave infrared (LWIR) spectral ranges, these different spectral ranges were analysed and interpreted separately. This limits the complexity of the final interpretation. In this study a presentation is made of how multiple absorption features, which are directly linked to the mineral composition and are present throughout the VIS, NIR, SWIR and LWIR ranges, can be automatically derived and, moreover, how these new datasets can be successfully used for mineral/lithology mapping. The biggest advantage of this approach is that it overcomes the issue of prior definition of endmembers, which is a requested routine employed in all widely used spectral mapping techniques. In this study, two different airborne image datasets were analysed, HyMap (VIS/NIR/SWIR image data) and Airborne Hyperspectral Scanner (AHS, LWIR image data). Both datasets were acquired over the Sokolov lignite open-cast mines in the Czech Republic. It is further demonstrated that even in this case, when the absorption feature information derived from multispectral LWIR data is integrated with the absorption feature information derived from hyperspectral VIS/NIR/SWIR data, an important improvement in terms of more complex mineral mapping is achieved. Full article
(This article belongs to the Special Issue Hyperspectral Imaging and Applications)
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Open AccessArticle Monitoring Urban Clusters Expansion in the Middle Reaches of the Yangtze River, China, Using Time-Series Nighttime Light Images
Remote Sens. 2017, 9(10), 1007; https://doi.org/10.3390/rs9101007
Received: 7 August 2017 / Revised: 14 September 2017 / Accepted: 22 September 2017 / Published: 28 September 2017
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Abstract
The urban clusters in the Middle Reaches of the Yangtze River (MRYR) in China include the Chang-Zhu-Tan urban agglomeration, the Wuhan metropolitan area, and the Poyang Lake urban agglomeration. While previous studies of urban expansion in China focused mainly on the coastal regions,
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The urban clusters in the Middle Reaches of the Yangtze River (MRYR) in China include the Chang-Zhu-Tan urban agglomeration, the Wuhan metropolitan area, and the Poyang Lake urban agglomeration. While previous studies of urban expansion in China focused mainly on the coastal regions, this study aimed to investigate urban expansion patterns and factors in the MRYR, which are crucial for urban development in Central China. A neighborhood statistics analysis (NSA) method and a local-optimized threshold method were used to detect urban changes during 1992–2011 from the time-series Defense Meteorological Satellite Program’s Operational Linescan System (DMSP/OLS) nighttime light (NTL) images. The evolution of urban expansion intensity and landscape metrics were analyzed at multiple spatial scales, including the whole region, urban agglomeration, and city scales. Finally, the expanded STochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model was built to explore the factors that controlled NTL intensity. The results revealed that urban areas extracted from the NTL data were consistent with those extracted from the Landsat Thematic Mapper data, with an overall accuracy of 81.74% and a Kappa of 0.40. A relatively slow urbanization pace was observed from 1992 to 2002 in the MRYR region, which then accelerated in the period of 2002 to 2007 and then slowed down between 2007 and 2011. Additionally, urban expansion exhibited a radial pattern. The results further indicated that major factors controlling NTL intensity were gross domestic product, followed by total investment in fixed assets, tertiary industry, urban construction area, non-agricultural population, and industrial output in the city clusters. The study provides important insights for further studies on the urbanization processes in the MRYR region. Full article
(This article belongs to the Special Issue Societal and Economic Benefits of Earth Observation Technologies)
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Open AccessArticle Band Selection-Based Dimensionality Reduction for Change Detection in Multi-Temporal Hyperspectral Images
Remote Sens. 2017, 9(10), 1008; https://doi.org/10.3390/rs9101008
Received: 31 August 2017 / Revised: 23 September 2017 / Accepted: 26 September 2017 / Published: 29 September 2017
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Abstract
This paper proposes to use band selection-based dimensionality reduction (BS-DR) technique in addressing a challenging multi-temporal hyperspectral images change detection (HSI-CD) problem. The aim of this work is to analyze and evaluate in detail the CD performance by selecting the most informative band
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This paper proposes to use band selection-based dimensionality reduction (BS-DR) technique in addressing a challenging multi-temporal hyperspectral images change detection (HSI-CD) problem. The aim of this work is to analyze and evaluate in detail the CD performance by selecting the most informative band subset from the original high-dimensional data space. In particular, for cases where ground reference data are available or unavailable, either supervised or unsupervised CD approaches are designed. The following sub-problems in HSI-CD are investigated, including: (1) the estimated number of multi-class changes; (2) the binary CD; (3) the multiple CD; (4) the estimated optimal number of selected bands; and (5) computational efficiency. The main contribution of this paper is to provide for the first time a thorough analysis of the impacts of band selection on the HSI-CD problem, thus to fix the gap in the state-of-the-art techniques either by simply utilizing the full dimensionality of the data or exploring a complex hierarchical change analysis. It is applicable to CD problems in multispectral or PolSAR images when the feature space is expanded for discriminant feature extraction. Two real multi-temporal hyperspectral Hyperion datasets are used to validate the proposed approaches. Quantitative and qualitative experimental results demonstrated that by selecting a subset of the most informative and distinct spectral bands, the proposed approaches offered better CD performance than the state-of-the-art techniques using original full bands, without losing the change representative and discriminable capabilities of a detector. Full article
(This article belongs to the Section Remote Sensing Image Processing)
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Open AccessArticle An Integrated Method for Simulation of Synthetic Aperture Radar (SAR) Raw Data in Moving Target Detection
Remote Sens. 2017, 9(10), 1009; https://doi.org/10.3390/rs9101009
Received: 7 August 2017 / Revised: 18 September 2017 / Accepted: 25 September 2017 / Published: 29 September 2017
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Abstract
SAR (Synthetic Aperture Radar) raw data simulation has a significant role in the evaluation of newly-proposed methods for the estimation of moving target parameters. The evaluation of methods in different cases emphasizes the importance of the need to have fast simulators. Using reverse
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SAR (Synthetic Aperture Radar) raw data simulation has a significant role in the evaluation of newly-proposed methods for the estimation of moving target parameters. The evaluation of methods in different cases emphasizes the importance of the need to have fast simulators. Using reverse SAR imaging methods for the raw data simulations has achieved good results in the simulation of the static targets, but for the simulation of moving targets these methods have a few shortcomings. In this paper, we propose a method to simulate a speckled scene with moving targets in the hybrid domain. First, the scene is simulated, including speckle, which is statistically in accordance with real SAR image behavior. Then, a reverse imaging algorithm (inverse chirp scaling) was applied on the scene to generate the SAR raw data. The moving target simulation was also done in the time-domain as the next step. Finally, the results from two prior steps were superposed to generate the SAR raw data with moving targets. All steps of the proposed method were evaluated separately. The speckle procedure was evaluated by comparing the speckled SAR image before the simulation and the image of the SAR simulated raw data. The results show similar variations in real and imaginary parts of these data. The correlation between the reflectivity map and the SAR images after the simulation was calculated and the obtained correlation coefficient was about 0.95 for different images. The final data were further analyzed for the displacement of moving targets’ positions. The results show similar displacement between moving target SAR raw data with a background and without a background. Full article
(This article belongs to the Section Remote Sensing Image Processing)
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Open AccessEditor’s ChoiceArticle 3D Monitoring of Buildings Using TerraSAR-X InSAR, DInSAR and PolSAR Capacities
Remote Sens. 2017, 9(10), 1010; https://doi.org/10.3390/rs9101010
Received: 30 June 2017 / Revised: 21 August 2017 / Accepted: 22 September 2017 / Published: 29 September 2017
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Abstract
The rapid expansion of cities increases the need of urban remote sensing for a large scale monitoring. This paper provides greater understanding of how TerraSAR-X (TSX) high-resolution abilities enable to reach the spatial precision required to monitor individual buildings, through the use of
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The rapid expansion of cities increases the need of urban remote sensing for a large scale monitoring. This paper provides greater understanding of how TerraSAR-X (TSX) high-resolution abilities enable to reach the spatial precision required to monitor individual buildings, through the use of a 4 year temporal stack of 100 images over Paris (France). Three different SAR modes are investigated for this purpose. First a method involving a whole time-series is proposed to measure realistic heights of buildings. Then, we show that the small wavelength of TSX makes the interferometric products very sensitive to the ordinary building-deformation, and that daily deformation can be measured over the entire building with a centimetric accuracy, and without any a priori on the deformation evolution, even when neglecting the impact of the atmosphere. Deformations up to 4 cm were estimated for the Eiffel Tower and up to 1 cm for other lower buildings. These deformations were analyzed and validated with weather and in situ local data. Finally, four TSX polarimetric images were used to investigate geometric and dielectric properties of buildings under the deterministic framework. Despite of the resolution loss of this mode, the possibility to estimate the structural elements of a building orientations and their relative complexity in the spatial organization are demonstrated. Full article
(This article belongs to the Special Issue Recent Advances in Polarimetric SAR Interferometry)
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Open AccessArticle Assessing Terrestrial Water Storage and Flood Potential Using GRACE Data in the Yangtze River Basin, China
Remote Sens. 2017, 9(10), 1011; https://doi.org/10.3390/rs9101011
Received: 14 August 2017 / Revised: 20 September 2017 / Accepted: 22 September 2017 / Published: 29 September 2017
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Abstract
Floods have caused tremendous economic, societal and ecological losses in the Yangtze River Basin (YRB) of China. To reduce the impact of these disasters, it is important to understand the variables affecting the hydrological state of the basin. In this study, we used
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Floods have caused tremendous economic, societal and ecological losses in the Yangtze River Basin (YRB) of China. To reduce the impact of these disasters, it is important to understand the variables affecting the hydrological state of the basin. In this study, we used Gravity Recovery and Climate Experiment (GRACE) satellite data, flood potential index (FPI), precipitation data (Tropical Rainfall Measuring Mission, TRMM 3B43), and other meteorological data to generate monthly terrestrial water storage anomalies (TWSA) and to evaluate flood potential in the YRB. The results indicate that the basin contained increasing amounts of water from 2003 to 2014, with a slight increase of 3.04 mm/year in the TWSA. The TWSA and TRMM data exhibit marked seasonal characteristics with summer peaks and winter dips. Estimates of terrestrial water storage based on GRACE, measured as FPI, are critical for understanding and predicting flooding. The 2010 flood (FPI ~ 0.36) was identified as the most serious disaster during the study period, with discharge and precipitation values 37.95% and 19.44% higher, respectively, than multi-year average values for the same period. FPI can assess reliably hydrological extremes with high spatial and temporal resolution, but currently, it is not suitable for smaller and/or short-term flood events. Thus, we conclude that GRACE data can be effectively used for monitoring and examining large floods in the YRB and elsewhere, thus improving the current knowledge and presenting potentially important political and economic implications. Full article
(This article belongs to the Special Issue Remote Sensing for Flood Mapping and Monitoring of Flood Dynamics)
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Open AccessArticle Comparison of Electrochemical Concentration Cell Ozonesonde and Microwave Limb Sounder Satellite Remote Sensing Ozone Profiles for the Center of the South Asian High
Remote Sens. 2017, 9(10), 1012; https://doi.org/10.3390/rs9101012
Received: 1 August 2017 / Revised: 23 September 2017 / Accepted: 23 September 2017 / Published: 29 September 2017
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Abstract
To further verify the ozone profile reliability of satellite remote sensing for the ozone valley over the Tibetan Plateau in the core area of the South Asian high in summer, we validate the ozone products from the microwave limb sounder (MLS) onboard the
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To further verify the ozone profile reliability of satellite remote sensing for the ozone valley over the Tibetan Plateau in the core area of the South Asian high in summer, we validate the ozone products from the microwave limb sounder (MLS) onboard the Aura satellite over the Tibetan plateau using electrochemical concentration cell (ECC) ozonesonde data of 2016 for Ngari, Tibet. The MLS version four ozone profiles have lower standard deviation in the middle stratosphere (38–10 hPa), whereas the ozonesonde profiles have lower standard deviation in the upper troposphere and lower stratosphere region (200–83 hPa). There are statistically significant differences between these two datasets in most of the stratosphere (10–83 hPa). The mean value of MLS ozone is about 0.8–1.5 mPa greater than that of ECC ozone, which corresponds to a relative deviation of 59 ± 24% at 83 hPa, 24 ± 13% at 68 hPa, 20 ± 5% at 56 hPa, 14 ± 4% at 46 hPa and 38 hPa, and 9 ± 4% in the layers between 32 and 10 hPa. However, there is no statistically significant difference between the two datasets in the upper troposphere (100–200 hPa). Full article
(This article belongs to the Section Atmosphere Remote Sensing)
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Open AccessArticle A Spectral Unmixing Method with Ensemble Estimation of Endmembers: Application to Flood Mapping in the Caprivi Floodplain
Remote Sens. 2017, 9(10), 1013; https://doi.org/10.3390/rs9101013
Received: 18 July 2017 / Revised: 23 September 2017 / Accepted: 25 September 2017 / Published: 30 September 2017
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Abstract
The Caprivi basin in Namibia has been affected by severe flooding in recent years resulting in deaths, displacements and destruction of infrastructure. The negative consequences of these floods have emphasized the need for timely, accurate and objective information about the extent and location
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The Caprivi basin in Namibia has been affected by severe flooding in recent years resulting in deaths, displacements and destruction of infrastructure. The negative consequences of these floods have emphasized the need for timely, accurate and objective information about the extent and location of affected areas. Due to the high temporal variability of flood events, Earth Observation (EO) data at high revisit frequency is preferred for accurate flood monitoring. Currently, EO data has either high temporal or coarse spatial resolution. Accurate methodologies for the estimation and monitoring of flooding extent using coarse spatial resolution optical image data are needed in order to capture spatial details in heterogeneous areas such as Caprivi. The objective of this work was the retrieval of the fractional abundance of water ( γ w ) by applying a new spectral indices-based unmixing algorithm to Medium Resolution Imaging Spectrometer Full Resolution (MERIS FR) data using a minimum number of spectral bands. These images are technically similar to the OLCI image data acquired by the Sentinel-3 satellite, which are to be systematically provided in the near future. The normalized difference wetness index (NDWI) was applied to delineate the water surface and combined with normalized difference vegetation index (NDVI) to account for emergent vegetation within the water bodies. The challenge to map flooded areas by applying spectral unmixing is the estimation of spectral endmembers, i.e., pure spectra of land cover features. In our study, we developed and applied a new unmixing method based on the use of an ensemble of spectral endmembers to capture and take into account spectral variability within each endmember. In our case study, forty realizations of the spectral endmembers gave a stable frequency distribution of γ w . Quality of the flood map derived from the Envisat MERIS (MERIS) data was assessed against high (30 m) spatial resolution Landsat Thematic Mapper (TM) images on two different dates (17 April 2008 and 22 May 2009) during which floods occurred. The findings show that both the spatial and the frequency distribution of the γ w extracted from the MERIS data were in good agreement with the high-resolution TM retrievals. The use of conventional linear unmixing, instead, applied using the entire available spectra for each image, resulted in relatively large differences between TM and MERIS retrievals. Full article
(This article belongs to the Special Issue Remote Sensing for Flood Mapping and Monitoring of Flood Dynamics)
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Open AccessFeature PaperEditor’s ChoiceArticle Global Registration of 3D LiDAR Point Clouds Based on Scene Features: Application to Structured Environments
Remote Sens. 2017, 9(10), 1014; https://doi.org/10.3390/rs9101014
Received: 4 August 2017 / Revised: 22 September 2017 / Accepted: 26 September 2017 / Published: 30 September 2017
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Abstract
Acquiring 3D data with LiDAR systems involves scanning multiple scenes from different points of view. In actual systems, the ICP algorithm (Iterative Closest Point) is commonly used to register the acquired point clouds together to form a unique one. However, this method faces
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Acquiring 3D data with LiDAR systems involves scanning multiple scenes from different points of view. In actual systems, the ICP algorithm (Iterative Closest Point) is commonly used to register the acquired point clouds together to form a unique one. However, this method faces local minima issues and often needs a coarse initial alignment to converge to the optimum. This paper develops a new method for registration adapted to indoor environments and based on structure priors of such scenes. Our method works without odometric data or physical targets. The rotation and translation of the rigid transformation are computed separately, using, respectively, the Gaussian image of the point clouds and a correlation of histograms. To evaluate our algorithm on challenging registration cases, two datasets were acquired and are available for comparison with other methods online. The evaluation of our algorithm on four datasets against six existing methods shows that the proposed method is more robust against sampling and scene complexity. Moreover, the time performances enable a real-time implementation. Full article
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Open AccessArticle SCaMF–RM: A Fused High-Resolution Land Cover Product of the Rocky Mountains
Remote Sens. 2017, 9(10), 1015; https://doi.org/10.3390/rs9101015
Received: 6 July 2017 / Revised: 15 September 2017 / Accepted: 25 September 2017 / Published: 30 September 2017
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Abstract
Land cover (LC) products, derived primarily from satellite spectral imagery, are essential inputs for environmental studies because LC is a critical driver of processes involved in hydrology, ecology, and climatology, among others. However, existing LC products each have different temporal and spatial resolutions
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Land cover (LC) products, derived primarily from satellite spectral imagery, are essential inputs for environmental studies because LC is a critical driver of processes involved in hydrology, ecology, and climatology, among others. However, existing LC products each have different temporal and spatial resolutions and different LC classes that rarely provide the detail required by these studies. Using multiple existing LC products, we implement our Spatiotemporal Categorical Map Fusion (SCaMF) methodology over a large region of the Rocky Mountains (RM), encompassing sections of six states, to create a new LC product, SCaMF–RM. To do this, we must adapt SCaMF to address the prediction of LC in large space–time regions that present nonstationarities, and we add more flexibility in the LC classifications of the predicted product. SCaMF–RM is produced at two high spatial resolutions, 30 and 50 m, and a yearly frequency for the 30-year period 1983–2012. When multiple products are available in time, we illustrate how SCaMF–RM captures relevant information from the different LC products and improves upon flaws observed in other products. Future work needed includes an exhaustive validation not only of SCaMF–RM but also of all input LC products. Full article
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