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Remote Sens., Volume 12, Issue 7 (April-1 2020) – 177 articles

Cover Story (view full-size image): In the context of the climate and biodiversity crisis facing our planet, tropical forests playing a key role in global carbon flux and containing over half of Earth’s species are important to preserve. They are today threatened by deforestation but also by forest degradation, which is more difficult to study and to accurately measure. Remote sensing tools provide opportunities to monitor tropical moist forest degradation. Here, we performed a systematic review of studies on moist tropical forest degradation using remote sensing and fitting indicators of forest resilience to perturbations. The cover image was taken with a UAV in a central African moist forest just after logging. Holes in the canopy represent logging gaps and logging tracks allowing access to the timber resource.View this paper.
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Article
Experimental Calibration of the Overlap Factor for the Pulsed Atmospheric Lidar by Employing a Collocated Scheimpflug Lidar
Remote Sens. 2020, 12(7), 1227; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12071227 - 10 Apr 2020
Cited by 3 | Viewed by 1011
Abstract
Lidar techniques have been widely employed for atmospheric remote sensing during past decades. However, an important drawback of the traditional atmospheric pulsed lidar technique is the large blind range, typically hundreds of meters, due to incomplete overlap between the transmitter and the receiver, [...] Read more.
Lidar techniques have been widely employed for atmospheric remote sensing during past decades. However, an important drawback of the traditional atmospheric pulsed lidar technique is the large blind range, typically hundreds of meters, due to incomplete overlap between the transmitter and the receiver, etc. The large blind range prevents the successful retrieval of the near-ground aerosol profile, which is of great significance for both meteorological studies and environmental monitoring. In this work, we have demonstrated a new experimental approach to calibrate the overlap factor of the Mie-scattering pulsed lidar system by employing a collocated Scheimpflug lidar (SLidar) system. A calibration method of the overlap factor has been proposed and evaluated with lidar data measured in different ranges. The overlap factor, experimentally determined by the collocated SLidar system, has also been validated through horizontal comparison measurements. It has been found out that the median overlap factor evaluated by the proposed method agreed very well with the overlap factor obtained by the linear fitting approach with the assumption of homogeneous atmospheric conditions, and the discrepancy was generally less than 10%. Meanwhile, simultaneous measurements employing the SLidar system and the pulsed lidar system have been carried out to extend the measurement range of lidar techniques by gluing the lidar curves measured by the two systems. The profile of the aerosol extinction coefficient from the near surface at around 90 m up to 28 km can be well resolved in a slant measurement geometry during nighttime. This work has demonstrated a great potential of employing the SLidar technique for the calibration of the overlap factor and the extension of the measurement range for pulsed lidar techniques. Full article
(This article belongs to the Special Issue Advances in Atmospheric Remote Sensing with Lidar)
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Letter
Azimuth Ambiguity Suppression for Hybrid Polarimetric Synthetic Aperture Radar via Waveform Diversity
Remote Sens. 2020, 12(7), 1226; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12071226 - 10 Apr 2020
Cited by 4 | Viewed by 923
Abstract
Hybrid quadrature polarimetric (hybrid quad-pol) synthetic aperture radar (SAR) is proposed as a potential candidate for the full-polarimetric SAR mode. It allows balanced range ambiguity performance and simplified system structure. System based on hybrid-pol SAR mode can also implement the conventional quad-pol mode [...] Read more.
Hybrid quadrature polarimetric (hybrid quad-pol) synthetic aperture radar (SAR) is proposed as a potential candidate for the full-polarimetric SAR mode. It allows balanced range ambiguity performance and simplified system structure. System based on hybrid-pol SAR mode can also implement the conventional quad-pol mode and the compact-pol mode via few adjustments. However, the azimuth ambiguity performance in cross-pol channels is proved deteriorated in hybrid quad-pol mode due to the lopsided energy distribution of ambiguities. As are generally called “ghost” targets, azimuth ambiguities usually influence the recognition of the targets in SAR imaging. This letter describes how to remove the false targets that arise from azimuth ambiguities by means of waveform diversity and dual-focus post-processing (DFPP) technique. The proposed method exploits the feature of azimuth ambiguity and yields improved image quality in cross-pol channels with strong co-pol azimuth ambiguities removed in hybrid quad-pol SAR at a low system cost. Furthermore, it offers remarkable benefits for target detecting and recognition with strong false targets removed. Full article
(This article belongs to the Section Remote Sensing Letter)
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Article
Spatio-Temporal Analysis of Oil Spill Impact and Recovery Pattern of Coastal Vegetation and Wetland Using Multispectral Satellite Landsat 8-OLI Imagery and Machine Learning Models
Remote Sens. 2020, 12(7), 1225; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12071225 - 10 Apr 2020
Cited by 7 | Viewed by 2253
Abstract
Oil spills are a global phenomenon with impacts that cut across socio-economic, health, and environmental dimensions of the coastal ecosystem. However, comprehensive assessment of oil spill impacts and selection of appropriate remediation approaches have been restricted due to reliance on laboratory experiments which [...] Read more.
Oil spills are a global phenomenon with impacts that cut across socio-economic, health, and environmental dimensions of the coastal ecosystem. However, comprehensive assessment of oil spill impacts and selection of appropriate remediation approaches have been restricted due to reliance on laboratory experiments which offer limited area coverage and classification accuracy. Thus, this study utilizes multispectral Landsat 8-OLI remote sensing imagery and machine learning models to assess the impacts of oil spills on coastal vegetation and wetland and monitor the recovery pattern of polluted vegetation and wetland in a coastal city. The spatial extent of polluted areas was also precisely quantified for effective management of the coastal ecosystem. Using Johor, a coastal city in Malaysia as a case study, a total of 49 oil spill (ground truth) locations, 54 non-oil-spill locations and Landsat 8-OLI data were utilized for the study. The ground truth points were divided into 70% training and 30% validation parts for the classification of polluted vegetation and wetland. Sixteen different indices that have been used to monitor vegetation and wetland stress in literature were adopted for impact and recovery analysis. To eliminate similarities in spectral appearance of oil-spill-affected vegetation, wetland and other elements like burnt and dead vegetation, Support Vector Machine (SVM) and Random Forest (RF) machine learning models were used for the classification of polluted and nonpolluted vegetation and wetlands. Model optimization was performed using a random search method to improve the models’ performance, and accuracy assessments confirmed the effectiveness of the two machine learning models to identify, classify and quantify the area extent of oil pollution on coastal vegetation and wetland. Considering the harmonic mean (F1), overall accuracy (OA), User’s accuracy (UA), and producers’ accuracy (PA), both models have high accuracies. However, the RF outperformed the SVM with F1, OA, PA and UA values of 95.32%, 96.80%, 98.82% and 95.11%, respectively, while the SVM recorded accuracy values of F1 (80.83%), OA (92.87%), PA (95.18%) and UA (93.81%), respectively, highlighting 1205.98 hectares of polluted vegetation and 1205.98 hectares of polluted wetland. Analysis of the vegetation indices revealed that spilled oil had a significant impact on the vegetation and wetland, although steady recovery was observed between 2015-2018. This study concludes that Chlorophyll Vegetation Index, Modified Difference Water Index, Normalized Difference Vegetation Index and Green Chlorophyll Index vegetation indices are more sensitive for impact and recovery assessment of both vegetation and wetland, in addition to Modified Normalized Difference Vegetation Index for wetlands. Thus, remote sensing and Machine Learning models are essential tools capable of providing accurate information for coastal oil spill impact assessment and recovery analysis for appropriate remediation initiatives. Full article
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Article
Automated Feature-Based Down-Sampling Approaches for Fine Registration of Irregular Point Clouds
Remote Sens. 2020, 12(7), 1224; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12071224 - 10 Apr 2020
Cited by 4 | Viewed by 1251
Abstract
The integration of three-dimensional (3D) data defined in different coordinate systems requires the use of well-known registration procedures, which aim to align multiple models relative to a common reference frame. Depending on the achieved accuracy of the estimated transformation parameters, the existing registration [...] Read more.
The integration of three-dimensional (3D) data defined in different coordinate systems requires the use of well-known registration procedures, which aim to align multiple models relative to a common reference frame. Depending on the achieved accuracy of the estimated transformation parameters, the existing registration procedures are classified as either coarse or fine registration. Coarse registration is typically used to establish a rough alignment between the involved point clouds. Fine registration starts from coarsely aligned point clouds to achieve more precise alignment of the involved datasets. In practice, the acquired/derived point clouds from laser scanning and image-based dense matching techniques usually include an excessive number of points. Fine registration of huge datasets is time-consuming and sometimes difficult to accomplish in a reasonable timeframe. To address this challenge, this paper introduces two down-sampling approaches, which aim to improve the efficiency and accuracy of the iterative closest patch (ICPatch)-based fine registration. The first approach is based on a planar-based adaptive down-sampling strategy to remove redundant points in areas with high point density while keeping the points in lower density regions. The second approach starts with the derivation of the surface normals for the constituents of a given point cloud using their local neighborhoods, which are then represented on a Gaussian sphere. Down-sampling is ultimately achieved by removing the points from the detected peaks in the Gaussian sphere. Experiments were conducted using both simulated and real datasets to verify the feasibility of the proposed down-sampling approaches for providing reliable transformation parameters. Derived experimental results have demonstrated that for most of the registration cases, in which the points are obtained from various mapping platforms (e.g., mobile/static laser scanner or aerial photogrammetry), the first proposed down-sampling approach (i.e., adaptive down-sampling approach) was capable of exceeding the performance of the traditional approaches, which utilize either the original or randomly down-sampled points, in terms of providing smaller Root Mean Square Errors (RMSE) values and a faster convergence rate. However, for some challenging cases, in which the acquired point cloud only has limited geometric constraints, the Gaussian sphere-based approach was capable of providing superior performance as it preserves some critical points for the accurate estimation of the transformation parameters relating the involved point clouds. Full article
(This article belongs to the Special Issue Point Cloud Processing and Analysis in Remote Sensing)
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Article
Direct and Lagged Effects of Spring Phenology on Net Primary Productivity in the Alpine Grasslands on the Tibetan Plateau
Remote Sens. 2020, 12(7), 1223; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12071223 - 10 Apr 2020
Cited by 2 | Viewed by 1182
Abstract
As a key biotic factor, phenology exerts fundamental influences on ecosystem carbon sequestration. However, whether spring phenology affects the subsequent seasonal ecosystem productivity and the underlying resource limitation mechanism remains unclear for the alpine grasslands of the Tibetan Plateau (TP). In this study, [...] Read more.
As a key biotic factor, phenology exerts fundamental influences on ecosystem carbon sequestration. However, whether spring phenology affects the subsequent seasonal ecosystem productivity and the underlying resource limitation mechanism remains unclear for the alpine grasslands of the Tibetan Plateau (TP). In this study, we investigated the direct and lagged seasonal responses of net primary productivity (NPP) to the beginning of growing season (BGS) along a precipitation gradient by integrating field observations, remote sensing monitoring and ecosystem model simulations. The results revealed distinct response patterns of seasonal NPP to BGS. Specifically, the BGS showed a significant and negative correlation with spring NPP (R = −0.73, p < 0.01), as evidenced by the direct boosting effects of earlier BGS on spring NPP. Moreover, spring NPP was more responsive to BGS in areas with more annual precipitation. The boosting effects of earlier BGS on NPP tended to weaken in summer compared with that in spring. Sequentially, BGS exhibited stronger positive correlation with autumn NPP in areas with less annual precipitation, which suggested the enhanced lagged suppressing effects of earlier spring phenology on ecosystem carbon assimilation during the later growing season under aggravated water stress. Overall, the strengthened NPP in spring was offset by its decrement in autumn, resulting in no obvious relationship between BGS and annual NPP (R = −0.34, p > 0.05) for the entire grasslands on the TP. The findings of this study imply that the lagged effects of phenology on the ecosystem productivity during the subsequent seasons should not be neglected in the future studies. Full article
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Article
Object-Based Ensemble Learning for Pan-European Riverscape Units Mapping Based on Copernicus VHR and EU-DEM Data Fusion
Remote Sens. 2020, 12(7), 1222; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12071222 - 10 Apr 2020
Cited by 4 | Viewed by 1279
Abstract
Recent developments in the fields of geographical object-based image analysis (GEOBIA) and ensemble learning (EL) have led the way to the development of automated processing frameworks suitable to tackle large-scale problems. Mapping riverscape units has been recognized in fluvial remote sensing as an [...] Read more.
Recent developments in the fields of geographical object-based image analysis (GEOBIA) and ensemble learning (EL) have led the way to the development of automated processing frameworks suitable to tackle large-scale problems. Mapping riverscape units has been recognized in fluvial remote sensing as an important concern for understanding the macrodynamics of a river system and, if applied at large scales, it can be a powerful tool for monitoring purposes. In this study, the potentiality of GEOBIA and EL algorithms were tested for the mapping of key riverscape units along the main European river network. The Copernicus VHR Image Mosaic and the EU Digital Elevation Model (EU-DEM)—both made available through the Copernicus Land Monitoring Service—were integrated within a hierarchical object-based architecture. In a first step, the most well-known EL techniques (bagging, boosting and voting) were tested for the automatic classification of water, sediment bars, riparian vegetation and other floodplain units. Random forest was found to be the best-to-use classifier, and therefore was used in a second phase to classify the entire object-based river network. Finally, an independent validation was performed taking into consideration the polygon area within the accuracy assessment, hence improving the efficiency of the classification accuracy of the GEOBIA-derived map, both globally and by geographical zone. As a result, we automatically processed almost 2 million square kilometers at a spatial resolution of 2.5 meters, producing a riverscape-units map with a global overall accuracy of 0.915, and with per-class F1 accuracies in the range 0.79–0.97. The obtained results may allow for future studies aimed at quantitative, objective and continuous monitoring of river evolutions and fluvial geomorphological processes at the scale of Europe. Full article
(This article belongs to the Special Issue Object Based Image Analysis for Remote Sensing)
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Article
Global Trends in Evapotranspiration Dominated by Increases across Large Cropland Regions
Remote Sens. 2020, 12(7), 1221; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12071221 - 10 Apr 2020
Cited by 10 | Viewed by 1910
Abstract
Irrigated croplands require large annual water inputs and are critical to global food production. Actual evapotranspiration (AET) is a main index of water use in croplands, and several remote-sensing products have been developed to quantify AET at the global scale. In this study, [...] Read more.
Irrigated croplands require large annual water inputs and are critical to global food production. Actual evapotranspiration (AET) is a main index of water use in croplands, and several remote-sensing products have been developed to quantify AET at the global scale. In this study, we estimate global trends in actual AET, potential ET (PET), and precipitation rate (PP) utilizing the MODIS Evapotranspiration product (2001–2018) within the Google Earth Engine cloud-computing environment. We then introduce a new index based on a combination of AET, PET, and PP estimates—the evapotranspiration warning index (ETWI)—which we use to evaluate the sustainability of observed AET trends. We show that while AET has not considerably changed across global natural lands, it has significantly increased across global croplands (+14% ± 5%). The average ETWI for global croplands is −0.40 ± 0.25, which is largely driven by an extreme trend in AET, exceeding both PET and PP trends. Furthermore, the trends in water and energy limited areas demonstrate, on a global scale, while AET and PET do not have significant trends in both water and energy limited areas, the increasing trend of PP in energy-limited areas is more than water-limited areas. Averaging cropland ETWI trends at the country level further revealed nonsustainable trends in cropland water consumptions in Thailand, Brazil, and China. These regions were also found to experiencing some of the largest increases in net primary production (NPP) and solar-induced fluorescence (SIF), suggesting that recent increases in food production may be dependent on unsustainable water inputs. Globally, irrigated maize was found to be associated with nonsustainable AET trends relative to other crop types. We present an online open access application designed to enable near real-time monitoring and improve the understanding of global water consumption and availability. Full article
(This article belongs to the Special Issue Remote Sensing of Evapotranspiration (ET) II)
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Article
Combining Radar and Optical Imagery to Map Oil Palm Plantations in Sumatra, Indonesia, Using the Google Earth Engine
Remote Sens. 2020, 12(7), 1220; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12071220 - 10 Apr 2020
Cited by 7 | Viewed by 2112
Abstract
Monitoring the expansion of commodity crops in the tropics is crucial to safeguard forests for biodiversity and ecosystem services. Oil palm (Elaeis guineensis) is one such crop that is a major driver of deforestation in Southeast Asia. We evaluated the use [...] Read more.
Monitoring the expansion of commodity crops in the tropics is crucial to safeguard forests for biodiversity and ecosystem services. Oil palm (Elaeis guineensis) is one such crop that is a major driver of deforestation in Southeast Asia. We evaluated the use of a semi-automated approach with random forest as a classifier and combined optical and radar datasets to classify oil palm land-cover in 2015 in Sumatra, Indonesia, using Google Earth Engine. We compared our map with two existing remotely-sensed oil palm land-cover products that utilized visual and semi-automated approaches for the same year. We evaluated the accuracy of oil palm land-cover classification from optical (Landsat), radar (synthetic aperture radar (SAR)), and combined optical and radar satellite imagery (Combined). Combining Landsat and SAR data resulted in the highest overall classification accuracy (84%) and highest producer’s and user’s accuracy for oil palm classification (84% and 90%, respectively). The amount of oil palm land-cover in our Combined map was closer to official government statistics than the two existing land-cover products that used visual interpretation techniques. Our analysis of the extents of disagreement in oil palm land-cover indicated that our map had comparable accuracy to one of them and higher accuracy than the other. Our results demonstrate that a combination of optical and radar data outperforms the use of optical-only or radar-only datasets for oil palm classification and that our technique of preprocessing and classifying combined optical and radar data in the Google Earth Engine can be applied to accurately monitor oil-palm land-cover in Southeast Asia. Full article
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Article
Satellite Observations for Detecting and Forecasting Sea-Ice Conditions: A Summary of Advances Made in the SPICES Project by the EU’s Horizon 2020 Programme
Remote Sens. 2020, 12(7), 1214; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12071214 - 10 Apr 2020
Cited by 8 | Viewed by 2151
Abstract
The detection, monitoring, and forecasting of sea-ice conditions, including their extremes, is very important for ship navigation and offshore activities, and for monitoring of sea-ice processes and trends. We summarize here recent advances in the monitoring of sea-ice conditions and their extremes from [...] Read more.
The detection, monitoring, and forecasting of sea-ice conditions, including their extremes, is very important for ship navigation and offshore activities, and for monitoring of sea-ice processes and trends. We summarize here recent advances in the monitoring of sea-ice conditions and their extremes from satellite data as well as the development of sea-ice seasonal forecasting capabilities. Our results are the outcome of the three-year (2015–2018) SPICES (Space-borne Observations for Detecting and Forecasting Sea-Ice Cover Extremes) project funded by the EU’s Horizon 2020 programme. New SPICES sea-ice products include pancake ice thickness and degree of ice ridging based on synthetic aperture radar imagery, Arctic sea-ice volume and export derived from multisensor satellite data, and melt pond fraction and sea-ice concentration using Soil Moisture and Ocean Salinity (SMOS) radiometer data. Forecasts of July sea-ice conditions from initial conditions in May showed substantial improvement in some Arctic regions after adding sea-ice thickness (SIT) data to the model initialization. The SIT initialization also improved seasonal forecasts for years with extremely low summer sea-ice extent. New SPICES sea-ice products have a demonstrable level of maturity, and with a reasonable amount of further work they can be integrated into various operational sea-ice services. Full article
(This article belongs to the Special Issue Polar Sea Ice: Detection, Monitoring and Modeling)
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Article
Flexible Hierarchical Gaussian Mixture Model for High-Resolution Remote Sensing Image Segmentation
Remote Sens. 2020, 12(7), 1219; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12071219 - 09 Apr 2020
Cited by 2 | Viewed by 1119
Abstract
The Gaussian mixture model (GMM) plays an important role in image segmentation, but the difficulty of GMM for modeling asymmetric, heavy-tailed, or multimodal distributions of pixel intensities significantly limits its application. One effective way to improve the segmentation accuracy is to accurately model [...] Read more.
The Gaussian mixture model (GMM) plays an important role in image segmentation, but the difficulty of GMM for modeling asymmetric, heavy-tailed, or multimodal distributions of pixel intensities significantly limits its application. One effective way to improve the segmentation accuracy is to accurately model the statistical distributions of pixel intensities. In this study, an innovative high-resolution remote sensing image segmentation algorithm is proposed based on a flexible hierarchical GMM (HGMM). The components are first defined by the weighted sums of elements, in order to accurately model the complicated distributions of pixel intensities in object regions. The elements of components are defined by Gaussian distributions to model the distributions of pixel intensities in local regions of the object region. Following the Bayesian theorem, the segmentation model is then built by combining the HGMM and the prior distributions of parameters. Finally, a novel birth or death Markov chain Monte Carlo (BDMCMC) is designed to simulate the segmentation model, which can automatically determine the number of elements and flexibly model complex distributions of pixel intensities. Experiments were implemented on simulated and real high-resolution remote sensing images. The results show that the proposed algorithm is able to flexibly model the complicated distributions and accurately segment images. Full article
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Article
Drill-Core Mineral Abundance Estimation Using Hyperspectral and High-Resolution Mineralogical Data
Remote Sens. 2020, 12(7), 1218; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12071218 - 09 Apr 2020
Cited by 7 | Viewed by 1837
Abstract
Due to the extensive drilling performed every year in exploration campaigns for the discovery and evaluation of ore deposits, drill-core mapping is becoming an essential step. While valuable mineralogical information is extracted during core logging by on-site geologists, the process is time consuming [...] Read more.
Due to the extensive drilling performed every year in exploration campaigns for the discovery and evaluation of ore deposits, drill-core mapping is becoming an essential step. While valuable mineralogical information is extracted during core logging by on-site geologists, the process is time consuming and dependent on the observer and individual background. Hyperspectral short-wave infrared (SWIR) data is used in the mining industry as a tool to complement traditional logging techniques and to provide a rapid and non-invasive analytical method for mineralogical characterization. Additionally, Scanning Electron Microscopy-based image analyses using a Mineral Liberation Analyser (SEM-MLA) provide exhaustive high-resolution mineralogical maps, but can only be performed on small areas of the drill-cores. We propose to use machine learning algorithms to combine the two data types and upscale the quantitative SEM-MLA mineralogical data to drill-core scale. This way, quasi-quantitative maps over entire drill-core samples are obtained. Our upscaling approach increases result transparency and reproducibility by employing physical-based data acquisition (hyperspectral imaging) combined with mathematical models (machine learning). The procedure is tested on 5 drill-core samples with varying training data using random forests, support vector machines and neural network regression models. The obtained mineral abundance maps are further used for the extraction of mineralogical parameters such as mineral association. Full article
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Article
Vehicle and Vessel Detection on Satellite Imagery: A Comparative Study on Single-Shot Detectors
Remote Sens. 2020, 12(7), 1217; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12071217 - 09 Apr 2020
Cited by 6 | Viewed by 2864
Abstract
In this paper, we investigate the feasibility of automatic small object detection, such as vehicles and vessels, in satellite imagery with a spatial resolution between 0.3 and 0.5 m. The main challenges of this task are the small objects, as well as the [...] Read more.
In this paper, we investigate the feasibility of automatic small object detection, such as vehicles and vessels, in satellite imagery with a spatial resolution between 0.3 and 0.5 m. The main challenges of this task are the small objects, as well as the spread in object sizes, with objects ranging from 5 to a few hundred pixels in length. We first annotated 1500 km2, making sure to have equal amounts of land and water data. On top of this dataset we trained and evaluated four different single-shot object detection networks: YOLOV2, YOLOV3, D-YOLO and YOLT, adjusting the many hyperparameters to achieve maximal accuracy. We performed various experiments to better understand the performance and differences between the models. The best performing model, D-YOLO, reached an average precision of 60% for vehicles and 66% for vessels and can process an image of around 1 Gpx in 14 s. We conclude that these models, if properly tuned, can thus indeed be used to help speed up the workflows of satellite data analysts and to create even bigger datasets, making it possible to train even better models in the future. Full article
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Article
On-Board Wind Scatterometry
Remote Sens. 2020, 12(7), 1216; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12071216 - 09 Apr 2020
Cited by 2 | Viewed by 989
Abstract
Real-time (RT) ocean surface wind can make key improvements to disaster alarmingand safety of maritime navigation to avoid loss in property and human lives. Wind scatterometry is a well-acquainted way of obtaining good quality ocean surface winds, and it has been in application [...] Read more.
Real-time (RT) ocean surface wind can make key improvements to disaster alarmingand safety of maritime navigation to avoid loss in property and human lives. Wind scatterometry is a well-acquainted way of obtaining good quality ocean surface winds, and it has been in application for decades. Existing wind-obtaining chains employ ground stations for receiving observations and can, at best, provide products in around 30 minutes for limited regions. In recent years, a satellite information-obtaining and transmission network is the new trend of Earth observation. In this research, on-board wind retrieval environment and procedures, which are different from traditional wind-obtaining chains, are proposed. First, the establishment of the on-board environment is instructed. Structures of each module are provided. The ground simulation system is been established based on this. After that, existing observing and processing routines of wind scatterometry are described, and then an on-board processing chain proposed and described. Modifications to existing satellite-ground chains are highlighted. The proposed method is validated in Level 0 data from the Chinese–French Oceanic SATellite (CFOSAT). Experiments indicate that the proposed on-board processing procedure can provide comparable results to ground-processed wind products. The root-mean-square error (RMSE) of wind speed for a track of data used in the experiment was about 0.26 m/s, and it was about 0.8° for wind direction. By decreasing wind field result quality, calculation time can be lessened in the on-board environment. However, it is found that in the whole chain of on-board wind generation, the most time-consuming procedure is observation-obtaining. The proposed on-board processing method can achieve good wind accuracy while meeting RT applications with good processing time. This provides a good complement to existing on-board-observing-ground-processing chains for RT applications. Full article
(This article belongs to the Special Issue Tropical Cyclones Remote Sensing and Data Assimilation)
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Article
Assessment and Comparison of Broadcast Ionospheric Models: NTCM-BC, BDGIM, and Klobuchar
Remote Sens. 2020, 12(7), 1215; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12071215 - 09 Apr 2020
Cited by 4 | Viewed by 956
Abstract
For single-frequency Global Navigation Satellite Systems (GNSSs) users, ionospheric delay is the main error source affecting the accuracy of positioning. Applying a broadcast ionospheric correction model to mitigate the ionospheric delay is essential for meter-to-decimeter-level accuracy positioning. To provide support for real-time single-frequency [...] Read more.
For single-frequency Global Navigation Satellite Systems (GNSSs) users, ionospheric delay is the main error source affecting the accuracy of positioning. Applying a broadcast ionospheric correction model to mitigate the ionospheric delay is essential for meter-to-decimeter-level accuracy positioning. To provide support for real-time single-frequency operations, particularly in the China area, we assessed the performance of three broadcast ionospheric correction models, namely, the Neustrelitz total electron content (TEC) broadcast model (NTCM-BC), the BeiDou global broadcast ionospheric delay correction model (BDGIM), and the Klobuchar model. In this study, the broadcast coefficients of Klobuchar and BDGIM are obtained from the navigation data files directly. Two sets of coefficients of NTCM-BC for China and global areas are estimated. The slant total electron contents (STEC) data from more than 80 validation stations and the final vertical TEC (VTEC) data of the Center for Orbit Determination in Europe (CODE) are used as independent benchmarks for comparison. Compared to GPS STEC during the period of Day of Year (DOY) 101~199, 2019, the ionospheric correction ratio of NTCM-BC, BDGIM, and Klobuchar are 79.4%, 64.9%, and 57.7% in China, respectively. For the global area, the root-mean-square (RMS) errors of these three models are 3.67 TECU (1 TECU = 1016 electrons/m2), 5.48 TECU, and 8.92 TECU, respectively. Compared to CODE VTEC in the same period, NTCM-BC, BDGIM, and Klobuchar can correct 72.6%, 69.8%, and 61.7% of ionospheric delay, respectively. Hence, NTCM-BC is recommended for use as the broadcast ionospheric model for the new-generation BeiDou satellite navigation system (BDS) and its satellite-based augmentation system. Full article
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Article
Exploring the Potential of High-Resolution Satellite Imagery for the Detection of Soybean Sudden Death Syndrome
Remote Sens. 2020, 12(7), 1213; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12071213 - 09 Apr 2020
Cited by 7 | Viewed by 1702
Abstract
Sudden death syndrome (SDS) is one of the major yield-limiting soybean diseases in the Midwestern United States. Effective management for SDS requires accurate detection in soybean fields. Since traditional scouting methods are time-consuming, labor-intensive, and often destructive, alternative methods to monitor SDS in [...] Read more.
Sudden death syndrome (SDS) is one of the major yield-limiting soybean diseases in the Midwestern United States. Effective management for SDS requires accurate detection in soybean fields. Since traditional scouting methods are time-consuming, labor-intensive, and often destructive, alternative methods to monitor SDS in large soybean fields are needed. This study explores the potential of using high-resolution (3 m) PlanetScope satellite imagery for detection of SDS using the random forest classification algorithm. Image data from blue, green, red, and near-infrared (NIR) spectral bands, the calculated normalized difference vegetation index (NDVI), and crop rotation information were used to detect healthy and SDS-infected quadrats in a soybean field experiment with different rotation treatments, located in Boone County, Iowa. Datasets collected during the 2016, 2017, and 2018 soybean growing seasons were analyzed. The results indicate that spectral features, when combined with ground-based information, can detect areas in soybean plots that are at risk for disease, even before foliar symptoms develop. The classification of healthy and diseased soybean quadrats was >75% accurate and the area under the receiver operating characteristic curve (AUROC) was >70%. Our results indicate that high-resolution satellite imagery and random forest analyses have the potential to detect SDS in soybean fields, and that this approach may facilitate large-scale monitoring of SDS (and possibly other economically important soybean diseases). It may also be useful for guiding recommendations for site-specific management in current and future seasons. Full article
(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)
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Article
Attenuation Factor Estimation of Direct Normal Irradiance Combining Sky Camera Images and Mathematical Models in an Inter-Tropical Area
Remote Sens. 2020, 12(7), 1212; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12071212 - 09 Apr 2020
Cited by 3 | Viewed by 1016
Abstract
Nowadays, it is of great interest to know and forecast the solar energy resource that will be constantly available in order to optimize its use. The generation of electrical energy using CSP (concentrated solar power) plants is mostly affected by atmospheric changes. Therefore, [...] Read more.
Nowadays, it is of great interest to know and forecast the solar energy resource that will be constantly available in order to optimize its use. The generation of electrical energy using CSP (concentrated solar power) plants is mostly affected by atmospheric changes. Therefore, forecasting solar irradiance is essential for planning a plant’s operation. Solar irradiance/atmospheric (clouds) interaction studies using satellite and sky images can help to prepare plant operators for solar surface irradiance fluctuations. In this work, we present three methodologies that allow us to estimate direct normal irradiance (DNI). The study was carried out at the Solar Irradiance Observatory (SIO) at the Geophysics Institute (UNAM) in Mexico City using corresponding images obtained with a sky camera and starting from a clear sky model. The multiple linear regression and polynomial regression models as well as the neural networks model designed in the present study, were structured to work under all sky conditions (cloudy, partly cloudy and cloudless), obtaining estimation results with 82% certainty for all sky types. Full article
(This article belongs to the Special Issue Assessment of Renewable Energy Resources with Remote Sensing)
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Article
Dynamic Modal Identification of Telecommunication Towers Using Ground Based Radar Interferometry
Remote Sens. 2020, 12(7), 1211; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12071211 - 09 Apr 2020
Cited by 4 | Viewed by 970
Abstract
This work presents a methodology to monitor the dynamic behaviour of tall metallic towers based on ground-based radar interferometry, and apply it to the case of telecommunication towers. Ground-based radar displacement measurements of metallic towers are acquired without installing any Corner Reflector (CR) [...] Read more.
This work presents a methodology to monitor the dynamic behaviour of tall metallic towers based on ground-based radar interferometry, and apply it to the case of telecommunication towers. Ground-based radar displacement measurements of metallic towers are acquired without installing any Corner Reflector (CR) on the structure. Each structural element of the tower is identified based on its range distance with respect to the radar. The interferometric processing of a time series of radar profiles is used to measure the vibration frequencies of each structural element and estimate the amplitude of its oscillation. A methodology is described to visualize the results and provide a useful tool for the real-time analysis of the dynamic behaviour of metallic towers. Full article
(This article belongs to the Special Issue Remote Sensing for Monitoring Infrastructure Deformation)
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Article
A Random Forest Modelling Procedure for a Multi-Sensor Assessment of Tree Species Diversity
Remote Sens. 2020, 12(7), 1210; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12071210 - 09 Apr 2020
Cited by 3 | Viewed by 1430
Abstract
Earth observation data can provide important information for tree species diversity mapping and monitoring. The relatively recent advances in remote sensing data characteristics and processing systems elevate the potential of satellite imagery for providing accurate, timely, consistent, and robust spatially explicit estimates of [...] Read more.
Earth observation data can provide important information for tree species diversity mapping and monitoring. The relatively recent advances in remote sensing data characteristics and processing systems elevate the potential of satellite imagery for providing accurate, timely, consistent, and robust spatially explicit estimates of tree species diversity over forest ecosystems. This study was conducted in Northern Pindos National Park, the largest terrestrial park in Greece and aimed to assess the potential of four satellite sensors with different instrumental characteristics, for the estimation of tree diversity. Through field measurements, we originally quantified two diversity indices, namely the Shannon diversity index (H’) and Simpson’s diversity (D1). Random forest regression models were developed for associating remotely sensed spectral signal with tree species diversity within the area. The models generated from the use of the WorldView-2 image were the most accurate with a coefficient of determination of up to 0.44 for H’ and 0.37 for D1. The Sentinel-2 -based models of tree species diversity performed slightly worse, but were better than the Landsat-8 and RapidEye models. The coefficient of variation quantifying internal variability of spectral values within each plot provided little or no usage for improving the modelling accuracy. Our results suggest that very-high-spatial-resolution imagery provides the most important information for the assessment of tree species diversity in heterogeneous Mediterranean ecosystems. Full article
(This article belongs to the Special Issue Remote Sensing for Biodiversity Mapping and Monitoring)
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Article
Global Glacial Isostatic Adjustment Constrained by GPS Measurements: Spherical Harmonic Analyses of Uplifts and Geopotential Variations
Remote Sens. 2020, 12(7), 1209; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12071209 - 09 Apr 2020
Cited by 2 | Viewed by 1117
Abstract
In addition to studies of sea level change and mantle rheology, reliable Glacial Isostatic Adjustment (GIA) models are necessary as a background model to correct the widely used Gravity Recovery and Climate Experiment (GRACE) monthly gravity solutions to determine subsecular, nonviscous variations. Based [...] Read more.
In addition to studies of sea level change and mantle rheology, reliable Glacial Isostatic Adjustment (GIA) models are necessary as a background model to correct the widely used Gravity Recovery and Climate Experiment (GRACE) monthly gravity solutions to determine subsecular, nonviscous variations. Based on spherical harmonic analyses, we developed a method using degree-dependent weighting to assimilate the Global Positioning System (GPS) derived crustal uplift rates into GIA model predictions, in which the good global pattern of GIA model predictions and better local resolution of GPS solutions are both retained. Some systematic errors in global GPS uplift rates were also corrected during the spherical harmonic analyses. Further, we used the refined GIA uplift rates to infer the GIA-induced rates of Stokes coefficients (complete to degree/order 120) relying on the accurate relationship between GIA vertical surface deformation and gravitational potential changes. The results show notable improvements relative to GIA model outputs, and may serve as a GIA-correction model for GRACE time-variable gravity data. Full article
(This article belongs to the Section Environmental Remote Sensing)
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Article
Untangling the Incoherent and Coherent Scattering Components in GNSS-R and Novel Applications
Remote Sens. 2020, 12(7), 1208; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12071208 - 09 Apr 2020
Cited by 7 | Viewed by 1342
Abstract
As opposed to monostatic radars where incoherent backscattering dominates, in bistatic radars, such as Global Navigation Satellite Systems Reflectometry (GNSS-R), the forward scattered signals exhibit both an incoherent and a coherent component. Current models assume that either one or the other are dominant, [...] Read more.
As opposed to monostatic radars where incoherent backscattering dominates, in bistatic radars, such as Global Navigation Satellite Systems Reflectometry (GNSS-R), the forward scattered signals exhibit both an incoherent and a coherent component. Current models assume that either one or the other are dominant, and the calibration and geophysical parameter retrieval (e.g., wind speed, soil moisture, etc.) are developed accordingly. Even the presence of the coherent component of a GNSS reflected signal itself has been a matter of discussion in the last years. In this work, a method developed to separate the leakage of the direct signal in the reflected one is applied to a data set of GNSS-R signals collected over the ocean by the Microwave Interferometer Reflectometer (MIR) instrument, an airborne dual-band (L1/E1 and L5/E5a), multi-constellation (GPS and Galileo) GNSS-R instrument with two 19-elements antenna arrays with 4 beam-steered each. The presented results demonstrate the feasibility of the proposed technique to untangle the coherent and incoherent components from the total power waveform in GNSS reflected signals. This technique allows the processing of these components separately, which increases the calibration accuracy (as today both are mixed and processed together), allowing higher resolution applications since the spatial resolution of the coherent component is determined by the size of the first Fresnel zone (300–500 meters from a LEO satellite), and not by the size of the glistening zone (25 km from a LEO satellite). The identification of the coherent component enhances also the location of the specular reflection point by determining the peak maximum from this coherent component rather than the point of maximum derivative of the incoherent one, which is normally noisy and it is blurred by all the glistening zone contributions. Full article
(This article belongs to the Special Issue Applications of GNSS Reflectometry for Earth Observation)
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Article
Gap Fill of Land Surface Temperature and Reflectance Products in Landsat Analysis Ready Data
Remote Sens. 2020, 12(7), 1192; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12071192 - 09 Apr 2020
Cited by 1 | Viewed by 1351
Abstract
The recently released Landsat analysis ready data (ARD) over the United States provides the opportunity to investigate landscape dynamics using dense time series observations at 30-m resolution. However, the dataset often contains data gaps (or missing data) because of cloud contamination or data [...] Read more.
The recently released Landsat analysis ready data (ARD) over the United States provides the opportunity to investigate landscape dynamics using dense time series observations at 30-m resolution. However, the dataset often contains data gaps (or missing data) because of cloud contamination or data acquisition strategy, which result in different capabilities for seasonality modeling. We present a new algorithm that focuses on data gap filling using clear observations from orbit overlap regions. Multiple linear regression models were established for each pixel time series to estimate stable predictions and uncertainties. The model’s training data came from stratified random samples based on the time series similarity between the pixel and data from the overlap regions. The algorithm was first evaluated using four tiles (5000 × 5000 30-m pixels for each tile) from 2018 land surface temperature data (LST) in Atlanta, Georgia. The accuracy was assessed using randomly masked clear observations with an average Root Mean Square Error (RMSE) of 3.88 and an average bias of −0.37, which were comparable to the product accuracy. We also applied the method on ARD surface reflectance bands at Fairbanks, Alaska. The accuracy assessment suggested a majority RMSE of less than 0.04 and a bias of less than 0.0023. The gap-filled time series can be of help for reliable seasonal modeling and reducing artifacts related to data availability. This approach can also be applied to other datasets, vegetation indexes, or spectral reflectance bands of other sensors. Full article
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Article
Assessing the Effect of Real Spatial Resolution of In Situ UAV Multispectral Images on Seedling Rapeseed Growth Monitoring
Remote Sens. 2020, 12(7), 1207; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12071207 - 08 Apr 2020
Cited by 4 | Viewed by 1441
Abstract
The spatial resolution of in situ unmanned aerial vehicle (UAV) multispectral images has a crucial effect on crop growth monitoring and image acquisition efficiency. However, existing studies about optimal spatial resolution for crop monitoring are mainly based on resampled images. Therefore, the resampled [...] Read more.
The spatial resolution of in situ unmanned aerial vehicle (UAV) multispectral images has a crucial effect on crop growth monitoring and image acquisition efficiency. However, existing studies about optimal spatial resolution for crop monitoring are mainly based on resampled images. Therefore, the resampled spatial resolution in these studies might not be applicable to in situ UAV images. In order to obtain optimal spatial resolution of in situ UAV multispectral images for crop growth monitoring, a RedEdge Micasense 3 camera was installed onto a DJI M600 UAV flying at different heights of 22, 29, 44, 88, and 176m to capture images of seedling rapeseed with ground sampling distances (GSD) of 1.35, 1.69, 2.61, 5.73, and 11.61 cm, respectively. Meanwhile, the normalized difference vegetation index (NDVI) measured by a GreenSeeker (GS-NDVI) and leaf area index (LAI) were collected to evaluate the performance of nine vegetation indices (VIs) and VI*plant height (PH) at different GSDs for rapeseed growth monitoring. The results showed that the normalized difference red edge index (NDRE) had a better performance for estimating GS-NDVI (R2 = 0.812) and LAI (R2 = 0.717), compared with other VIs. Moreover, when GSD was less than 2.61 cm, the NDRE*PH derived from in situ UAV images outperformed the NDRE for LAI estimation (R2 = 0.757). At oversized GSD (≥5.73 cm), imprecise PH information and a large heterogeneity within the pixel (revealed by semi-variogram analysis) resulted in a large random error for LAI estimation by NDRE*PH. Furthermore, the image collection and processing time at 1.35 cm GSD was about three times as long as that at 2.61 cm. The result of this study suggested that NDRE*PH from UAV multispectral images with a spatial resolution around 2.61 cm could be a preferential selection for seedling rapeseed growth monitoring, while NDRE alone might have a better performance for low spatial resolution images. Full article
(This article belongs to the Special Issue Remote Sensing for Precision Agriculture)
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Article
Hyperspectral Estimation of Soil Organic Matter Content using Different Spectral Preprocessing Techniques and PLSR Method
Remote Sens. 2020, 12(7), 1206; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12071206 - 08 Apr 2020
Cited by 11 | Viewed by 1523
Abstract
Soil organic matter (SOM) is the main source of soil nutrients, which are essential for the growth and development of agricultural crops. Hyperspectral remote sensing is one of the most efficient ways of estimating the SOM content. Visible, near infrared, and mid-infrared reflectance [...] Read more.
Soil organic matter (SOM) is the main source of soil nutrients, which are essential for the growth and development of agricultural crops. Hyperspectral remote sensing is one of the most efficient ways of estimating the SOM content. Visible, near infrared, and mid-infrared reflectance spectroscopy, combined with the partial least squares regression (PLSR) method is considered to be an effective way of determining soil properties. In this study, we used 54 different spectral pretreatments to preprocess soil spectral data. These spectral pretreatments were composed of three denoising methods, six data transformations, and three dimensionality reduction methods. The three denoising methods included no denoising (ND), Savitzky–Golay denoising (SGD), and wavelet packet denoising (WPD). The six data transformations included original spectral data, R; reciprocal, 1/R; logarithmic, log(R); reciprocal logarithmic, log(1/R); first derivative, R’; and first derivative of reciprocal, (1/R)’. The three dimensionality reduction methods included no dimensionality reduction (NDR), sensitive waveband dimensionality reduction (SWDR), and principal component analysis (PCA) dimensionality reduction (PCADR). The processed spectra were then employed to construct PLSR models for predicting the SOM content. The main results were as follows—(1) the wavelet packet denoising (WPD)-R’ and WPD-(1/R)’ data showed stronger correlations with the SOM content. Furthermore, these methods could effectively limit the correlation between the adjacent bands and, thus, prevent “overfitting”. (2) Of the 54 pretreatments investigated, WPD-(1/R)’-PCADR yielded the model with the highest accuracy and stability. (3) For the same denoising method and spectral transformation data, the accuracy of the SOM content estimation model based on SWDR was higher than that of the model based on NDR. Furthermore, the accuracy in the case of PCADR was higher than that for SWDR. (4) Dimensionality reduction was effective in preventing data overfitting. (5) The quality of the spectral data could be improved and the accuracy of the SOM content estimation model could be enhanced effectively, by using some appropriate preprocessing methods (one combining WPD and PCADR in this study). Full article
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Article
Extracting Agricultural Fields from Remote Sensing Imagery Using Graph-Based Growing Contours
Remote Sens. 2020, 12(7), 1205; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12071205 - 08 Apr 2020
Cited by 6 | Viewed by 1410
Abstract
Knowledge of the location and extent of agricultural fields is required for many applications, including agricultural statistics, environmental monitoring, and administrative policies. Furthermore, many mapping applications, such as object-based classification, crop type distinction, or large-scale yield prediction benefit significantly from the accurate delineation [...] Read more.
Knowledge of the location and extent of agricultural fields is required for many applications, including agricultural statistics, environmental monitoring, and administrative policies. Furthermore, many mapping applications, such as object-based classification, crop type distinction, or large-scale yield prediction benefit significantly from the accurate delineation of fields. Still, most existing field maps and observation systems rely on historic administrative maps or labor-intensive field campaigns. These are often expensive to maintain and quickly become outdated, especially in regions of frequently changing agricultural patterns. However, exploiting openly available remote sensing imagery (e.g., from the European Union’s Copernicus programme) may allow for frequent and efficient field mapping with minimal human interaction. We present a new approach to extracting agricultural fields at the sub-pixel level. It consists of boundary detection and a field polygon extraction step based on a newly developed, modified version of the growing snakes active contours model we refer to as graph-based growing contours. This technique is capable of extracting complex networks of boundaries present in agricultural landscapes, and is largely automatic with little supervision required. The whole detection and extraction process is designed to work independently of sensor type, resolution, or wavelength. As a test case, we applied the method to two regions of interest in a study area in the northern Germany using multi-temporal Sentinel-2 imagery. Extracted fields were compared visually and quantitatively to ground reference data. The technique proved reliable in producing polygons closely matching reference data, both in terms of boundary location and statistical proxies such as median field size and total acreage. Full article
(This article belongs to the Special Issue Remote Sensing for Crop Mapping)
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Article
Super-Resolution for Hyperspectral Remote Sensing Images Based on the 3D Attention-SRGAN Network
Remote Sens. 2020, 12(7), 1204; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12071204 - 08 Apr 2020
Cited by 2 | Viewed by 1387
Abstract
Hyperspectral remote sensing images (HSIs) have a higher spectral resolution compared to multispectral remote sensing images, providing the possibility for more reasonable and effective analysis and processing of spectral data. However, rich spectral information usually comes at the expense of low spatial resolution [...] Read more.
Hyperspectral remote sensing images (HSIs) have a higher spectral resolution compared to multispectral remote sensing images, providing the possibility for more reasonable and effective analysis and processing of spectral data. However, rich spectral information usually comes at the expense of low spatial resolution owing to the physical limitations of sensors, which brings difficulties for identifying and analyzing targets in HSIs. In the super-resolution (SR) field, many methods have been focusing on the restoration of the spatial information while ignoring the spectral aspect. To better restore the spectral information in the HSI SR field, a novel super-resolution (SR) method was proposed in this study. Firstly, we innovatively used three-dimensional (3D) convolution based on SRGAN (Super-Resolution Generative Adversarial Network) structure to not only exploit the spatial features but also preserve spectral properties in the process of SR. Moreover, we used the attention mechanism to deal with the multiply features from the 3D convolution layers, and we enhanced the output of our model by improving the content of the generator’s loss function. The experimental results indicate that the 3DASRGAN (3D Attention-based Super-Resolution Generative Adversarial Network) is both visually quantitatively better than the comparison methods, which proves that the 3DASRGAN model can reconstruct high-resolution HSIs with high efficiency. Full article
(This article belongs to the Section Remote Sensing Image Processing)
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Article
Automated Mapping of Antarctic Supraglacial Lakes Using a Machine Learning Approach
Remote Sens. 2020, 12(7), 1203; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12071203 - 08 Apr 2020
Cited by 9 | Viewed by 1793
Abstract
Supraglacial lakes can have considerable impact on ice sheet mass balance and global sea-level-rise through ice shelf fracturing and subsequent glacier speedup. In Antarctica, the distribution and temporal development of supraglacial lakes as well as their potential contribution to increased ice mass loss [...] Read more.
Supraglacial lakes can have considerable impact on ice sheet mass balance and global sea-level-rise through ice shelf fracturing and subsequent glacier speedup. In Antarctica, the distribution and temporal development of supraglacial lakes as well as their potential contribution to increased ice mass loss remains largely unknown, requiring a detailed mapping of the Antarctic surface hydrological network. In this study, we employ a Machine Learning algorithm trained on Sentinel-2 and auxiliary TanDEM-X topographic data for automated mapping of Antarctic supraglacial lakes. To ensure the spatio-temporal transferability of our method, a Random Forest was trained on 14 training regions and applied over eight spatially independent test regions distributed across the whole Antarctic continent. In addition, we employed our workflow for large-scale application over Amery Ice Shelf where we calculated interannual supraglacial lake dynamics between 2017 and 2020 at full ice shelf coverage. To validate our supraglacial lake detection algorithm, we randomly created point samples over our classification results and compared them to Sentinel-2 imagery. The point comparisons were evaluated using a confusion matrix for calculation of selected accuracy metrics. Our analysis revealed wide-spread supraglacial lake occurrence in all three Antarctic regions. For the first time, we identified supraglacial meltwater features on Abbott, Hull and Cosgrove Ice Shelves in West Antarctica as well as for the entire Amery Ice Shelf for years 2017–2020. Over Amery Ice Shelf, maximum lake extent varied strongly between the years with the 2019 melt season characterized by the largest areal coverage of supraglacial lakes (~763 km2). The accuracy assessment over the test regions revealed an average Kappa coefficient of 0.86 where the largest value of Kappa reached 0.98 over George VI Ice Shelf. Future developments will involve the generation of circum-Antarctic supraglacial lake mapping products as well as their use for further methodological developments using Sentinel-1 SAR data in order to characterize intraannual supraglacial meltwater dynamics also during polar night and independent of meteorological conditions. In summary, the implementation of the Random Forest classifier enabled the development of the first automated mapping method applied to Sentinel-2 data distributed across all three Antarctic regions. Full article
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Article
OCO-2 Solar-Induced Chlorophyll Fluorescence Variability across Ecoregions of the Amazon Basin and the Extreme Drought Effects of El Niño (2015–2016)
Remote Sens. 2020, 12(7), 1202; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12071202 - 08 Apr 2020
Cited by 3 | Viewed by 2157
Abstract
Amazonian ecosystems are major biodiversity hotspots and carbon sinks that may lose species to extinction and become carbon sources due to extreme dry or warm conditions. We investigated the seasonal patterns of high-resolution solar-induced chlorophyll fluorescence (SIF) measured by the satellite Orbiting Carbon [...] Read more.
Amazonian ecosystems are major biodiversity hotspots and carbon sinks that may lose species to extinction and become carbon sources due to extreme dry or warm conditions. We investigated the seasonal patterns of high-resolution solar-induced chlorophyll fluorescence (SIF) measured by the satellite Orbiting Carbon Observatory-2 (OCO-2) across the Amazonian ecoregions to assess the area´s phenology and extreme drought vulnerability. SIF is an indicator of the photosynthetic activity of chlorophyll molecules and is assumed to be directly related to gross primary production (GPP). We analyzed SIF variability in the Amazon basin during the period between September 2014 and December 2018. In particular, we focused on the SIF drought response under the extreme drought period during the strong El Niño in 2015–2016, as well as the 6-month drought peak period. During the drought´s peak months, the SIF decreased and increased with different intensities across the ecoregions of the Amazonian moist broadleaf forest (MBF) biome. Under a high temperature, a high vapor pressure deficit, and extreme drought conditions, the SIF presented differences from −31.1% to +17.6%. Such chlorophyll activity variations have been observed in plant-level measurements of active fluorescence in plants undergoing physiological responses to water or heat stress. Thus, it is plausible that the SIF variations in the ecoregions’ ecosystems occurred as a result of water and heat stress, and arguably because of drought-driven vegetation mortality and collateral effects in their species composition and community structures. The SIF responses to drought at the ecoregional scale indicate that there are different levels of resilience to drought across MBF ecosystems that the currently used climate- and biome-region scales do not capture. Finally, we identified monthly SIF values of 32 ecoregions, including non-MBF biomes, which may give the first insights into the photosynthetic activity dynamics of Amazonian ecoregions. Full article
(This article belongs to the Section Forest Remote Sensing)
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Article
Landsat Images Classification Algorithm (LICA) to Automatically Extract Land Cover Information in Google Earth Engine Environment
Remote Sens. 2020, 12(7), 1201; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12071201 - 08 Apr 2020
Cited by 7 | Viewed by 2033
Abstract
Remote sensing has been recognized as the main technique to extract land cover/land use (LC/LU) data, required to address many environmental issues. Therefore, over the years, many approaches have been introduced and explored to optimize the resultant classification maps. Particularly, index-based methods have [...] Read more.
Remote sensing has been recognized as the main technique to extract land cover/land use (LC/LU) data, required to address many environmental issues. Therefore, over the years, many approaches have been introduced and explored to optimize the resultant classification maps. Particularly, index-based methods have highlighted its efficiency and effectiveness in detecting LC/LU in a multitemporal and multisensors analysis perspective. Nevertheless, the developed indices are suitable to extract a specific class but not to completely classify the whole area. In this study, a new Landsat Images Classification Algorithm (LICA) is proposed to automatically detect land cover (LC) information using satellite open data provided by different Landsat missions in order to perform a multitemporal and multisensors analysis. All the steps of the proposed method were implemented within Google Earth Engine (GEE) to automatize the procedure, manage geospatial big data, and quickly extract land cover information. The algorithm was tested on the experimental site of Siponto, a historic municipality located in Apulia Region (Southern Italy) using 12 radiometrically and atmospherically corrected satellite images collected from Landsat archive (four images, one for each season, were selected from Landsat 5, 7, and 8, respectively). Those images were initially used to assess the performance of 82 traditional spectral indices. Since their classification accuracy and the number of identified LC categories were not satisfying, an analysis of the different spectral signatures existing in the study area was also performed, generating a new algorithm based on the sequential application of two new indices (SwirTirRed (STRed) index and SwiRed index). The former was based on the integration of shortwave infrared (SWIR), thermal infrared (TIR), and red bands, whereas the latter featured a combination of SWIR and red bands. The performance of LICA was preferable to those of conventional indices both in terms of accuracy and extracted classes number (water, dense and sparse vegetation, mining areas, built-up areas versus water, and dense and sparse vegetation). GEE platform allowed us to go beyond desktop system limitations, reducing acquisition and processing times for geospatial big data. Full article
(This article belongs to the Special Issue Multitemporal Land Cover and Land Use Mapping)
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Article
Groundwater Potential Mapping Using Remote Sensing and GIS-Based Machine Learning Techniques
Remote Sens. 2020, 12(7), 1200; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12071200 - 08 Apr 2020
Cited by 19 | Viewed by 2262
Abstract
Adequate groundwater development for the rural population is essential because groundwater is an important source of drinking water and agricultural water. In this study, ensemble models of decision tree-based machine learning algorithms were used with geographic information system (GIS) to map and test [...] Read more.
Adequate groundwater development for the rural population is essential because groundwater is an important source of drinking water and agricultural water. In this study, ensemble models of decision tree-based machine learning algorithms were used with geographic information system (GIS) to map and test groundwater yield potential in Yangpyeong-gun, South Korea. Groundwater control factors derived from remote sensing data were used for mapping, including nine topographic factors, two hydrological factors, forest type, soil material, land use, and two geological factors. A total of 53 well locations with both specific capacity (SPC) data and transmissivity (T) data were selected and randomly divided into two classes for model training (70%) and testing (30%). First, the frequency ratio (FR) was calculated for SPC and T, and then the boosted classification tree (BCT) method of the machine learning model was applied. In addition, an ensemble model, FR-BCT, was applied to generate and compare groundwater potential maps. Model performance was evaluated using the receiver operating characteristic (ROC) method. To test the model, the area under the ROC curve was calculated; the curve for the predicted dataset of SPC showed values of 80.48% and 87.75% for the BCT and FR-BCT models, respectively. The accuracy rates from T were 72.27% and 81.49% for the BCT and FR-BCT models, respectively. Both the BCT and FR-BCT models measured the contributions of individual groundwater control factors, which showed that soil was the most influential factor. The machine learning techniques used in this study showed effective modeling of groundwater potential in areas where data are relatively scarce. The results of this study may be used for sustainable development of groundwater resources by identifying areas of high groundwater potential. Full article
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Article
Identification of Apple Orchard Planting Year Based on Spatiotemporally Fused Satellite Images and Clustering Analysis of Foliage Phenophase
Remote Sens. 2020, 12(7), 1199; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12071199 - 08 Apr 2020
Cited by 7 | Viewed by 1579
Abstract
The planting year of apple orchard not only determines the fruit output but also provides information for the governmental management of the fruit industry. However, considering that different orchards use different management and cultivation methods, this may result in some trees having similar [...] Read more.
The planting year of apple orchard not only determines the fruit output but also provides information for the governmental management of the fruit industry. However, considering that different orchards use different management and cultivation methods, this may result in some trees having similar outlines but different planting years, and it is, therefore, difficult to effectively determine the actual planting year based on textural or structural characteristics. Therefore, the monitoring method provided in this paper is not to monitor the growing year positively from the planting of orchard seedlings but to use time series remote sensing data to reverse determine the continuous growth age of each existing orchard. The city of Qixia, Shandong Province, China, was used as a case study. Firstly, the spatial distribution of apple orchards was accurately extracted using the Sentinel-2 normalized difference vegetation index (NDVI) spatiotemporally fused images and phenological vegetation information. Secondly, using region of interest (ROI) data for different vegetation types obtained from a field survey, NDVI time series were extracted from the Sentinel-2 NDVI spatiotemporally fused image. Among them, three characteristic phenological periods were selected, and the NDVI time series for apple orchards was used as a template to extract the apple orchard distribution area from 2000 to 2017. Then, the distribution area of apple orchards was defined as the area of interest in the planting year, combined with the Landsat NDVI time series image composed of three characteristic phenological periods each year from 2000 to 2017, and the apple orchard phenological curve. Subsequently, a Euclidean distance (ED) method was used to calculate the distribution area of apple orchards for each year between 2000 and 2017. Finally, a pixel-by-pixel inverse time series calculation method was used to obtain the planting year of apple orchards in the study area. This study provides a new way to accurately identify the planting year of apple orchards using satellite remote sensing images. Full article
(This article belongs to the Special Issue Remote Sensing and Decision Support for Precision Orchard Production)
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