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Remote Sens., Volume 10, Issue 4 (April 2018)

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Cover Story (view full-size image) Ocean surface currents and winds are closely coupled essential climate variables and should be [...] Read more.
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Open AccessArticle Evaluating Landsat and RapidEye Data for Winter Wheat Mapping and Area Estimation in Punjab, Pakistan
Remote Sens. 2018, 10(4), 489; doi:10.3390/rs10040489
Received: 30 January 2018 / Revised: 17 March 2018 / Accepted: 18 March 2018 / Published: 21 March 2018
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Abstract
While publicly available, cost-free coarse and medium spatial resolution satellite data such as MODIS and Landsat perform well in characterizing industrial cropping systems, commercial high spatial resolution satellite data are often preferred alternative for fine scale land tenure agricultural systems such as found
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While publicly available, cost-free coarse and medium spatial resolution satellite data such as MODIS and Landsat perform well in characterizing industrial cropping systems, commercial high spatial resolution satellite data are often preferred alternative for fine scale land tenure agricultural systems such as found in Pakistan. In this article, we integrated commercial 5 m spatial resolution RapidEye and free 30 m Landsat imagery in characterizing winter wheat in Punjab province, Pakistan. Specifically, we used 5 m spatial resolution RapidEye imagery from peak of the winter wheat growing season to derive training data for the characterization of time-series Landsat data. After co-registration, each RapidEye image was classified into wheat/no wheat labels at the 5 m resolution and then aggregated as percent cover to 30 m Landsat grid cells. We produced four maps, two using RapidEye derived continuous training data (of percent wheat cover) as input to a regression tree model, and two using RapidEye derived categorical training data as input to a classification tree model. From the RapidEye-derived 30 m continuous training data, we derived Map 1 as percent wheat per pixel, and Map 2 as binary wheat/no wheat classification derived using a 50% threshold applied to Map 1. To create the categorical wheat/no wheat training data, we first converted the continuous training data to a wheat/no wheat classification, and then used these categorical RapidEye training data to produce a categorical wheat map from the Landsat data. Two methods for categorizing the training data were used. The first method used a 50% wheat/no wheat threshold to produce Map 3, and the second method used only pure wheat (≥75% cover) and no wheat (≤25% cover) training pixels to produce Map 4. The approach of Map 4 is analogous to a standard method in which whole, pure, high-confidence training pixels are delineated. We validated the wheat maps with field data collected using a stratified, two-stage cluster design. Accuracy of the maps produced from the percent cover training data (Map 1 and Map 2) was not substantially better than the accuracy of the maps produced from the categorical training data as all methods yielded similar overall accuracies (±standard error): 88% (±4%) for Map 1, 90% (±4%) for Map 2, 90% (±4%) for Map 3, and 87% (±4%) for Map 4. Because the percent cover training data did not produce significantly higher accuracies, sub-pixel training data are not required for winter wheat mapping in Punjab. Given sufficient expertise in supervised classification model calibration, freely available Landsat data are sufficient for crop mapping in the fine-scale land tenure system of Punjab. For winter wheat mapping in Punjab and other like landscapes, training data for supervised classification may be collected directly from Landsat images without the need for high resolution reference imagery. Full article
(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)
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Open AccessArticle Dust Detection and Intensity Estimation Using Himawari-8/AHI Observation
Remote Sens. 2018, 10(4), 490; doi:10.3390/rs10040490
Received: 23 January 2018 / Revised: 16 March 2018 / Accepted: 20 March 2018 / Published: 21 March 2018
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Abstract
In this study, simple dust detection and intensity estimation methods using Himawari-8 Advanced Himawari Imager (AHI) data are developed. Based on the differences of thermal radiation characteristics between dust and other typical objects, brightness temperature difference (BTD) among four channels (BT11–BT
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In this study, simple dust detection and intensity estimation methods using Himawari-8 Advanced Himawari Imager (AHI) data are developed. Based on the differences of thermal radiation characteristics between dust and other typical objects, brightness temperature difference (BTD) among four channels (BT11–BT12, BT8–BT11, and BT3–BT11) are used together for dust detection. When considering the thermal radiation variation of dust particles over different land cover types, a dynamic threshold scheme for dust detection is adopted. An enhanced dust intensity index (EDII) is developed based on the reflectance of visible/near-infrared bands, BT of thermal-infrared bands, and aerosol optical depth (AOD), and is applied to the detected dust area. The AOD is retrieved using multiple temporal AHI observations by assuming little surface change in a short time period (i.e., 1–2 days) and proved with high accuracy using the Aerosol Robotic Network (AERONET) and cross-compared with MODIS AOD products. The dust detection results agree qualitatively with the dust locations that were revealed by AHI true color images. The results were also compared quantitatively with dust identification results from the AERONET AOD and Ångström exponent, achieving a total dust detection accuracy of 84%. A good agreement is obtained between EDII and the visibility data from National Climatic Data Center ground measurements, with a correlation coefficient of 0.81, indicating the effectiveness of EDII in dust monitoring. Full article
(This article belongs to the Section Atmosphere Remote Sensing)
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Open AccessArticle Estimating Sub-Pixel Soybean Fraction from Time-Series MODIS Data Using an Optimized Geographically Weighted Regression Model
Remote Sens. 2018, 10(4), 491; doi:10.3390/rs10040491
Received: 26 December 2017 / Revised: 15 March 2018 / Accepted: 19 March 2018 / Published: 21 March 2018
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Abstract
Soybean cultivation in China has significantly decreased due to the rising import of genetically modified soybeans from other countries. Understanding soybean’s extent and change information is of great value for national agricultural policy implications and global food security. Some previous studies have explored
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Soybean cultivation in China has significantly decreased due to the rising import of genetically modified soybeans from other countries. Understanding soybean’s extent and change information is of great value for national agricultural policy implications and global food security. Some previous studies have explored the quantitative relationships between crop area and spectral variables derived from remote sensing data. However, both those linear or non-linear relationships were expressed by global regression models, which ignored the spatial non-stationarity of crop spectral signature and may limit the prediction accuracy. This study presented a geographically weighted regression model (GWR) to estimate fractional soybean at 250 m spatial resolution in Heilongjiang Province, one of the most important food production regions in China, using time-series MODIS data and high-quality calibration information derived from Landsat data. A forward stepwise optimization strategy was embedded with the GWR model to select the optimal subset of independent variables for soybeans. Normalized Difference Vegetation Index (NDVI) of Julian day 233 to 257 when soybeans are filling seed was found to be the most important temporal period for sub-pixel soybean area estimation. Our MODIS-based soybean area compared well with Landsat-based results at pixel-level. Also, there was a good agreement between the MODIS-based result and census data at county level, with the coefficient of determination (R2) of 0.80 and the root mean square error (RMSE) was 340.21 km2. Additionally, F-test results showed GWR model had better model goodness-of-fit and higher prediction accuracy than the traditional ordinary least squares (OLS) model. These promising results suggest crop spectral variations both at temporal and spatial scales should be considered when exploring its relationship with pixel-level crop acreage. The optimized GWR model by combining an automated feature selection strategy has great potential for estimating sub-pixel crop area at regional scale based on remote sensing time-series data. Full article
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Open AccessArticle Detecting Forest Road Wearing Course Damage Using Different Methods of Remote Sensing
Remote Sens. 2018, 10(4), 492; doi:10.3390/rs10040492
Received: 17 January 2018 / Revised: 6 March 2018 / Accepted: 19 March 2018 / Published: 21 March 2018
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Abstract
Currently, a large part of forest roads with a bituminous surface course constructed in the Czech Republic in the second half of the last century has been worn out. The aim of the study is to verify the possibility and the accuracy of
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Currently, a large part of forest roads with a bituminous surface course constructed in the Czech Republic in the second half of the last century has been worn out. The aim of the study is to verify the possibility and the accuracy of the road wearing course damage detected by four different remote sensing methods: close range photogrammetry, terrestrial laser scanning, mobile laser scanning and airborne laser scanning. At the beginning of verification, cross sections of the road surface were surveyed geodetically and then compared with the cross sections created in the DTMs which were acquired using the four methods mentioned above. The differences calculated between particular models and geodetic measurements show that close range photogrammetry achieved an RMSE of 0.0110 m and the RMSE of terrestrial laser scanning was 0.0243 m. Based on these results, we can conclude that these two methods are sufficient for the monitoring of the asphalt wearing course of forest roads. These methods allow precise and objective localization, size and quantification of the road damage. By contrast, mobile laser scanning with an RMSE of 0.3167 m does not reach the required precision for the damage detection of forest roads due to the vegetation that affects the precision of the measurements. Similar results are achieved by airborne laser scanning, with an RMSE of 0.1392 m. As regards the time needed, close range photogrammetry appears to be the most appropriate method for damage detection of forest roads. Full article
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Open AccessArticle Groundwater Depletion in the West Liaohe River Basin, China and Its Implications Revealed by GRACE and In Situ Measurements
Remote Sens. 2018, 10(4), 493; doi:10.3390/rs10040493
Received: 30 January 2018 / Revised: 7 March 2018 / Accepted: 19 March 2018 / Published: 21 March 2018
Cited by 1 | PDF Full-text (2460 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
The West Liaohe River Basin (WLRB) is one of the most sensitive areas to climate change in China and an important grain production base in the Inner Mongolia Autonomous Region of China. Groundwater depletion in this region is becoming a critical issue. Here,
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The West Liaohe River Basin (WLRB) is one of the most sensitive areas to climate change in China and an important grain production base in the Inner Mongolia Autonomous Region of China. Groundwater depletion in this region is becoming a critical issue. Here, we used the Gravity Recovery and Climate Experiment (GRACE) satellite data and in situ well observations to estimate groundwater storage (GWS) variations and discussed the driving factors of GWS changes in the WLRB. GRACE detects a GWS decline rate of −0.92 ± 0.49 km3/yr in the WLRB during 2005–2011, consistent with the estimate from in situ observations (−0.96 ± 0.19 km3/yr). This long-term GWS depletion is attributed to reduced precipitation and extensive groundwater overexploitation in the 2000s. Long-term groundwater level observations and reconstructed total water storage variations since 1980 show favorable agreement with precipitation anomalies at interannual timescales, both of which are significantly influenced by the El Niño-Southern Oscillation (ENSO). Generally, the WLRB receives more/less precipitation during the El Niño/La Niña periods. One of the strongest El Niño events on record in 1997–1998 and a subsequent strong La Niña drastically transform the climate of WLRB into a decade-long drought period, and accelerate the groundwater depletion in the WLRB after 1998. This study demonstrates the significance of integrating satellite observations, ground-based measurements, and climatological data for interpreting regional GWS changes from a long-term perspective. Full article
(This article belongs to the Special Issue Remote Sensing of Groundwater from River Basin to Global Scales)
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Open AccessArticle Thaw Subsidence of a Yedoma Landscape in Northern Siberia, Measured In Situ and Estimated from TerraSAR-X Interferometry
Remote Sens. 2018, 10(4), 494; doi:10.3390/rs10040494
Received: 2 February 2018 / Revised: 14 March 2018 / Accepted: 19 March 2018 / Published: 21 March 2018
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Abstract
In permafrost areas, seasonal freeze-thaw cycles result in upward and downward movements of the ground. For some permafrost areas, long-term downward movements were reported during the last decade. We measured seasonal and multi-year ground movements in a yedoma region of the Lena River
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In permafrost areas, seasonal freeze-thaw cycles result in upward and downward movements of the ground. For some permafrost areas, long-term downward movements were reported during the last decade. We measured seasonal and multi-year ground movements in a yedoma region of the Lena River Delta, Siberia, in 2013–2017, using reference rods installed deep in the permafrost. The seasonal subsidence was 1.7 ± 1.5 cm in the cold summer of 2013 and 4.8 ± 2 cm in the warm summer of 2014. Furthermore, we measured a pronounced multi-year net subsidence of 9.3 ± 5.7 cm from spring 2013 to the end of summer 2017. Importantly, we observed a high spatial variability of subsidence of up to 6 cm across a sub-meter horizontal scale. In summer 2013, we accompanied our field measurements with Differential Synthetic Aperture Radar Interferometry (DInSAR) on repeat-pass TerraSAR-X (TSX) data from the summer of 2013 to detect summer thaw subsidence over the same study area. Interferometry was strongly affected by a fast phase coherence loss, atmospheric artifacts, and possibly the choice of reference point. A cumulative ground movement map, built from a continuous interferogram stack, did not reveal a subsidence on the upland but showed a distinct subsidence of up to 2 cm in most of the thermokarst basins. There, the spatial pattern of DInSAR-measured subsidence corresponded well with relative surface wetness identified with the near infra-red band of a high-resolution optical image. Our study suggests that (i) although X-band SAR has serious limitations for ground movement monitoring in permafrost landscapes, it can provide valuable information for specific environments like thermokarst basins, and (ii) due to the high sub-pixel spatial variability of ground movements, a validation scheme needs to be developed and implemented for future DInSAR studies in permafrost environments. Full article
(This article belongs to the Special Issue Remote Sensing of Dynamic Permafrost Regions)
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Open AccessArticle New Tropical Peatland Gas and Particulate Emissions Factors Indicate 2015 Indonesian Fires Released Far More Particulate Matter (but Less Methane) than Current Inventories Imply
Remote Sens. 2018, 10(4), 495; doi:10.3390/rs10040495
Received: 31 January 2018 / Revised: 5 March 2018 / Accepted: 19 March 2018 / Published: 21 March 2018
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Abstract
Deforestation and draining of the peatlands in equatorial SE Asia has greatly increased their flammability, and in September–October 2015 a strong El Niño-related drought led to further drying and to widespread burning across parts of Indonesia, primarily on Kalimantan and Sumatra. These fires
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Deforestation and draining of the peatlands in equatorial SE Asia has greatly increased their flammability, and in September–October 2015 a strong El Niño-related drought led to further drying and to widespread burning across parts of Indonesia, primarily on Kalimantan and Sumatra. These fires resulted in some of the worst sustained outdoor air pollution ever recorded, with atmospheric particulate matter (PM) concentrations exceeding those considered “extremely hazardous to health” by up to an order of magnitude. Here we report unique in situ air quality data and tropical peatland fire emissions factors (EFs) for key carbonaceous trace gases (CO2, CH4 and CO) and PM2.5 and black carbon (BC) particulates, based on measurements conducted on Kalimantan at the height of the 2015 fires, both at locations of “pure” sub-surface peat burning and spreading vegetation fires atop burning peat. PM2.5 are the most significant smoke constituent in terms of human health impacts, and we find in situ PM2.5 emissions factors for pure peat burning to be 17.8 to 22.3 g·kg−1, and for spreading vegetation fires atop burning peat 44 to 61 g·kg−1, both far higher than past laboratory burning of tropical peat has suggested. The latter are some of the highest PM2.5 emissions factors measured worldwide. Using our peatland CO2, CH4 and CO emissions factors (1779 ± 55 g·kg−1, 238 ± 36 g·kg−1, and 7.8 ± 2.3 g·kg−1 respectively) alongside in situ measured peat carbon content (610 ± 47 g-C·kg−1) we provide a new 358 Tg (± 30%) fuel consumption estimate for the 2015 Indonesian fires, which is less than that provided by the GFEDv4.1s and GFASv1.2 global fire emissions inventories by 23% and 34% respectively, and which due to our lower EFCH4 produces far less (~3×) methane. However, our mean in situ derived EFPM2.5 for these extreme tropical peatland fires (28 ± 6 g·kg−1) is far higher than current emissions inventories assume, resulting in our total PM2.5 emissions estimate (9.1 ± 3.5 Tg) being many times higher than GFEDv4.1s, GFASv1.2 and FINNv2, despite our lower fuel consumption. We find that two thirds of the emitted PM2.5 come from Kalimantan, one third from Sumatra, and 95% from burning peatlands. Using new geostationary fire radiative power (FRP) data we map the fire emissions’ spatio-temporal variations in far greater detail than ever before (hourly, 0.05°), identifying a tropical peatland fire diurnal cycle twice as wide as in neighboring non-peat areas and peaking much later in the day. Our data show that a combination of greatly elevated PM2.5 emissions factors, large areas of simultaneous, long-duration burning, and very high peat fuel consumption per unit area made these Sept to Oct tropical peatland fires the greatest wildfire source of particulate matter globally in 2015, furthering evidence for a regional atmospheric pollution impact whose particulate matter component in particular led to millions of citizens being exposed to extremely poor levels of air quality for substantial periods. Full article
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Open AccessArticle Assessment of the Impact of GNSS Processing Strategies on the Long-Term Parameters of 20 Years IWV Time Series
Remote Sens. 2018, 10(4), 496; doi:10.3390/rs10040496
Received: 2 March 2018 / Revised: 16 March 2018 / Accepted: 19 March 2018 / Published: 21 March 2018
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Abstract
Advanced processing of collected global navigation satellite systems (GNSS) observations allows for the estimation of zenith tropospheric delay (ZTD), which in turn can be converted to the integrated water vapour (IWV). The proper estimation of GNSS IWV can be affected by the adopted
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Advanced processing of collected global navigation satellite systems (GNSS) observations allows for the estimation of zenith tropospheric delay (ZTD), which in turn can be converted to the integrated water vapour (IWV). The proper estimation of GNSS IWV can be affected by the adopted GNSS processing strategy. To verify which of its elements cause deterioration and which improve the estimated GNSS IWV, we conducted eight reprocessings of 20 years of GPS observations (01.1996–12.2015). In each of them, we applied a different mapping function, the zenith hydrostatic delay (ZHD) a priori value, the cut-off angle, software, and the positioning method. Obtained in such a way, the ZTD time series were converted to the IWV using the meteorological parameters sourced from the ERA-Interim. Then, based on them, the long-term parameters were estimated and compared to those obtained from the IWV derived from the radio sounding (RS) observations. In this paper, we analyzed long-term parameters such as IWV mean values, linear trends, and amplitudes of annual and semiannual oscillations. A comparative analysis showed, inter alia, that in terms of the investigation of the IWV linear trend the precise point positioning (PPP) method is characterized by higher accuracy than the differential one. It was also found that using the GPT2 model and the higher elevation mask brings benefits to the GNSS IWV linear trend estimation. Full article
(This article belongs to the Section Atmosphere Remote Sensing)
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Open AccessArticle Quality Assessment of Sea Surface Temperature from ATSRs of the Climate Change Initiative (Phase 1)
Remote Sens. 2018, 10(4), 497; doi:10.3390/rs10040497
Received: 30 January 2018 / Revised: 6 March 2018 / Accepted: 16 March 2018 / Published: 21 March 2018
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Abstract
Sea Surface Temperature (SST) observations from space have been made by the Along Track Scanning Radiometers (ATSRs) providing 20 years (August 1991–April 2012) of high quality data. As part of the ESA Climate Change Initiative (CCI) project, SSTs have been retrieved from the
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Sea Surface Temperature (SST) observations from space have been made by the Along Track Scanning Radiometers (ATSRs) providing 20 years (August 1991–April 2012) of high quality data. As part of the ESA Climate Change Initiative (CCI) project, SSTs have been retrieved from the ATSRs. Here, the quality of CCI SST (Phase 1) from ATSRs is validated against drifting buoys. Only CCI ATSR SSTs (Version 1.1) are considered, to facilitate the comparison with the precursor dataset ATSR Reprocessing for Climate (ARC). The CCI retrievals compared with drifting buoys have a median difference slightly larger than 0.1 K. The median SST difference is larger in the tropics (∼0.3 K) during the day, with the night time showing a spatially homogeneous pattern. ATSR-2 and AATSR show similar performance in terms of Robust Standard Deviation (RSD) being 0.2–0.3 K during night and about 0.1 K higher during day. On the other hand, ATSR-1 shows increasing RSD with time from 0.3 K to over 0.6 K. Triple collocation analysis has been applied for the first time on TMI/ATSR-2 observations and for daytime conditions when the wind speed is greater than 10 m/s. Both day and night results indicate that since 2004, the random uncertainty of drifting buoys and CCI AATSR is rather stable at about 0.22 K. Before 2004, drifting buoys have larger values (∼0.3 K), while ATSR-2 shows slightly lower values (∼0.2 K). The random uncertainty for AMSR-E is about 0.47 K, also rather stable with time, while as expected, the TMI has higher values of ∼0.55 K. It is shown for the first time that the AMSR-E random uncertainty changes with latitude, being ∼0.3 K in the tropics and about double this value at mid-latitudes. The SST uncertainties provided with the CCI data are slightly overestimated above 0.45 K and underestimated below 0.3 K during the day. The uncertainty model does not capture correctly the periods with instrument problems after the ATSR-1 3.7 μ m channel failed and the gyro failure of ERS-2. During the night, the uncertainties are slightly underestimated. The CCI SSTs (Phase 1) do not yet match the quality of the ARC dataset when comparing to drifting buoys. The value of the ARC median bias is closer to zero than for CCI, while the RSD is about 0.05 K lower for ARC. ARC also shows a more homogeneous geographical distribution of median bias and RSD, although the differences between the two datasets are small. The observed discrepancies between CCI and ARC during the period of ATSR-1 are unexplained given that both datasets use the same retrieval method. Full article
(This article belongs to the collection Sea Surface Temperature Retrievals from Remote Sensing)
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Open AccessArticle Improving the Remote Sensing Retrieval of Phytoplankton Functional Types (PFT) Using Empirical Orthogonal Functions: A Case Study in a Coastal Upwelling Region
Remote Sens. 2018, 10(4), 498; doi:10.3390/rs10040498
Received: 12 January 2018 / Revised: 8 March 2018 / Accepted: 14 March 2018 / Published: 22 March 2018
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Abstract
An approach that improves the spectral-based PHYSAT method for identifying phytoplankton functional types (PFT) in satellite ocean-color imagery is developed and applied to one study case. This new approach, called PHYSTWO, relies on the assumption that the dominant effect of chlorophyll-a (Chl-a) in
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An approach that improves the spectral-based PHYSAT method for identifying phytoplankton functional types (PFT) in satellite ocean-color imagery is developed and applied to one study case. This new approach, called PHYSTWO, relies on the assumption that the dominant effect of chlorophyll-a (Chl-a) in the normalized water-leaving radiance (nLw) spectrum can be effectively isolated from the signal of accessory pigment biomarkers of different PFT by using Empirical Orthogonal Function (EOF) decomposition. PHYSTWO operates in the dimensionless plane composed by the first two EOF modes generated through the decomposition of a space–nLw matrix at seven wavelengths (412, 443, 469, 488, 531, 547, and 555 nm). PFT determination is performed using orthogonal models derived from the acceptable ranges of anomalies proposed by PHYSAT but adjusted with the available regional and global data. In applying PHYSTWO to study phytoplankton community structures in the coastal upwelling system off central Chile, we find that this method increases the accuracy of PFT identification, extends the application of this tool to waters with high Chl-a concentration, and significantly decreases (~60%) the undetermined retrievals when compared with PHYSAT. The improved accuracy of PHYSTWO and its applicability for the identification of new PFT are discussed. Full article
(This article belongs to the Special Issue Remote Sensing of Ocean Colour)
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Open AccessArticle Optimisation of Savannah Land Cover Characterisation with Optical and SAR Data
Remote Sens. 2018, 10(4), 499; doi:10.3390/rs10040499
Received: 28 January 2018 / Revised: 12 March 2018 / Accepted: 18 March 2018 / Published: 22 March 2018
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Abstract
Accurately mapping savannah land cover at the regional scale can provide useful input to policy decision making efforts regarding, for example, bush control or overgrazing, as well as to global carbon emissions models. Recent attempts have employed Earth observation data, either from optical
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Accurately mapping savannah land cover at the regional scale can provide useful input to policy decision making efforts regarding, for example, bush control or overgrazing, as well as to global carbon emissions models. Recent attempts have employed Earth observation data, either from optical or radar sensors, and most commonly from the dry season when the spectral difference between woody vegetation, crops and grasses is maximised. By far the most common practice has been the use of Landsat optical bands, but some studies have also used vegetation indices or SAR data. However, conflicting reports with regards to the effectiveness of the different approaches have emerged, leaving the respective land cover mapping community with unclear methodological pathways to follow. We address this issue by employing Landsat and Advanced Land Observing Satellite Phased Array type L-band Synthetic Aperture Radar (ALOS PALSAR) data to assess the accuracy of mapping the main savannah land cover types of woody vegetation, grassland, cropland and non-vegetated land. The study area is in southern Africa, covering approximately 44,000 km2. We test the performance of 15 different models comprised of combinations of optical and radar data from the dry and wet seasons. Our results show that a number of models perform well and very similarly. The highest overall accuracy is achieved by the model that incorporates both optical and synthetic-aperture radar (SAR) data from both dry and wet seasons with an overall accuracy of 91.1% (±1.7%): this is almost a 10% improvement from using only the dry season Landsat data (81.7 ± 2.3%). The SAR-only models were capable of mapping woody cover effectively, achieving similar or lower omission and commission errors than the optical models, but other classes were detected with lower accuracies. Our main conclusion is that the combination of metrics from different sensors and seasons improves results and should be the preferred methodological pathway for accurate savannah land cover mapping, especially now with the availability of Sentinel-1 and Sentinel-2 data. Our findings can provide much needed assistance to land cover monitoring efforts to savannahs in general, and in particular to southern African savannahs, where a number of land cover change processes have been related with the observed land degradation in the region. Full article
(This article belongs to the Section Remote Sensing Image Processing)
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Open AccessArticle An Objective Assessment of Hyperspectral Indicators for the Detection of Buried Archaeological Relics
Remote Sens. 2018, 10(4), 500; doi:10.3390/rs10040500
Received: 30 January 2018 / Revised: 19 March 2018 / Accepted: 20 March 2018 / Published: 22 March 2018
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Abstract
Hyperspectral images can highlight crop marks in vegetated areas, which may indicate the presence of underground buried structures, by exploiting the spectral information conveyed in reflected solar radiation. In recent years, different vegetation indices and several other image features have been used, with
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Hyperspectral images can highlight crop marks in vegetated areas, which may indicate the presence of underground buried structures, by exploiting the spectral information conveyed in reflected solar radiation. In recent years, different vegetation indices and several other image features have been used, with varying success, to improve the interpretation of remotely sensed images for archaeological research. However, it is difficult to assess the derived maps quantitatively and select the most meaningful one for a given task, in particular for a non-specialist in image processing. This paper estimates for the first time objectively the suitability of maps derived from spectral features for the detection of buried archaeological structures in vegetated areas based on information theory. This is achieved by computing the statistical dependence between the extracted features and a digital map indicating the presence of buried structures using information theoretical notions. Based on the obtained scores on known targets, the features can be ranked and the most suitable can be chosen to aid in the discovery of previously undetected crop marks in the area under similar conditions. Three case studies are reported: the Roman buried remains of Carnuntum (Austria), the underground structures of Selinunte in the South of Italy, and the buried street relics of Pherai (Velestino) in central Greece. Full article
(This article belongs to the Special Issue Advances in Remote Sensing for Archaeological Heritage)
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Open AccessArticle SAR Automatic Target Recognition Using a Roto-Translational Invariant Wavelet-Scattering Convolution Network
Remote Sens. 2018, 10(4), 501; doi:10.3390/rs10040501
Received: 11 February 2018 / Revised: 17 March 2018 / Accepted: 18 March 2018 / Published: 22 March 2018
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Abstract
The algorithm of synthetic aperture radar (SAR) for automatic target recognition consists of two stages: feature extraction and classification. The quality of extracted features has significant impacts on the final classification performance. This paper presents a SAR automatic target classification method based on
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The algorithm of synthetic aperture radar (SAR) for automatic target recognition consists of two stages: feature extraction and classification. The quality of extracted features has significant impacts on the final classification performance. This paper presents a SAR automatic target classification method based on the wavelet-scattering convolution network. By introducing a deep scattering convolution network with complex wavelet filters over spatial and angular variables, robust feature representations can be extracted across various scales and angles without training data. Conventional dimension reduction and a support vector machine classifier are followed to complete the classification task. The proposed method is then tested on the moving and stationary target acquisition and recognition (MSTAR) benchmark data set and achieves an average accuracy of 97.63% on the classification of ten-class targets without data augmentation. Full article
(This article belongs to the Special Issue Analysis of Big Data in Remote Sensing)
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Open AccessFeature PaperArticle An Approach for High-Resolution Mapping of Hawaiian Metrosideros Forest Mortality Using Laser-Guided Imaging Spectroscopy
Remote Sens. 2018, 10(4), 502; doi:10.3390/rs10040502
Received: 2 February 2018 / Revised: 27 February 2018 / Accepted: 20 March 2018 / Published: 22 March 2018
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Abstract
Rapid ‘Ōhi‘a Death (ROD) is a disease aggressively killing large numbers of Metrosideros polymorpha (‘ōhi‘a), a native keystone tree species on Hawaii Island. This loss threatens to deeply alter the biological make-up of this unique island ecosystem. Spatially explicit information about the present
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Rapid ‘Ōhi‘a Death (ROD) is a disease aggressively killing large numbers of Metrosideros polymorpha (‘ōhi‘a), a native keystone tree species on Hawaii Island. This loss threatens to deeply alter the biological make-up of this unique island ecosystem. Spatially explicit information about the present and past advancement of the disease is essential for its containment; yet, currently such data are severely lacking. To this end, we used the Carnegie Airborne Observatory to collect Laser-Guided Imaging Spectroscopy data and high-resolution digital imagery across >500,000 ha of Hawaii Island in June–July 2017. We then developed a method to map individual tree crowns matching the symptoms of both active (brown; desiccated ‘ōhi‘a crowns) and past (leafless tree crowns) ROD infection using an ensemble of two distinct machine learning approaches. Employing a very conservative classification scheme for minimizing false-positives, model sensitivity rates were 86.9 and 82.5, and precision rates were 97.4 and 95.3 for browning and leafless crowns, respectively. Across the island of Hawaii, we found 43,134 individual crowns suspected of exhibiting the active (browning) stage of ROD infection. Hotspots of potential ROD infection are apparent in the maps. The peninsula on the eastern side of Hawaii known as the Puna district, where the ROD outbreak likely originated, contained a particularly high density of brown crown detections. In comparison, leafless crown detections were much more numerous (547,666 detected leafless crowns in total) and more dispersed across the island. Mapped hotspots of likely ROD incidence across the island will enable scientists, administrators, and land managers to better understand both where and how ROD spreads and how to apply limited resources to limiting this spread. Full article
(This article belongs to the Special Issue Remote Sensing of Tropical Forest Biodiversity)
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Open AccessArticle A New Strategy for Extracting ENSO Related Signals in the Troposphere and Lower Stratosphere from GNSS RO Specific Humidity Observations
Remote Sens. 2018, 10(4), 503; doi:10.3390/rs10040503
Received: 16 January 2018 / Revised: 19 March 2018 / Accepted: 20 March 2018 / Published: 22 March 2018
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Abstract
El Niño-Southern Oscillation related signals (ENSORS) in the troposphere and lower stratosphere (TLS) are the prominent source of inter-annual variability in the weather and climate system of the Earth, and are especially important for monitoring El Niño-Southern Oscillation (ENSO). In order to reduce
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El Niño-Southern Oscillation related signals (ENSORS) in the troposphere and lower stratosphere (TLS) are the prominent source of inter-annual variability in the weather and climate system of the Earth, and are especially important for monitoring El Niño-Southern Oscillation (ENSO). In order to reduce the influence of quasi-biennial oscillations and other unknown signals compared with the traditional empirical orthogonal functions (EOF) method, a new processing strategy involving fusion of a low-pass filter with an optimal filtering frequency (hereafter called the optimal low-pass filter) and EOF is proposed in this paper for the extraction of ENSORS in the TLS. Using this strategy, ENSORS in the TLS over different areas were extracted effectively from the specific humidity profiles provided by the Global Navigation Satellite System (GNSS) radio occultation (RO) of the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) mission from June 2006 to June 2014. The spatial and temporal responses of the extracted ENSORS to ENSO at different altitudes in the TLS were analyzed. The results show that the most suitable areas for extracting ENSORS are over the areas of G25 (−25°S–25°N, 180°W–180°E) −G65(−65°S–65°N, 180°W–180°E) in the upper troposphere (250–200 hpa) which show a lag time of 3 months relative to the Oceanic Niño index (ONI). In the troposphere, ENSO manifests as a major inter-annual variation. The ENSORS extracted from the N3.4 (−5°S to 5°N, 120°W to 170°W) area are responsible for 83.59% of the variability of the total specific humidity anomaly (TSHA) at an altitude of 250 hpa. Over all other defined areas which contain the N3.4 areas, ENSORS also explain the major variability in TSHA. In the lower stratosphere, the extracted ENSORS present an unstable pattern at different altitudes because of the weak ENSO effect. Moreover, the spatial and temporal responses of ENSORS and ONI to ENSO across the globe are in good agreement. Over the areas with strong correlation between ENSORS and ONI, the larger the correlation coefficient is, the shorter the lag time between them. Furthermore, the ENSORS from zonal-mean specific humidity monthly anomalies at different altitudes can clearly present the vertical structure of ENSO in the troposphere. This study provides a new approach for monitoring ENSO events. Full article
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Open AccessArticle SAR Image Recognition with Monogenic Scale Selection-Based Weighted Multi-task Joint Sparse Representation
Remote Sens. 2018, 10(4), 504; doi:10.3390/rs10040504
Received: 13 February 2018 / Revised: 10 March 2018 / Accepted: 21 March 2018 / Published: 22 March 2018
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Abstract
The monogenic signal, which is defined as a linear combination of a signal and its Riesz-transformed one, provides a great opportunity for synthetic aperture radar (SAR) image recognition. However, the incredibly large number of components at different scales may result in too much
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The monogenic signal, which is defined as a linear combination of a signal and its Riesz-transformed one, provides a great opportunity for synthetic aperture radar (SAR) image recognition. However, the incredibly large number of components at different scales may result in too much of a burden for onboard computation. There is great information redundancy in monogenic signals because components at some scales are less discriminative or even have negative impact on classification. In addition, the heterogeneity of the three types of components will lower the quality of decision-making. To solve the problems above, a scale selection method, based on a weighted multi-task joint sparse representation, is proposed. A scale selection model is designed and the Fisher score is presented to measure the discriminative ability of components at each scale. The components with high Fisher scores are concatenated to three component-specific features, and an overcomplete dictionary is built. Meanwhile, the scale selection model produces the weight vector. The three component-specific features are then fed into a multi-task joint sparse representation classification framework. The final decision is made in terms of accumulated weighted reconstruction error. Experiments on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset have proved the effectiveness and superiority of our method. Full article
(This article belongs to the Section Remote Sensing Image Processing)
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Open AccessArticle Evaluating the Applications of the Near-Infrared Region in Mapping Foliar N in the Miombo Woodlands
Remote Sens. 2018, 10(4), 505; doi:10.3390/rs10040505
Received: 23 November 2017 / Revised: 13 January 2018 / Accepted: 20 January 2018 / Published: 23 March 2018
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Abstract
Remote sensing has been widely used to estimate the distribution of foliar nitrogen (N) in a cost-effective manner. Although hyperspectral remote sensing targeting the red edge and shortwave infrared regions has proved successful at estimating foliar N, research has recently shifted to include
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Remote sensing has been widely used to estimate the distribution of foliar nitrogen (N) in a cost-effective manner. Although hyperspectral remote sensing targeting the red edge and shortwave infrared regions has proved successful at estimating foliar N, research has recently shifted to include exploring the benefits of using the near-infrared (NIR) region, especially when using broadband sensing. Bootstrapped random forest regression analysis was applied on Sentinel 2 data to test the significance of using the NIR in foliar N estimation in miombo woodlands. The results revealed a low ranking for individual NIR bands, but the ranking improved when spectral indices were used. In addition, the results indicated a marginal increase in the normalised root mean square error of prediction (nRMSE) from 11.35% N when all bands were used to 11.69% N when the NIR bands were excluded from the model. Bootstrapping results show higher accuracy and better consistency in the prediction of foliar N using combined spectral indices and individual bands. This study therefore underscores the significance of spectral indices to increase the NIR region’s importance in estimating the distribution of foliar N as a key indicator of ecosystem health at the landscape scale in miombo systems. Full article
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Open AccessArticle Reducing Uncertainties in Applying Remotely Sensed Land Use and Land Cover Maps in Land-Atmosphere Interaction: Identifying Change in Space and Time
Remote Sens. 2018, 10(4), 506; doi:10.3390/rs10040506
Received: 15 February 2018 / Revised: 19 March 2018 / Accepted: 19 March 2018 / Published: 23 March 2018
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Abstract
Land use and land cover (LULC) data are a central component of most land-atmosphere interaction studies, but there are two common and highly problematic scale mismatches between LULC and climate data. First, in the spatial domain, researchers rarely consider the impact of scaling
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Land use and land cover (LULC) data are a central component of most land-atmosphere interaction studies, but there are two common and highly problematic scale mismatches between LULC and climate data. First, in the spatial domain, researchers rarely consider the impact of scaling up fine-scale LULC data to match coarse-scale climate datasets. Second, in the temporal domain, climate data typically have sub-daily, daily, monthly, or annual resolution, but LULC datasets often have much coarser (e.g., decadal) resolution. We first explored the effect of three spatial scaling methods on correlations among LULC data and a land surface climatic variable, latent heat flux in China. Scaling by a fractional method preserved significant correlations among LULC data and latent heat flux at all three studied scales (0.5°, 1.0°, and 2.5°), whereas nearest-neighbor and majority-aggregation methods caused these correlations to diminish and even become statistically non-significant at coarser spatial scales (i.e., 2.5°). In the temporal domain, we identified fractional changes in croplands, forests, and grasslands in China using a recently developed and annually resolved time series of LULC maps from 1982 to 2012. Relative to common LULC change (LULCC) analyses conducted over two-time steps or several time periods, this annually resolved, 31-year time series of LULC maps enables robust interpretation of LULCC. Specifically, the annual resolution of these data enabled us to more precisely observe three key and statistically significant LULCC trends and transitions that could have consequential effects on land-atmosphere interaction: (1) decreasing grasslands to increasing croplands in the Northeast China plain and the Yellow river basin, (2) decreasing croplands to increasing forests in the Yangtze river basin, and (3) decreasing grasslands to increasing forests in Southwest China. Our study not only demonstrates the importance of using a fractional spatial rescaling method, but also illustrates the value of annually resolved LULC time series for detecting significant trends and transitions in LULCC, thus potentially facilitating a more robust use of remotely sensed data in land-atmosphere interaction studies. Full article
(This article belongs to the Special Issue Remote Sensing of Land-Atmosphere Interactions)
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Open AccessArticle HY-2A Altimeter Data Initial Assessment and Corresponding Two-Pass Waveform Retracker
Remote Sens. 2018, 10(4), 507; doi:10.3390/rs10040507
Received: 30 January 2018 / Revised: 10 March 2018 / Accepted: 21 March 2018 / Published: 23 March 2018
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Abstract
The accuracy and resolution of the marine gravity field derived from multisatellite altimeter data sets mainly depend on the corresponding range precision and spatial distribution. Here, we preliminarily investigate the performance of HY-2A altimeter data by analyzing cross-mission sea surface height discrepancies with
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The accuracy and resolution of the marine gravity field derived from multisatellite altimeter data sets mainly depend on the corresponding range precision and spatial distribution. Here, we preliminarily investigate the performance of HY-2A altimeter data by analyzing cross-mission sea surface height discrepancies with SARAL/AltiKa and calculating correlation coefficients with respect to tide gauge measurements. We also explore the improved range precision that can be achieved using a two-pass weighted least squares retracker which was proposed for the purpose of optimal gravity field recovery. Firstly, both the exact repetitive mission and the geodetic mission for HY-2A provide new track orientations and different data coverage for recovering the marine gravity field, and these dense geographical distributions are more greatly attributed to the geodetic mission in recent years. Secondly, HY-2A provides reliable sea surface height measurements based on exterior verifications by SARAL/AltiKa geophysical data records and tide gauge measurements, although the accuracy level is slightly lower than SARAL/AltiKa. Another more exciting finding is that the statistics of along-track sea surface heights in one-second intervals show that the two-pass retracking does further improve the range precision by a factor of 1.6 with respect to 20 Hz retracked results in sensor data records. In conclusion, the HY-2A mission can substantially improve the global accuracy and resolution of the marine gravity field and will reveal new tectonic features such as microplates, abyssal hill fabric, and new uncharted seamounts on the ocean floor. Full article
(This article belongs to the Special Issue Satellite Altimetry for Earth Sciences)
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Open AccessArticle Modeling Orbital Error in InSAR Interferogram Using Frequency and Spatial Domain Based Methods
Remote Sens. 2018, 10(4), 508; doi:10.3390/rs10040508
Received: 27 January 2018 / Revised: 26 February 2018 / Accepted: 19 March 2018 / Published: 23 March 2018
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Abstract
Synthetic Aperture Radar Interferometry (SAR, InSAR) is increasingly being used for deformation monitoring. Uncertainty in satellite state vectors is considered to be one of the main sources of errors in applications such as this. In this paper, we present frequency and spatial domain
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Synthetic Aperture Radar Interferometry (SAR, InSAR) is increasingly being used for deformation monitoring. Uncertainty in satellite state vectors is considered to be one of the main sources of errors in applications such as this. In this paper, we present frequency and spatial domain based algorithms to model orbital errors in InSAR interferograms. The main advantage of this method, when applied to the spatial domain, is that the order of the polynomial coefficient is automatically determined according to the features of the orbital errors, using K-cross validation. In the frequency domain, a maximum likelihood fringe rate estimate is deployed to resolve linear orbital patterns in strong noise interferograms, where spatial-domain-based algorithms are unworkable. Both methods were tested and compared with synthetic data and applied to historical Environmental Satellite Advanced Synthetic Aperture Radar (ENVISAT ASAR) sensor and modern instruments such as Gaofen-3 (GF-3) and Sentinel-1. The validation from the simulation demonstrated that an accuracy of ~1mm can be obtained under optimal conditions. Using an independent GPS measurement that is discontinuous from the InSAR measurement over the Tohoku-Oki area, we found a 31.45% and 73.22% reduction in uncertainty after applying our method for ASAR tracks 347 and 74, respectively. Full article
(This article belongs to the Special Issue Advances in SAR: Sensors, Methodologies, and Applications)
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Open AccessArticle An Automatic Sparse Pruning Endmember Extraction Algorithm with a Combined Minimum Volume and Deviation Constraint
Remote Sens. 2018, 10(4), 509; doi:10.3390/rs10040509
Received: 24 January 2018 / Revised: 14 March 2018 / Accepted: 21 March 2018 / Published: 23 March 2018
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Abstract
In this paper, an automatic sparse pruning endmember extraction algorithm with a combined minimum volume and deviation constraint (SPEEVD) is proposed. The proposed algorithm can adaptively determine the number of endmembers through a sparse pruning method and, at the same time, can weaken
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In this paper, an automatic sparse pruning endmember extraction algorithm with a combined minimum volume and deviation constraint (SPEEVD) is proposed. The proposed algorithm can adaptively determine the number of endmembers through a sparse pruning method and, at the same time, can weaken the noise interference by a minimum volume and deviation constraint. A non-negative matrix factorization solution based on the projection gradient is mathematically applied to solve the combined constrained optimization problem, which makes sure that the convergence is steady and robust. Experiments were carried out on both simulated data sets and real AVIRIS data sets. The experimental results indicate that the proposed method does not require a predetermined endmember number, but it still manifests an improvement in both the root-mean-square error (RMSE) and the endmember spectra, compared to the other state-of-the-art methods, most of which need an accurate pre-estimation of endmember number. Full article
(This article belongs to the Section Remote Sensing Image Processing)
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Open AccessArticle Total Variation Regularization Term-Based Low-Rank and Sparse Matrix Representation Model for Infrared Moving Target Tracking
Remote Sens. 2018, 10(4), 510; doi:10.3390/rs10040510
Received: 13 January 2018 / Revised: 27 February 2018 / Accepted: 22 March 2018 / Published: 24 March 2018
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Abstract
Infrared moving target tracking plays a fundamental role in many burgeoning research areas of Smart City. Challenges in developing a suitable tracker for infrared images are particularly caused by pose variation, occlusion, and noise. In order to overcome these adverse interferences, a total
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Infrared moving target tracking plays a fundamental role in many burgeoning research areas of Smart City. Challenges in developing a suitable tracker for infrared images are particularly caused by pose variation, occlusion, and noise. In order to overcome these adverse interferences, a total variation regularization term-based low-rank and sparse matrix representation (TV-LRSMR) model is designed in order to exploit a robust infrared moving target tracker in this paper. First of all, the observation matrix that is derived from the infrared sequence is decomposed into a low-rank target matrix and a sparse occlusion matrix. For the purpose of preventing the noise pixel from being separated into the occlusion term, a total variation regularization term is proposed to further constrain the occlusion matrix. Then an alternating algorithm combing principal component analysis and accelerated proximal gradient methods is employed to separately optimize the two matrices. For long-term tracking, the presented algorithm is implemented using a Bayesien state inference under the particle filtering framework along with a dynamic model update mechanism. Both qualitative and quantitative experiments that were examined on real infrared video sequences verify that our algorithm outperforms other state-of-the-art methods in terms of precision rate and success rate. Full article
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Open AccessArticle Automatic Ship Classification from Optical Aerial Images with Convolutional Neural Networks
Remote Sens. 2018, 10(4), 511; doi:10.3390/rs10040511
Received: 22 February 2018 / Revised: 14 March 2018 / Accepted: 21 March 2018 / Published: 24 March 2018
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Abstract
The automatic classification of ships from aerial images is a considerable challenge. Previous works have usually applied image processing and computer vision techniques to extract meaningful features from visible spectrum images in order to use them as the input for traditional supervised classifiers.
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The automatic classification of ships from aerial images is a considerable challenge. Previous works have usually applied image processing and computer vision techniques to extract meaningful features from visible spectrum images in order to use them as the input for traditional supervised classifiers. We present a method for determining if an aerial image of visible spectrum contains a ship or not. The proposed architecture is based on Convolutional Neural Networks (CNN), and it combines neural codes extracted from a CNN with a k-Nearest Neighbor method so as to improve performance. The kNN results are compared to those obtained with the CNN Softmax output. Several CNN models have been configured and evaluated in order to seek the best hyperparameters, and the most suitable setting for this task was found by using transfer learning at different levels. A new dataset (named MASATI) composed of aerial imagery with more than 6000 samples has also been created to train and evaluate our architecture. The experimentation shows a success rate of over 99% for our approach, in contrast with the 79% obtained with traditional methods in classification of ship images, also outperforming other methods based on CNNs. A dataset of images (MWPU VHR-10) used in previous works was additionally used to evaluate the proposed approach. Our best setup achieves a success ratio of 86% with these data, significantly outperforming previous state-of-the-art ship classification methods. Full article
(This article belongs to the Section Remote Sensing Image Processing)
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Open AccessArticle Focal Mechanisms of the 2016 Central Italy Earthquake Sequence Inferred from High-Rate GPS and Broadband Seismic Waveforms
Remote Sens. 2018, 10(4), 512; doi:10.3390/rs10040512
Received: 18 December 2017 / Revised: 9 March 2018 / Accepted: 21 March 2018 / Published: 25 March 2018
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Abstract
Numerous shallow earthquakes, including a multitude of small shocks and three moderate mainshocks, i.e., the Amatrice earthquake on 24 August, the Visso earthquake on 26 October and the Norcia earthquake on 30 October, occurred throughout central Italy in late 2016 and resulted in
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Numerous shallow earthquakes, including a multitude of small shocks and three moderate mainshocks, i.e., the Amatrice earthquake on 24 August, the Visso earthquake on 26 October and the Norcia earthquake on 30 October, occurred throughout central Italy in late 2016 and resulted in many casualties and property losses. The three mainshocks were successfully recorded by high-rate Global Positioning System (GPS) receivers located near the epicenters, while the broadband seismograms in this area were mostly clipped due to the strong shaking. We retrieved the dynamic displacements from these high-rate GPS records using kinematic precise point positioning analysis. The focal mechanisms of the three mainshocks were estimated both individually and jointly using high-rate GPS waveforms in a very small epicentral distance range (<100 km) and unclipped regional broadband waveforms (100~600 km). The results show that the moment magnitudes of the Amatrice, Visso, and Norcia events are Mw 6.1, Mw 5.9, and Mw 6.5, respectively. Their focal mechanisms are dominated by normal faulting, which is consistent with the local tectonic environment. The moment tensor solution for the Norcia earthquake demonstrates a significant non-double-couple component, which suggests that the faulting interface is complicated. Sparse network tests were conducted to retrieve stable focal mechanisms using a limited number of GPS records. Our results confirm that high-rate GPS waveforms can act as a complement to clipped near-field long-period seismic waveform signals caused by the strong motion and can effectively constrain the focal mechanisms of moderate- to large-magnitude earthquakes. Thus, high-rate GPS observations extremely close to the epicenter can be utilized to rapidly obtain focal mechanisms, which is critical for earthquake emergency response operations. Full article
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Open AccessArticle Evaluating Different Methods for Estimating Diameter at Breast Height from Terrestrial Laser Scanning
Remote Sens. 2018, 10(4), 513; doi:10.3390/rs10040513
Received: 4 January 2018 / Revised: 16 March 2018 / Accepted: 23 March 2018 / Published: 25 March 2018
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Abstract
The accurate measurement of diameter at breast height (DBH) is essential to forest operational management, forest inventory, and carbon cycle modeling. Terrestrial laser scanning (TLS) is a measurement technique that allows rapid, automatic, and periodical estimates of DBH information. With the multitude of
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The accurate measurement of diameter at breast height (DBH) is essential to forest operational management, forest inventory, and carbon cycle modeling. Terrestrial laser scanning (TLS) is a measurement technique that allows rapid, automatic, and periodical estimates of DBH information. With the multitude of DBH estimation approaches available, a systematic study is needed to compare different algorithms and evaluate the ideal situations to use a specific algorithm. To contribute to such an approach, this study evaluated three commonly used DBH estimation algorithms: Hough-transform, linear least square circle fitting, and nonlinear least square circle fitting. They were each evaluated on their performance using two forest types of TLS data under numerous preprocessing conditions. The two forest types were natural secondary forest and plantation. The influences of preprocessing conditions on the performance of the algorithms were also investigated. Results showed that among the three algorithms, the linear least square circle fitting algorithm was the most appropriate for the natural secondary forest, and the nonlinear least square circle fitting algorithm was the most appropriate for the plantation. In the natural secondary forest, a moderate gray scale threshold of three and a slightly large height bin of 0.24 m were the optimal parameters for the appropriate algorithm of the multi-scan scanning method, and a moderate gray scale threshold of three and a large height bin of 1.34 m were the optimal parameters for the appropriate algorithm of the single-scan scanning method. A small gray scale threshold of one and a small height bin of 0.1 m were the optimal parameters for the appropriate algorithm of the single-scan scanning method in the plantation. Full article
(This article belongs to the Special Issue Lidar for Forest Science and Management)
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Open AccessArticle Wind Gust Detection and Impact Prediction for Wind Turbines
Remote Sens. 2018, 10(4), 514; doi:10.3390/rs10040514
Received: 25 January 2018 / Revised: 17 March 2018 / Accepted: 23 March 2018 / Published: 25 March 2018
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Abstract
Wind gusts on a scale from 100 m to 1000 m are studied due to their significant influence on wind turbine performance. A detecting and tracking algorithm is proposed to extract gusts from a wind field and track their movement. The algorithm utilizes
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Wind gusts on a scale from 100 m to 1000 m are studied due to their significant influence on wind turbine performance. A detecting and tracking algorithm is proposed to extract gusts from a wind field and track their movement. The algorithm utilizes the “peak over threshold method,” Moore-Neighbor tracing algorithm, and Taylor’s frozen turbulence hypothesis. The algorithm was implemented for a three-hour, two-dimensional wind field retrieved from the measurements of a coherent Doppler lidar. The Gaussian shape distribution of the gust spanwise deviation from the streamline was demonstrated. Size dependency of gust deviations is discussed, and an empirical power function is derived. A prediction model estimating the impact of gusts with respect to arrival time and the probability of arrival locations is introduced, in which the Gaussian plume model and random walk theory including size dependency are applied. The prediction model was tested and the results reveal that the prediction model can represent the spanwise deviation of the gusts and capture the effect of gust size. The prediction model was applied to a virtual wind turbine array, and estimates are given for which wind turbines would be impacted. Full article
(This article belongs to the Special Issue Remote Sensing of Atmospheric Conditions for Wind Energy Applications)
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Open AccessArticle Semi-Supervised Classification of Hyperspectral Images Based on Extended Label Propagation and Rolling Guidance Filtering
Remote Sens. 2018, 10(4), 515; doi:10.3390/rs10040515
Received: 20 December 2017 / Revised: 20 March 2018 / Accepted: 23 March 2018 / Published: 25 March 2018
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Abstract
Semi-supervised classification methods result in higher performance for hyperspectral images, because they can utilize the relationship between unlabeled samples and labeled samples to obtain pseudo-labeled samples. However, how generating an effective training sample set is a major challenge for semi-supervised methods, In this
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Semi-supervised classification methods result in higher performance for hyperspectral images, because they can utilize the relationship between unlabeled samples and labeled samples to obtain pseudo-labeled samples. However, how generating an effective training sample set is a major challenge for semi-supervised methods, In this paper, we propose a novel semi-supervised classification method based on extended label propagation (ELP) and a rolling guidance filter (RGF) called ELP-RGF, in which ELP is a new two-step process to make full use of unlabeled samples. The first step is to implement the graph-based label propagation algorithm to propagate the label information from labeled samples to the neighboring unlabeled samples. This is then followed by the second step, which uses superpixel propagation to assign the same labels to all pixels within the superpixels that are generated by the image segmentation method, so that some labels wrongly labeled by the above step can be modified. As a result, so obtained pseudo-labeled samples could be used to improve the performance of the classifier. Subsequently, an effective feature extraction method, i.e., RGF is further used to remove the noise and the small texture structures to optimize the features of the initial hyperspectral image. Finally, these produced initial labeled samples and high-confidence pseudo-labeled samples are used as a training set for support vector machine (SVM). The experimental results show that the proposed method can produce better classification performance for three widely-used real hyperspectral datasets, particularly when the number of training samples is relatively small. Full article
(This article belongs to the Special Issue Hyperspectral Imaging and Applications)
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Open AccessArticle A Deep Pipelined Implementation of Hyperspectral Target Detection Algorithm on FPGA Using HLS
Remote Sens. 2018, 10(4), 516; doi:10.3390/rs10040516
Received: 2 January 2018 / Revised: 15 March 2018 / Accepted: 22 March 2018 / Published: 25 March 2018
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Abstract
Real-time target detection for hyperspectral images (HSI) has received considerable interest in recent years. However, owing to enormous data volume provided by HSI, detection algorithms are generally computationally complex, thus developing rapid processing techniques for target detection has encountered several challenging issues. It
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Real-time target detection for hyperspectral images (HSI) has received considerable interest in recent years. However, owing to enormous data volume provided by HSI, detection algorithms are generally computationally complex, thus developing rapid processing techniques for target detection has encountered several challenging issues. It seems that using a deep pipelined structure can improve the detection speed, and implementing on field programmable gate arrays (FPGAs) can also achieve concurrent operations rather than run streams of sequential instruction. This paper presents a deep pipelined background statistics (DPBS) approach to optimizing and implementing a well-known subpixel target detection algorithm, called constrained energy minimization (CEM) on FPGA by using high-level synthesis (HLS). This approach offers significant benefits in terms of increasing data throughput and improving design efficiency. To overcome a drawback of HLS on implementing a task-level pipelined circuit that includes a feedback data path, a script based circuit design method is further developed to make connections between some of the modules created by HLS. Experimental results show that the proposed method can detect targets on a real-hyperspectral data set (HyMap Data) only in 0.15 s without compromising detection accuracy. Full article
(This article belongs to the Special Issue Hyperspectral Imaging and Applications)
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Open AccessArticle Monitoring Quarry Area with Landsat Long Time-Series for Socioeconomic Study
Remote Sens. 2018, 10(4), 517; doi:10.3390/rs10040517
Received: 8 January 2018 / Revised: 6 March 2018 / Accepted: 20 March 2018 / Published: 26 March 2018
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Abstract
Quarry sites result from human activity, which includes the removal of original vegetation and the overlying soil to dig out stones for building use. Therefore, the dynamics of the quarry area provide a unique view of human mining activities. Actually, the topographic changes
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Quarry sites result from human activity, which includes the removal of original vegetation and the overlying soil to dig out stones for building use. Therefore, the dynamics of the quarry area provide a unique view of human mining activities. Actually, the topographic changes caused by mining activities are also a result of the development of the local economy. Thus, monitoring the quarry area can provide information about the policies of the economy and environmental protection. In this paper, we developed a combined method of machine learning classification and quarry region analysis to estimate the quarry area in a quarry region near Beijing. A temporal smoothing based on the classification results of all years was applied in post-processing to remove outliers and obtain gently changing sequences along the monitoring term. The method was applied to Landsat images to derive a quarry distribution map and quarry area time series from 1984 to 2017, revealing significant inter-annual variability. The time series revealed a five-stage development of the quarry area with different growth patterns. As the study region lies on two jurisdictions—Tianjin and Hebei—a comparison of the quarry area changes in the two jurisdictions was applied, which revealed that the different policies in the two regions could impose different impacts on the development of a quarry area. An analysis concerning the relationship between quarry area and gross regional product (GRP) was performed to explore the potential application on socioeconomic studies, and we found a strong positive correlation between quarry area and GRP in Langfang City, Hebei Province. These results demonstrate the potential benefit of annual monitoring over the long-term for socioeconomic studies, which can be used for mining decision making. Full article
(This article belongs to the Special Issue Analysis of Multi-temporal Remote Sensing Images)
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Open AccessArticle Haze Optical Properties from Long-Term Ground-Based Remote Sensing over Beijing and Xuzhou, China
Remote Sens. 2018, 10(4), 518; doi:10.3390/rs10040518
Received: 22 January 2018 / Revised: 15 March 2018 / Accepted: 23 March 2018 / Published: 26 March 2018
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Abstract
Aerosol haze pollution has had a significant impact on both global climate and the regional air quality of Eastern China, which has a high proportion of high level pollution days. Statistical analyses of aerosol optical properties and direct radiative forcing at two AERONET
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Aerosol haze pollution has had a significant impact on both global climate and the regional air quality of Eastern China, which has a high proportion of high level pollution days. Statistical analyses of aerosol optical properties and direct radiative forcing at two AERONET sites (Beijing and Xuzhou) were conducted from 2013 to 2016. Results indicate: (1) Haze pollution days accounted for 26% and 20% of days from 2013 to 2016 in Beijing and Xuzhou, respectively, with the highest proportions in winter; (2) The averaged aerosol optical depth (AOD) at 550 nm on haze days were about 3.7 and 1.6 times greater than those on clean days in Beijing and Xuzhou, respectively. At both sites, the maximum AOD occurred in summer; (3) Hazes were dominated by fine particles at both sites. However, as compared to Xuzhou, Beijing had larger coarse mode AOD and higher percentage of small α. This data, together with an analysis of size distribution, suggests that the hazes in Beijing were more susceptible to coarse dust particles than Xuzhou; (4) During hazes in Beijing, the single scattering albedo (SSA) is significantly higher when compared to clean conditions (0.874 vs. 0.843 in SSA440 nm), an increase much less evident in Xuzhou. The most noticeable differences in both SSA and the imaginary part of the complex refractive index between Beijing and Xuzhou were found in winter; (5) In Beijing, the haze radiative forcing produced an averaged cooling effect of −113.6 ± 63.7 W/m2 at the surface, whereas the averaged heating effect of 77.5 ± 49.7 W/m2 within the atmosphere was at least twice as strong as clean days. In Xuzhou, such a radiative forcing effect appeared to be much smaller and the difference between haze and clean days was insignificant. Derived from long-term observation, these findings are more significant for the improvement of our understanding of haze formation in China and the assessment of its impacts on radiative forcing of climate change than previous short-term case studies. Full article
(This article belongs to the Special Issue Aerosol Remote Sensing)
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Open AccessArticle Estimation of Penetration Depth from Soil Effective Temperature in Microwave Radiometry
Remote Sens. 2018, 10(4), 519; doi:10.3390/rs10040519
Received: 27 February 2018 / Revised: 18 March 2018 / Accepted: 21 March 2018 / Published: 26 March 2018
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Abstract
Soil moisture is an essential variable in Earth surface modeling. Two dedicated satellite missions, the Soil Moisture and Ocean Salinity (SMOS) and the Soil Moisture Active Passive (SMAP), are currently in operation to map the global distribution of soil moisture. However, at the
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Soil moisture is an essential variable in Earth surface modeling. Two dedicated satellite missions, the Soil Moisture and Ocean Salinity (SMOS) and the Soil Moisture Active Passive (SMAP), are currently in operation to map the global distribution of soil moisture. However, at the longer L-band wavelength of these satellites, the emitting behavior of the land becomes very complex due to the unknown deeper penetration depth. This complexity leads to more uncertainty in calibration and validation of satellite soil moisture product and their applications. In the framework of zeroth-order incoherent microwave radiative transfer model, the soil effective temperature is the only component that contains depth information and thus provides the necessary link to quantify the penetration depth. By means of the multi-layer soil effective temperature (Lv’s T e f f ) scheme, we have determined the relationship between the penetration depth and soil effective temperature and verified it against field observations at the Maqu Network. The key findings are that the penetration depth can be estimated according to Lv’s T e f f scheme with the assumption of linear soil temperature gradient along the optical depth; and conversely, the soil temperature at the penetration depth should be equal to the soil effective temperature with the same linear assumption. The accuracy of this inference depends on to what extent the assumption of linear soil temperature gradient is satisfied. The result of this study is expected to advance understanding of the soil moisture products retrieved by SMOS and SMAP and improve the techniques in data assimilation and climate research. Full article
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Open AccessArticle A Hybrid Color Mapping Approach to Fusing MODIS and Landsat Images for Forward Prediction
Remote Sens. 2018, 10(4), 520; doi:10.3390/rs10040520
Received: 6 March 2018 / Revised: 22 March 2018 / Accepted: 24 March 2018 / Published: 26 March 2018
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Abstract
We present a new, simple, and efficient approach to fusing MODIS and Landsat images. It is well known that MODIS images have high temporal resolution and low spatial resolution, whereas Landsat images are just the opposite. Similar to earlier approaches, our goal is
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We present a new, simple, and efficient approach to fusing MODIS and Landsat images. It is well known that MODIS images have high temporal resolution and low spatial resolution, whereas Landsat images are just the opposite. Similar to earlier approaches, our goal is to fuse MODIS and Landsat images to yield high spatial and high temporal resolution images. Our approach consists of two steps. First, a mapping is established between two MODIS images, where one is at an earlier time, t1, and the other one is at the time of prediction, tp. Second, this mapping is applied to map a known Landsat image at t1 to generate a predicted Landsat image at tp. Similar to the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), SpatioTemporal Image-Fusion Model (STI-FM), and the Flexible Spatiotemporal DAta Fusion (FSDAF) approaches, only one pair of MODIS and Landsat images is needed for prediction. Using seven performance metrics, experiments involving actual Landsat and MODIS images demonstrated that the proposed approach achieves comparable or better fusion performance than that of STARFM, STI-FM, and FSDAF. Full article
(This article belongs to the Section Remote Sensing Image Processing)
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Open AccessArticle Individual and Interactive Influences of Anthropogenic and Ecological Factors on Forest PM2.5 Concentrations at an Urban Scale
Remote Sens. 2018, 10(4), 521; doi:10.3390/rs10040521
Received: 4 January 2018 / Revised: 21 March 2018 / Accepted: 24 March 2018 / Published: 26 March 2018
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Abstract
Integration of Landsat images and multisource data using spatial statistical analysis and geographical detector models can reveal the individual and interactive influences of anthropogenic activities and ecological factors on concentrations of atmospheric particulate matter less than 2.5 microns in diameter (PM2.5).
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Integration of Landsat images and multisource data using spatial statistical analysis and geographical detector models can reveal the individual and interactive influences of anthropogenic activities and ecological factors on concentrations of atmospheric particulate matter less than 2.5 microns in diameter (PM2.5). This approach has been used in many studies to estimate biomass and forest disturbance patterns and to monitor carbon sinks. However, the approach has rarely been used to comprehensively analyze the individual and interactive influences of anthropogenic factors (e.g., population density, impervious surface percentage) and ecological factors (e.g., canopy density, stand age, and elevation) on PM2.5 concentrations. To do this, we used Landsat-8 images and meteorological data to retrieve quantitative data on the concentrations of particulates (PM2.5), then integrated a forest management planning inventory (FMPI), population density distribution data, meteorological data, and topographic data in a Geographic Information System database, and applied a spatial statistical analysis model to identify aggregated areas (hot spots and cold spots) of particulates in the urban area of Jinjiang city, China. A geographical detector model was used to analyze the individual and interactive influences of anthropogenic and ecological factors on PM2.5 concentrations. We found that particulate concentration hot spots are mainly distributed in urban centers and suburbs, while cold spots are mainly distributed in the suburbs and exurban region. Elevation was the dominant individual factor affecting PM2.5 concentrations, followed by dominant tree species and meteorological factors. A combination of human activities (e.g., population density, impervious surface percentage) and multiple ecological factors caused the dominant interactive effects, resulting in increased PM2.5 concentrations. Our study suggests that human activities and multiple ecological factors effect PM2.5 concentrations both individually and interactively. We conclude that in order to reveal the direct and indirect effects of human activities and multiple factors on PM2.5 concentrations in urban forests, quantification of fusion satellite data and spatial statistical methods should be conducted in urban areas. Full article
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Open AccessArticle Modeling Environments Hierarchically with Omnidirectional Imaging and Global-Appearance Descriptors
Remote Sens. 2018, 10(4), 522; doi:10.3390/rs10040522
Received: 19 January 2018 / Revised: 20 March 2018 / Accepted: 23 March 2018 / Published: 26 March 2018
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Abstract
In this work, a framework is proposed to build topological models in mobile robotics, using an omnidirectional vision sensor as the only source of information. The model is structured hierarchically into three layers, from one high-level layer which permits a coarse estimation of
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In this work, a framework is proposed to build topological models in mobile robotics, using an omnidirectional vision sensor as the only source of information. The model is structured hierarchically into three layers, from one high-level layer which permits a coarse estimation of the robot position to one low-level layer to refine this estimation efficiently. The algorithm is based on the use of clustering approaches to obtain compact topological models in the high-level layers, combined with global appearance techniques to represent robustly the omnidirectional scenes. Compared to the classical approaches based on the extraction and description of local features, global-appearance descriptors lead to models that can be interpreted and handled more intuitively. However, while local-feature techniques have been extensively studied in the literature, global-appearance ones require to be evaluated in detail to test their efficacy in map-building tasks. The proposed algorithms are tested with a set of publicly available panoramic images captured in realistic environments. The results show that global-appearance descriptors along with some specific clustering algorithms constitute a robust alternative to create a hierarchical representation of the environment. Full article
(This article belongs to the Section Remote Sensing Image Processing)
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Open AccessArticle Geometric Accuracy of Sentinel-1A and 1B Derived from SAR Raw Data with GPS Surveyed Corner Reflector Positions
Remote Sens. 2018, 10(4), 523; doi:10.3390/rs10040523
Received: 22 December 2017 / Revised: 21 March 2018 / Accepted: 23 March 2018 / Published: 27 March 2018
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Abstract
The geometric accuracy of synthetic aperture radar (SAR) data is usually derived from level-1 products using accurately surveyed corner reflector positions. This paper introduces a novel approach that derives the range delay and azimuth shift from acquired SAR raw data (level-0 products). Therefore,
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The geometric accuracy of synthetic aperture radar (SAR) data is usually derived from level-1 products using accurately surveyed corner reflector positions. This paper introduces a novel approach that derives the range delay and azimuth shift from acquired SAR raw data (level-0 products). Therefore, the propagation path is completely retrieved from SAR pulse transmission up to the reception of the point target’s backscatter. The procedure includes simple pulse compression in range and azimuth instead of full SAR data processing. By applying this method, the geometric accuracy of ESA’s Sentinel-1 SAR satellites (Sentinel-1A and Sentinel-1B) is derived for each satellite overpass by using corner reflectors with precisely surveyed GPS positions. The results show that the azimuth bias of about 2 m found in level-1 products for Stripmap acquisitions is reduced to about 15 cm. This indicates an artificial bias arising from operational Sentinel-1 SAR data processing. The remaining range bias of about 1.0 m, observed in L0-products, is interpreted as the offset between the SAR antenna phase center and the spacecraft’s center of gravity. The relative pixel localization accuracy derived with the proposed method is about 12 cm for the evaluated acquisitions. Compared to the full processed level-1 SAR data products, this accuracy is similar in the range direction, but, for the azimuth direction, it is improved by about 50% with the proposed method. Full article
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Open AccessArticle Assessment of Methods for Passive Microwave Snow Cover Mapping Using FY-3C/MWRI Data in China
Remote Sens. 2018, 10(4), 524; doi:10.3390/rs10040524
Received: 10 January 2018 / Revised: 27 February 2018 / Accepted: 23 March 2018 / Published: 27 March 2018
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Abstract
Ongoing information on snow and its extent is critical for understanding global water and energy cycles. Passive microwave data have been widely used in snow cover mapping given their long-time observation capabilities under all-weather conditions. However, assessments of different passive microwave (PMW) snow
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Ongoing information on snow and its extent is critical for understanding global water and energy cycles. Passive microwave data have been widely used in snow cover mapping given their long-time observation capabilities under all-weather conditions. However, assessments of different passive microwave (PMW) snow cover area (SCA) mapping algorithms have rarely been reported, especially in China. In this study, the performances of seven PMW SCA mapping algorithms were tested using in situ snow depth measurements and a one-kilometer Interactive Multisensor Snow and Ice Mapping System (IMS) snow cover product over China. The selected algorithms are the FY3 algorithm, Grody’s algorithm, the South China algorithm, Kelly’s algorithm, Singh’s algorithm, Hall’s algorithm and Neal’s algorithm. During the test period, most algorithms performed reasonably well. The overall accuracy of all algorithms is higher than 0.895 against in situ observations and higher than 0.713 against the IMS product. In general, Singh’s algorithm, Hall’s algorithm and Neal’s algorithm had poor performance during the test. Their misclassification errors were larger than those of the remaining algorithms. Grody’s algorithm, the South China algorithm and Kelly’s algorithm had higher positive predictive values and lower omission errors than those of the others. The errors of these three algorithms were mainly caused by variations in commission errors. Comparing to Grody’s algorithm, the South China algorithm and Kelly’s algorithm, the FY3 algorithm presented a conservative snow cover estimation to balance the problem between snow identification and overestimation. As a result, the overall accuracy of the FY3 algorithm was the highest of all the tested algorithms. The accuracy of all algorithms tended to decline with a decreased snow cover fraction as well as SD < 5 cm. All tested algorithms have severe omission errors over barren land and grasslands. The results shown in this study contribute to ongoing efforts to improve the performance and applicability of PMW SCA algorithms. Full article
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Open AccessArticle Wavelet-Based Rust Spectral Feature Set (WRSFs): A Novel Spectral Feature Set Based on Continuous Wavelet Transformation for Tracking Progressive Host–Pathogen Interaction of Yellow Rust on Wheat
Remote Sens. 2018, 10(4), 525; doi:10.3390/rs10040525
Received: 10 February 2018 / Revised: 20 March 2018 / Accepted: 24 March 2018 / Published: 27 March 2018
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Abstract
Understanding the progression of host–pathogen interaction through time by hyperspectral features is vital for tracking yellow rust (Puccinia striiformis) development, one of the major diseases of wheat. However, well-designed features are still open issues that impact the performance of relevant models
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Understanding the progression of host–pathogen interaction through time by hyperspectral features is vital for tracking yellow rust (Puccinia striiformis) development, one of the major diseases of wheat. However, well-designed features are still open issues that impact the performance of relevant models to nondestructively detect pathological progress of wheat rust. The aim of this paper is (1) to propose a novel wavelet-based rust spectral feature set (WRSFs) to uncover wheat rust-related processes; and (2) to evaluate the performance and robustness of the proposed WRSFs and models for retrieving the progression of host–pathogen interaction and tracking rust development. A hyperspectral dataset was collected by analytical spectral devices (ASD) spectroradiometer and Headwall spectrograph, along with corresponding physiological measurements of chlorophyll index (CHL), nitrogen balance index (NBI), anthocyanin index (ANTH), and percentile dry matter (PDM) from the 7th to 41st day after inoculation (dai) under controlled conditions. The resultant findings suggest that the progression of yellow rust on wheat is better characterized by the proposed WRSFs (R2 > 0.7). The WRSFs-based PLSR model provides insight into specific leaf biophysical variations in the rust pathological progress. To evaluate the efficiency of the proposed WRSFs on yellow rust discrimination during different infestation stages, the identified WRSFs and vegetation indices (VIs) were fed into linear discriminant analysis (LDA) and support vector machine (SVM) classification frames. The WRSFs in conjunction with a SVM classifier can obtain better performance than that of LDA method and the VIs-based models. Overall, synthesizing the biophysical analysis, retrieving accuracy, and classification performance, we recommend the proposed WRSFs for monitoring the progression of the host–pathogen interaction of yellow rust on wheat cross various hyperspectral sensors. Full article
(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)
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Open AccessArticle Comparison of Satellite-Derived Phytoplankton Size Classes Using In-Situ Measurements in the South China Sea
Remote Sens. 2018, 10(4), 526; doi:10.3390/rs10040526
Received: 9 January 2018 / Revised: 23 March 2018 / Accepted: 23 March 2018 / Published: 27 March 2018
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Abstract
Ocean colour remote sensing is used as a tool to detect phytoplankton size classes (PSCs). In this study, the Medium Resolution Imaging Spectrometer (MERIS), Moderate Resolution Imaging Spectroradiometer (MODIS), and Sea-viewing Wide Field-of-view Sensor (SeaWiFS) phytoplankton size classes (PSCs) products were compared with
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Ocean colour remote sensing is used as a tool to detect phytoplankton size classes (PSCs). In this study, the Medium Resolution Imaging Spectrometer (MERIS), Moderate Resolution Imaging Spectroradiometer (MODIS), and Sea-viewing Wide Field-of-view Sensor (SeaWiFS) phytoplankton size classes (PSCs) products were compared with in-situ High Performance Liquid Chromatography (HPLC) data for the South China Sea (SCS), collected from August 2006 to September 2011. Four algorithms were evaluated to determine their ability to detect three phytoplankton size classes. Chlorophyll-a (Chl-a) and absorption spectra of phytoplankton (aph(λ)) were also measured to help understand PSC’s algorithm performance. Results show that the three abundance-based approaches performed better than the inherent optical property (IOP)-based approach in the SCS. The size detection of microplankton and picoplankton was generally better than that of nanoplankton. A three-component model was recommended to produce maps of surface PSCs in the SCS. For the IOP-based approach, satellite retrievals of inherent optical properties and the PSCs algorithm both have impacts on inversion accuracy. However, for abundance-based approaches, the selection of the PSCs algorithm seems to be more critical, owing to low uncertainty in satellite Chl-a input data Full article
(This article belongs to the Special Issue Remote Sensing of Ocean Colour)
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Open AccessArticle Spatio-Temporal Variability of Annual Sea Level Cycle in the Baltic Sea
Remote Sens. 2018, 10(4), 528; doi:10.3390/rs10040528
Received: 28 February 2018 / Revised: 19 March 2018 / Accepted: 28 March 2018 / Published: 29 March 2018
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Abstract
In coastal and semi-enclosed seas, the mean local sea level can significantly influence the magnitude of flooding in inundation areas. Using the cyclostationary empirical orthogonal function (CSEOF) method, we examine the spatial patterns and temporal variations of annual sea level cycle in the
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In coastal and semi-enclosed seas, the mean local sea level can significantly influence the magnitude of flooding in inundation areas. Using the cyclostationary empirical orthogonal function (CSEOF) method, we examine the spatial patterns and temporal variations of annual sea level cycle in the Baltic Sea based on satellite altimetry data, tide gauge data, and regional model reanalysis during 1993 and 2014. All datasets demonstrate coherent spatial and temporal annual sea level variability, although the model reanalysis shows a smaller interannual variation of annual sea level amplitude than other datasets. A large annual sea level cycle is observed in the Baltic Sea, except in the Danish straits from December to February. Compared with altimetry data, tide gauge data exhibit a stronger annual sea level cycle in the Baltic Sea (e.g., along the coasts and in the Gulf of Finland and the Gulf of Bothnia), particularly in the winter. Moreover, the maps of the maximum and minimum annual sea level amplitude imply that all datasets underestimate the maximum annual sea level amplitude. Analysis of the atmospheric forcing factors (e.g., sea level pressure, North Atlantic Oscillation (NAO), winds and air temperature), which may contribute to the interannual variation of the annual sea level cycle shows that both the zonal wind and winter NAO (e.g., from December to March) are highly correlated with the annual cycle variations in the tide gauge data in 1900–2012. In the altimetry era (1993–2014), all the atmospheric forcing factors are linked to the annual sea level cycle variations, particularly in 1996, 2010 and 2012, when a significant increase and drop of annual sea level amplitude are observed from all datasets, respectively. Full article
(This article belongs to the Section Ocean Remote Sensing)
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Open AccessArticle Local Effects of Forests on Temperatures across Europe
Remote Sens. 2018, 10(4), 529; doi:10.3390/rs10040529
Received: 20 December 2017 / Revised: 22 March 2018 / Accepted: 27 March 2018 / Published: 29 March 2018
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Abstract
Forests affect local climate through biophysical processes in terrestrial ecosystems. Due to the spatial and temporal heterogeneity of ecosystems in Europe, climate responses to forests vary considerably with diverse geographic and seasonal patterns. Few studies have used an empirical analysis to examine the
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Forests affect local climate through biophysical processes in terrestrial ecosystems. Due to the spatial and temporal heterogeneity of ecosystems in Europe, climate responses to forests vary considerably with diverse geographic and seasonal patterns. Few studies have used an empirical analysis to examine the effect of forests on temperature and the role of the background climate in Europe. In this study, we aimed to quantitatively determine the effects of forest on temperature in different seasons with MODIS (MODerate-resolution Imaging Spectroradiometer) land surface temperature (LST) data and in situ air temperature measurements. First, we compared the differences in LSTs between forests and nearby open land. Then, we paired 48 flux sites with nearby weather stations to quantify the effects of forests on surface air temperature. Finally, we explored the role of background temperatures on the above forests effects. The results showed that (1) forest in Europe generally increased LST and air temperature in northeastern Europe and decreased LST and air temperature in other areas; (2) the daytime cooling effect was dominate and produced a net cooling effect from forests in the warm season. In the cold season, daytime and nighttime warming effects drove the net effect of forests; (3) the effects of forests on temperatures were mainly negatively correlated with the background temperatures in Europe. Under extreme climate conditions, the cooling effect of forests will be stronger during heatwaves or weaker during cold spring seasons; (4) the background temperature affects the spatiotemporal distribution of differences in albedo and evapotranspiration (forest minus open land), which determines the spatial, seasonal and interannual effects of forests on temperature. The extrapolation of the results could contribute not only to model validation and development but also to appropriate land use policies for future decades under the background of global warming. Full article
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Open AccessFeature PaperArticle TU1208 Open Database of Radargrams: The Dataset of the IFSTTAR Geophysical Test Site
Remote Sens. 2018, 10(4), 530; doi:10.3390/rs10040530
Received: 16 February 2018 / Revised: 16 March 2018 / Accepted: 26 March 2018 / Published: 29 March 2018
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Abstract
This paper aims to present a wide dataset of ground penetrating radar (GPR) profiles recorded on a full-size geophysical test site, in Nantes (France). The geophysical test site was conceived to reproduce objects and obstacles commonly met in the urban subsurface, in a
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This paper aims to present a wide dataset of ground penetrating radar (GPR) profiles recorded on a full-size geophysical test site, in Nantes (France). The geophysical test site was conceived to reproduce objects and obstacles commonly met in the urban subsurface, in a completely controlled environment; since the design phase, the site was especially adapted to the context of radar-based techniques. After a detailed description of the test site and its building process, the GPR profiles included in the dataset are presented and commented on. Overall, 67 profiles were recorded along eleven parallel lines crossing the test site in the transverse direction; three pulsed radar systems were used to perform the measurements, manufactured by different producers and equipped with various antennas having central frequencies from 200 MHz to 900 MHz. An archive containing all profiles (raw data) is enclosed to this paper as supplementary material. This dataset is the core part of the Open Database of Radargrams initiative of COST (European Cooperation in Science and Technology) Action TU1208 “Civil engineering applications of Ground Penetrating Radar”. The idea beyond such initiative is to share with the scientific community a selection of interesting and reliable GPR responses, to enable an effective benchmark for direct and inverse electromagnetic approaches, imaging methods and signal processing algorithms. We hope that the dataset presented in this paper will be enriched by the contributions of further users in the future, who will visit the test site and acquire new data with their GPR systems. Moreover, we hope that the dataset will be made alive by researchers who will perform advanced analyses of the profiles, measure the electromagnetic characteristics of the host materials, contribute with synthetic radargrams obtained by modeling the site with electromagnetic simulators, and more in general share results achieved by applying their techniques on the available profiles. Full article
(This article belongs to the Special Issue Recent Advances in GPR Imaging)
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Open AccessFeature PaperArticle Pole-Like Road Furniture Detection and Decomposition in Mobile Laser Scanning Data Based on Spatial Relations
Remote Sens. 2018, 10(4), 531; doi:10.3390/rs10040531
Received: 16 February 2018 / Revised: 20 March 2018 / Accepted: 28 March 2018 / Published: 30 March 2018
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Abstract
Road furniture plays an important role in road safety. To enhance road safety, policies that encourage the road furniture inventory are prevalent in many countries. Such an inventory can be remarkably facilitated by the automatic recognition of road furniture. Current studies typically detect
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Road furniture plays an important role in road safety. To enhance road safety, policies that encourage the road furniture inventory are prevalent in many countries. Such an inventory can be remarkably facilitated by the automatic recognition of road furniture. Current studies typically detect and classify road furniture as one single above-ground component only, which is inadequate for road furniture with multiple functions such as a streetlight with a traffic sign attached. Due to the recent developments in mobile laser scanners, more accurate data is available that allows for the segmentation of road furniture at a detailed level. In this paper, we propose an automatic framework to decompose road furniture into different components based on their spatial relations in a three-step procedure: first, pole-like road furniture are initially detected by removing ground points and an initial classification. Then, the road furniture is decomposed into poles and attachments. The result of the decomposition is taken as a feedback to remove spurious pole-like road furniture as a third step. If there are no poles extracted in the decomposition stage, these incorrectly detected pole-like road furniture—such as the pillars of buildings—will be removed from the detection list. We further propose a method to evaluate the results of the decomposition. Compared with our previous work, the performance of decomposition has been much improved. In our test sites, the correctness of detection is higher than 90% and the completeness is approximately 95%, showing that our procedure is competitive to state of the art methods in the field of pole-like road furniture detection. Compared to our previous work, the optimized decomposition improves the correctness by 7.3% and 18.4% in the respective test areas. In conclusion, we demonstrate that our method decomposes pole-like road furniture into poles and attachments with respect to their spatial relations, which is crucial for road furniture interpretation. Full article
(This article belongs to the Special Issue Mobile Laser Scanning)
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Open AccessArticle Integrating Airborne LiDAR and Optical Data to Estimate Forest Aboveground Biomass in Arid and Semi-Arid Regions of China
Remote Sens. 2018, 10(4), 532; doi:10.3390/rs10040532
Received: 4 December 2017 / Revised: 25 February 2018 / Accepted: 28 March 2018 / Published: 30 March 2018
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Abstract
Forest Aboveground Biomass (AGB) is a key parameter for assessing forest productivity and global carbon content. In previous studies, AGB has been estimated using various prediction methods and types of remote sensing data. Increasingly, there is a trend towards integrating various data sources
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Forest Aboveground Biomass (AGB) is a key parameter for assessing forest productivity and global carbon content. In previous studies, AGB has been estimated using various prediction methods and types of remote sensing data. Increasingly, there is a trend towards integrating various data sources such as Light Detection and Ranging (LiDAR) and optical data. In this study, we constructed and compared the accuracies of five models for estimating AGB of forests in the upper Heihe River Basin in Northwest China. The five models were constructed using field and remotely-sensed data (optical and LiDAR) and algorithms including Random Forest (RF), Support Vector Machines (SVM), Back Propagation Neural Networks (BPNN), K-Nearest Neighbor (KNN) and the Generalized Linear Mixed Model (GLMM). Models based on the RF algorithm emerged as being the best among the five algorithms irrespective of the datasets used. The Random Forest AGB model, using only LiDAR data (R2 = 0.899, RMSE = 14.0 t/ha) as the input data, was more effective than the one using optical data (R2 = 0.835, RMSE = 22.724 t/ha). Compared to LiDAR or optical data alone, the AGB model (R2 = 0.913, RMSE = 13.352 t/ha) that used the RF algorithm and integrated LiDAR and optical data was found to be optimal. Incorporation of terrain variables with optical data resulted in only slight improvements in accuracy. The models developed in this study could be useful for using integrated airborne LiDAR and passive optical data to accurately estimate forest biomass. Full article
(This article belongs to the Special Issue Biomass Remote Sensing in Forest Landscapes)
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Open AccessFeature PaperArticle An Approach for Unsupervised Change Detection in Multitemporal VHR Images Acquired by Different Multispectral Sensors
Remote Sens. 2018, 10(4), 533; doi:10.3390/rs10040533
Received: 1 February 2018 / Revised: 15 March 2018 / Accepted: 29 March 2018 / Published: 30 March 2018
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Abstract
This paper proposes an approach for the detection of changes in multitemporal Very High Resolution (VHR) optical images acquired by different multispectral sensors. The proposed approach, which is inspired by a recent framework developed to support the design of change-detection systems for single-sensor
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This paper proposes an approach for the detection of changes in multitemporal Very High Resolution (VHR) optical images acquired by different multispectral sensors. The proposed approach, which is inspired by a recent framework developed to support the design of change-detection systems for single-sensor VHR remote sensing images, addresses and integrates in the general approach a strategy to effectively deal with multisensor information, i.e., to perform change detection between VHR images acquired by different multispectral sensors on two dates. This is achieved by the definition of procedures for the homogenization of radiometric, spectral and geometric image properties. These procedures map images into a common feature space where the information acquired by different multispectral sensors becomes comparable across time. Although the approach is general, here we optimize it for the detection of changes in vegetation and urban areas by employing features based on linear transformations (Tasseled Caps and Orthogonal Equations), which are shown to be effective for representing the multisensor information in a homogeneous physical way irrespectively of the considered sensor. Experiments on multitemporal images acquired by different VHR satellite systems (i.e., QuickBird, WorldView-2 and GeoEye-1) confirm the effectiveness of the proposed approach. Full article
(This article belongs to the Special Issue Analysis of Multi-temporal Remote Sensing Images)
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Open AccessArticle The Consideration of Formal Errors in Spatiotemporal Filtering Using Principal Component Analysis for Regional GNSS Position Time Series
Remote Sens. 2018, 10(4), 534; doi:10.3390/rs10040534
Received: 18 January 2018 / Revised: 12 March 2018 / Accepted: 29 March 2018 / Published: 30 March 2018
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Abstract
In the daily operation of regional GNSS (Global Navigation Satellite System) networks, the formal errors of all stations’ coordinate components are calculated. However, spatiotemporal filtering based on traditional Principal Component Analysis (PCA) for regional GNSS position time series does not take these formal
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In the daily operation of regional GNSS (Global Navigation Satellite System) networks, the formal errors of all stations’ coordinate components are calculated. However, spatiotemporal filtering based on traditional Principal Component Analysis (PCA) for regional GNSS position time series does not take these formal errors into account. This paper developed a PCA-based approach to extract Common Mode Error (CME) from the position time series of a regional GNSS station network, where formal errors were applied to construct a weight factor. Because coordinate components with larger errors have smaller weight factors in extracting CME, the performance of our proposed approach was anticipated to be better than the traditional PCA approach. The position time series of 25 stations in the Yunnan Province, China, were analyzed using our approach, as well as the traditional PCA approach. The average errors of the residual time series after removing the CMEs with our approach were 1.30 mm, 1.72 mm, and 4.62 mm for North, East and Up components, and the reductions with respect to those of the original time series were 18.23%, 15.42%, and 18.25%, respectively. If CMEs were removed from the traditional PCA approach, the corresponding average errors were 1.34 mm, 1.81 mm, and 4.84 mm, with reductions of 15.84%, 10.86%, and 14.32%, respectively. Compared to the traditional PCA approach, the average errors of our approach were reduced by about 2.39%, 4.56%, and 3.93% in the North, East and Up components, respectively. Analysis of CME indicated that it mainly contained white and flicker noise. In the synthetic position time series with 500 repeated simulations, the CME extracted by our approach was closer to the true simulated values than those extracted by the traditional PCA approach, regardless of whether local effects were considered or not. Specifically, the mean root mean square (RMS) reduction of our approach, relative to PCA, ranged from 1.35% to 3.93%. Our simulations illustrated that the RMS reductions depended not only on the magnitude, but also the variation of the formal error series, which further highlights the necessity of considering formal errors in spatiotemporal filtering. Full article
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Open AccessArticle The Evaluation of SMAP Enhanced Soil Moisture Products Using High-Resolution Model Simulations and In-Situ Observations on the Tibetan Plateau
Remote Sens. 2018, 10(4), 535; doi:10.3390/rs10040535
Received: 4 February 2018 / Revised: 18 March 2018 / Accepted: 28 March 2018 / Published: 31 March 2018
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Abstract
The Soil Moisture Active Passive (SMAP) mission was designed to provide a global mapping of soil moisture (SM) measured by L-band passive and active microwave sensors. In this study, we evaluate the newly released SMAP enhanced SM products over the Tibetan Plateau by
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The Soil Moisture Active Passive (SMAP) mission was designed to provide a global mapping of soil moisture (SM) measured by L-band passive and active microwave sensors. In this study, we evaluate the newly released SMAP enhanced SM products over the Tibetan Plateau by performing comparisons among SMAP standard products, in-situ observations and Community Land Model (CLM) simulations driven by high-resolution meteorological forcing. At local scales, the enhanced SMAP products, the standard products and CLM simulations all generally compare well with the in-situ observations. The SMAP products show stronger correlations (0.64–0.88) but slightly larger unbiased root mean square errors (ubRMSE, ~0.06) relative to the CLM simulations (0.58–0.79 and 0.037–0.047, for correlation and ubRMSE, respectively). At the regional scale, both SMAP products show similar spatial distributions of SM on the TP (Tibetan Plateau), although, as expected, the enhanced product provides more fine details. The SMAP enhanced product is in good agreement with model simulations with respect to temporal and spatial variations in SM over most of the TP. Regions with low correlation between SMAP enhanced products and model simulations are mainly located in the northwestern TP and regions of complex topography, where meteorological stations are sparse and non-existent or elevation is highly variable. In such remote regions, CLM simulations may be problematic due to inaccurate land cover maps and/or uncertainties in meteorological forcing. The independent, high-resolution observations provided by SMAP could help to constrain the model simulation and, ultimately, improve the skill of models in these problematic regions. Full article
(This article belongs to the Special Issue Soil Moisture Remote Sensing Across Scales)
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Open AccessArticle Inferring Water Table Depth Dynamics from ENVISAT-ASAR C-Band Backscatter over a Range of Peatlands from Deeply-Drained to Natural Conditions
Remote Sens. 2018, 10(4), 536; doi:10.3390/rs10040536
Received: 28 February 2018 / Revised: 21 March 2018 / Accepted: 29 March 2018 / Published: 31 March 2018
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Abstract
Water table depth (WTD) is one of the key variables controlling many processes in peatlands. Reliable WTD estimates based on remote sensing data would advance peatland research from global-scale climate monitoring to field-scale ecosystem management. Here, we evaluate the relationship between ENVISAT Advanced
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Water table depth (WTD) is one of the key variables controlling many processes in peatlands. Reliable WTD estimates based on remote sensing data would advance peatland research from global-scale climate monitoring to field-scale ecosystem management. Here, we evaluate the relationship between ENVISAT Advanced Synthetic Aperture Radar (ASAR) C-band backscatter (σ°) and in situ observed WTD dynamics over 17 peatlands in Germany covering deeply-drained to natural conditions, excluding peatlands dominated by forest or inundation periods. The results show increasing σ° with shallower WTD (=wetter conditions), with average temporal Pearson correlation coefficients of 0.38 and 0.54 (-) for natural (also including disturbed and rewetted/restored states) and agriculturally-used drained peatlands, respectively. The anomaly correlation further highlights the potential of ASAR backscatter to capture interannual variations with values of 0.33 and 0.43 (-), for natural and drained peatlands. The skill metrics, which are similar to those for evaluations of top soil moisture from C-band over mineral soils, indicate a strong capillary connection between WTD and the ‘C-band-sensitive’ top 1–2 cm of peat soils, even during dry periods with WTD at around −1 m. Various backscatter processing algorithms were tested without significant differences. The cross-over angle concept for correcting dynamical vegetation effects was tested, but not superior, to constant incidence angle correction. Full article
(This article belongs to the Special Issue Remote Sensing of Peatlands)
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Open AccessArticle A Combined Approach to Classifying Land Surface Cover of Urban Domestic Gardens Using Citizen Science Data and High Resolution Image Analysis
Remote Sens. 2018, 10(4), 537; doi:10.3390/rs10040537
Received: 27 February 2018 / Revised: 18 March 2018 / Accepted: 28 March 2018 / Published: 31 March 2018
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Abstract
Domestic gardens are an important component of cities, contributing significantly to urban green infrastructure (GI) and its associated ecosystem services. However, domestic gardens are incredibly heterogeneous which presents challenges for quantifying their GI contribution and associated benefits for sustainable urban development. This study
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Domestic gardens are an important component of cities, contributing significantly to urban green infrastructure (GI) and its associated ecosystem services. However, domestic gardens are incredibly heterogeneous which presents challenges for quantifying their GI contribution and associated benefits for sustainable urban development. This study applies an innovative methodology that combines citizen science data with high resolution image analysis to create a garden dataset in the case study city of Manchester, UK. An online Citizen Science Survey (CSS) collected estimates of proportional coverage for 10 garden land surface types from 1031 city residents. High resolution image analysis was conducted to validate the CSS estimates, and to classify 7 land surface cover categories for all garden parcels in the city. Validation of the CSS land surface estimations revealed a mean accuracy of 76.63% (s = 15.24%), demonstrating that citizens are able to provide valid estimates of garden surface coverage proportions. An Object Based Image Analysis (OBIA) classification achieved an estimated overall accuracy of 82%, with further processing required to classify shadow objects. CSS land surface estimations were then extrapolated across the entire classification through calculation of within image class proportions, to provide the proportional coverage of 10 garden land surface types (buildings, hard impervious surfaces, hard pervious surfaces, bare soil, trees, shrubs, mown grass, rough grass, cultivated land, water) within every garden parcel in the city. The final dataset provides a better understanding of the composition of GI in domestic gardens and how this varies across the city. An average garden in Manchester has 50.23% GI, including trees (16.54%), mown grass (14.46%), shrubs (9.19%), cultivated land (7.62%), rough grass (1.97%) and water (0.45%). At the city scale, Manchester has 49.0% GI, and around one fifth (20.94%) of this GI is contained within domestic gardens. This is useful evidence to inform local urban development policies. Full article
(This article belongs to the Special Issue Earth Observation in Planning for Sustainable Urban Development)
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Open AccessArticle Comparing Three Different Ground Based Laser Scanning Methods for Tree Stem Detection
Remote Sens. 2018, 10(4), 538; doi:10.3390/rs10040538
Received: 4 February 2018 / Revised: 28 March 2018 / Accepted: 30 March 2018 / Published: 31 March 2018
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Abstract
A forest inventory is often carried out using airborne laser data combined with ground measured reference data. Traditionally, the ground reference data have been collected manually with a caliper combined with land surveying equipment. During recent years, studies have shown that the caliper
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A forest inventory is often carried out using airborne laser data combined with ground measured reference data. Traditionally, the ground reference data have been collected manually with a caliper combined with land surveying equipment. During recent years, studies have shown that the caliper can be replaced by equipment and methods that capture the ground reference data more efficiently. In this study, we compare three different ground based laser measurement methods: terrestrial laser scanner, handheld laser scanner and a backpack laser scanner. All methods are compared with traditional measurements. The study area is located in southeastern Norway and divided into seven different locations with different terrain morphological characteristics and tree density. The main tree species are boreal, dominated by Norway spruce and Scots pine. To compare the different methods, we analyze the estimated tree stem diameter, tree position and data capture efficiency. The backpack laser scanning method captures the data in one operation. For this method, the estimated diameter at breast height has the smallest mean differences of 0.1 cm, the smallest root mean square error of 2.2 cm and the highest number of detected trees with 87.5%, compared to the handheld laser scanner method and the terrestrial laser scanning method. We conclude that the backpack laser scanner method has the most efficient data capture and can detect the largest number of trees. Full article
(This article belongs to the Special Issue Optical Remote Sensing of Boreal Forests)
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Open AccessArticle Elevation Change Derived from SARAL/ALtiKa Altimetric Mission: Quality Assessment and Performance of the Ka-Band
Remote Sens. 2018, 10(4), 539; doi:10.3390/rs10040539
Received: 17 January 2018 / Revised: 27 March 2018 / Accepted: 28 March 2018 / Published: 1 April 2018
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Abstract
The waveform retracking algorithm is a key factor that affects the accuracy of elevation change from satellite altimetry over an ice sheet. The elevation change results from four waveform retracker algorithms (ICE1/ICE2/Sea Ice/OCEAN) provided by the Satellite with ARgos and ALtiKa (SARAL/ALtiKa) data
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The waveform retracking algorithm is a key factor that affects the accuracy of elevation change from satellite altimetry over an ice sheet. The elevation change results from four waveform retracker algorithms (ICE1/ICE2/Sea Ice/OCEAN) provided by the Satellite with ARgos and ALtiKa (SARAL/ALtiKa) data were compared using repeated SARAL data between March 2013 and April 2016 to determine the optimal retracker in the crossovers of descending and ascending orbits over a Greenland ice sheet (GrIS). The ICE1 provided slightly better results than the three other algorithms with the lowest standard deviation (SD) of 0.30 m year−1. Further comparison was also conducted between the Satellite with ARgos and ALtiKa (SARAL) and Operation ICEBridge laser data, thereby indicating that ICE1 was the best retracker with an Root Mean Square Error (RMSE) of 0.43 m year−1. The distribution of elevation change rate and uncertainties over Greenland from SARAL were presented using the selected ICE1 retracker with a volume loss of 40 ± 12 km3 year−1. This volume loss did not include the fast-changing coastal areas of the GrIS. A large thinning was observed in Jakobshavn Isbræ, and a trend that extended far inland was also found from 2013–2016. Furthermore, a melting ice sheet was observed in the large areas northwest over the GrIS. Full article
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Open AccessArticle Comparing Terrestrial Laser Scanning (TLS) and Wearable Laser Scanning (WLS) for Individual Tree Modeling at Plot Level
Remote Sens. 2018, 10(4), 540; doi:10.3390/rs10040540
Received: 3 March 2018 / Revised: 25 March 2018 / Accepted: 29 March 2018 / Published: 1 April 2018
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Abstract
This study presents a comparison between the use of wearable laser scanning (WLS) and terrestrial laser scanning (TLS) devices for automatic tree detection with an estimation of two dendrometric variables: diameter at breast height (DBH) and total tree height (TH). Operative processes for
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This study presents a comparison between the use of wearable laser scanning (WLS) and terrestrial laser scanning (TLS) devices for automatic tree detection with an estimation of two dendrometric variables: diameter at breast height (DBH) and total tree height (TH). Operative processes for data collection and automatic forest inventory are described in detail. The approach used is based on the clustering of points belonging to each individual tree, the isolation of the trunks, the iterative fitting of circles for the DBH calculation and the computation of the TH of each tree. TLS and WLS point clouds were compared by the statistical analysis of both estimated forest dendrometric parameters and the possible presence of bias. Results show that the apparent differences in point density and relative precision between both 3D forest models do not affect tree detection and DBH estimation. Nevertheless, tree height estimation using WLS appears to be affected by the limited scanning range of the WLS used in this study. TH estimations for trees below a certain height are equivalent using WLS or TLS, whereas TH of taller trees is clearly underestimated using WLS. Full article
(This article belongs to the Special Issue Mobile Laser Scanning)
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Open AccessArticle Fog Detection Based on Meteosat Second Generation-Spinning Enhanced Visible and InfraRed Imager High Resolution Visible Channel
Remote Sens. 2018, 10(4), 541; doi:10.3390/rs10040541
Received: 30 January 2018 / Revised: 16 March 2018 / Accepted: 29 March 2018 / Published: 1 April 2018
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Abstract
In this study, the Meteosat Second Generation (MSG)—Spinning Enhanced Visible and Infrared Imager (SEVIRI) High Resolution Visible channel (HRV) is used in synergy with the narrow band MSG-SEVIRI channels for daytime fog detection. A new algorithm, named MSG-SEVIRI SatFog, has been designed and
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In this study, the Meteosat Second Generation (MSG)—Spinning Enhanced Visible and Infrared Imager (SEVIRI) High Resolution Visible channel (HRV) is used in synergy with the narrow band MSG-SEVIRI channels for daytime fog detection. A new algorithm, named MSG-SEVIRI SatFog, has been designed and implemented. MSG-SEVIRI SatFog provides the indication of the presence of fog in near real time and at the high spatial resolution of the HRV channel. The HRV resolution is useful for detecting small scale daytime fog that would be missed in the MSG-SEVIRI low spatial resolution channels. By combining textural, physical and tonal tests, a distinction between fog and low stratus is performed for pixels identified as low/middle clouds or clear by the Classification-MAsk Coupling of Statistical and Physical Methods (C-MACSP) cloud detection algorithm. Suitable thresholds have been determined using a specific dataset covering different geographical areas, seasons and time of the day. MSG-SEVIRI SatFog is evaluated against METeorological Aerodrome Reports (METAR) data observations. Evaluation results in an accuracy of 69.9%, a probability of detection of 68.7%, a false alarm ratio of 31.3% and a probability of false detection of 30.0%. Full article
(This article belongs to the Special Issue Remote Sensing of Low-Level Liquid Water Clouds and Fog)
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Open AccessArticle Diurnal Variation of Light Absorption in the Yellow River Estuary
Remote Sens. 2018, 10(4), 542; doi:10.3390/rs10040542
Received: 31 January 2018 / Revised: 29 March 2018 / Accepted: 29 March 2018 / Published: 2 April 2018
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Abstract
Considering the influence of river discharge and strong winds, the diurnal variability of ocean optical absorption properties in the Yellow River Estuary (YRE) is quantified, using in-situ measurements. The study finds that terrestrial sources due to the Yellow River discharge can cause high
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Considering the influence of river discharge and strong winds, the diurnal variability of ocean optical absorption properties in the Yellow River Estuary (YRE) is quantified, using in-situ measurements. The study finds that terrestrial sources due to the Yellow River discharge can cause high diurnal variation of water absorption because of the movement of river plume in the YRE, but such an influence diminishes far away from the Yellow River plume. The diurnal variability of water absorption, affected by strong winds, is found to be strengthened with a rapid increase of particles and colored dissolved organic matter (CDOM) arising from re-suspended sediment induced by wave forcing. The diurnal variability of particle absorption is controlled by non-algal particle absorption in the YRE, and the ratio of non-algal particle absorption (aNAP) and total particle absorption for most wavelengths is more than 0.56. The diurnal variation of spectral slope of non-algal particle absorption (SNAP) is found to vary within a narrow range, although large variability in the aNAP spectrum is observed. The CDOM is correlated negatively with salinity, and such negative correlation becomes weaker with the decreasing influence of riverine input. The spectral slope of CDOM absorption (Sg) may reflect the formation and constituents of CDOM with weak relationship to its concentration, and its relationship with the absorption of CDOM at 440 nm may be associated with the source of CDOM. The value of Sg, which is affected by re-suspended bottom sediment, is much lower than that derived from CDOM affected by Yellow River runoff. Disregarding the absorption of pure water, the diurnal variability of total water absorption stems principally from changes in non-algal particle matter rather than CDOM and Chl-a. By the observations of hourly GOCI (Geostationary Ocean Color Imager) data, the major diurnal variations of remote sensing reflectance at 680 nm are observed in near-coastal waters and the estuary of the Yellow River, which are mainly influenced by the flow discharge of Yellow River and strong winds. Finally, the seasonal differences of diurnal variations of water absorption caused by strong winds and river discharge are determined. Full article
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Open AccessArticle Use of Multispectral Airborne Images to Improve In-Season Nitrogen Management, Predict Grain Yield and Estimate Economic Return of Maize in Irrigated High Yielding Environments
Remote Sens. 2018, 10(4), 543; doi:10.3390/rs10040543
Received: 3 March 2018 / Revised: 22 March 2018 / Accepted: 28 March 2018 / Published: 2 April 2018
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Abstract
Vegetation indices (VIs) derived from active or passive sensors have been used for maize growth monitoring and real-time nitrogen (N) management at field scale. In the present multilocation two-year study, multispectral VIs (green- and red-based), chlorophyll meter (SPAD) and plant height (PltH) measured
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Vegetation indices (VIs) derived from active or passive sensors have been used for maize growth monitoring and real-time nitrogen (N) management at field scale. In the present multilocation two-year study, multispectral VIs (green- and red-based), chlorophyll meter (SPAD) and plant height (PltH) measured at V12–VT stage of maize development, were used to distinguish among the N status of maize, to predict grain yield and economic return in high yielding environments. Moreover, linear plateau-models were performed with VIs, SPAD and PltH measurements to determine the amount of N needed to achieve maximum maize grain yields and economic return. The available N in the topsoil (0–30 cm) was measured, and its relationship with VIs, maize yield and maize N requirements was analyzed. Green-based VIs were the most accurate indices to predict grain yield and to estimate the grain yield optimum N rate (GYONr) (216.8 kg N ha−1), but underestimated the grain yield optimum N available (GYONa) (248.6 kg N ha−1). Red-based VIs slightly overestimated the GYONr and GYONa, while SPAD highly underestimated both of them. The determination of the available N did not improve the accuracy of the VIs to determine the grain yield. The green chlorophyll index (GCI) distinguished maize that would yield less than 84% of the maximum yield, showing a high potential to detect and correct maize N deficiencies at V12 stage. The economic optimum nitrogen rate (EONr) and economic optimum nitrogen available (EONa) were determined below the GYONr and the GYONa, demonstrating that maximum grain yield strategies in maize are not normally the most profitable for farmers. Further research is needed to fine-tune the response of maize to N applications when deficiencies are detected at V12 stage, but airborne imagery could be useful for practical farming implementation in irrigated high yielding environments. Full article
(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)
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Open AccessArticle Towards Operational Monitoring of Forest Canopy Disturbance in Evergreen Rain Forests: A Test Case in Continental Southeast Asia
Remote Sens. 2018, 10(4), 544; doi:10.3390/rs10040544
Received: 23 February 2018 / Revised: 19 March 2018 / Accepted: 26 March 2018 / Published: 2 April 2018
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Abstract
This study presents an approach to forest canopy disturbance monitoring in evergreen forests in continental Southeast Asia, based on temporal differences of a modified normalized burn ratio (NBR) vegetation index. We generate NBR values from each available Landsat 8 scene of a given
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This study presents an approach to forest canopy disturbance monitoring in evergreen forests in continental Southeast Asia, based on temporal differences of a modified normalized burn ratio (NBR) vegetation index. We generate NBR values from each available Landsat 8 scene of a given period. A step of ‘self-referencing’ normalizes the NBR values, largely eliminating illumination/topography effects, thus maximizing inter-comparability. We then create yearly composites of these self-referenced NBR (rNBR) values, selecting per pixel the maximum rNBR value over each observation period, which reflects the most open canopy cover condition of that pixel. The ΔrNBR is generated as the difference between the composites of two reference periods. The methodology produces seamless and consistent maps, highlighting patterns of canopy disturbances (e.g., encroachment, selective logging), and keeping artifacts at minimum level. The monitoring approach was validated within four test sites with an overall accuracy of almost 78% using very high resolution satellite reference imagery. The methodology was implemented in a Google Earth Engine (GEE) script requiring no user interaction. A threshold is applied to the final output dataset in order to separate signal from noise. The approach, capable of detecting sub-pixel disturbance events as small as 0.005 ha, is transparent and reproducible, and can help to increase the credibility of monitoring, reporting and verification (MRV), as required in the context of reducing emissions from deforestation and forest degradation (REDD+). Full article
(This article belongs to the Special Issue Remote Sensing of Forest Cover Change)
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Open AccessArticle Color Enhancement for Four-Component Decomposed Polarimetric SAR Image Based on a CIE-Lab Encoding
Remote Sens. 2018, 10(4), 545; doi:10.3390/rs10040545
Received: 31 January 2018 / Revised: 14 March 2018 / Accepted: 14 March 2018 / Published: 2 April 2018
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Abstract
Color enhancement of decomposed fully polarimetric synthetic aperture radar (PolSAR) image is vital for visual understanding and interpretation of the polarimetric information about the target. It is common practice to use RGB or HIS color space to display the chromatic information for polarization-encoded,
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Color enhancement of decomposed fully polarimetric synthetic aperture radar (PolSAR) image is vital for visual understanding and interpretation of the polarimetric information about the target. It is common practice to use RGB or HIS color space to display the chromatic information for polarization-encoded, Pauli-basis images, or model-based target decomposition of PolSAR images. However, to represent the chroma for multi-polarization SAR data, the region of basic RGB color space does not fully cover the human perceptual system, leading to information loss. In this paper, we propose a color-encoding framework based on the CIE-Lab, a perceptually uniform color space, aiming at a better visual perception and information exploration. The effective interpretability in increasing chromatic, and thus visual enhancement, is presented using extensive datasets. In particular, the four decomposed components—volume scattering, surface scattering, double bounce, and helix scattering—along with total return power, are simultaneously mapped into the color space to improve the discernibility among the scattering components. The five channels derived from the four-component decomposition method can be simultaneously mapped to CIE-Lab color space intuitively. Results show that the proposed color enhancement not only preserves the color tone of the polarization signatures, but also magnifies the target information embedded in the total returned power. Full article
(This article belongs to the Special Issue Data Restoration and Denoising of Remote Sensing Data)
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Open AccessArticle Evaluation of One-Class Support Vector Classification for Mapping the Paddy Rice Planting Area in Jiangsu Province of China from Landsat 8 OLI Imagery
Remote Sens. 2018, 10(4), 546; doi:10.3390/rs10040546
Received: 27 December 2017 / Revised: 29 March 2018 / Accepted: 30 March 2018 / Published: 3 April 2018
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Abstract
Identification of paddy fields is essential for monitoring the rice cultivated area and predicting rice productivity. Timely and accurate extraction of rice distribution can bring vital information for national food security, agricultural policy formulation, and regional environmental sustainability. Conventional classification methods usually suffered
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Identification of paddy fields is essential for monitoring the rice cultivated area and predicting rice productivity. Timely and accurate extraction of rice distribution can bring vital information for national food security, agricultural policy formulation, and regional environmental sustainability. Conventional classification methods usually suffered from low accuracy, multi-class training samples, or demanding imagery requirements. This paper proposes to use one-class support vector classification (OCSVC) to extract rice cultivated area with Landsat Optical Land Imager (OLI) imagery. Instead of sampling and training all land cover types as performed by multi-class classification methods, OCSVC only used the training samples of target class (rice) for rice mapping. The performance of OCSVC was evaluated in terms of the classification accuracy of rice mapping and rice acreage estimation based on high-resolution imagery, field survey data and rice acreage data from government reports for Jiangsu Province, China. At the county-level, OCSVC was also compared with the commonly used multi-class support vector classification (MCSVC), decision tree classification (DTC), and vegetation index-based thresholding (VIT). Our results demonstrated that OCSVC produced a comparable overall accuracy to DTC and outperformed MCSVC and VIT. The computational efficiency of OCSVC increased approximately ten times as compared to MCSVC. The OCSVC produced the best correlation between its classified area and reported area among the four classification methods evaluated. When applied to the provincial level, the classification overall accuracy for OCSVC was 88.54%. The detected rice planting area for Jiangsu Province was 22,602 km2, which was consistent with the statistics from the National Bureau of Statistics (22,948 km2). This OCSVC-based mapping strategy provides a practical and efficient way to detect the rice planting extent with Landsat imagery at a large scale. Full article
(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)
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Open AccessArticle Multi-Annual Kinematics of an Active Rock Glacier Quantified from Very High-Resolution DEMs: An Application-Case in the French Alps
Remote Sens. 2018, 10(4), 547; doi:10.3390/rs10040547
Received: 11 January 2018 / Revised: 26 March 2018 / Accepted: 29 March 2018 / Published: 3 April 2018
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Abstract
Rock glaciers result from the long-term creeping of ice-rich permafrost along mountain slopes. Under warming conditions, deformation is expected to increase, and potential destabilization of those landforms may lead to hazardous phenomena. Monitoring the kinematics of rock glaciers at fine spatial resolution is
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Rock glaciers result from the long-term creeping of ice-rich permafrost along mountain slopes. Under warming conditions, deformation is expected to increase, and potential destabilization of those landforms may lead to hazardous phenomena. Monitoring the kinematics of rock glaciers at fine spatial resolution is required to better understand at which rate, where and how they deform. We present here the results of several years of in situ surveys carried out between 2005 and 2015 on the Laurichard rock glacier, an active rock glacier located in the French Alps. Repeated terrestrial laser-scanning (TLS) together with aerial laser-scanning (ALS) and structure-from-motion-multi-view-stereophotogrammetry (SFM-MVS) were used to accurately quantify surface displacement of the Laurichard rock glacier at interannual and pluri-annual scales. Six very high-resolution digital elevation models (DEMs, pixel size <50 cm) of the rock glacier surface were generated, and their respective quality was assessed. The relative horizontal position accuracy (XY) of the individual DEMs is in general less than 2 cm with a co-registration error on stable areas ranging from 20–50 cm. The vertical accuracy is around 20 cm. The direction and amplitude of surface displacements computed between DEMs are very consistent with independent geodetic field measurements (e.g., DGPS). Using these datasets, local patterns of the Laurichard rock glacier kinematics were quantified, pointing out specific internal (rheological) and external (bed topography) controls. The evolution of the surface velocity shows few changes on the rock glacier’s snout for the first years of the observed period, followed by a major acceleration between 2012 and 2015 affecting the upper part of the tongue and the snout. Full article
(This article belongs to the Special Issue Remote Sensing of Dynamic Permafrost Regions)
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Open AccessArticle Spatio-Temporal Analysis and Uncertainty of Fractional Vegetation Cover Change over Northern China during 2001–2012 Based on Multiple Vegetation Data Sets
Remote Sens. 2018, 10(4), 549; doi:10.3390/rs10040549
Received: 7 January 2018 / Revised: 19 March 2018 / Accepted: 2 April 2018 / Published: 3 April 2018
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Abstract
Northern China is one of the most sensitive and vulnerable regions in the country. To combat environmental degradation in northern China, a series of vegetation protection programs, such as the Three-North Shelter Forest Program (TNFSP), have been implemented. Whether the implementation of these
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Northern China is one of the most sensitive and vulnerable regions in the country. To combat environmental degradation in northern China, a series of vegetation protection programs, such as the Three-North Shelter Forest Program (TNFSP), have been implemented. Whether the implementation of these programs in northern China has improved the vegetation conditions has merited global attention. Therefore, quantifying vegetation changes in northern China is essential for meteorological, hydrological, ecological, and societal implications. Fractional vegetation cover (FVC) is a crucial biophysical parameter which describes land surface vegetation conditions. In this study, four FVC data sets derived from remote sensing data over northern China are employed for a spatio-temporal analysis to determine the uncertainty of fractional vegetation cover change from 2001 to 2012. Trend analysis of these data sets (including an annually varying estimate of error) reveals that FVC has increased at the rate of 0.26 ± 0.13%, 0.30 ± 0.25%, 0.12 ± 0.03%, 0.49 ± 0.21% per year in northern China, Northeast China, Northwest China, and North China during the period 2001–2012, respectively. In all of northern China, only 33.03% of pixels showed a significant increase in vegetation cover whereas approximately 16.81% of pixels showed a significant decrease and 50.16% remained relatively stable. Full article
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Open AccessArticle Comparison of the Retrieval of Sea Surface Salinity Using Different Instrument Configurations of MICAP
Remote Sens. 2018, 10(4), 550; doi:10.3390/rs10040550
Received: 25 January 2018 / Revised: 23 March 2018 / Accepted: 29 March 2018 / Published: 4 April 2018
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Abstract
The Microwave Imager Combined Active/Passive (MICAP) has been designed to simultaneously retrieve sea surface salinity (SSS), sea surface temperature (SST) and wind speed (WS), and its performance has also been preliminarily analyzed. To determine the influence of the first guess values uncertainties on
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The Microwave Imager Combined Active/Passive (MICAP) has been designed to simultaneously retrieve sea surface salinity (SSS), sea surface temperature (SST) and wind speed (WS), and its performance has also been preliminarily analyzed. To determine the influence of the first guess values uncertainties on the retrieved parameters of MICAP, the retrieval accuracies of SSS, SST, and WS are estimated at various noise levels. The results suggest that the errors on the retrieved SSS have not increased dues poorly known initial values of SST and WS, since the MICAP can simultaneously acquire SST information and correct ocean surface roughness. The main objective of this paper is to obtain the simplified instrument configuration of MICAP without loss of the SSS, SST, and WS retrieval accuracies. Comparisons are conducted between three different instrument configurations in retrieval mode, based on the simulation measurements of MICAP. The retrieval results tend to prove that, without the 23.8 GHz channel, the errors on the retrieved SSS, SST, and WS for MICAP could also satisfy the accuracy requirements well globally during only one satellite pass. By contrast, without the 1.26 GHz scatterometer, there are relatively large increases in the SSS, SST, and WS errors at middle/low latitudes. Full article
(This article belongs to the Special Issue Sea Surface Salinity Remote Sensing)
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Open AccessArticle Simplified Normalization of C-Band Synthetic Aperture Radar Data for Terrestrial Applications in High Latitude Environments
Remote Sens. 2018, 10(4), 551; doi:10.3390/rs10040551
Received: 31 January 2018 / Revised: 28 March 2018 / Accepted: 3 April 2018 / Published: 4 April 2018
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Abstract
Synthetic aperture radar (SAR) applications often require normalization to a common incidence angle. Angular signatures of radar backscatter depend on surface roughness and vegetation cover, and thus differ, from location to location. Comprehensive reference datasets are therefore required in heterogeneous landscapes. Multiple acquisitions
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Synthetic aperture radar (SAR) applications often require normalization to a common incidence angle. Angular signatures of radar backscatter depend on surface roughness and vegetation cover, and thus differ, from location to location. Comprehensive reference datasets are therefore required in heterogeneous landscapes. Multiple acquisitions from overlapping orbits with sufficient incidence angle range are processed in order to obtain parameters of the location specific normalization function. We propose a simpler method for C-band data, using single scenes only. It requires stable dielectric properties (no variations of liquid water content). This method is therefore applicable for frozen conditions. Winter C-band data have been shown of high value for a number of applications in high latitudes before. In this paper we explore the relationship of incidence angle and Sentinel-1 backscatter across the tundra to boreal transition zone. A linear relationship (coefficient of determination R 2 = 0.64) can be found between backscatter and incidence angle dependence (slope of normalization function) as determined by multiple acquisitions on a pixel by pixel basis for typical land cover classes in these regions. This allows a simplified normalization and thus reduced processing effort for applications over larger areas. Full article
(This article belongs to the Section Remote Sensing Image Processing)
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Open AccessArticle Spatiotemporal Evolution of Land Subsidence in the Beijing Plain 2003–2015 Using Persistent Scatterer Interferometry (PSI) with Multi-Source SAR Data
Remote Sens. 2018, 10(4), 552; doi:10.3390/rs10040552
Received: 17 January 2018 / Revised: 20 March 2018 / Accepted: 28 March 2018 / Published: 4 April 2018
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Abstract
Land subsidence is one of the most important geological hazards in Beijing, China, and its scope and magnitude have been growing rapidly over the past few decades, mainly due to long-term groundwater withdrawal. Interferometric Synthetic Aperture Radar (InSAR) has been used to monitor
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Land subsidence is one of the most important geological hazards in Beijing, China, and its scope and magnitude have been growing rapidly over the past few decades, mainly due to long-term groundwater withdrawal. Interferometric Synthetic Aperture Radar (InSAR) has been used to monitor the deformation in Beijing, but there is a lack of analysis of the long-term spatiotemporal evolution of land subsidence. This study focused on detecting and characterizing spatiotemporal changes in subsidence in the Beijing Plain by using Persistent Scatterer Interferometry (PSI) and geographic spatial analysis. Land subsidence during 2003–2015 was monitored by using ENVISAT ASAR (2003–2010), RADARSAT-2 (2011–2015) and TerraSAR-X (2010–2015) images, with results that are consistent with independent leveling measurements. The radar-based deformation velocity ranged from −136.9 to +15.2 mm/year during 2003–2010, and −149.4 to +8.9 mm/year during 2011–2015 relative to the reference point. The main subsidence areas include Chaoyang, Tongzhou, Shunyi and Changping districts, where seven subsidence bowls were observed between 2003 and 2015. Equal Fan Analysis Method (EFAM) shows that the maximum extensive direction was eastward, with a growing speed of 11.30 km2/year. Areas of differential subsidence were mostly located at the boundaries of the seven subsidence bowls, as indicated by the subsidence rate slope. Notably, the area of greatest subsidence was generally consistent with the patterns of groundwater decline in the Beijing Plain. Full article
(This article belongs to the Special Issue Radar Interferometry for Geohazards)
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Open AccessArticle A Consistent Combination of Brightness Temperatures from SMOS and SMAP over Polar Oceans for Sea Ice Applications
Remote Sens. 2018, 10(4), 553; doi:10.3390/rs10040553
Received: 14 March 2018 / Revised: 29 March 2018 / Accepted: 30 March 2018 / Published: 4 April 2018
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Abstract
Passive microwave measurements at L-band from ESA’s Soil Moisture and Ocean Salinity (SMOS) mission can be used to retrieve sea ice thickness of up to 0.5–1.0 m. Since 2015, NASA’s Soil Moisture Active Passive (SMAP) mission provides brightness temperatures (TB) at the same
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Passive microwave measurements at L-band from ESA’s Soil Moisture and Ocean Salinity (SMOS) mission can be used to retrieve sea ice thickness of up to 0.5–1.0 m. Since 2015, NASA’s Soil Moisture Active Passive (SMAP) mission provides brightness temperatures (TB) at the same frequency. Here, we explore the possibility of combining SMOS and SMAP TBs for sea ice thickness retrieval. First, we compare daily TBs over polar ocean and sea ice regions. For this purpose, the multi-angular SMOS measurements have to be fitted to the SMAP incidence angle of 40 . Using a synthetical dataset for testing, we evaluate the performance of different fitting methods. We find that a two-step regression fitting method performs best, yielding a high accuracy even for a small number of measurements of only 15. Generally, SMOS and SMAP TBs agree very well with correlations exceeding 0.99 over sea ice but show an intensity bias of about 2.7 K over both ocean and sea ice regions. This bias can be adjusted using a linear fit resulting in a very good agreement of the retrieved sea ice thicknesses. The main advantages of a combined product are the increased number of daily overpasses leading to an improved data coverage also towards lower latitudes, as well as a continuation of retrieved timeseries if one of the sensors stops delivering data. Full article
(This article belongs to the Section Ocean Remote Sensing)
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Open AccessArticle Regional Daily ET Estimates Based on the Gap-Filling Method of Surface Conductance
Remote Sens. 2018, 10(4), 554; doi:10.3390/rs10040554
Received: 9 March 2018 / Revised: 22 March 2018 / Accepted: 2 April 2018 / Published: 4 April 2018
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Abstract
Remote sensing allows regional evapotranspiration (ET) values to be obtained. Surface conductance is a key variable in estimating ET and controls surface flux interactions between the underlying surface and atmosphere. Limited by the influence of clouds, ET can only be estimated on cloud-free
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Remote sensing allows regional evapotranspiration (ET) values to be obtained. Surface conductance is a key variable in estimating ET and controls surface flux interactions between the underlying surface and atmosphere. Limited by the influence of clouds, ET can only be estimated on cloud-free days. In this study, a gap-filling method is proposed to acquire daily surface conductance, which was coupled into a Penman-Monteith (P-M) equation, to estimate the regional daily ET over the Hai River Basin. The gap-filling method is coupled with the canopy conductance, surface conductance and a simple time extension method, which provides more mechanisms and is more comprehensive. Field observations, including eddy covariance (EC) fluxes and meteorological elements from automatic weather station (AWS), were collected from two sites for calibration and validation. One site is located in Guantao County, which is cropped in a circular pattern with winter wheat and summer maize. The other site is located in Miyun County, which has orchard and summer maize crops. The P-M equation was inverted to the computed surface conductance at the field scale, and latent heat fluxes from EC were processed and converted to daily ET. The results show that the surface conductance model used in the gap-filling method performs well compared with the inverted surface conductance, which suggests that the model used here is reasonable. In addition, the relationship between the results estimated from the gap-filling method and EC measurements is more pronounced than that between the other method and the EC measurements. The R 2 values improve from 0.68 to 0.75 at the Guantao site and from 0.79 to 0.88 at the Miyun site. The improvement mainly occurs during the growing crop season, according to the temporal variations in the results. Full article
(This article belongs to the Special Issue Remote Sensing of Land-Atmosphere Interactions)
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