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Open AccessEditor’s ChoiceArticle A Self-Calibrated Non-Parametric Time Series Analysis Approach for Assessing Insect Defoliation of Broad-Leaved Deciduous Nothofagus pumilio Forests
Remote Sens. 2019, 11(2), 204; https://doi.org/10.3390/rs11020204
Received: 30 November 2018 / Revised: 5 January 2019 / Accepted: 12 January 2019 / Published: 21 January 2019
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Abstract
Folivorous insects cause some of the most ecologically and economically important disturbances in forests worldwide. For this reason, several approaches have been developed to exploit the temporal richness of available satellite time series data to detect and quantify insect forest defoliation. Current approaches [...] Read more.
Folivorous insects cause some of the most ecologically and economically important disturbances in forests worldwide. For this reason, several approaches have been developed to exploit the temporal richness of available satellite time series data to detect and quantify insect forest defoliation. Current approaches rely on parametric functions to describe the natural annual phenological cycle of the forest, from which anomalies are calculated and used to assess defoliation. Quantification of the natural variability of the annual phenological baseline is limited in parametric approaches, which is critical to evaluating whether an observed anomaly is “true” defoliation or only part of the natural forest variability. We present here a fully self-calibrated, non-parametric approach to reconstruct the annual phenological baseline along with its confidence intervals using the historical frequency of a vegetation index (VI) density, accounting for the natural forest phenological variability. This baseline is used to calculate per pixel (1) a VI anomaly per date and (2) an anomaly probability flag indicating its probability of being a “true” anomaly. Our method can be self-calibrated when applied to deciduous forests, where the winter VI values are used as the leafless reference to calculate the VI loss (%). We tested our approach with dense time series from the MODIS enhanced vegetation index (EVI) to detect and map a massive outbreak of the native Ormiscodes amphimone caterpillars which occurred in 2015–2016 in Chilean Patagonia. By applying the anomaly probability band, we filtered out all pixels with a probability <0.9 of being “true” defoliation. Our method enabled a robust spatiotemporal assessment of the O. amphimone outbreak, showing severe defoliation (60–80% and >80%) over an area of 15,387 ha of Nothofagus pumilio forests in only 40 days (322 ha/day in average) with a total of 17,850 ha by the end of the summer. Our approach is useful for the further study of the apparent increasing frequency of insect outbreaks due to warming trends in Patagonian forests; its generality means it can be applied in deciduous broad-leaved forests elsewhere. Full article
(This article belongs to the Special Issue Dense Image Time Series Analysis for Ecosystem Monitoring)
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Open AccessEditor’s ChoiceArticle Adaptive Framework for the Delineation of Homogeneous Forest Areas Based on LiDAR Points
Remote Sens. 2019, 11(2), 189; https://doi.org/10.3390/rs11020189
Received: 8 November 2018 / Revised: 14 January 2019 / Accepted: 14 January 2019 / Published: 18 January 2019
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Abstract
We propose a flexible framework for automated forest patch delineations that exploits a set of canopy structure features computed from airborne laser scanning (ALS) point clouds. The approach is based on an iterative subdivision of the point cloud using k-means clustering followed by [...] Read more.
We propose a flexible framework for automated forest patch delineations that exploits a set of canopy structure features computed from airborne laser scanning (ALS) point clouds. The approach is based on an iterative subdivision of the point cloud using k-means clustering followed by an iterative merging step to tackle oversegmentation. The framework can be adapted for different applications by selecting relevant input features that best measure the intended homogeneity. In our study, the performance of the segmentation framework was tested for the delineation of forest patches with a homogeneous canopy height structure on the one hand and with similar water cycle conditions on the other. For the latter delineation, canopy components that impact interception and evapotranspiration were used, and the delineation was mainly driven by leaf area, tree functional type, and foliage density. The framework was further tested on two scenes covering a variety of forest conditions and topographies. We demonstrate that the delineated patches capture well the spatial distributions of relevant canopy features that are used for defining the homogeneity. The consistencies range from R 2 = 0.84 to R 2 = 0.86 and from R 2 = 0.80 to R 2 = 0.91 for the most relevant features in the delineation of patches with similar height structure and water cycle conditions, respectively. Full article
(This article belongs to the Special Issue 3D Point Clouds in Forests)
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Open AccessEditor’s ChoiceArticle A Structural Classification of Australian Vegetation Using ICESat/GLAS, ALOS PALSAR, and Landsat Sensor Data
Remote Sens. 2019, 11(2), 147; https://doi.org/10.3390/rs11020147
Received: 29 October 2018 / Revised: 4 January 2019 / Accepted: 5 January 2019 / Published: 14 January 2019
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Abstract
Australia has historically used structural descriptors of height and cover to characterize, differentiate, and map the distribution of woody vegetation across the continent but no national satellite-based structural classification has been available. In this study, we present a new 30-m spatial resolution reference [...] Read more.
Australia has historically used structural descriptors of height and cover to characterize, differentiate, and map the distribution of woody vegetation across the continent but no national satellite-based structural classification has been available. In this study, we present a new 30-m spatial resolution reference map of Australian forest and woodland structure (height and cover), with this generated by integrating Landsat Thematic Mapper (TM) and Enhanced TM, Advanced Land Observing Satellite (ALOS) Phased Arrayed L-band Synthetic Aperture Radar (PALSAR) and Ice, Cloud, and land Elevation (ICESat),and Geoscience Laser Altimeter System (GLAS) data. ALOS PALSAR and Landsat-derived Foliage Projective Cover (FPC) were used to segment and classify the Australian landscape. Then, from intersecting ICESat waveform data, vertical foliage profiles and height metrics (e.g., 95% percentile height, mean height and the height to maximum vegetation density) were extracted for each of the classes generated. Within each class, and for selected areas, the variability in ICESat profiles was found to be similar with differences between segments of the same class attributed largely to clearance or disturbance events. ICESat metrics and profiles were then assigned to all remaining segments across Australia with the same class allocation. Validation against airborne LiDAR for a range of forest structural types indicated a high degree of correspondence in estimated height measures. On this basis, a map of vegetation height was generated at a national level and was combined with estimates of cover to produce a revised structural classification based on the scheme of the Australian National Vegetation Information System (NVIS). The benefits of integrating the three datasets for segmenting and classifying the landscape and retrieving biophysical attributes was highlighted with this leading the way for future mapping using ALOS-2 PALSAR-2, Landsat/Sentinel-2, Global Ecosystem Dynamics Investigation (GEDI), and ICESat-2 LiDAR data. The ability to map across large areas provides considerable benefits for quantifying carbon dynamics and informing on biodiversity metrics. Full article
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Open AccessFeature PaperEditor’s ChoiceArticle Bathymetry of Northwest Greenland Using “Ocean Melting Greenland” (OMG) High-Resolution Airborne Gravity and Other Data
Remote Sens. 2019, 11(2), 131; https://doi.org/10.3390/rs11020131
Received: 20 November 2018 / Revised: 7 January 2019 / Accepted: 7 January 2019 / Published: 11 January 2019
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Abstract
Marine-terminating glaciers dominate the evolution of the Greenland Ice Sheet (GrIS) and its contribution to sea-level rise. Widespread glacier acceleration has been linked to the warming of ocean waters around the periphery of Greenland but a lack of information on the bathymetry of [...] Read more.
Marine-terminating glaciers dominate the evolution of the Greenland Ice Sheet (GrIS) and its contribution to sea-level rise. Widespread glacier acceleration has been linked to the warming of ocean waters around the periphery of Greenland but a lack of information on the bathymetry of the continental shelf and glacial fjords has limited our ability to understand how subsurface, warm, salty ocean waters of Atlantic origin (AW) reach the glaciers and melt them from below. Here, we employ high-resolution, airborne gravity data (AIRGrav) in combination with multibeam echo sounding (MBES) data, to infer the bathymetry of the coastal areas of Northwest Greenland for NASA’s Ocean Melting Greenland (OMG) mission. High-resolution, AIRGrav data acquired on a 2 km spacing, 150 m ground clearance, with 1.5 mGal crossover error, is inverted in three dimensions to map the bathymetry. To constrain the inversion away from MBES data, we compare two methods: one based on the Direct Current (DC) shift of the gravity field (absolute minus observed gravity) and another based on the density of the bedrock. We evaluate and compare the two methods in areas with complete MBES coverage. We find the lowest standard error in bed elevation (±60 m) using the DC shift method. When applied to the entire coast of Northwest Greenland, the three-dimensional inversion reveals a complex network of connected sea bed channels, not known previously, that provide natural and varied pathways for AW to reach the glaciers across the continental shelf. The study demonstrates that the gravity approach offers an efficient and practical alternative to extensive ship mapping in ice-filled waters to obtain information critical to understanding and modeling ice-ocean interaction along ice sheet margins. Full article
(This article belongs to the Section Remote Sensing in Geology, Geomorphology and Hydrology)
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Open AccessEditor’s ChoiceArticle Assessing Correlation of High-Resolution NDVI with Fertilizer Application Level and Yield of Rice and Wheat Crops Using Small UAVs
Remote Sens. 2019, 11(2), 112; https://doi.org/10.3390/rs11020112
Received: 20 November 2018 / Revised: 2 January 2019 / Accepted: 4 January 2019 / Published: 9 January 2019
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Abstract
The aim of this study was to use small unmanned aerial vehicles (UAVs) for determining high-resolution normalized difference vegetation index (NDVI) values. Subsequently, these results were used to assess their correlations with fertilizer application levels and the yields of rice and wheat crops. [...] Read more.
The aim of this study was to use small unmanned aerial vehicles (UAVs) for determining high-resolution normalized difference vegetation index (NDVI) values. Subsequently, these results were used to assess their correlations with fertilizer application levels and the yields of rice and wheat crops. For multispectral sensing, we flew two types of small UAVs (DJI Phantom 4 and DJI Phantom 4 Pro)—each equipped with a compact multispectral sensor (Parrot Sequoia). The information collected was composed of numerous RGB orthomosaic images as well as reflectance maps with spatial resolution greater than a ground sampling distance of 10.5 cm. From 223 UAV flight campaigns over 120 fields with a total area coverage of 77.48 ha, we determined that the highest efficiency for the UAV-based remote sensing measurement was approximately 19.8 ha per 10 min while flying 100 m above ground level. During image processing, we developed and used a batch image alignment algorithm—a program written in Python language–to calculate the NDVI values in experimental plots or fields in a batch of NDVI index maps. The color NDVI distribution maps of wide rice fields identified differences in stages of ripening and lodging-injury areas, which accorded with practical crop growth status from aboveground observation. For direct-seeded rice, variation in the grain yield was most closely related to that in the NDVI at the early reproductive and late ripening stages. For wheat, the NDVI values were highly correlated with the yield ( R 2 = 0.601–0.809) from the middle reproductive to the early ripening stages. Furthermore, using the NDVI values, it was possible to differentiate the levels of fertilizer application for both rice and wheat. These results indicate that the small UAV-derived NDVI values are effective for predicting yield and detecting fertilizer application levels during rice and wheat production. Full article
(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)
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Open AccessEditor’s ChoiceArticle Evaluation of Ground Surface Models Derived from Unmanned Aerial Systems with Digital Aerial Photogrammetry in a Disturbed Conifer Forest
Remote Sens. 2019, 11(1), 84; https://doi.org/10.3390/rs11010084
Received: 15 November 2018 / Revised: 11 December 2018 / Accepted: 27 December 2018 / Published: 4 January 2019
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Abstract
Detailed vertical forest structure information can be remotely sensed by combining technologies of unmanned aerial systems (UAS) and digital aerial photogrammetry (DAP). A key limitation in the application of DAP methods, however, is the inability to produce accurate digital elevation models (DEM) in [...] Read more.
Detailed vertical forest structure information can be remotely sensed by combining technologies of unmanned aerial systems (UAS) and digital aerial photogrammetry (DAP). A key limitation in the application of DAP methods, however, is the inability to produce accurate digital elevation models (DEM) in areas of dense vegetation. This study investigates the terrain modeling potential of UAS-DAP methods within a temperate conifer forest in British Columbia, Canada. UAS-acquired images were photogrammetrically processed to produce high-resolution DAP point clouds. To evaluate the terrain modeling ability of DAP, first, a sensitivity analysis was conducted to estimate optimal parameters of three ground-point classification algorithms designed for airborne laser scanning (ALS). Algorithms tested include progressive triangulated irregular network (TIN) densification (PTD), hierarchical robust interpolation (HRI) and simple progressive morphological filtering (SMRF). Points were classified as ground from the ALS and served as ground-truth data to which UAS-DAP derived DEMs were compared. The proportion of area with root mean square error (RMSE) <1.5 m were 56.5%, 51.6% and 52.3% for the PTD, HRI and SMRF methods respectively. To assess the influence of terrain slope and canopy cover, error values of DAP-DEMs produced using optimal parameters were compared to stratified classes of canopy cover and slope generated from ALS point clouds. Results indicate that canopy cover was approximately three times more influential on RMSE than terrain slope. Full article
(This article belongs to the Special Issue Aerial and Near-Field Remote Sensing Developments in Forestry)
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Open AccessFeature PaperEditor’s ChoiceArticle A Multi-Platform Hydrometeorological Analysis of the Flash Flood Event of 15 November 2017 in Attica, Greece
Remote Sens. 2019, 11(1), 45; https://doi.org/10.3390/rs11010045
Received: 13 November 2013 / Revised: 17 December 2018 / Accepted: 21 December 2018 / Published: 28 December 2018
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Abstract
Urban areas often experience high precipitation rates and heights associated with flash flood events. Atmospheric and hydrological models in combination with remote-sensing and surface observations are used to analyze these phenomena. This study aims to conduct a hydrometeorological analysis of a flash flood [...] Read more.
Urban areas often experience high precipitation rates and heights associated with flash flood events. Atmospheric and hydrological models in combination with remote-sensing and surface observations are used to analyze these phenomena. This study aims to conduct a hydrometeorological analysis of a flash flood event that took place in the sub-urban area of Mandra, western Attica, Greece, using remote-sensing observations and the Chemical Hydrological Atmospheric Ocean Wave System (CHAOS) modeling system that includes the Advanced Weather Research Forecasting (WRF-ARW) model and the hydrological model (WRF-Hydro). The flash flood was caused by a severe storm during the morning of 15 November 2017 around Mandra area resulting in extensive damages and 24 fatalities. The X-band dual-polarization (XPOL) weather radar of the National Observatory of Athens (NOA) observed precipitation rates reaching 140 mm/h in the core of the storm. CHAOS simulation unveils the persistent orographic convergence of humid southeasterly airflow over Pateras mountain as the dominant parameter for the evolution of the storm. WRF-Hydro simulated the flood using three different precipitation estimations as forcing data, obtained from the CHAOS simulation (CHAOS-hydro), the XPOL weather radar (XPOL-hydro) and the Global Precipitation Measurement (GMP)/Integrated Multi-satellitE Retrievals for GPM (IMERG) satellite dataset (GPM/IMERG-hydro). The findings indicate that GPM/IMERG-hydro underestimated the flood magnitude. On the other hand, XPOL-hydro simulation resulted to discharge about 115 m3/s and water level exceeding 3 m in Soures and Agia Aikaterini streams, which finally inundated. CHAOS-hydro estimated approximately the half water level and even lower discharge compared to XPOL-hydro simulation. Comparing site-detailed post-surveys of flood extent, XPOL-hydro is characterized by overestimation while CHAOS-hydro and GPM/IMERG-hydro present underestimation. However, CHAOS-hydro shows enough skill to simulate the flooded areas despite the forecast inaccuracies of numerical weather prediction. Overall, the simulation results demonstrate the potential benefit of using high-resolution observations from a X-band dual-polarization radar as an additional forcing component in model precipitation simulations. Full article
(This article belongs to the Special Issue Remote Sensing of Precipitation)
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Open AccessEditor’s ChoiceArticle On the Synergistic Use of Optical and SAR Time-Series Satellite Data for Small Mammal Disease Host Mapping
Remote Sens. 2019, 11(1), 39; https://doi.org/10.3390/rs11010039
Received: 29 November 2018 / Revised: 18 December 2018 / Accepted: 21 December 2018 / Published: 27 December 2018
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Abstract
(1) Background: Echinococcus multilocularis (Em), a highly pathogenic parasitic tapeworm, is responsible for a significant burden of human disease. In this study, optical and time-series Synthetic Aperture Radar (SAR) data is used synergistically to model key land cover characteristics driving the spatial distributions [...] Read more.
(1) Background: Echinococcus multilocularis (Em), a highly pathogenic parasitic tapeworm, is responsible for a significant burden of human disease. In this study, optical and time-series Synthetic Aperture Radar (SAR) data is used synergistically to model key land cover characteristics driving the spatial distributions of two small mammal intermediate host species, Ellobius tancrei and Microtus gregalis, which facilitate Em transmission in a highly endemic area of Kyrgyzstan. (2) Methods: A series of land cover maps are derived from (a) single-date Landsat Operational Land Imager (OLI) imagery, (b) time-series Sentinel-1 SAR data, and (c) Landsat OLI and time-series Sentinel-1 SAR data in combination. Small mammal distributions are analyzed in relation to the surrounding land cover class coverage using random forests, before being applied predictively over broader areas. A comparison of models derived from the three land cover maps are made, assessing their potential for use in cloud-prone areas. (3) Results: Classification accuracies demonstrated the combined OLI-SAR classification to be of highest accuracy, with the single-date OLI and time-series SAR derived classifications of equivalent quality. Random forest analysis identified statistically significant positive relationships between E. tancrei density and agricultural land, and between M. gregalis density and water and bushes. Predictive application of random forest models identified hotspots of high relative density of E. tancrei and M. gregalis across the broader study area. (4) Conclusions: This offers valuable information to improve the targeting of limited-resource disease control activities to disrupt disease transmission in this area. Time-series SAR derived land cover maps are shown to be of equivalent quality to those generated from single-date optical imagery, which enables application of these methods in cloud-affected areas where, previously, this was not possible due to the sparsity of cloud-free optical imagery. Full article
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Open AccessEditor’s ChoiceArticle Sea Ice Albedo from MISR and MODIS: Production, Validation, and Trend Analysis
Remote Sens. 2019, 11(1), 9; https://doi.org/10.3390/rs11010009
Received: 16 October 2018 / Revised: 13 December 2018 / Accepted: 17 December 2018 / Published: 20 December 2018
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Abstract
The Multi-angle Imaging SpectroRadiometer (MISR) sensor onboard the Terra satellite provides high accuracy albedo products. MISR deploys nine cameras each at different view angles, which allow a near-simultaneous angular sampling of the surface anisotropy. This is particularly important to measure the near-instantaneous albedo [...] Read more.
The Multi-angle Imaging SpectroRadiometer (MISR) sensor onboard the Terra satellite provides high accuracy albedo products. MISR deploys nine cameras each at different view angles, which allow a near-simultaneous angular sampling of the surface anisotropy. This is particularly important to measure the near-instantaneous albedo of dynamic surface features such as clouds or sea ice. However, MISR’s cloud mask over snow or sea ice is not yet sufficiently robust because MISR’s spectral bands are only located in the visible and the near infrared. To overcome this obstacle, we performed data fusion using a specially processed MISR sea ice albedo product (that was generated at Langley Research Center using Rayleigh correction) combining this with a cloud mask of a sea ice mask product, MOD29, which is derived from the MODerate Resolution Imaging Spectroradiometer (MODIS), which is also, like MISR, onboard the Terra satellite. The accuracy of the MOD29 cloud mask has been assessed as >90% due to the fact that MODIS has a much larger number of spectral bands and covers a much wider range of the solar spectrum. Four daily sea ice products have been created, each with a different averaging time window (24 h, 7 days, 15 days, 31 days). For each time window, the number of samples, mean and standard deviation of MISR cloud-free sea ice albedo is calculated. These products are publicly available on a predefined polar stereographic grid at three spatial resolutions (1 km, 5 km, 25 km). The time span of the generated sea ice albedo covers the months between March and September of each year from 2000 to 2016 inclusive. In addition to data production, an evaluation of the accuracy of sea ice albedo was performed through a comparison with a dataset generated from a tower based albedometer from NOAA/ESRL/GMD/GRAD. This comparison confirms the high accuracy and stability of MISR’s sea ice albedo since its launch in February 2000. We also performed an evaluation of the day-of-year trend of sea ice albedo between 2000 and 2016, which confirm the reduction of sea ice shortwave albedo with an order of 0.4–1%, depending on the day of year and the length of observed time window. Full article
(This article belongs to the Special Issue MISR)
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Open AccessEditor’s ChoiceArticle Identification of Dust Sources in a Saharan Dust Hot-Spot and Their Implementation in a Dust-Emission Model
Remote Sens. 2019, 11(1), 4; https://doi.org/10.3390/rs11010004
Received: 20 November 2018 / Revised: 14 December 2018 / Accepted: 18 December 2018 / Published: 20 December 2018
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Abstract
Although mineral dust plays a key role in the Earth’s climate system and in climate and weather prediction, models still have difficulties in predicting the amount and distribution of mineral dust in the atmosphere. One reason for this is the limited understanding of [...] Read more.
Although mineral dust plays a key role in the Earth’s climate system and in climate and weather prediction, models still have difficulties in predicting the amount and distribution of mineral dust in the atmosphere. One reason for this is the limited understanding of the distribution of dust sources and their behavior with respect to their spatiotemporal variability in activity. For a better estimation of the atmospheric dust load, this paper presents an approach to localize dust sources and thereby estimate the sediment supply for a study area centered on the Aïr Massif in Niger with a north–south extent of 16 –22 N and an east–west extent of 4 –12 E. This approach uses optical Sentinel-2 data at visible and near infrared wavelengths together with HydroSHEDS flow accumulation data to localize ephemeral riverbeds. Visible channels from Sentinel-2 data are used to detect sand sheets and dunes. The identified sediment supply map was compared to the dust source activation frequency derived from the analysis of Desert-Dust-RGB imagery from the Meteosat Second Generation series of satellites. This comparison reveals the strong connection between dust activity, prevailing meteorology and sediment supply. In a second step, the sediment supply information was implemented in a dust-emission model. The simulated emission flux shows how much the model results benefit from the updated sediment supply information in localizing the main dust sources and in retrieving the seasonality of dust activity from these sources. The described approach to characterize dust sources can be implemented in other regional model studies, or even globally, and can thereby help to get a more accurate picture of dust source distribution and a more realistic estimation of the atmospheric dust load. Full article
(This article belongs to the Special Issue Remote Sensing in Support of Aeolian Research)
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Open AccessEditor’s ChoiceArticle Monitoring Crop Evapotranspiration and Crop Coefficients over an Almond and Pistachio Orchard Throughout Remote Sensing
Remote Sens. 2018, 10(12), 2001; https://doi.org/10.3390/rs10122001
Received: 23 October 2018 / Revised: 5 December 2018 / Accepted: 6 December 2018 / Published: 10 December 2018
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Abstract
In California, water is a perennial concern. As competition for water resources increases due to growth in population, California’s tree nut farmers are committed to improving the efficiency of water used for food production. There is an imminent need to have reliable methods [...] Read more.
In California, water is a perennial concern. As competition for water resources increases due to growth in population, California’s tree nut farmers are committed to improving the efficiency of water used for food production. There is an imminent need to have reliable methods that provide information about the temporal and spatial variability of crop water requirements, which allow farmers to make irrigation decisions at field scale. This study focuses on estimating the actual evapotranspiration and crop coefficients of an almond and pistachio orchard located in Central Valley (California) during an entire growing season by combining a simple crop evapotranspiration model with remote sensing data. A dataset of the vegetation index NDVI derived from Landsat-8 was used to facilitate the estimation of the basal crop coefficient (Kcb), or potential crop water use. The soil water evaporation coefficient (Ke) was measured from microlysimeters. The water stress coefficient (Ks) was derived from airborne remotely sensed canopy thermal-based methods, using seasonal regressions between the crop water stress index (CWSI) and stem water potential (Ψstem). These regressions were statistically-significant for both crops, indicating clear seasonal differences in pistachios, but not in almonds. In almonds, the estimated maximum Kcb values ranged between 1.05 to 0.90, while for pistachios, it ranged between 0.89 to 0.80. The model indicated a difference of 97 mm in transpiration over the season between both crops. Soil evaporation accounted for an average of 16% and 13% of the total actual evapotranspiration for almonds and pistachios, respectively. Verification of the model-based daily crop evapotranspiration estimates was done using eddy-covariance and surface renewal data collected in the same orchards, yielding an R2 ≥ 0.7 and average root mean square errors (RMSE) of 0.74 and 0.91 mm·day−1 for almond and pistachio, respectively. It is concluded that the combination of crop evapotranspiration models with remotely-sensed data is helpful for upscaling irrigation information from plant to field scale and thus may be used by farmers for making day-to-day irrigation management decisions. Full article
(This article belongs to the Special Issue Remote Sensing for Crop Water Management)
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Open AccessEditor’s ChoiceArticle GPS Time Series Analysis from Aboa the Finnish Antarctic Research Station
Remote Sens. 2018, 10(12), 1937; https://doi.org/10.3390/rs10121937
Received: 30 October 2018 / Revised: 23 November 2018 / Accepted: 28 November 2018 / Published: 1 December 2018
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Abstract
Continuous Global Positioning System (GPS) observations have been logged at the Finnish Antarctic research station (Aboa) since February 2003. The station is located in Dronning Maud Land, East Antarctica. Almost 5000 daily observation files have been archived based on yearly scientific expeditions. These [...] Read more.
Continuous Global Positioning System (GPS) observations have been logged at the Finnish Antarctic research station (Aboa) since February 2003. The station is located in Dronning Maud Land, East Antarctica. Almost 5000 daily observation files have been archived based on yearly scientific expeditions. These files have not been fully analysed until now. This study reports for the first time on the consistent and homogeneous data processing and analysis of the 15-year long time series. Daily coordinates are obtained using Precise Point Positioning (PPP) processing based on two approaches. The first approach is based on the Kalman filter and uses the RTKLIB open source library to produce daily solutions by unconventionally running the filter in the forward and backward direction. The second approach uses APPS web service and is based on GIPSY scientific processing engine. The two approaches show an excellent agreement with less than 3 mm rms error horizontally and 6 mm rms error vertically. The derived position time series is analysed in terms of trend, periodicity and noise characteristics. The noise of the time series was found to be power-law noise model with spectral index closer to flicker noise. In addition, several periodic signals were found at 5, 14, 183 and 362 days. Furthermore, most of the horizontal movement was found to be in the North direction at a rate of 11.23 ± 0.09 mm/y, whereas the rate in the East direction was estimated to be 1.46 ± 0.05 mm/y. Lastly, the 15-year long time series revealed a movement upwards at a rate of 0.79 ± 0.35 mm/y. Despite being an unattended station, Aboa provides one of the most continuous and longest GPS time series in Antarctica. Therefore, we believe that this research increases the awareness of local geophysical phenomena in a less reported area of the Antarctic continent. Full article
(This article belongs to the Special Issue Environmental Research with Global Navigation Satellite System (GNSS))
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Open AccessEditor’s ChoiceArticle Deforestation and Forest Degradation Due to Gold Mining in the Peruvian Amazon: A 34-Year Perspective
Remote Sens. 2018, 10(12), 1903; https://doi.org/10.3390/rs10121903
Received: 2 November 2018 / Revised: 21 November 2018 / Accepted: 27 November 2018 / Published: 29 November 2018
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Abstract
While deforestation rates decline globally they are rising in the Western Amazon. Artisanal-scale gold mining (ASGM) is a large cause of this deforestation and brings with it extensive environmental, social, governance, and public health impacts, including large carbon emissions and mercury pollution. Underlying [...] Read more.
While deforestation rates decline globally they are rising in the Western Amazon. Artisanal-scale gold mining (ASGM) is a large cause of this deforestation and brings with it extensive environmental, social, governance, and public health impacts, including large carbon emissions and mercury pollution. Underlying ASGM is a broad network of factors that influence its growth, distribution, and practices such as poverty, flows of legal and illegal capital, conflicting governance, and global economic trends. Despite its central role in land use and land cover change in the Western Amazon and the severity of its social and environmental impacts, it is relatively poorly studied. While ASGM in Southeastern Peru has been quantified previously, doing so is difficult due to the heterogeneous nature of the resulting landscape. Using a novel approach to classify mining that relies on a fusion of CLASlite and the Global Forest Change dataset, two Landsat-based deforestation detection tools, we sought to quantify ASGM-caused deforestation in the period 1984–2017 in the southern Peruvian Amazon and examine trends in the geography, methods, and impacts of ASGM across that time. We identify nearly 100,000 ha of deforestation due to ASGM in the 34-year study period, an increase of 21% compared to previous estimates. Further, we find that 10% of that deforestation occurred in 2017, the highest annual amount of deforestation in the study period, with 53% occurring since 2011. Finally, we demonstrate that not all mining is created equal by examining key patterns and changes in ASGM activity and techniques through time and space. We discuss their connections with, and impacts on, socio-economic factors, such as land tenure, infrastructure, international markets, governance efforts, and social and environmental impacts. Full article
(This article belongs to the Section Forest Remote Sensing)
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Open AccessEditor’s ChoiceArticle CubeSats Enable High Spatiotemporal Retrievals of Crop-Water Use for Precision Agriculture
Remote Sens. 2018, 10(12), 1867; https://doi.org/10.3390/rs10121867
Received: 28 September 2018 / Revised: 19 November 2018 / Accepted: 20 November 2018 / Published: 22 November 2018
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Abstract
Remote sensing based estimation of evapotranspiration (ET) provides a direct accounting of the crop water use. However, the use of satellite data has generally required that a compromise between spatial and temporal resolution is made, i.e., one could obtain low spatial resolution data [...] Read more.
Remote sensing based estimation of evapotranspiration (ET) provides a direct accounting of the crop water use. However, the use of satellite data has generally required that a compromise between spatial and temporal resolution is made, i.e., one could obtain low spatial resolution data regularly, or high spatial resolution occasionally. As a consequence, this spatiotemporal trade-off has tended to limit the impact of remote sensing for precision agricultural applications. With the recent emergence of constellations of small CubeSat-based satellite systems, these constraints are rapidly being removed, such that daily 3 m resolution optical data are now a reality for earth observation. Such advances provide an opportunity to develop new earth system monitoring and assessment tools. In this manuscript we evaluate the capacity of CubeSats to advance the estimation of ET via application of the Priestley-Taylor Jet Propulsion Laboratory (PT-JPL) retrieval model. To take advantage of the high-spatiotemporal resolution afforded by these systems, we have integrated a CubeSat derived leaf area index as a forcing variable into PT-JPL, as well as modified key biophysical model parameters. We evaluate model performance over an irrigated farmland in Saudi Arabia using observations from an eddy covariance tower. Crop water use retrievals were also compared against measured irrigation from an in-line flow meter installed within a center-pivot system. To leverage the high spatial resolution of the CubeSat imagery, PT-JPL retrievals were integrated over the source area of the eddy covariance footprint, to allow an equivalent intercomparison. Apart from offering new precision agricultural insights into farm operations and management, the 3 m resolution ET retrievals were shown to explain 86% of the observed variability and provide a relative RMSE of 32.9% for irrigated maize, comparable to previously reported satellite-based retrievals. An observed underestimation was diagnosed as a possible misrepresentation of the local surface moisture status, highlighting the challenge of high-resolution modeling applications for precision agriculture and informing future research directions. Full article
(This article belongs to the Special Issue Advances in the Remote Sensing of Terrestrial Evaporation)
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Open AccessEditor’s ChoiceArticle Object-Based Image Analysis for Sago Palm Classification: The Most Important Features from High-Resolution Satellite Imagery
Remote Sens. 2018, 10(8), 1319; https://doi.org/10.3390/rs10081319
Received: 13 August 2018 / Accepted: 17 August 2018 / Published: 20 August 2018
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Abstract
Sago palm (Metroxylon sagu) is a palm tree species originating in Indonesia. In the future, this starch-producing tree will play an important role in food security and biodiversity. Local governments have begun to emphasize the sustainable development of sago palm plantations; therefore, they [...] Read more.
Sago palm (Metroxylon sagu) is a palm tree species originating in Indonesia. In the future, this starch-producing tree will play an important role in food security and biodiversity. Local governments have begun to emphasize the sustainable development of sago palm plantations; therefore, they require near-real-time geospatial information on palm stands. We developed a semi-automated classification scheme for mapping sago palm using machine learning within an object-based image analysis framework with Pleiades-1A imagery. In addition to spectral information, arithmetic, geometric, and textural features were employed to enhance the classification accuracy. Recursive feature elimination was applied to samples to rank the importance of 26 input features. A support vector machine (SVM) was used to perform classifications and resulted in the highest overall accuracy of 85.00% after inclusion of the eight most important features, including three spectral features, three arithmetic features, and two textural features. The SVM classifier showed normal fitting up to the eighth most important feature. According to the McNemar test results, using the top seven to 14 features provided a better classification accuracy. The significance of this research is the revelation of the most important features in recognizing sago palm among other similar tree species. Full article
(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)
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Open AccessEditor’s ChoiceArticle Assessment of the SMAP-Derived Soil Water Deficit Index (SWDI-SMAP) as an Agricultural Drought Index in China
Remote Sens. 2018, 10(8), 1302; https://doi.org/10.3390/rs10081302
Received: 5 July 2018 / Revised: 2 August 2018 / Accepted: 16 August 2018 / Published: 18 August 2018
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Abstract
China is frequently subjected to local and regional drought disasters, and thus, drought monitoring is vital. Drought assessments based on available surface soil moisture (SM) can account for soil water deficit directly. Microwave remote sensing techniques enable the estimation of global SM with [...] Read more.
China is frequently subjected to local and regional drought disasters, and thus, drought monitoring is vital. Drought assessments based on available surface soil moisture (SM) can account for soil water deficit directly. Microwave remote sensing techniques enable the estimation of global SM with a high temporal resolution. At present, the evaluation of Soil Moisture Active Passive (SMAP) SM products is inadequate, and L-band microwave data have not been applied to agricultural drought monitoring throughout China. In this study, first, we provide a pivotal evaluation of the SMAP L3 radiometer-derived SM product using in situ observation data throughout China, to assist in subsequent drought assessment, and then the SMAP-Derived Soil Water Deficit Index (SWDI-SMAP) is compared with the atmospheric water deficit (AWD) and vegetation health index (VHI). It is found that the SMAP can obtain SM with relatively high accuracy and the SWDI-SMAP has a good overall performance on drought monitoring. Relatively good performance of SWDI-SMAP is shown, except in some mountain regions; the SWDI-SMAP generally performs better in the north than in the south for less dry bias, although better performance of SMAP SM based on the R is shown in the south than in the north; differences between the SWDI-SMAP and VHI are mainly shown in areas without vegetation or those containing drought-resistant plants. In summary, the SWDI-SMAP shows great application potential in drought monitoring. Full article
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Open AccessEditor’s ChoiceArticle Synergetic Use of Sentinel-1 and Sentinel-2 Data for Soil Moisture Mapping at Plot Scale
Remote Sens. 2018, 10(8), 1285; https://doi.org/10.3390/rs10081285
Received: 23 May 2018 / Revised: 27 June 2018 / Accepted: 2 July 2018 / Published: 15 August 2018
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Abstract
This paper presents an approach for retrieval of soil moisture content (SMC) by coupling single polarization C-band synthetic aperture radar (SAR) and optical data at the plot scale in vegetated areas. The study was carried out at five different sites with dominant vegetation [...] Read more.
This paper presents an approach for retrieval of soil moisture content (SMC) by coupling single polarization C-band synthetic aperture radar (SAR) and optical data at the plot scale in vegetated areas. The study was carried out at five different sites with dominant vegetation cover located in Kenya. In the initial stage of the process, different features are extracted from single polarization mode (VV polarization) SAR and optical data. Subsequently, proper selection of the relevant features is conducted on the extracted features. An advanced state-of-the-art machine learning regression approach, the support vector regression (SVR) technique, is used to retrieve soil moisture. This paper takes a new look at soil moisture retrieval in vegetated areas considering the needs of practical applications. In this context, we tried to work at the object level instead of the pixel level. Accordingly, a group of pixels (an image object) represents the reality of the land cover at the plot scale. Three approaches, a pixel-based approach, an object-based approach, and a combination of pixel- and object-based approaches, were used to estimate soil moisture. The results show that the combined approach outperforms the other approaches in terms of estimation accuracy (4.94% and 0.89 compared to 6.41% and 0.62 in terms of root mean square error (RMSE) and R2), flexibility on retrieving the level of soil moisture, and better quality of visual representation of the SMC map. Full article
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Open AccessEditor’s ChoiceArticle SLALOM: An All-Surface Snow Water Path Retrieval Algorithm for the GPM Microwave Imager
Remote Sens. 2018, 10(8), 1278; https://doi.org/10.3390/rs10081278
Received: 19 June 2018 / Revised: 10 August 2018 / Accepted: 12 August 2018 / Published: 14 August 2018
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Abstract
This paper describes a new algorithm that is able to detect snowfall and retrieve the associated snow water path (SWP), for any surface type, using the Global Precipitation Measurement (GPM) Microwave Imager (GMI). The algorithm is tuned and evaluated against coincident observations of [...] Read more.
This paper describes a new algorithm that is able to detect snowfall and retrieve the associated snow water path (SWP), for any surface type, using the Global Precipitation Measurement (GPM) Microwave Imager (GMI). The algorithm is tuned and evaluated against coincident observations of the Cloud Profiling Radar (CPR) onboard CloudSat. It is composed of three modules for (i) snowfall detection, (ii) supercooled droplet detection and (iii) SWP retrieval. This algorithm takes into account environmental conditions to retrieve SWP and does not rely on any surface classification scheme. The snowfall detection module is able to detect 83% of snowfall events including light SWP (down to 1 × 10−3 kg·m−2) with a false alarm ratio of 0.12. The supercooled detection module detects 97% of events, with a false alarm ratio of 0.05. The SWP estimates show a relative bias of −11%, a correlation of 0.84 and a root mean square error of 0.04 kg·m−2. Several applications of the algorithm are highlighted: Three case studies of snowfall events are investigated, and a 2-year high resolution 70°S–70°N snowfall occurrence distribution is presented. These results illustrate the high potential of this algorithm for snowfall detection and SWP retrieval using GMI. Full article
(This article belongs to the Special Issue Remote Sensing of Precipitation)
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Open AccessEditor’s ChoiceArticle Canopy Hyperspectral Sensing of Paddy Fields at the Booting Stage and PLS Regression can Assess Grain Yield
Remote Sens. 2018, 10(8), 1249; https://doi.org/10.3390/rs10081249
Received: 26 June 2018 / Revised: 6 August 2018 / Accepted: 6 August 2018 / Published: 8 August 2018
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Abstract
Canopy hyperspectral (HS) sensing is a promising tool for estimating rice (Oryza sativa L.) yield. However, the timing of HS measurements is crucial for assessing grain yield prior to harvest because rice growth stages strongly influence the sensitivity to different wavelengths and [...] Read more.
Canopy hyperspectral (HS) sensing is a promising tool for estimating rice (Oryza sativa L.) yield. However, the timing of HS measurements is crucial for assessing grain yield prior to harvest because rice growth stages strongly influence the sensitivity to different wavelengths and the evaluation performance. To clarify the optimum growth stage for HS sensing-based yield assessments, the grain yield of paddy fields during the reproductive phase to the ripening phase was evaluated from field HS data in conjunction with iterative stepwise elimination partial least squares (ISE-PLS) regression. The field experiments involved three different transplanting dates (12 July, 26 July, and 9 August) in 2017 for six cultivars with three replicates (n = 3 × 6 × 3 = 54). Field HS measurements were performed on 2 October 2017, during the panicle initiation, booting, and ripening growth stages. The predictive accuracy of ISE-PLS was compared with that of the standard full-spectrum PLS (FS-PLS) via coefficient of determination (R2) values and root mean squared errors of cross-validation (RMSECV), and the robustness was evaluated by the residual predictive deviation (RPD). Compared with the FS-PLS models, the ISE-PLS models exhibited higher R2 values and lower RMSECV values for all data sets. Overall, the highest R2 values and the lowest RMSECV values were obtained from the ISE-PLS model at the booting stage (R2 = 0.873, RMSECV = 22.903); the RPD was >2.4. Selected HS wavebands in the ISE-PLS model were identified in the red-edge (710–740 nm) and near-infrared (830 nm) regions. Overall, these results suggest that the booting stage might be the best time for in-season rice grain assessment and that rice yield could be evaluated accurately from the HS sensing data via the ISE-PLS model. Full article
(This article belongs to the Special Issue Remote Sensing in Support of Transforming Smallholder Agriculture)
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Open AccessEditor’s ChoiceArticle Assisting Flood Disaster Response with Earth Observation Data and Products: A Critical Assessment
Remote Sens. 2018, 10(8), 1230; https://doi.org/10.3390/rs10081230
Received: 22 June 2018 / Revised: 25 July 2018 / Accepted: 3 August 2018 / Published: 6 August 2018
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Abstract
Floods are among the top-ranking natural disasters in terms of annual cost in insured and uninsured losses. Since high-impact events often cover spatial scales that are beyond traditional regional monitoring operations, remote sensing, in particular from satellites, presents an attractive approach. Since the [...] Read more.
Floods are among the top-ranking natural disasters in terms of annual cost in insured and uninsured losses. Since high-impact events often cover spatial scales that are beyond traditional regional monitoring operations, remote sensing, in particular from satellites, presents an attractive approach. Since the 1970s, there have been many studies in the scientific literature about mapping and monitoring of floods using data from various sensors onboard different satellites. The field has now matured and hence there is a general consensus among space agencies, numerous organizations, scientists, and end-users to strengthen the support that satellite missions can offer, particularly in assisting flood disaster response activities. This has stimulated more research in this area, and significant progress has been achieved in recent years in fostering our understanding of the ways in which remote sensing can support flood monitoring and assist emergency response activities. This paper reviews the products and services that currently exist to deliver actionable information about an ongoing flood disaster to emergency response operations. It also critically discusses requirements, challenges and perspectives for improving operational assistance during flood disaster using satellite remote sensing products. Full article
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Open AccessEditor’s ChoiceArticle Accuracy Assessment of GlobeLand30 2010 Land Cover over China Based on Geographically and Categorically Stratified Validation Sample Data
Remote Sens. 2018, 10(8), 1213; https://doi.org/10.3390/rs10081213
Received: 27 June 2018 / Revised: 27 July 2018 / Accepted: 31 July 2018 / Published: 2 August 2018
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Abstract
Land cover information is vital for research and applications concerning natural resources and environmental modeling. Accuracy assessment is an important dimension in use and production of land cover information. GlobeLand30 is a relatively new global land cover information product with a fine spatial [...] Read more.
Land cover information is vital for research and applications concerning natural resources and environmental modeling. Accuracy assessment is an important dimension in use and production of land cover information. GlobeLand30 is a relatively new global land cover information product with a fine spatial resolution of 30 m and is potentially useful for many applications. This paper describes the methods for and results from the first country-wide and statistically based accuracy assessment of GlobeLand30 2010 land cover dataset over China. For this, a total of 8400 validation sample pixels were collected based on a sampling design featuring two levels of stratification (ten geographical regions, each with nine or eight land-cover classes). Validation sample data with reference class labels were acquired from visual interpretation based on Google Earth high-resolution satellite images. Error matrices for individual regions and entire China were estimated properly based on the sampling design adopted, with the former aggregated to get the latter through suitable weighting. Results were obtained, with agreement at a sample pixel defined both as a match between the map (class) label and either the primary or alternate reference label therein and, more strictly, as a match between the map label and the primary reference label only. Based on the former definition of agreement, the overall accuracy of GlobeLand30 2010 land cover for China was assessed to be 84.2%. User’s accuracy and producer’s accuracy were both greater than 80% for cultivated land, forest, permanent snow and ice, and bareland, with user’s accuracy for water bodies estimated 94.2% (82.1% for wetland, 79.8% for artificial surface) and producer’s accuracy for grassland estimated 89.0%. These indicate that GlobeLand30 2010 depicts land cover circa 2010 in China quite accurately, although estimates of accuracy indicators based on the latter definition of agreement were lower as expected with an estimated national overall accuracy of 81.0%. Regional and class variations in accuracy were revealed and examined in the light of their associations with land cover distributions and patterns. Implications for use and production of GlobeLand30 land cover information were discussed, so were commonality and lack of it between GlobeLand30 and other fine-resolution land cover products. Full article
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Open AccessEditor’s ChoiceArticle Spectral-Spatial Classification of Hyperspectral Images: Three Tricks and a New Learning Setting
Remote Sens. 2018, 10(7), 1156; https://doi.org/10.3390/rs10071156
Received: 18 May 2018 / Revised: 10 July 2018 / Accepted: 19 July 2018 / Published: 21 July 2018
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Abstract
Spectral-spatial classification of hyperspectral images has been the subject of many studies in recent years. When there are only a few labeled pixels for training and a skewed class label distribution, this task becomes very challenging because of the increased risk of overfitting [...] Read more.
Spectral-spatial classification of hyperspectral images has been the subject of many studies in recent years. When there are only a few labeled pixels for training and a skewed class label distribution, this task becomes very challenging because of the increased risk of overfitting when training a classifier. In this paper, we show that in this setting, a convolutional neural network with a single hidden layer can achieve state-of-the-art performance when three tricks are used: a spectral-locality-aware regularization term and smoothing- and label-based data augmentation. The shallow network architecture prevents overfitting in the presence of many features and few training samples. The locality-aware regularization forces neighboring wavelengths to have similar contributions to the features generated during training. The new data augmentation procedure favors the selection of pixels in smaller classes, which is beneficial for skewed class label distributions. The accuracy of the proposed method is assessed on five publicly available hyperspectral images, where it achieves state-of-the-art results. As other spectral-spatial classification methods, we use the entire image (labeled and unlabeled pixels) to infer the class of its unlabeled pixels. To investigate the positive bias induced by the use of the entire image, we propose a new learning setting where unlabeled pixels are not used for building the classifier. Results show the beneficial effect of the proposed tricks also in this setting and substantiate the advantages of using labeled and unlabeled pixels from the image for hyperspectral image classification. Full article
(This article belongs to the Section Remote Sensing Image Processing)
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Open AccessEditor’s ChoiceArticle Aboveground Forest Biomass Estimation Combining L- and P-Band SAR Acquisitions
Remote Sens. 2018, 10(7), 1151; https://doi.org/10.3390/rs10071151
Received: 8 June 2018 / Revised: 16 July 2018 / Accepted: 19 July 2018 / Published: 20 July 2018
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Abstract
While considerable research has focused on using either L-band or P-band SAR (Synthetic Aperture Radar) on their own for forest biomass retrieval, the use of the two bands simultaneously to improve forest biomass retrieval remains less explored. In this paper, we make use [...] Read more.
While considerable research has focused on using either L-band or P-band SAR (Synthetic Aperture Radar) on their own for forest biomass retrieval, the use of the two bands simultaneously to improve forest biomass retrieval remains less explored. In this paper, we make use of L- and P-band airborne SAR and in situ data measured in the field together with laser scanning data acquired over one hemi-boreal (Remningstorp) and one boreal (Krycklan) forest study area in Sweden. We fit statistical models to different combinations of topographic-corrected SAR backscatter and forest heights estimated from PolInSAR for the biomass estimation, and evaluate retrieval performance in terms of R2 and using 10-fold cross-validation. The study shows that specific combinations of radar observables from L- and P-band lead to biomass predictions that are more accurate in comparison with single-band retrievals. The correlations and accuracies between the combinations of SAR features and aboveground biomass are consistent across the two study areas, whereas the retrieval performance varied for individual bands. P-band-based retrievals were more accurate than L-band for the hemi-boreal Remningstorp site and less accurate than L-band for the boreal Krycklan site. The aboveground biomass levels as well as the ground topography differ between the two sites. The results suggest that P-band is more sensitive to higher biomass and L-band to lower biomass forests. The forest height from PolInSAR improved the results at L-band in the higher biomass substantially, whereas no improvement was observed at P-band in both study areas. These results are relevant in the context of combining information over boreal forests from future low-frequency SAR missions such as the European Space Agency (ESA) BIOMASS mission, which will operate at P-band, and future L-band missions planned by several space agencies. Full article
(This article belongs to the Section Forest Remote Sensing)
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Open AccessEditor’s ChoiceArticle Opportunities and Challenges for the Estimation of Aquaculture Production Based on Earth Observation Data
Remote Sens. 2018, 10(7), 1076; https://doi.org/10.3390/rs10071076
Received: 8 June 2018 / Revised: 25 June 2018 / Accepted: 5 July 2018 / Published: 6 July 2018
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Abstract
Aquaculture makes a crucial contribution to global food security and protein intake and is a basis for many livelihoods. Every second fish consumed today is produced in aquaculture systems, mainly in land-based water ponds situated along the coastal areas. Satellite remote sensing enables [...] Read more.
Aquaculture makes a crucial contribution to global food security and protein intake and is a basis for many livelihoods. Every second fish consumed today is produced in aquaculture systems, mainly in land-based water ponds situated along the coastal areas. Satellite remote sensing enables high-resolution mapping of pond aquaculture, facilitating inventory analyses to support sustainable development of the planet’s valuable coastal ecosystems. Free, full and open data from the Copernicus earth observation missions opens up new potential for the detection and monitoring of aquaculture from space. High-resolution time series data acquired by active microwave instruments aboard the Sentinel-1 satellites and fully automated, object-based image analysis allow the identification of aquaculture ponds. In view of the diversity and complexity in the production of aquaculture products, yield and production varies greatly among species. Although national statistics on aquaculture production exist, there is a large gap of pond-specific aquaculture production quantities. In this regard, earth observation-based mapping and monitoring of pond aquaculture can be used to estimate production and has great potential for global production projections. For the deltas of the Mekong River, Red River, Pearl River, and Yellow River, as one of the world’s most significant aquaculture production regions, we detected aquaculture ponds from high spatial resolution Sentinel-1 Synthetic Aperture Radar (SAR) data. We collected aquaculture production and yield statistics at national, regional and local levels to link earth observation-based findings to the size, number and distribution of aquaculture ponds with production estimation. With the SAR derived mapping product, it is possible for the first time to assess aquaculture on single pond level at a regional scale and use that information for spatial analyses and production estimation. Full article
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Open AccessEditor’s ChoiceArticle Antarctic Surface Ice Velocity Retrieval from MODIS-Based Mosaic of Antarctica (MOA)
Remote Sens. 2018, 10(7), 1045; https://doi.org/10.3390/rs10071045
Received: 17 May 2018 / Revised: 12 June 2018 / Accepted: 26 June 2018 / Published: 2 July 2018
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Abstract
The velocity of ice flow in the Antarctic is a crucial factor to determine ice discharge and thus future sea level rise. Feature tracking has been widely used in optical and radar imagery with fine resolution to retrieve flow parameters, although the primitive [...] Read more.
The velocity of ice flow in the Antarctic is a crucial factor to determine ice discharge and thus future sea level rise. Feature tracking has been widely used in optical and radar imagery with fine resolution to retrieve flow parameters, although the primitive result may be contaminated by noise. In this paper, we present a series of modified post-processing steps, such as SNR thresholding by residual, complex Butterworth filters, and triple standard deviation truncation, to improve the performance of primitive results, and apply it to MODIS-based Mosaic of Antarctica (MOA) datasets. The final velocity field result displays the general flow pattern of the peripheral Antarctic. Seventy-eight out of 97 streamlines starting from seed points are smooth and continuous. The RMSE with 178 manually selected tie points is within 60 m·a−1. The systematic comparison with Making Earth System Data Records for Use in Research Environments (MEaSUREs) datasets in seven drainages shows that the results regarding high magnitude and large-scale ice shelf are highly reliable; absolute mean and median difference are less than 18 m·a−1, while the result of localized drainage suffered from too much tracking error. The relative differences from manually selected and random points are controlled within 8% when speed is beyond 500 m·a−1, but bias and uncertainty are pronounced when speed is lower than that. The result through our accuracy control strategy highlights that coarse remote-sensed images such as Moderate Resolution Imaging Spectrophotometer (MODIS) can still offer the capability for comprehensive and long-term continental ice sheet surface velocity mapping. Full article
(This article belongs to the Section Remote Sensing in Geology, Geomorphology and Hydrology)
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Open AccessEditor’s ChoiceArticle The Combined ASTER and MODIS Emissivity over Land (CAMEL) Global Broadband Infrared Emissivity Product
Remote Sens. 2018, 10(7), 1027; https://doi.org/10.3390/rs10071027
Received: 16 May 2018 / Revised: 12 June 2018 / Accepted: 22 June 2018 / Published: 28 June 2018
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Abstract
Infrared surface emissivity is needed for the calculation of net longwave radiation, a critical parameter in weather and climate models and Earth’s radiation budget. Due to a prior lack of spatially and temporally variant global broadband emissivity (BBE) measurements of the surface, it [...] Read more.
Infrared surface emissivity is needed for the calculation of net longwave radiation, a critical parameter in weather and climate models and Earth’s radiation budget. Due to a prior lack of spatially and temporally variant global broadband emissivity (BBE) measurements of the surface, it is common practice in land surface and climate models to set BBE to a single constant over the globe. This can lead to systematic biases in the estimated net and longwave radiation for any particular location and time of year. Under the National Aeronautics and Space Administration’s (NASA) Making Earth System Data Records for Use in Research Environments (MEaSUREs) project, a new global, high spectral resolution land surface emissivity dataset has recently been made available at monthly at 0.05 degree resolution since 2000. Called the Combined ASTER MODIS Emissivity over Land (CAMEL), this dataset is created by the merging of the MODIS baseline-fit emissivity database developed at the University of Wisconsin-Madison and the ASTER Global Emissivity Dataset (GED) produced at the Jet Propulsion Laboratory. CAMEL has 13 hinge points between 3.6–14.3 µm which are expanded to cover 417 infrared spectral channels within the same wavelength region using a principal component regression approach. This work presents the method for calculating BBE using the new CAMEL dataset. BBE is computed via numerical integration over the CAMEL High Spectral Resolution product for two different wavelength ranges—3.6–14.3 µm which takes advantage of the full, available CAMEL spectra and 8.0–13.5 µm which has been determined to be an optimal range for computing the most representative all wavelength, longwave net radiation. CAMEL BBE uncertainty estimates are computed, and comparisons are made to BBE computed from lab validation data for selected case sites. Variations of BBE over time and land cover classification schemes are investigated and converted into flux to demonstrate the equivalent error in longwave radiation which would be made by the use of a single, constant BBE value. Misrepresentations in BBE by 0.05 at 310 K corresponds to potential errors in longwave radiation of over 25 W/m2. Full article
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Open AccessEditor’s ChoiceArticle Landsat-Based Land Use Change Assessment in the Brazilian Atlantic Forest: Forest Transition and Sugarcane Expansion
Remote Sens. 2018, 10(7), 996; https://doi.org/10.3390/rs10070996
Received: 27 March 2018 / Revised: 19 May 2018 / Accepted: 23 May 2018 / Published: 22 June 2018
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Abstract
In this study, we examine the hypothesis of a forest transition in an area of early expansion of the agricultural frontier over the Brazilian Atlantic Forest in the south-central part of the State of São Paulo. Large scale land use/cover changes were assessed [...] Read more.
In this study, we examine the hypothesis of a forest transition in an area of early expansion of the agricultural frontier over the Brazilian Atlantic Forest in the south-central part of the State of São Paulo. Large scale land use/cover changes were assessed by integrating Landsat imagery, census data, and landscape metrics. Two Landsat multi-temporal datasets were assembled for two consecutive periods—1995–2006 and 2006–2013—to assess changes in forest cover according to four classes: (i) transition from non-forest cover to planted forest (NF-PF); (ii) transition from non-forest to secondary (successional) forest (NF-SF); (iii) conservation of planted forest (PF) and (iv) conservation of forest remnants (REM). Data from the two most recent, 1995/96 and 2006 agricultural censuses were analyzed to single out major changes in agricultural production. The total area of forest cover, including primary, secondary, and planted forest, increased 30% from 1995 to 2013, whereas forest planted in non-forest areas (NF-PF) and conservation of planted forest (PF) accounted for 14.1% and 19.6%, respectively, of the total forest area by 2013. Such results showed a relatively important forest transition that would be explained mostly by forest plantations though. Analysis of the landscape metrics indicated an increase in connectivity among forest fragments during the period of study, and revealed that nearly half of the forest fragments were located within 50 m from riverbeds, possibly suggesting some level of compliance with environmental laws. Census data showed an increase in both the area and productivity of sugarcane plantations, while pasture and citrus area decreased by a relatively important level, suggesting that sugarcane production has expanded at the expense of these land uses. Both satellite and census data helped to delineate the establishment of two major production systems, the first one dominated by sugarcane plantations approximately located in the NE part of the study area, and a second one concentrating most of the forest plantations in the SW portion of the study area, where most of the forest transition could be observed. Full article
(This article belongs to the Special Issue Remote Sensing of Forest Cover Change)
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Open AccessFeature PaperEditor’s ChoiceArticle Intercomparison of MODIS AQUA and VIIRS I-Band Fires and Emissions in an Agricultural Landscape—Implications for Air Pollution Research
Remote Sens. 2018, 10(7), 978; https://doi.org/10.3390/rs10070978
Received: 16 May 2018 / Revised: 18 June 2018 / Accepted: 19 June 2018 / Published: 21 June 2018
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Abstract
Quantifying emissions from crop residue burning is crucial as it is a significant source of air pollution. In this study, we first compared the fire products from two different sensors, the Visible Infrared Imaging Radiometer Suite (VIIRS) 375 m active fire product (VNP14IMG) [...] Read more.
Quantifying emissions from crop residue burning is crucial as it is a significant source of air pollution. In this study, we first compared the fire products from two different sensors, the Visible Infrared Imaging Radiometer Suite (VIIRS) 375 m active fire product (VNP14IMG) and Moderate Resolution Imaging Spectroradiometer (MODIS) 1 km fire product (MCD14ML) in an agricultural landscape, Punjab, India. We then performed an intercomparison of three different approaches for estimating total particulate matter (TPM) emissions which includes the fire radiative power (FRP) based approach using VIIRS and MODIS data, the Global Fire Emissions Database (GFED) burnt area emissions and a bottom-up emissions approach involving agricultural census data. Results revealed that VIIRS detected fires were higher by a factor of 4.8 compared to MODIS Aqua and Terra sensors. Further, VIIRS detected fires were higher by a factor of 6.5 than Aqua. The mean monthly MODIS Aqua FRP was found to be higher than the VIIRS FRP; however, the sum of FRP from VIIRS was higher than MODIS data due to the large number of fires detected by the VIIRS. Besides, the VIIRS sum of FRP was 2.5 times more than the MODIS sum of FRP. MODIS and VIIRS monthly FRP data were found to be strongly correlated (r2 = 0.98). The bottom-up approach suggested TPM emissions in the range of 88.19–91.19 Gg compared to 42.0–61.71 Gg, 42.59–58.75 Gg and 93.98–111.72 Gg using the GFED, MODIS FRP, and VIIRS FRP based approaches, respectively. Of the different approaches, VIIRS FRP TPM emissions were highest. Since VIIRS data are only available since 2012 compared to MODIS Aqua data which have been available since May 2002, a prediction model combining MODIS and VIIRS FRP was derived to obtain potential TPM emissions from 2003–2016. The results suggested a range of 2.56–63.66 (Gg) TPM emissions per month, with the highest crop residue emissions during November of each year. Our results on TPM emissions for seasonality matched the ground-based data from the literature. As a mitigation option, stringent policy measures are recommended to curtail agricultural residue burning in the study area. Full article
(This article belongs to the Special Issue Remote Sensing of Wildfire)
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Open AccessFeature PaperEditor’s ChoiceArticle Realistic Forest Stand Reconstruction from Terrestrial LiDAR for Radiative Transfer Modelling
Remote Sens. 2018, 10(6), 933; https://doi.org/10.3390/rs10060933
Received: 2 May 2018 / Revised: 29 May 2018 / Accepted: 11 June 2018 / Published: 13 June 2018
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Abstract
Forest biophysical variables derived from remote sensing observations are vital for climate research. The combination of structurally and radiometrically accurate 3D “virtual” forests with radiative transfer (RT) models creates a powerful tool to facilitate the calibration and validation of remote sensing data and [...] Read more.
Forest biophysical variables derived from remote sensing observations are vital for climate research. The combination of structurally and radiometrically accurate 3D “virtual” forests with radiative transfer (RT) models creates a powerful tool to facilitate the calibration and validation of remote sensing data and derived biophysical products by helping us understand the assumptions made in data processing algorithms. We present a workflow that uses highly detailed 3D terrestrial laser scanning (TLS) data to generate virtual forests for RT model simulations. Our approach to forest stand reconstruction from a co-registered point cloud is unique as it models each tree individually. Our approach follows three steps: (1) tree segmentation; (2) tree structure modelling and (3) leaf addition. To demonstrate this approach, we present the measurement and construction of a one hectare model of the deciduous forest in Wytham Woods (Oxford, UK). The model contains 559 individual trees. We matched the TLS data with traditional census data to determine the species of each individual tree and allocate species-specific radiometric properties. Our modelling framework is generic, highly transferable and adjustable to data collected with other TLS instruments and different ecosystems. The Wytham Woods virtual forest is made publicly available through an online repository. Full article
(This article belongs to the Special Issue Radiative Transfer Modelling and Applications in Remote Sensing)
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Open AccessEditor’s ChoiceArticle East Africa Rainfall Trends and Variability 1983–2015 Using Three Long-Term Satellite Products
Remote Sens. 2018, 10(6), 931; https://doi.org/10.3390/rs10060931
Received: 26 April 2018 / Revised: 28 May 2018 / Accepted: 8 June 2018 / Published: 13 June 2018
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Abstract
Daily time series from the Climate Prediction Center (CPC) Africa Rainfall Climatology version 2.0 (ARC2), Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) and Tropical Applications of Meteorology using SATellite (TAMSAT) African Rainfall Climatology And Time series version 2 (TARCAT) high-resolution long-term satellite [...] Read more.
Daily time series from the Climate Prediction Center (CPC) Africa Rainfall Climatology version 2.0 (ARC2), Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) and Tropical Applications of Meteorology using SATellite (TAMSAT) African Rainfall Climatology And Time series version 2 (TARCAT) high-resolution long-term satellite rainfall products are exploited to study the spatial and temporal variability of East Africa (EA, 5S–20N, 28–52E) rainfall between 1983 and 2015. Time series of selected rainfall indices from the joint CCl/CLIVAR/JCOMM Expert Team on Climate Change Detection and Indices are computed at yearly and seasonal scales. Rainfall climatology and spatial patterns of variability are extracted via the analysis of the total rainfall amount (PRCPTOT), the simple daily intensity (SDII), the number of precipitating days (R1), the number of consecutive dry and wet days (CDD and CWD), and the number of very heavy precipitating days (R20). Our results show that the spatial patterns of such trends depend on the selected rainfall product, as much as on the geographic areas characterized by statistically significant trends for a specific rainfall index. Nevertheless, indications of rainfall trends were extracted especially at the seasonal scale. Increasing trends were identified for the October–November–December PRCPTOT, R1, and SDII indices over eastern EA, with the exception of Kenya. In March–April–May, rainfall is decreasing over a large part of EA, as demonstrated by negative trends of PRCPTOT, R1, CWD, and R20, even if a complete convergence of all satellite products is not achieved. Full article
(This article belongs to the Special Issue Remote Sensing of Essential Climate Variables and Their Applications)
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Open AccessEditor’s ChoiceArticle Seasonal and Decadal Groundwater Changes in African Sedimentary Aquifers Estimated Using GRACE Products and LSMs
Remote Sens. 2018, 10(6), 904; https://doi.org/10.3390/rs10060904
Received: 4 April 2018 / Revised: 31 May 2018 / Accepted: 5 June 2018 / Published: 8 June 2018
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Abstract
Increased groundwater abstraction is important to the economic development of Africa and to achieving many of the Sustainable Development Goals. However, there is little information on long-term or seasonal groundwater trends due to a lack of in situ monitoring. Here, we used GRACE [...] Read more.
Increased groundwater abstraction is important to the economic development of Africa and to achieving many of the Sustainable Development Goals. However, there is little information on long-term or seasonal groundwater trends due to a lack of in situ monitoring. Here, we used GRACE data from three products (the Centre for Space Research land solution (CSR), the Jet Propulsion Laboratory’s Global Mascon solution (JPL-MSCN), and the Centre National D’etudes Spatiales / Groupe de Recherches de Géodésie Spatiale solution (GRGS)), to examine terrestrial water storage (TWS) changes in 12 African sedimentary aquifers, to examine relationships between TWS and rainfall , and estimate groundwater storage (GWS) changes using four Land Surface Models (LSMs) (Community Land Model (CLM2.0), the Variable Infiltration Capacity model (VIC), the Mosaic model (MOSAIC) and the Noah model (NOAH)). We find that there are no substantial continuous long-term decreasing trends in groundwater storage from 2002 to 2016 in any of the African basins, however, consistent rising groundwater trends amounting to ~1 km3/year and 1.5 km3/year are identified in the Iullemmeden and Senegal basins, respectively, and longer term variations in ΔTWS in several basins associated with rainfall patterns. Discrete seasonal ΔTWS responses of ±1–5 cm/year are indicated by GRACE for each of the basins, with the exception of the Congo, North Kalahari, and Senegal basins, which display larger seasonal ΔTWS equivalent to approx. ±11–20 cm/year. The different seasonal responses in ΔTWS provide useful information about groundwater, including the identification of 5 to 9 month accumulation periods of rainfall in many semi-arid and arid basins as well as differences in ΔTWS responses between Sahelian and southern African aquifers to similar rainfall, likely reflecting differences in landcover. Seasonal ΔGWS estimated by combining GRACE ΔTWS with LSM outputs compare inconsistently to available in situ measurements of groundwater recharge from different basins, highlighting the need to further develop the representation of the recharge process in LSMs and the need for more in situ observations from piezometry. Full article
(This article belongs to the Special Issue Remote Sensing of Groundwater from River Basin to Global Scales)
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Open AccessFeature PaperEditor’s ChoiceArticle Rimaal: A Sand Buried Structure of Possible Impact Origin in the Sahara: Optical and Radar Remote Sensing Investigation
Remote Sens. 2018, 10(6), 880; https://doi.org/10.3390/rs10060880
Received: 24 April 2018 / Revised: 13 May 2018 / Accepted: 4 June 2018 / Published: 5 June 2018
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Abstract
This work communicates the discovery of a sandy buried 10.5 km diameter near-circular structure in the eastern part of the Great Sahara in North Africa. Rimaal, meaning “sand” in Arabic, is given as the name for this structure since it is largely concealed [...] Read more.
This work communicates the discovery of a sandy buried 10.5 km diameter near-circular structure in the eastern part of the Great Sahara in North Africa. Rimaal, meaning “sand” in Arabic, is given as the name for this structure since it is largely concealed beneath the Sahara Aeolian sand. Remote sensing image fusion and transformation of multispectral data (from Landsat-8) and synthetic aperture radar (from Sentinel-1 and ALOS PALSAR), of dual wavelengths (C and L-bands) and multi-polarization (HV, VV, HH, and HV), were adopted in this work. The optical and microwave hybrid imagery enabled the combining of surface spectral properties and subsurface roughness information for better understanding of the Rimaal structure. The long wavelength of the radar, in particular, enabled the penetration of desert sands and the revealing of the proposed structure. The structure exhibits a clear outer rim with traces of concentric faults, an annular flat basin and an inner ring surrounding remnants of a highly eroded central peak. Radar imagery clearly shows the interior wall of the structure is incised with radial pattern gullies that originate at or near the crater periphery, implying a much steeper rim wall in the past. In addition, data reveals a circumferential of a paleoriver course that flows along a curved path parallel to the crater’s western margin indicating the plausible presence of a concentric ring graben related to the inferred structure. The defined crater boundary is coincident with a shallow semi-circular-like basin in the SRTM elevation data. The structure portrays considerable modifications by extensive long-term Aeolian and fluvial erosion. Residing in the Cretaceous Nubian Sandstone formation suggests an old age of ≤65 Ma for the structure. If proven to be of an impact origin, the Rimaal structure could help in understanding the early evolution of the landscape of the Eastern Sahara and holds promise for hosting economically valuable ore deposits and hydrocarbon resources in the region. Full article
(This article belongs to the Section Remote Sensing in Geology, Geomorphology and Hydrology)
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Open AccessEditor’s ChoiceArticle Deep Cube-Pair Network for Hyperspectral Imagery Classification
Remote Sens. 2018, 10(5), 783; https://doi.org/10.3390/rs10050783
Received: 17 March 2018 / Revised: 23 April 2018 / Accepted: 16 May 2018 / Published: 18 May 2018
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Abstract
Advanced classification methods, which can fully utilize the 3D characteristic of hyperspectral image (HSI) and generalize well to the test data given only limited labeled training samples (i.e., small training dataset), have long been the research objective for HSI classification problem. Witnessing the [...] Read more.
Advanced classification methods, which can fully utilize the 3D characteristic of hyperspectral image (HSI) and generalize well to the test data given only limited labeled training samples (i.e., small training dataset), have long been the research objective for HSI classification problem. Witnessing the success of deep-learning-based methods, a cube-pair-based convolutional neural networks (CNN) classification architecture is proposed to cope this objective in this study, where cube-pair is used to address the small training dataset problem as well as preserve the 3D local structure of HSI data. Within this architecture, a 3D fully convolutional network is further modeled, which has less parameters compared with traditional CNN. Provided the same amount of training samples, the modeled network can go deeper than traditional CNN and thus has superior generalization ability. Experimental results on several HSI datasets demonstrate that the proposed method has superior classification results compared with other state-of-the-art competing methods. Full article
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Open AccessEditor’s ChoiceArticle Unveiling 25 Years of Planetary Urbanization with Remote Sensing: Perspectives from the Global Human Settlement Layer
Remote Sens. 2018, 10(5), 768; https://doi.org/10.3390/rs10050768
Received: 28 February 2018 / Revised: 7 May 2018 / Accepted: 8 May 2018 / Published: 16 May 2018
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Abstract
In the last few decades the magnitude and impacts of planetary urban transformations have become increasingly evident to scientists and policymakers. The ability to understand these processes remained limited in terms of territorial scope and comparative capacity for a long time: data availability [...] Read more.
In the last few decades the magnitude and impacts of planetary urban transformations have become increasingly evident to scientists and policymakers. The ability to understand these processes remained limited in terms of territorial scope and comparative capacity for a long time: data availability and harmonization were among the main constraints. Contemporary technological assets, such as remote sensing and machine learning, allow for analyzing global changes in the settlement process with unprecedented detail. The Global Human Settlement Layer (GHSL) project set out to produce detailed datasets to analyze and monitor the spatial footprint of human settlements and their population, which are key indicators for the global policy commitments of the 2030 Development Agenda. In the GHSL, Earth Observation plays a key role in the detection of built-up areas from the Landsat imagery upon which population distribution is modelled. The combination of remote sensing imagery and population modelling allows for generating globally consistent and detailed information about the spatial distribution of built-up areas and population. The GHSL data facilitate a multi-temporal analysis of human settlements with global coverage. The results presented in this article focus on the patterns of development of built-up areas, population and settlements. The article reports about the present status of global urbanization (2015) and its evolution since 1990 by applying to the GHSL the Degree of Urbanisation definition of the European Commission Directorate General for Regional and Urban Policy (DG-Regio) and the Statistical Office of the European Communities (EUROSTAT). The analysis portrays urbanization dynamics at a regional level and per country income classes to show disparities and inequalities. This study analyzes how the 6.1 billion urban dwellers are distributed worldwide. Results show the degree of global urbanization (which reached 85% in 2015), the more than 100 countries in which urbanization has increased between 1990 and 2015, and the tens of countries in which urbanization is today above the global average and where urbanization grows the fastest. The paper sheds light on the key role of urban areas for development, on the patterns of urban development across the regions of the world and on the role of a new generation of data to advance urbanization theory and reporting. Full article
(This article belongs to the Special Issue Earth Observation in Planning for Sustainable Urban Development)
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Open AccessEditor’s ChoiceArticle A Critical Comparison of Remote Sensing Leaf Area Index Estimates over Rice-Cultivated Areas: From Sentinel-2 and Landsat-7/8 to MODIS, GEOV1 and EUMETSAT Polar System
Remote Sens. 2018, 10(5), 763; https://doi.org/10.3390/rs10050763
Received: 31 March 2018 / Revised: 7 May 2018 / Accepted: 13 May 2018 / Published: 15 May 2018
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Abstract
Leaf area index (LAI) is a key biophysical variable fundamental in natural vegetation and agricultural land monitoring and modelling studies. This paper is aimed at comparing, validating and discussing different LAI satellite products from operational services and customized solution based on innovative Earth [...] Read more.
Leaf area index (LAI) is a key biophysical variable fundamental in natural vegetation and agricultural land monitoring and modelling studies. This paper is aimed at comparing, validating and discussing different LAI satellite products from operational services and customized solution based on innovative Earth Observation (EO) data such as Landsat-7/8 and Sentinel-2A. The comparison was performed to assess overall quality of LAI estimates for rice, as a fundamental input of different scale (regional to local) operational crop monitoring systems such as the ones developed during the “An Earth obseRvation Model based RicE information Service” (ERMES) project. We adopted a multiscale approach following international recognized protocols of the Committee on Earth Observation Satellites (CEOS) Land Product Validation (LPV) guidelines in different steps: (1) acquisition of representative field sample measurements, (2) validation of decametric satellite product (10–30 m spatial resolution), and (3) exploitation of such data to assess quality of medium-resolution operational products (~1000 m). The study areas were located in the main European rice areas in Spain, Italy and Greece. Field campaigns were conducted during three entire rice seasons (2014, 2015 and 2016—from sowing to full-flowering) to acquire multi-temporal ground LAI measurements and to assess Landsat-7/8 LAI estimates. Results highlighted good correspondence between Landsat-7/8 LAI estimates and ground measurements revealing high correlations (R2 ≥ 0.89) and low root mean squared errors (RMSE ≤ 0.75) in all seasons. Landsat-7/8 as well as Sentinel-2A high-resolution LAI retrievals, were compared with satellite LAI products operationally derived from MODIS (MOD15A2), Copernicus PROBA-V (GEOV1), and the recent EUMETSAT Polar System (EPS) LAI product. Good agreement was observed between high- and medium-resolution LAI estimates. In particular, the EPS LAI product was the most correlated product with both Landsat/7-8 and Sentinel-2A estimates, revealing R2 ≥ 0.93 and RMSE ≤ 0.53 m2/m2. In addition, a comparison exercise of EPS, GEOV1 and MODIS revealed high correlations (R2 ≥ 0.90) and RMSE ≤ 0.80 m2/m2 in all cases and years. The temporal assessment shows that the three satellite products capture well the seasonality during the crop phenological cycle. Discrepancies are observed mainly in absolute values retrieved for the peak of rice season. This is the first study that provides a quantitative assessment on the quality of available operational LAI product for rice monitoring to both the scientific community and users of agro-monitoring operational services. Full article
(This article belongs to the Special Issue Quantitative Remote Sensing of Land Surface Variables)
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Open AccessEditor’s ChoiceArticle Preliminary Investigation of a New AHI Aerosol Optical Depth (AOD) Retrieval Algorithm and Evaluation with Multiple Source AOD Measurements in China
Remote Sens. 2018, 10(5), 748; https://doi.org/10.3390/rs10050748
Received: 16 March 2018 / Revised: 19 April 2018 / Accepted: 2 May 2018 / Published: 14 May 2018
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Abstract
The Himawari-8 geostationary weather satellite, which is an Earth observing satellite launched in October 2014, has been applied in climate, environment, and air quality studies. Using hourly observation data from the Advanced Himawari Imager (AHI) on board Himawari-8, a new dark target algorithm [...] Read more.
The Himawari-8 geostationary weather satellite, which is an Earth observing satellite launched in October 2014, has been applied in climate, environment, and air quality studies. Using hourly observation data from the Advanced Himawari Imager (AHI) on board Himawari-8, a new dark target algorithm was proposed to retrieve the aerosol optical depth (AOD) at 1 km and 5 km resolutions over mainland China. Because of the short satellite operation time and lack of AErosol RObotic NETwork (AERONET) sites across China, we cannot derive robust and representative surface reflectance relationships for the visible to near-infrared channels by atmospheric correction. Therefore, we inherited the empirical reflectance relationship from the Moderate Resolution Imaging Spectroradiometer (MODIS) and we used the AHI and MODIS spectral response functions to make the relationship more suitable for AHI. Ultimately, our AOD products can better reflect the regional characteristics with the AHI sensor. Seasonal averages showed that our product is more similar to MODIS Collection 6 (C6) Dark Target (DT) AOD than the Japan Aerospace Exploration Agency (JAXA) AHI AOD, but the difference is largest in winter. In addition, we evaluated several satellite retrieval products (our AHI AOD, JAXA AHI AOD, the National Oceanic and Atmospheric Administration (NOAA) VIIRS AOD, MODIS DT AOD, and MODIS DB AOD) against AERONET AOD from July 2016 to June 2017. The results showed that our AHI measurements demonstrate good agreement with, but exhibit a little overestimation, as compared to ground-based AERONET measurements with a correlation coefficient of 0.83 and an root-mean-square error (RMSE) of 0.112. The hourly validation also showed stable statistical results. A time series comparison with ground-based observations from two AERONET sites (Beijing-CAMS and XiangHe) showed that our AHI AOD products have trends as those in MODIS DB AOD, but that the bias in Beijing-CAMS is positive and higher than that in XiangHe. This error and the slight overestimation may be caused by the single continental aerosol model assumption and not considering the Normalized Difference Vegetation Index (NDVI). Full article
(This article belongs to the Special Issue Aerosol Remote Sensing)
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Open AccessEditor’s ChoiceArticle The Combined ASTER MODIS Emissivity over Land (CAMEL) Part 2: Uncertainty and Validation
Remote Sens. 2018, 10(5), 664; https://doi.org/10.3390/rs10050664
Received: 28 February 2018 / Revised: 29 March 2018 / Accepted: 16 April 2018 / Published: 24 April 2018
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Abstract
Under the National Aeronautics and Space Administration’s (NASA) Making Earth System Data Records for Use in Research Environments (MEaSUREs) Land Surface Temperature and Emissivity project, a new global land surface emissivity dataset has been produced by the University of Wisconsin–Madison Space Science and [...] Read more.
Under the National Aeronautics and Space Administration’s (NASA) Making Earth System Data Records for Use in Research Environments (MEaSUREs) Land Surface Temperature and Emissivity project, a new global land surface emissivity dataset has been produced by the University of Wisconsin–Madison Space Science and Engineering Center and NASA’s Jet Propulsion Laboratory (JPL). This new dataset termed the Combined ASTER MODIS Emissivity over Land (CAMEL), is created by the merging of the UW–Madison MODIS baseline-fit emissivity dataset (UWIREMIS) and JPL’s ASTER Global Emissivity Dataset v4 (GEDv4). CAMEL consists of a monthly, 0.05° resolution emissivity for 13 hinge points within the 3.6–14.3 µm region and is extended to 417 infrared spectral channels using a principal component regression approach. An uncertainty product is provided for the 13 hinge point emissivities by combining temporal, spatial, and algorithm variability as part of a total uncertainty estimate. Part 1 of this paper series describes the methodology for creating the CAMEL emissivity product and the corresponding high spectral resolution algorithm. This paper, Part 2 of the series, details the methodology of the CAMEL uncertainty calculation and provides an assessment of the CAMEL emissivity product through comparisons with (1) ground site lab measurements; (2) a long-term Infrared Atmospheric Sounding Interferometer (IASI) emissivity dataset derived from 8 years of data; and (3) forward-modeled IASI brightness temperatures using the Radiative Transfer for TOVS (RTTOV) radiative transfer model. Global monthly results are shown for different seasons and International Geosphere-Biosphere Programme land classifications, and case study examples are shown for locations with different land surface types. Full article
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Open AccessEditor’s ChoiceArticle Vegetation Response to the 2012–2014 California Drought from GPS and Optical Measurements
Remote Sens. 2018, 10(4), 630; https://doi.org/10.3390/rs10040630
Received: 9 March 2018 / Revised: 4 April 2018 / Accepted: 14 April 2018 / Published: 19 April 2018
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Abstract
We compare microwave GPS and optical-based remote sensing observations of the vegetation response to a recent drought in California, USA. The microwave data are based on reflected GPS signals that were collected by a geodetic network. These data are sensitive to temporal variations [...] Read more.
We compare microwave GPS and optical-based remote sensing observations of the vegetation response to a recent drought in California, USA. The microwave data are based on reflected GPS signals that were collected by a geodetic network. These data are sensitive to temporal variations in vegetation water content and are made available via the Normalized Microwave Reflection Index (NMRI). NMRI data are complementary to information of plant greenness provided by the Normalized Difference Vegetation Index (NDVI). NMRI data from 146 sites in California are compared to collocated NDVI observations, over the interval of 2007–2016. This period includes a severe, three-year drought (2012–2014). We quantify the seasonal variations in vegetation state by calculating a series of phenology metrics at each site, using both NMRI and NDVI. We examine how the phenology metrics vary from year-to-year, as related to the observed fluctuations in accumulated precipitation. The amplitude of seasonal vegetation growth exhibits the greatest sensitivity to prior accumulated precipitation. Above-normal precipitation from 4 to 12 months before peak growth yields a stronger seasonal growth pulse, and vice versa. The amplitude of seasonal growth, as determined from NDVI, varies linearly with precipitation during dry years, but is largely insensitive to precipitation amount in years with above-normal precipitation. In contrast, the amplitude of seasonal growth from NMRI varies approximately linearly with precipitation across the entire range of conditions observed. The length of season is positively correlated with prior accumulated precipitation, more strongly with NDVI than NMRI. The recovery from drought was similar for a one-year (2007) and the more severe three-year drought (2012–2014). In both cases, the amplitude of growth returned to typical values in the first year with near-normal precipitation. Growing season length, only based on NDVI, was greatly reduced in 2014, the driest and final year of the three-year California drought. Full article
(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)
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Open AccessEditor’s ChoiceArticle Assessing the Spatial and Occupation Dynamics of the Brazilian Pasturelands Based on the Automated Classification of MODIS Images from 2000 to 2016
Remote Sens. 2018, 10(4), 606; https://doi.org/10.3390/rs10040606
Received: 18 March 2018 / Revised: 31 March 2018 / Accepted: 4 April 2018 / Published: 14 April 2018
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Abstract
The pasturelands areas of Brazil constitute an important asset for the country, as the main food source for the world’s largest commercial herd, representing the largest stock of open land in the country, occupying ~21% of the national territory. Understanding the spatio-temporal dynamics [...] Read more.
The pasturelands areas of Brazil constitute an important asset for the country, as the main food source for the world’s largest commercial herd, representing the largest stock of open land in the country, occupying ~21% of the national territory. Understanding the spatio-temporal dynamics of these areas is of fundamental importance for the goal of promoting improved territorial governance, emission mitigation and productivity gains. To this effect, this study mapped, through objective criteria and automatic classification methods (Random Forest) applied to MODIS (Moderate Resolution Imaging Spectroradiometer) images, the totality of the Brazilian pastures between 2000 and 2016. Based on 90 spectro-temporal metrics derived from the Red, NIR and SWIR1 bands and distinct vegetation indices, distributed between dry and wet seasons, a total of 17 pasture maps with an approximate overall accuracy of 80% were produced with cloud-computing (Google Earth Engine). During this period, the pasture area varied from ~152 (2000) to ~179 (2016) million hectares. This expansion pattern was consistent with the bovine herd variation and mostly occurred in the Amazon, which increased its total pasture area by ~15 million hectares between 2000 and 2005, while the Cerrado, Caatinga and Pantanal biomes showed an increase of ~8 million hectares in this same period. The Atlantic Forest was the only biome in which there was a retraction of pasture areas throughout this series. In general, the results of this study suggest the existence of two relevant moments for the Brazilian pasture land uses. The first, strongly supported by the opening of new grazing areas, prevailed between 2000 and 2005 and mostly occurred in the Deforestation Arc and in the Matopiba regions. From 2006 on, the total pasture area in Brazil showed a trend towards stabilization, indicating a slight intensification of livestock activity in recent years. Full article
(This article belongs to the collection Google Earth Engine Applications)
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Open AccessEditor’s ChoiceArticle Comparing Three Different Ground Based Laser Scanning Methods for Tree Stem Detection
Remote Sens. 2018, 10(4), 538; https://doi.org/10.3390/rs10040538
Received: 4 February 2018 / Revised: 28 March 2018 / Accepted: 30 March 2018 / Published: 31 March 2018
Cited by 3 | PDF Full-text (16263 KB) | HTML Full-text | XML Full-text | Supplementary Files
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 [...] Read more.
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 AccessEditor’s ChoiceArticle 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; https://doi.org/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 [...] Read more.
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 AccessEditor’s ChoiceArticle 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; https://doi.org/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 [...] Read more.
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 AccessEditor’s ChoiceArticle An Objective Assessment of Hyperspectral Indicators for the Detection of Buried Archaeological Relics
Remote Sens. 2018, 10(4), 500; https://doi.org/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 [...] Read more.
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 AccessEditor’s ChoiceArticle Evaluating Eight Global Reanalysis Products for Atmospheric Correction of Thermal Infrared Sensor—Application to Landsat 8 TIRS10 Data
Remote Sens. 2018, 10(3), 474; https://doi.org/10.3390/rs10030474
Received: 1 February 2018 / Revised: 12 March 2018 / Accepted: 14 March 2018 / Published: 19 March 2018
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Abstract
Global reanalysis products have been widely used for correcting the atmospheric effects of thermal infrared data, but their performances have not been comprehensively evaluated. In this paper, we evaluate eight global reanalysis products (NCEP/FNL; NCEP/DOE Reanalysis2; MERRA-3; MERRA-6; MERRA2-3; MERRA2-6; JRA-55; and ERA-Interim) [...] Read more.
Global reanalysis products have been widely used for correcting the atmospheric effects of thermal infrared data, but their performances have not been comprehensively evaluated. In this paper, we evaluate eight global reanalysis products (NCEP/FNL; NCEP/DOE Reanalysis2; MERRA-3; MERRA-6; MERRA2-3; MERRA2-6; JRA-55; and ERA-Interim) commonly used in the atmospheric correction of Landsat 8 TIRS10 data by referencing global radiosonde observations collected from 163 stations. The atmospheric parameters (atmospheric transmittance, upward radiance, and downward radiance) simulated with MERRA-6 and ERA-Interim were accurate than those simulated with other reanalysis products for different water vapor contents and surface elevations. When global reanalysis products were applied to retrieve land surface temperature (LST) from simulated Landsat 8 TIRS10 data, ERA-Interim and MERRA-6 were accurate than other reanalysis products. The overall LST biases and RMSEs between the retrieved LSTs and LSTs that were used to generate the top-of-atmosphere radiances were less than 0.2 K and 1.09 K, respectively. When eight reanalysis products were used to estimate LSTs from thirty-two Landsat 8 TIRS10 images covering the Heihe River basin in China, the various reanalysis products showed similar validation accuracies for LSTs with low water vapor contents. The biases ranged from 0.07 K to 0.24 K, and the STDs (RMSEs) ranged from 1.93 K (1.93 K) to 2.02 K (2.04 K). Considering the above evaluation results, MERRA-6 and ERA-Interim are recommended for thermal infrared data atmospheric corrections. Full article
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Open AccessEditor’s ChoiceArticle A New Algorithm for the On-Board Compression of Hyperspectral Images
Remote Sens. 2018, 10(3), 428; https://doi.org/10.3390/rs10030428
Received: 1 February 2018 / Revised: 27 February 2018 / Accepted: 6 March 2018 / Published: 9 March 2018
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Abstract
Hyperspectral sensors are able to provide information that is useful for many different applications. However, the huge amounts of data collected by these sensors are not exempt of drawbacks, especially in remote sensing environments where the hyperspectral images are collected on-board satellites and [...] Read more.
Hyperspectral sensors are able to provide information that is useful for many different applications. However, the huge amounts of data collected by these sensors are not exempt of drawbacks, especially in remote sensing environments where the hyperspectral images are collected on-board satellites and need to be transferred to the earth’s surface. In this situation, an efficient compression of the hyperspectral images is mandatory in order to save bandwidth and storage space. Lossless compression algorithms have been traditionally preferred, in order to preserve all the information present in the hyperspectral cube for scientific purposes, despite their limited compression ratio. Nevertheless, the increment in the data-rate of the new-generation sensors is making more critical the necessity of obtaining higher compression ratios, making it necessary to use lossy compression techniques. A new transform-based lossy compression algorithm, namely Lossy Compression Algorithm for Hyperspectral Image Systems (HyperLCA), is proposed in this manuscript. This compressor has been developed for achieving high compression ratios with a good compression performance at a reasonable computational burden. An extensive amount of experiments have been performed in order to evaluate the goodness of the proposed HyperLCA compressor using different calibrated and uncalibrated hyperspectral images from the AVIRIS and Hyperion sensors. The results provided by the proposed HyperLCA compressor have been evaluated and compared against those produced by the most relevant state-of-the-art compression solutions. The theoretical and experimental evidence indicates that the proposed algorithm represents an excellent option for lossy compressing hyperspectral images, especially for applications where the available computational resources are limited, such as on-board scenarios. Full article
(This article belongs to the Special Issue Hyperspectral Imaging and Applications)
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Open AccessEditor’s ChoiceArticle Assessment of Water Management Changes in the Italian Rice Paddies from 2000 to 2016 Using Satellite Data: A Contribution to Agro-Ecological Studies
Remote Sens. 2018, 10(3), 416; https://doi.org/10.3390/rs10030416
Received: 16 January 2018 / Revised: 16 February 2018 / Accepted: 6 March 2018 / Published: 8 March 2018
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Abstract
The intensive rice cultivation area in northwestern Italy hosts the largest surface of rice paddies in Europe, and it is valued as a substantial habitat for aquatic biodiversity, with the paddies acting as a surrogate for the lost natural wetlands. The extent of [...] Read more.
The intensive rice cultivation area in northwestern Italy hosts the largest surface of rice paddies in Europe, and it is valued as a substantial habitat for aquatic biodiversity, with the paddies acting as a surrogate for the lost natural wetlands. The extent of submerged paddies strictly depends on crop management practices: in this framework, the recent diffusion of rice seeding in dry conditions has led to a reduction of flooded surfaces during spring and could have contributed to the observed decline of the populations of some waterbird species that exploit rice fields as foraging habitat. In order to test the existence and magnitude of a decreasing trend in the extent of submerged rice paddies during the rice-sowing period, MODIS remotely-sensed data were used to estimate the extent of the average flooded surface and the proportion of flooded rice fields in the years 2000–2016 during the nesting period of waterbirds. A general reduction of flooded rice fields during the rice-sowing season was observed, averaging 0.86 ± 0.20 % per year (p-value < 0.01). Overall, the loss in submerged surface area during the sowing season reached 44 % of the original extent in 2016, with a peak of 78 % in the sub-districts to the east of the Ticino River. Results highlight the usefulness of remote sensing data and techniques to map and monitor water dynamics within rice cropping systems. These techniques could be of key importance to analyze the effects at the regional scale of the recent increase of dry-seeded rice cultivations on watershed recharge and water runoff and to interpret the decline of breeding waterbirds via a loss of foraging habitat. Full article
(This article belongs to the Special Issue Satellite Remote Sensing for Water Resources in a Changing Climate)
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Open AccessEditor’s ChoiceArticle Analysis of Secular Ground Motions in Istanbul from a Long-Term InSAR Time-Series (1992–2017)
Remote Sens. 2018, 10(3), 408; https://doi.org/10.3390/rs10030408
Received: 31 January 2018 / Revised: 22 February 2018 / Accepted: 1 March 2018 / Published: 6 March 2018
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Abstract
The identification and measurement of ground deformations in urban areas is of great importance for determining the vulnerable parts of the cities that are prone to geohazards, which is a crucial element of both sustainable urban planning and hazard mitigation. Interferometric synthetic aperture [...] Read more.
The identification and measurement of ground deformations in urban areas is of great importance for determining the vulnerable parts of the cities that are prone to geohazards, which is a crucial element of both sustainable urban planning and hazard mitigation. Interferometric synthetic aperture radar (InSAR) time series analysis is a very powerful tool for the operational mapping of ground deformation related to urban subsidence and landslide phenomena. With an analysis spanning almost 25 years of satellite radar observations, we compute an InSAR time series of data from multiple satellites (European Remote Sensing satellites ERS-1 and ERS-2, Envisat, Sentinel-1A, and its twin sensor Sentinel-1B) in order to investigate the spatial extent and rate of ground deformation in the megacity of Istanbul. By combining the various multi-track InSAR datasets (291 images in total) and analysing persistent scatterers (PS-InSAR), we present mean velocity maps of ground surface displacement in selected areas of Istanbul. We identify several sites along the terrestrial and coastal regions of Istanbul that underwent vertical ground subsidence at varying rates, from 5 ± 1.2 mm/yr to 15 ± 2.1 mm/yr. The results reveal that the most distinctive subsidence patterns are associated with both anthropogenic factors and relatively weak lithologies along the Haramirede valley in particular, where the observed subsidence is up to 10 ± 2 mm/yr. We show that subsidence has been occurring along the Ayamama river stream at a rate of up to 10 ± 1.8 mm/yr since 1992, and has also been slowing down over time following the restoration of the river and stream system. We also identify subsidence at a rate of 8 ± 1.2 mm/yr along the coastal region of Istanbul, which we associate with land reclamation, as well as a very localised subsidence at a rate of 15 ± 2.3 mm/yr starting in 2016 around one of the highest skyscrapers of Istanbul, which was built in 2010. Full article
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Open AccessEditor’s ChoiceArticle Impacts of Climate Change on Tibetan Lakes: Patterns and Processes
Remote Sens. 2018, 10(3), 358; https://doi.org/10.3390/rs10030358
Received: 16 December 2017 / Revised: 14 February 2018 / Accepted: 21 February 2018 / Published: 26 February 2018
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Abstract
High-altitude inland-drainage lakes on the Tibetan Plateau (TP), the earth’s third pole, are very sensitive to climate change. Tibetan lakes are important natural resources with important religious, historical, and cultural significance. However, the spatial patterns and processes controlling the impacts of climate and [...] Read more.
High-altitude inland-drainage lakes on the Tibetan Plateau (TP), the earth’s third pole, are very sensitive to climate change. Tibetan lakes are important natural resources with important religious, historical, and cultural significance. However, the spatial patterns and processes controlling the impacts of climate and associated changes on Tibetan lakes are largely unknown. This study used long time series and multi-temporal Landsat imagery to map the patterns of Tibetan lakes and glaciers in 1977, 1990, 2000, and 2014, and further to assess the spatiotemporal changes of lakes and glaciers in 17 TP watersheds between 1977 and 2014. Spatially variable changes in lake and glacier area as well as climatic factors were analyzed. We identified four modes of lake change in response to climate and associated changes. Lake expansion was predominantly attributed to increased precipitation and glacier melting, whereas lake shrinkage was a main consequence of a drier climate or permafrost degradation. These findings shed new light on the impacts of recent environmental changes on Tibetan lakes. They suggest that protecting these high-altitude lakes in the face of further environmental change will require spatially variable policies and management measures. Full article
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Open AccessEditor’s ChoiceArticle An Automatic Random Forest-OBIA Algorithm for Early Weed Mapping between and within Crop Rows Using UAV Imagery
Remote Sens. 2018, 10(2), 285; https://doi.org/10.3390/rs10020285
Received: 26 December 2017 / Revised: 2 February 2018 / Accepted: 8 February 2018 / Published: 12 February 2018
Cited by 21 | PDF Full-text (10424 KB) | HTML Full-text | XML Full-text
Abstract
Accurate and timely detection of weeds between and within crop rows in the early growth stage is considered one of the main challenges in site-specific weed management (SSWM). In this context, a robust and innovative automatic object-based image analysis (OBIA) algorithm was developed [...] Read more.
Accurate and timely detection of weeds between and within crop rows in the early growth stage is considered one of the main challenges in site-specific weed management (SSWM). In this context, a robust and innovative automatic object-based image analysis (OBIA) algorithm was developed on Unmanned Aerial Vehicle (UAV) images to design early post-emergence prescription maps. This novel algorithm makes the major contribution. The OBIA algorithm combined Digital Surface Models (DSMs), orthomosaics and machine learning techniques (Random Forest, RF). OBIA-based plant heights were accurately estimated and used as a feature in the automatic sample selection by the RF classifier; this was the second research contribution. RF randomly selected a class balanced training set, obtained the optimum features values and classified the image, requiring no manual training, making this procedure time-efficient and more accurate, since it removes errors due to a subjective manual task. The ability to discriminate weeds was significantly affected by the imagery spatial resolution and weed density, making the use of higher spatial resolution images more suitable. Finally, prescription maps for in-season post-emergence SSWM were created based on the weed maps—the third research contribution—which could help farmers in decision-making to optimize crop management by rationalization of the herbicide application. The short time involved in the process (image capture and analysis) would allow timely weed control during critical periods, crucial for preventing yield loss. Full article
(This article belongs to the Special Issue Remote Sensing from Unmanned Aerial Vehicles (UAVs))
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Open AccessEditor’s ChoiceArticle Validation of Carbon Monoxide Total Column Retrievals from SCIAMACHY Observations with NDACC/TCCON Ground-Based Measurements
Remote Sens. 2018, 10(2), 223; https://doi.org/10.3390/rs10020223
Received: 6 December 2017 / Revised: 19 January 2018 / Accepted: 25 January 2018 / Published: 1 February 2018
Cited by 2 | PDF Full-text (3494 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
The objective was to validate the carbon monoxide (CO) total column product inferred from Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) full-mission (2003–2011) short-wave infrared (SWIR) nadir observations using the Beer InfraRed Retrieval Algorithm (BIRRA). Globally distributed Network for the Detection of [...] Read more.
The objective was to validate the carbon monoxide (CO) total column product inferred from Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) full-mission (2003–2011) short-wave infrared (SWIR) nadir observations using the Beer InfraRed Retrieval Algorithm (BIRRA). Globally distributed Network for the Detection of Atmospheric Composition Change (NDACC) and Total Carbon Column Observing Network (TCCON) ground-based (g-b) measurements were used as a true reference. Weighted averages of SCIAMACHY CO observations within a circle around the g-b observing system were utilized to minimize effects due to spatial mismatch of space-based (s-b) and g-b observations, i.e., disagreements due to representation errors rather than instrument and/or algorithm deficiencies. In addition, temporal weighted averages were examined and then the unweighted (classical) approach was compared to the weighted (non-classical) method. The delivered distance-based filtered SCIAMACHY data were in better agreement with respect to CO averages as compared to square-shaped sampling areas throughout the year. Errors in individual SCIAMACHY retrievals have increased substantially since 2005. The global bias was determined to be in the order of 10 parts per billion in volume (ppbv) depending on the reference network and validation strategy used. The largest negative bias was found to occur in the northern mid-latitudes in Europe and North America, and was partly caused by insufficient a priori estimates of CO and cloud shielding. Furthermore, no significant trend was identified in the global bias throughout the mission. The global analysis of the CO columns retrieved by the BIRRA shows results that are largely consistent with similar investigations in previous works. Full article
(This article belongs to the Section Atmosphere Remote Sensing)
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Open AccessEditor’s ChoiceArticle Exploring Multispectral ALS Data for Tree Species Classification
Remote Sens. 2018, 10(2), 183; https://doi.org/10.3390/rs10020183
Received: 15 November 2017 / Revised: 19 January 2018 / Accepted: 23 January 2018 / Published: 26 January 2018
Cited by 5 | PDF Full-text (7250 KB) | HTML Full-text | XML Full-text
Abstract
Multispectral Airborne Laser Scanning (ALS) is a new technology and its output data have not been fully explored for tree species classification purposes. The objective of this study was to investigate what type of features from multispectral ALS data (wavelengths of 1550 nm, [...] Read more.
Multispectral Airborne Laser Scanning (ALS) is a new technology and its output data have not been fully explored for tree species classification purposes. The objective of this study was to investigate what type of features from multispectral ALS data (wavelengths of 1550 nm, 1064 nm and 532 nm) are best suited for tree species classification. Remote sensing data were gathered over hemi-boreal forest in southern Sweden (58°27′18.35″N, 13°39′8.03″E) on 21 July 2016. The field data consisted of 179 solitary trees from nine genera and ten species. Two new methods for feature extraction were tested and compared to features of height and intensity distributions. The features that were most important for tree species classification were intensity distribution features. Features from the upper part of the upper and outer parts of the crown were better for classification purposes than others. The best classification model was created using distribution features of both intensity and height in multispectral data, with a leave-one-out cross-validated accuracy of 76.5%. As a comparison, only structural features resulted in an highest accuracy of 43.0%. Picea abies and Pinus sylvestris had high user’s and producer’s accuracies and were not confused with any deciduous species. Tilia cordata was the deciduous species with a large sample that was most frequently confused with many other deciduous species. The results, although based on a small and special data set, suggest that multispectral ALS is a technology with great potential for tree species classification. Full article
(This article belongs to the Special Issue Lidar for Forest Science and Management)
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