Open AccessArticle
Raman Lidar Observations of Aerosol Optical Properties in 11 Cities from France to Siberia
Remote Sens. 2017, 9(10), 978; doi:10.3390/rs9100978 (registering DOI) -
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
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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; doi:10.3390/rs9100979 (registering DOI) -
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
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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; doi:10.3390/rs9100985 (registering DOI) -
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
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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; doi:10.3390/rs9100980 (registering DOI) -
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
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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; doi:10.3390/rs9100981 (registering DOI) -
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
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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; doi:10.3390/rs9100972 (registering DOI) -
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
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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; doi:10.3390/rs9100976 (registering DOI) -
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
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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; doi:10.3390/rs9100973 (registering DOI) -
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; doi:10.3390/rs9100974 (registering DOI) -
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
Measurements of Surface-Layer Turbulence in a Wide Norwegian Fjord Using Synchronized Long-Range Doppler Wind Lidars
Remote Sens. 2017, 9(10), 977; doi:10.3390/rs9100977 (registering DOI) -
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 km. 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
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Open AccessTechnical Note
Examining the Influence of Crop Residue Burning on Local PM2.5 Concentrations in Heilongjiang Province Using Ground Observation and Remote Sensing Data
Remote Sens. 2017, 9(10), 971; doi:10.3390/rs9100971 (registering DOI) -
Abstract
Although a many studies concerning crop residue burning have been conducted, the influence of crop residue burning on local PM2.5 concentrations remains unclear. The number of crop residue burning spots was the highest in Heilongjiang province and we extracted crop residue burning
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Although a many studies concerning crop residue burning have been conducted, the influence of crop residue burning on local PM2.5 concentrations remains unclear. The number of crop residue burning spots was the highest in Heilongjiang province and we extracted crop residue burning spots for this region using MOD14A1 (Thermal Anomalies & Fire Daily L3 Global 1 km) data and national land cover data. By analyzing the temporal variation of crop residue burning and PM2.5 concentrations in Heilongjiang province, we found that the total number of crop residue burning spots was not correlated with the variations of PM2.5 concentrations at a provincial (regional) scale. However, crop residue burning exerted notable influence on the variations of PM2.5 concentrations at a local scale. We experimented with a set of buffer zone radiuses to examine the influencing area of crop residue burning. The results suggest that the valid influencing area of crop residue burning was between 50 and 80 km. The mean PM2.5 concentration measured at stations close to crop residue burning spots was more than 60 μg/m3 higher than that measured at stations not close to crop residue burning spots. However, no consistent, significant correlation existed between the existence of crop residue burning spots and local PM2.5 concentrations, indicating that local PM2.5 concentrations were influenced by a diversity of factors and not solely controlled by crop residue burning. This research also provides suggestions for better understanding the role of crop residue burning in local and regional air pollution. 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; doi:10.3390/rs9100975 (registering DOI) -
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
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Open AccessFeature PaperArticle
Calibration of the Water Cloud Model at C-Band for Winter Crop Fields and Grasslands
Remote Sens. 2017, 9(9), 969; doi:10.3390/rs9090969 -
Abstract
In a perspective to develop an inversion approach for estimating surface soil moisture of crop fields from Sentinel-1/2 data (radar and optical sensors), the Water Cloud Model (WCM) was calibrated from C-band Synthetic Aperture Radar (SAR) data and Normalized Difference Vegetation Index (NDVI)
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In a perspective to develop an inversion approach for estimating surface soil moisture of crop fields from Sentinel-1/2 data (radar and optical sensors), the Water Cloud Model (WCM) was calibrated from C-band Synthetic Aperture Radar (SAR) data and Normalized Difference Vegetation Index (NDVI) values collected over crops fields and grasslands. The soil contribution that depends on soil moisture and surface roughness (in addition to SAR instrumental parameters) was simulated using the physical backscattering model IEM (Integral Equation Model). The vegetation descriptor used in the WCM is the NDVI because it can be directly calculated from optical images. A large dataset consisting of radar backscattered signal in Vertical transmit and Vertical receive (VV) and Vertical transmit and Horizontal receive (VH) polarizations with wide range of incidence angle, soil moisture, surface roughness, and NDVI-values was used. It was collected over two agricultural study sites. Results show that the soil contribution to the total radar backscattered signal is lower in VH than in VV because VH is more sensitive to vegetation cover. Thus, the use of VH alone or in addition to VV for retrieving the soil moisture is not advantageous in presence of well-developed vegetation cover. Full article
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Open AccessArticle
Impacts of Urbanization on Vegetation Phenology over the Past Three Decades in Shanghai, China
Remote Sens. 2017, 9(9), 970; doi:10.3390/rs9090970 -
Abstract
Vegetation phenology manifests the rhythm of annual plant life activities. It has been extensively studied in natural ecosystems. However, major knowledge gaps still exist in understanding the impacts of urbanization on vegetation phenology. This study addresses two questions to fill the knowledge gaps:
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Vegetation phenology manifests the rhythm of annual plant life activities. It has been extensively studied in natural ecosystems. However, major knowledge gaps still exist in understanding the impacts of urbanization on vegetation phenology. This study addresses two questions to fill the knowledge gaps: (1) How does vegetation phenology vary spatially and temporally along a rural-to-urban transect in Shanghai, China, over the past three decades? (2) How do landscape composition and configuration affect those variations of vegetation phenology? To answer these questions, 30 m × 30 m mean vegetation phenology metrics, including the start of growing season (SOS), end of growing season (EOS), and length of growing season (LOS), were derived for urban vegetation using dense stacks of enhanced vegetation index (EVI) time series from images collected by Landsat 5–8 satellites from 1984 to 2015. Landscape pattern metrics were calculated using high spatial resolution aerial photos. We then used Pearson correlation analysis to quantify the associations between phenology patterns and landscape metrics. We found that vegetation in urban centers experienced advances of SOS for 5–10 days and delays of EOS for 5–11 days compared with those located in the surrounding rural areas. Additionally, we observed strong positive correlations between landscape composition (percentage of landscape area) of developed land and LOS of urban vegetation. We also found that the landscape configuration of local land cover types, especially patch density and edge density, was significantly correlated with the spatial patterns of vegetation phenology. These results demonstrate that vegetation phenology in the urban area is significantly different from its rural surroundings. These findings have implications for urban environmental management, ranging from biodiversity protection to public health risk reduction. Full article
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Open AccessArticle
Temporal Interpolation of Satellite-Derived Leaf Area Index Time Series by Introducing Spatial-Temporal Constraints for Heterogeneous Grasslands
Remote Sens. 2017, 9(9), 968; doi:10.3390/rs9090968 -
Abstract
Continuous satellite-derived leaf area index (LAI) time series are critical for modeling land surface process. In this study, we present an interpolation algorithm to predict the missing data in LAI time series for ecosystems with high within-ecosystem heterogeneity, particularly heterogeneous grasslands. The algorithm
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Continuous satellite-derived leaf area index (LAI) time series are critical for modeling land surface process. In this study, we present an interpolation algorithm to predict the missing data in LAI time series for ecosystems with high within-ecosystem heterogeneity, particularly heterogeneous grasslands. The algorithm is based on spatial-temporal constraints, i.e., the missing data in the LAI time series of a pixel are predicted by the phenological links with other pixels. To address the uncertainties in the construction and selection of reference curves in a heterogeneous landscape, the algorithm constructs a reference dataset for each missing data in the LAI time series from all pixels showing very strong linear phenological links with the target pixel within a region. We also use an iterative process to update the available spatial-temporal constraints. We tested the algorithm with an eight-day composite Moderate Resolution Imaging Spectroradiometer (MODIS) LAI product in the Songnen grasslands, Northeast China in 2010 and 2011. The validation dataset was generated based on high quality time series by artificially adding data gaps. The algorithm achieved high overall interpolation accuracies with high coefficient of determination R2 (>0.9) and low root mean square error (RMSE) (<0.2) in both dry (2010) and wet (2011) years. The algorithm showed advantages in predicting missing data for different seasons and proportions of missing data versus the algorithm that uses regional average LAI curve as a reference. These results suggest that the proposed algorithm could more effectively characterize spatial-temporal constraint information in heterogeneous grasslands for temporal interpolation. Full article
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Open AccessReview
Developments in Landsat Land Cover Classification Methods: A Review
Remote Sens. 2017, 9(9), 967; doi:10.3390/rs9090967 -
Abstract
Land cover classification of Landsat images is one of the most important applications developed from Earth observation satellites. The last four decades were marked by different developments in land cover classification methods of Landsat images. This paper reviews the developments in land cover
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Land cover classification of Landsat images is one of the most important applications developed from Earth observation satellites. The last four decades were marked by different developments in land cover classification methods of Landsat images. This paper reviews the developments in land cover classification methods for Landsat images from the 1970s to date and highlights key ways to optimize analysis of Landsat images in order to attain the desired results. This review suggests that the development of land cover classification methods grew alongside the launches of a new series of Landsat sensors and advancements in computer science. Most classification methods were initially developed in the 1970s and 1980s; however, many advancements in specific classifiers and algorithms have occurred in the last decade. The first methods of land cover classification to be applied to Landsat images were visual analyses in the early 1970s, followed by unsupervised and supervised pixel-based classification methods using maximum likelihood, K-means and Iterative Self-Organizing Data Analysis Technique (ISODAT) classifiers. After 1980, other methods such as sub-pixel, knowledge-based, contextual-based, object-based image analysis (OBIA) and hybrid approaches became common in land cover classification. Attaining the best classification results with Landsat images demands particular attention to the specifications of each classification method such as selecting the right training samples, choosing the appropriate segmentation scale for OBIA, pre-processing calibration, choosing the right classifier and using suitable Landsat images. All these classification methods applied on Landsat images have strengths and limitations. Most studies have reported the superior performance of OBIA on different landscapes such as agricultural areas, forests, urban settlements and wetlands; however, OBIA has challenges such as selecting the optimal segmentation scale, which can result in over or under segmentation, and the low spatial resolution of Landsat images. Other classification methods have the potential to produce accurate classification results when appropriate procedures are followed. More research is needed on the application of hybrid classifiers as they are considered more complex methods for land cover classification. Full article
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Open AccessArticle
A Satellite-Based Assessment of the Distribution and Biomass of Submerged Aquatic Vegetation in the Optically Shallow Basin of Lake Biwa
Remote Sens. 2017, 9(9), 966; doi:10.3390/rs9090966 -
Abstract
Assessing the abundance of submerged aquatic vegetation (SAV), particularly in shallow lakes, is essential for effective lake management activities. In the present study we applied satellite remote sensing (a Landsat-8 image) in order to evaluate the SAV coverage area and its biomass for
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Assessing the abundance of submerged aquatic vegetation (SAV), particularly in shallow lakes, is essential for effective lake management activities. In the present study we applied satellite remote sensing (a Landsat-8 image) in order to evaluate the SAV coverage area and its biomass for the peak growth period, which is mainly in September or October (2013 to 2016), in the eutrophic and shallow south basin of Lake Biwa. We developed and validated a satellite-based water transparency retrieval algorithm based on the linear regression approach (R2 = 0.77) to determine the water clarity (2013–2016), which was later used for SAV classification and biomass estimation. For SAV classification, we used Spectral Mixture Analysis (SMA), a Spectral Angle Mapper (SAM), and a binary decision tree, giving an overall classification accuracy of 86.5% and SAV classification accuracy of 76.5% (SAV kappa coefficient 0.74), based on in situ measurements. For biomass estimation, a new Spectral Decomposition Algorithm was developed. The satellite-derived biomass (R2 = 0.79) for the SAV classified area gives an overall root-mean-square error (RMSE) of 0.26 kg Dry Weight (DW) m-2. The mapped SAV coverage area was 20% and 40% in 2013 and 2016, respectively. Estimated SAV biomass for the mapped area shows an increase in recent years, with values of 3390 t (tons, dry weight) in 2013 as compared to 4550 t in 2016. The maximum biomass density (4.89 kg DW m-2) was obtained for a year with high water transparency (September 2014). With the change in water clarity, a slow change in SAV growth was noted from 2013 to 2016. The study shows that water clarity is important for the SAV detection and biomass estimation using satellite remote sensing in shallow eutrophic lakes. The present study also demonstrates the successful application of the developed satellite-based approach for SAV biomass estimation in the shallow eutrophic lake, which can be tested in other lakes. Full article
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Open AccessArticle
Optimal Weight Design Approach for the Geometrically-Constrained Matching of Satellite Stereo Images
Remote Sens. 2017, 9(9), 965; doi:10.3390/rs9090965 -
Abstract
This study presents an optimal weighting approach for combined image matching of high-resolution satellite stereo images (HRSI). When the rational polynomial coefficients (RPCs) for a pair of stereo images are available, some geometric constraints can be combined in image matching equations. Combining least
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This study presents an optimal weighting approach for combined image matching of high-resolution satellite stereo images (HRSI). When the rational polynomial coefficients (RPCs) for a pair of stereo images are available, some geometric constraints can be combined in image matching equations. Combining least squares image matching (LSM) equations with geometric constraints equations necessitates determining the appropriate weights for different types of observations. The common terms between the two sets of equations are the image coordinates of the corresponding points in the search image. Considering the fact that the RPCs of a stereo pair are produced in compliance with the coplanarity condition, geometric constraints are expected to play an important role in the image matching process. In this study, in order to control the impacts of the imposed constraint, optimal weights for observations were assigned through equalizing their average redundancy numbers. For a detailed assessment of the proposed approach, a pair of CARTOSAT-1 sub-images, along with their precise RPCs, were used. On top of obtaining different matching results, the dimension of the error ellipses of the intersection points in the object space were compared. It was shown through analysis that the geometric mean of the semi-minor and semi-major axis by our method was reduced 0.17 times relative to the unit weighting approach. Full article
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Open AccessArticle
A Method for Retrieving Vertical Air Velocities in Convective Clouds over the Tibetan Plateau from TIPEX-III Cloud Radar Doppler Spectra
Remote Sens. 2017, 9(9), 964; doi:10.3390/rs9090964 -
Abstract
In the summertime, convective cells occur frequently over the Tibetan Plateau (TP) because of the large dynamic and thermal effects of the landmass. Measurements of vertical air velocity in convective cloud are useful for advancing our understanding of the dynamic and microphysical mechanisms
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In the summertime, convective cells occur frequently over the Tibetan Plateau (TP) because of the large dynamic and thermal effects of the landmass. Measurements of vertical air velocity in convective cloud are useful for advancing our understanding of the dynamic and microphysical mechanisms of clouds and can be used to improve the parameterization of current numerical models. This paper presents a technique for retrieving high-resolution vertical air velocities in convective clouds over the TP through the use of Doppler spectra from vertically pointing Ka-band cloud radar. The method was based on the development of a “small-particle-traced” idea and its associated data processing, and it used three modes of radar. Spectral broadening corrections, uncertainty estimations, and results merging were used to ensure accurate results. Qualitative analysis of two typical convective cases showed that the retrievals were reliable and agreed with the expected results inferred from other radar measurements. A quantitative retrieval of vertical air motion from a ground-based optical disdrometer was used to compare with the radar-derived result. This comparison illustrated that, while the data trends from the two methods of retrieval were in agreement while identifying the updrafts and downdrafts, the cloud radar had a much higher resolution and was able to reveal the small-scale variations in vertical air motion. Full article
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Open AccessArticle
A Simplified Method for UAV Multispectral Images Mosaicking
Remote Sens. 2017, 9(9), 962; doi:10.3390/rs9090962 -
Abstract
This paper presents a method for mosaicking unmanned aerial vehicle (UAV) multispectral images. The main purpose of the proposed method is to reduce spatial distortion in the mosaicking process and increase robustness and the speed of the operation. Most UAV multispectral images have
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This paper presents a method for mosaicking unmanned aerial vehicle (UAV) multispectral images. The main purpose of the proposed method is to reduce spatial distortion in the mosaicking process and increase robustness and the speed of the operation. Most UAV multispectral images have multiple bands, and in every band, ground targets have a variety of reflection characteristics that will result in diverse feature quality for feature matching. In this research, an information entropy-based evaluation method is used to select the optimal band for feature matching among the UAV images. To produce more robust matching results for the following alignment step, the evaluation method takes the contrast and spatial distribution of the feature points into consideration at the same time. In most common image mosaicking processes, the digital orthophoto map (DOM) is generated to achieve maximum spatial accuracy. During this process, the original image data will experience considerable irregular resampling, and the process is also unstable in some circumstances. The alignment step uses a simplified projection model that treats the ground as planar is provided, by which the alignment parameters are applied directly to the images instead of generating 3D points, to avoid irregular resampling and unstable 3D reconstruction. The proposed method is proved to be more efficient and accurate and has lower spectral distortion than state-of-the-art mosaicking software. Full article
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