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Remote Sens., Volume 12, Issue 8 (April-2 2020) – 126 articles

Cover Story (view full-size image): The storage and processing of remotely sensed hyperspectral images (HSIs) face important challenges due to the computational requirements involved in the analysis of these images, characterized by continuous and narrow spectral channels. Although HSIs offer many opportunities for accurately modeling and mapping the surface of the Earth, they comprise massive data cubes. These huge amounts of data impose important requirements from storage and processing points of view. The support vector machine is one of the most powerful machine learning classifiers, capable of processing HSI data without applying previous feature extraction steps. Nevertheless, its training and prediction stages are very time-consuming, especially for large and complex problems that require intensive use of memory and computational resources.View this paper.
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Article
A Pathway to the Automated Global Assessment of Water Level in Reservoirs with Synthetic Aperture Radar (SAR)
Remote Sens. 2020, 12(8), 1353; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12081353 - 24 Apr 2020
Cited by 2 | Viewed by 1536
Abstract
Global measurements of reservoir water levels are crucial for understanding Earth’s hydrological dynamics, especially in the context of global industrialization and climate change. Although radar altimetry has been used to measure the water level of some reservoirs with high accuracy, it is not [...] Read more.
Global measurements of reservoir water levels are crucial for understanding Earth’s hydrological dynamics, especially in the context of global industrialization and climate change. Although radar altimetry has been used to measure the water level of some reservoirs with high accuracy, it is not yet feasible unless the water body is sufficiently large or directly located at the satellite’s nadir. This study proposes a gauging method applicable to a wide range of reservoirs using Sentinel–1 Synthetic Aperture Radar data and a digital elevation model (DEM). The method is straightforward to implement and involves estimating the mean slope–corrected elevation of points along the reservoir shoreline. We test the model on six case studies and show that the estimated water levels are accurate to around 10% error on average of independently verified values. This study represents a substantial step toward the global gauging of lakes and reservoirs of all sizes and in any location where a DEM is available. Full article
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Article
The Potentiality of Operational Mapping of Oil Pollution in the Mediterranean Sea near the Entrance of the Suez Canal Using Sentinel-1 SAR Data
Remote Sens. 2020, 12(8), 1352; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12081352 - 24 Apr 2020
Cited by 4 | Viewed by 1340
Abstract
The Suez Canal, being a main international maritime shipping route, experiences heavy ship traffic with probable illegal oil discharges. Oil pollution is harming the marine ecosystem and creates pressure on the coastal socio-economic activities particularly at Port Said city (the area of study). [...] Read more.
The Suez Canal, being a main international maritime shipping route, experiences heavy ship traffic with probable illegal oil discharges. Oil pollution is harming the marine ecosystem and creates pressure on the coastal socio-economic activities particularly at Port Said city (the area of study). It is anticipated that the damage of oil spills is not only during the event but it extends for a long time and normally requires more effort to remediate and recover the environment. Hence, early detection and volume estimation of these spills is the first and most important step for a successful clean-up operation. This study is the first to use Sentinel-1 space-borne Synthetic Aperture Radar (SAR) images for oil spill detection and mapping over the north entrance of the Suez Canal aiming to enable operational monitoring. SAR sensors are able to capture images day and night and are not affected by weather conditions. In addition, they have a wide swath that covers large geographical areas for possible oil spills. The present study examines a large amount of data (800 scenes of sentinel 1) for the study area over a period of five years from 2014 till 2019 which resulted in the detection of more than 20 events of oil pollution. The detection model is based on the quantitative analysis of the dark spot of the radar backscatter of oil spills. The largest case covered nearly 26 km2 of seawater. The spill drift direction in the area of spills indicated potential hazard on fishing activities, Port Said beaches and ports. This study can be the base for continuously monitoring and alarming pollution cases in the Canal area which is important for environmental agencies, decision-makers, and beneficiaries for coastal and marine socio-economic sustainability. Full article
(This article belongs to the Section Environmental Remote Sensing)
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Review
Review of Satellite Interferometry for Landslide Detection in Italy
Remote Sens. 2020, 12(8), 1351; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12081351 - 24 Apr 2020
Cited by 22 | Viewed by 2111
Abstract
Landslides recurrently impact the Italian territory, producing huge economic losses and casualties. Because of this, there is a large demand for monitoring tools to support landslide management strategies. Among the variety of remote sensing techniques, Interferometric Synthetic Aperture Radar (InSAR) has become one [...] Read more.
Landslides recurrently impact the Italian territory, producing huge economic losses and casualties. Because of this, there is a large demand for monitoring tools to support landslide management strategies. Among the variety of remote sensing techniques, Interferometric Synthetic Aperture Radar (InSAR) has become one of the most widely applied for landslide studies. This work reviews a variety of InSAR-related applications for landslide studies in Italy. More than 250 papers were analyzed in this review. The first application dates back to 1999. The average production of InSAR-related papers for landslide studies is around 12 per year, with a peak of 37 papers in 2015. Almost 70% of the papers are written by authors in academia. InSAR is used (i) for landslide back analysis (3% of the papers); (ii) for landslide characterization (40% of the papers); (iii) as input for landslide models (7% of the papers); (iv) to update landslide inventories (15% of the papers); (v) for landslide mapping (32% of the papers), and (vi) for monitoring (3% of the papers). Sixty-eight percent of the authors validated the satellite results with ground information or other remote sensing data. Although well-known limitations exist, this bibliographic overview confirms that InSAR is a consolidated tool for many landslide-related applications. Full article
(This article belongs to the Section Remote Sensing in Geology, Geomorphology and Hydrology)
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Article
Urban Heat Island Monitoring and Impacts on Citizen’s General Health Status in Isfahan Metropolis: A Remote Sensing and Field Survey Approach
Remote Sens. 2020, 12(8), 1350; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12081350 - 24 Apr 2020
Cited by 12 | Viewed by 1783
Abstract
Urban heat islands (UHIs) are one of the urban management challenges, especially in metropolises, which can affect citizens’ health and well-being. This study used a combination of remote sensing techniques with field survey to investigate systematically the effects of UHI on citizens’ health [...] Read more.
Urban heat islands (UHIs) are one of the urban management challenges, especially in metropolises, which can affect citizens’ health and well-being. This study used a combination of remote sensing techniques with field survey to investigate systematically the effects of UHI on citizens’ health in Isfahan metropolis, Iran. For this purpose, the land surface temperature (LST) over a three-year period was monitored by Landsat-8 satellite imagery based on the split window algorithm. Then, the areas where UHI and urban cold island (UCI) phenomena occurred were identified and a general health questionnaire-28 (GHQ-28) was applied to evaluate the health status of 800 citizens in terms of physical health, anxiety and sleep, social function, and depression in UHI and UCI treatments. The average LST during the study period was 45.5 ± 2.3 °C and results showed that the Zayandeh-Rood river and the surrounding greenery had an important role in regulating the ambient temperature and promoting the citizens’ health. Citizens living in the suburban areas were more exposed to the UHIs phenomena, and statistical analysis of the GHQ-28 results indicated that they showed severe significant (P < 0.05) responses in terms of non-physical health sub-scales (i.e., anxiety and sleep, social functioning, and depression). Therefore, it can be concluded that not all citizens in the Isfahan metropolis are in the same environmental conditions and city managers and planners should pay more attention to the citizens living in the UHIs. The most important proceedings in this area would be the creation and development of parks and green belts, as well as the allocation of health-medical facilities and citizen education. Full article
(This article belongs to the Special Issue Remote Sensing based Urban Development and Climate Change Research)
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Article
Prediction of Wheat Grain Protein by Coupling Multisource Remote Sensing Imagery and ECMWF Data
Remote Sens. 2020, 12(8), 1349; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12081349 - 24 Apr 2020
Cited by 4 | Viewed by 1117
Abstract
Industrialization production with high quality and effect on winter is an important measure for accelerating the shift from increasing agricultural production to improving quality in terms of grain protein content (GPC). Remote sensing technology achieved the GPC prediction. However, large deviations in interannual [...] Read more.
Industrialization production with high quality and effect on winter is an important measure for accelerating the shift from increasing agricultural production to improving quality in terms of grain protein content (GPC). Remote sensing technology achieved the GPC prediction. However, large deviations in interannual expansion and regional transfer still exist. The present experiment was carried out in wheat producing areas of Beijing (BJ), Renqiu (RQ), Quzhou, and Jinzhou in Hebei Province. First, the spectral consistency of Landsat 8 Operational Land Imager (LS8) and RapidEye (RE) was compared with Sentinel-2 (S2) satellites at the same ground point in the same period. The GPC prediction model was constructed by coupling the vegetation index with the meteorological data obtained by the European Center for Medium-range Weather Forecasts using hierarchical linear model (HLM) method. The prediction and spatial expansion of regional GPC were validated. Results were as follows: (1) Spectral information calculated from S2 imagery were highly consistent with LS8 (R2 = 1.00) and RE (R2 = 0.99) imagery, which could be jointly used for GPC modeling. (2) The predicted GPC by using the HLM method (R2 = 0.524) demonstrated higher accuracy than the empirical linear model (R2 = 0.286) and showed higher improvements across inter-annual and regional scales. (3) The GPC prediction results of the verification samples in RQ, BJ, Xiaotangshan (XTS) in 2018, and XTS in 2019 were ideal with root mean square errors of 0.61%, 1.13%, 0.91%, and 0.38%, and relative root mean square error of 4.11%, 6.83%, 6.41%, and 2.58%, respectively. This study has great application potential for regional and inter-annual quality prediction. Full article
(This article belongs to the Special Issue Digital Agriculture with Remote Sensing)
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Technical Note
Automated Inundation Mapping Over Large Areas Using Landsat Data and Google Earth Engine
Remote Sens. 2020, 12(8), 1348; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12081348 - 24 Apr 2020
Cited by 5 | Viewed by 2612
Abstract
Accurate inundation maps for flooded wetlands and rivers are a critical resource for their management and conservation. In this paper, we automate a method (thresholding of the short-wave infrared band) for classifying peak inundation in the Okavango Delta, northern Botswana, using Landsat imagery [...] Read more.
Accurate inundation maps for flooded wetlands and rivers are a critical resource for their management and conservation. In this paper, we automate a method (thresholding of the short-wave infrared band) for classifying peak inundation in the Okavango Delta, northern Botswana, using Landsat imagery and Google Earth Engine. Inundation classification in the Okavango Delta is complex owing to the spectral overlap between inundated areas covered with aquatic vegetation and dryland vegetation classes on satellite imagery, and classifications have predominately been implemented on broad spatial resolution imagery. We present the longest time series to date (1990–2019) of inundation maps for the peak flood season at a high spatial resolution (30 m) for the Okavango Delta. We validated the maps using image-based and in situ data accuracy assessments, with overall accuracy ranging from 91.5% to 98.1%. Use of Landsat imagery resulted in consistently lower (on average, 692 km2) estimates of inundation extent than previous studies that used Moderate Resolution Imaging Spectroradiometer (MODIS) and National Oceanic and Atmospheric Administration Advanced Very-High-Resolution Radiometer (NOAA AVHRR) imagery, likely owing to the increased number of mixed pixels that occur when using broad spatial resolution imagery, which can lead to overestimations of the size of inundated areas. We provide the inundation maps and Google Earth Engine code for public use. This classification method can likely be adapted for inundation mapping in other regions. Full article
(This article belongs to the Special Issue She Maps)
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Article
Coastal Wind Measurements Using a Single Scanning LiDAR
Remote Sens. 2020, 12(8), 1347; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12081347 - 24 Apr 2020
Cited by 4 | Viewed by 1254
Abstract
A wind measurement campaign using a single scanning light detection and ranging (LiDAR) device was conducted at the Hazaki Oceanographical Research Station (HORS) on the Hazaki coast of Japan to evaluate the performance of the device for coastal wind measurements. The scanning LiDAR [...] Read more.
A wind measurement campaign using a single scanning light detection and ranging (LiDAR) device was conducted at the Hazaki Oceanographical Research Station (HORS) on the Hazaki coast of Japan to evaluate the performance of the device for coastal wind measurements. The scanning LiDAR was deployed on the landward end of the HORS pier. We compared the wind speed and direction data recorded by the scanning LiDAR to the observations obtained from a vertical profiling LiDAR installed at the opposite end of the pier, 400 m from the scanning LiDAR. The best practice for offshore wind measurements using a single scanning LiDAR was evaluated by comparing results from a total of nine experiments using several different scanning settings. A two-parameter velocity volume processing (VVP) method was employed to retrieve the horizontal wind speed and direction from the radial wind speed. Our experiment showed that, at the current offshore site with a negligibly small vertical wind speed component, the accuracy of the scanning LiDAR wind speeds and directions was sensitive to the azimuth angle setting, but not to the elevation angle setting. In addition to the validations for the 10-minute mean wind speeds and directions, the application of LiDARs for the measurement of the turbulence intensity (TI) was also discussed by comparing the results with observations obtained from a sonic anemometer, mounted at the seaward end of the HORS pier, 400 m from the scanning LiDAR. The standard deviation obtained from the scanning LiDAR measurement showed a greater fluctuation than that obtained from the sonic anemometer measurement. However, the difference between the scanning LiDAR and sonic measurements appeared to be within an acceptable range for the wind turbine design. We discuss the variations in data availability and accuracy based on an analysis of the carrier-to-noise ratio (CNR) distribution and the goodness of fit for curve fitting via the VVP method. Full article
(This article belongs to the Special Issue Assessment of Renewable Energy Resources with Remote Sensing)
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Article
Seismogenic Source Model of the 2019, Mw 5.9, East-Azerbaijan Earthquake (NW Iran) through the Inversion of Sentinel-1 DInSAR Measurements
Remote Sens. 2020, 12(8), 1346; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12081346 - 24 Apr 2020
Cited by 3 | Viewed by 1753
Abstract
In this work, we investigate the Mw 5.9 earthquake occurred on 7 November 2019 in the East-Azerbaijan region, in northwestern Iran, which is inserted in the tectonic framework of the East-Azerbaijan Plateau, a complex mountain belt that contains internal major fold-and-thrust belts. [...] Read more.
In this work, we investigate the Mw 5.9 earthquake occurred on 7 November 2019 in the East-Azerbaijan region, in northwestern Iran, which is inserted in the tectonic framework of the East-Azerbaijan Plateau, a complex mountain belt that contains internal major fold-and-thrust belts. We first analyze the Differential Synthetic Aperture Radar Interferometry (DInSAR) measurements obtained by processing the data collected by the Sentinel-1 constellation along ascending and descending orbits; then, we invert the achieved results through analytical modelling, in order to better constrain the geometry and characteristics of the seismogenic source. The retrieved fault model shows a rather shallow seismic structure, with a center depth at about 3 km, approximately NE–SW-striking and southeast-dipping, characterized by a left-lateral strike-slip fault mechanism (strike = 29.17°, dip = 79.29°, rake = −4.94°) and by a maximum slip of 0.80 m. By comparing the inferred fault with the already published geological structures, the retrieved solution reveals a minor fault not reported in the geological maps available in the open literature, whose kinematics is compatible with that of the surrounding structures, with the local and regional stress states and with the performed field observations. Moreover, by taking into account the surrounding geological structures reported in literature, we also use the retrieved fault model to calculate the Coulomb Failure Function at the nearby receiver faults. We show that this event may have encouraged, with a positive loading, the activation of the considered receiver faults. This is also confirmed by the distribution of the aftershocks that occurred near the considered surrounding structures. The analysis of the seismic events nucleated along the left-lateral strike-slip minor faults of the East-Azerbaijan Plateau, such as the one analyzed in this work, is essential to improve our knowledge on the seismic hazard estimation in northwestern Iran. Full article
(This article belongs to the Special Issue Ground Deformation Patterns Detection by InSAR and GNSS Techniques)
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Article
A Conceptual Approach to Modeling the Geospatial Impact of Typical Urban Threats on the Habitat Quality of River Corridors
Remote Sens. 2020, 12(8), 1345; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12081345 - 23 Apr 2020
Cited by 6 | Viewed by 1265
Abstract
While for most of a landscape, urbanization leads to a significant habitat loss, rivers in urban areas are usually maintained or developed for their drainage function. Rivers are often the most important biophysical and ecological connection of cities with their surrounding ecosystems, although [...] Read more.
While for most of a landscape, urbanization leads to a significant habitat loss, rivers in urban areas are usually maintained or developed for their drainage function. Rivers are often the most important biophysical and ecological connection of cities with their surrounding ecosystems, although usually heavily altered due to urban impacts. For the conservation of urban rivers as ecological corridors, it is important to assess the impact of typical urban threats on habitat quality. In this study, we used the InVEST (Integrated Valuation of Environmental Services and Trade-offs) habitat quality model to assess the individual and combined impacts of built-up areas, first- and second-order road and water pollution from urban drainage, and wastewater discharge on habitat quality within a 200 m wide river corridor. The Pochote River in León, Nicaragua, was used as a case study. Our results show the spatial distribution and magnitude of the individual threat impacts, as well as the respective contribution of each threat to the overall impact of urbanization on the habitat quality within the river corridor. While close to the city center, all threats almost equally contributed to severe habitat degradation, while further downstream, an individual threat influence became more distinct with only water pollution having a consistent negative impact. We concluded that the InVEST habitat quality model can be used to assess the impact of typical urban threats on habitat quality in river corridors at a high spatial resolution. The results can help to improve urban planning and development to improve habitat conservation along urban rivers. Full article
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Article
The Autonomous Underwater Vehicle Integrated with the Unmanned Surface Vessel Mapping the Southern Ionian Sea. The Winning Technology Solution of the Shell Ocean Discovery XPRIZE
Remote Sens. 2020, 12(8), 1344; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12081344 - 23 Apr 2020
Cited by 10 | Viewed by 2327
Abstract
The methods of data collection, processing, and assessment of the quality of the results of a survey conducted at the Southern Ionian Sea off the Messinian Peninsula, Greece are presented. Data were collected by the GEBCO-Nippon Foundation Alumni Team, competing in the Shell [...] Read more.
The methods of data collection, processing, and assessment of the quality of the results of a survey conducted at the Southern Ionian Sea off the Messinian Peninsula, Greece are presented. Data were collected by the GEBCO-Nippon Foundation Alumni Team, competing in the Shell Ocean Discovery XPRIZE, during the Final Round of the competition. Data acquisition was conducted by the means of unmanned vehicles only. The mapping system was composed of a single deep water AUV (Autonomous Underwater Vehicle), equipped with a high-resolution synthetic aperture sonar HISAS 1032 and multibeam echosounder EM 2040, partnered with a USV (Unmanned Surface Vessel). The USV provided positioning data as well as mapping the seafloor from the surface, using a hull-mounted multibeam echosounder EM 304. Bathymetry and imagery data were collected for 24 h and then processed for 48 h, with the extensive use of cloud technology and automatic data processing. Finally, all datasets were combined to generate a 5-m resolution bathymetric surface, as an example of the deep-water mapping capabilities of the unmanned vehicles’ cooperation and their sensors’ integration. Full article
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Article
Towards the Concurrent Execution of Multiple Hyperspectral Imaging Applications by Means of Computationally Simple Operations
Remote Sens. 2020, 12(8), 1343; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12081343 - 23 Apr 2020
Cited by 2 | Viewed by 867
Abstract
The on-board processing of remotely sensed hyperspectral images is gaining momentum for applications that demand a quick response as an alternative to conventional approaches where the acquired images are off-line processed once they have been transmitted to the ground segment. However, the adoption [...] Read more.
The on-board processing of remotely sensed hyperspectral images is gaining momentum for applications that demand a quick response as an alternative to conventional approaches where the acquired images are off-line processed once they have been transmitted to the ground segment. However, the adoption of this on-board processing strategy brings further challenges for the remote-sensing research community due to the high data rate of the new-generation hyperspectral sensors and the limited amount of available on-board computational resources. This situation becomes even more stringent when different time-sensitive applications coexist, since different tasks must be sequentially processed onto the same computing device. In this work, we have dealt with this issue through the definition of a set of core operations that extracts spectral features useful for many hyperspectral analysis techniques, such as unmixing, compression and target/anomaly detection. Accordingly, it permits the concurrent execution of such techniques reusing operations and thereby requiring much less computational resources than if they were separately executed. In particular, in this manuscript we have verified the goodness of our proposal for the concurrent execution of both the lossy compression and anomaly detection processes in hyperspectral images. To evaluate the performance, several images taken by an unmanned aerial vehicle have been used. The obtained results clearly support the benefits of our proposal not only in terms of accuracy but also in terms of computational burden, achieving a reduction of roughly 50% fewer operations to be executed. Future research lines are focused on extending this methodology to other fields such as target detection, classification and dimensionality reduction. Full article
(This article belongs to the Section Remote Sensing Image Processing)
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Article
Enhancing Precipitation Estimates Through the Fusion of Weather Radar, Satellite Retrievals, and Surface Parameters
Remote Sens. 2020, 12(8), 1342; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12081342 - 23 Apr 2020
Cited by 18 | Viewed by 2221
Abstract
Accurate and timely monitoring of precipitation remains a challenge, particularly in hyper-arid regions such as the United Arab Emirates (UAE). The aim of this study is to improve the accuracy of the Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (GPM) mission’s latest [...] Read more.
Accurate and timely monitoring of precipitation remains a challenge, particularly in hyper-arid regions such as the United Arab Emirates (UAE). The aim of this study is to improve the accuracy of the Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (GPM) mission’s latest product release (IMERG V06B) locally over the UAE. Two distinct approaches, namely, geographically weighted regression (GWR), and artificial neural networks (ANNs) are tested. Daily soil moisture retrievals from the Soil Moisture Active Passive (SMAP) mission (9 km), terrain elevations from the Advanced Spaceborne Thermal Emission and Reflection digital elevation model (ASTER DEM, 30 m) and precipitation estimates (0.5 km) from a weather radar network are incorporated as explanatory variables in the proposed GWR and ANN model frameworks. First, the performances of the daily GPM and weather radar estimates are assessed using a network of 65 rain gauges from 1 January 2015 to 31 December 2018. Next, the GWR and ANN models are developed with 52 gauges used for training and 13 gauges reserved for model testing and seasonal inter-comparisons. GPM estimates record higher Pearson correlation coefficients (PCC) at rain gauges with increasing elevation (z) and higher rainfall amounts (PCC = 0.29 z0.12), while weather radar estimates perform better for lower elevations and light rain conditions (PCC = 0.81 z−0.18). Taylor diagrams indicate that both the GWR- and the ANN-adjusted precipitation products outperform the original GPM and radar estimates, with the poorest correction obtained by GWR during the summer period. The incorporation of soil moisture resulted in improved corrections by the ANN model compared to the GWR, with relative increases in Nash–Sutcliffe efficiency (NSE) coefficients of 56% (and 25%) for GPM estimates, and 34% (and 53%) for radar estimates during summer (and winter) periods. The ANN-derived precipitation estimates can be used to force hydrological models over ungauged areas across the UAE. The methodology is expandable to other arid and hyper-arid regions requiring improved precipitation monitoring. Full article
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Article
Ash Presence and Abundance Derived from Composite Landsat and Sentinel-2 Time Series and Lidar Surface Models in Minnesota, USA
Remote Sens. 2020, 12(8), 1341; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12081341 - 23 Apr 2020
Cited by 3 | Viewed by 950
Abstract
Ash trees (Fraxinus spp.) are a prominent species in Minnesota forests, with an estimated 1.1 billion trees in the state, totaling approximately 8% of all trees. Ash trees are threatened by the invasive emerald ash borer (Agrilus planipennis Fairmaire), which typically [...] Read more.
Ash trees (Fraxinus spp.) are a prominent species in Minnesota forests, with an estimated 1.1 billion trees in the state, totaling approximately 8% of all trees. Ash trees are threatened by the invasive emerald ash borer (Agrilus planipennis Fairmaire), which typically results in close to 100% tree mortality within one to five years of infestation. A detailed, wall-to-wall map of ash presence is highly desirable for forest management and monitoring applications. We used Google Earth Engine to compile Landsat time series analysis, which provided unique information on phenologic patterns across the landscape to identify ash species. Topographic position information derived from lidar was added to improve spatial maps of ash abundance. These input data were combined to produce a classification map and identify the abundance of ash forests that exist in the state of Minnesota. Overall, 12,524 km2 of forestland was predicted to have greater than 10% probability of ash species present. The overall accuracy of the composite ash presence/absence map was 64% for all ash species and 72% for black ash, and classification accuracy increased with the length of the time series. Average height derived from lidar was the best model predictor for ash basal area (R2 = 0.40), which, on average, was estimated as 16.1 m2 ha−1. Information produced from this map will be useful for natural resource managers and planners in developing forest management strategies which account for the spatial distribution of ash on the landscape. The approach used in this analysis is easily transferable and broadly scalable to other regions threatened with forest health problems such as invasive insects. Full article
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Article
Wave Signatures in Total Electron Content Variations: Filtering Problems
Remote Sens. 2020, 12(8), 1340; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12081340 - 23 Apr 2020
Cited by 2 | Viewed by 1117
Abstract
Over recent years, global navigation satellite systems (GNSSs) have been increasingly used to study near-Earth space. The basis for such studies is the total electron content (TEC) data. Standard procedures for detecting TEC wave signatures include variation selection and detrending. Our experience showed [...] Read more.
Over recent years, global navigation satellite systems (GNSSs) have been increasingly used to study near-Earth space. The basis for such studies is the total electron content (TEC) data. Standard procedures for detecting TEC wave signatures include variation selection and detrending. Our experience showed that the inaccurate procedure causes artifacts in datasets which might affect data interpretation, particularly in automated processing. We analyzed the features of various detrending and variation selection methods. We split the problem of the GNSS data filtering into two subproblems: detrending and variation selection. We examined centered moving average, centered moving median, 6th-order polynomial, Hodrick–Prescott filter, L1 filter, cubic smoothing spline, double-applied moving average for the GNSS-TEC detrending problem, and centered moving average, centered moving median, Butterworth filter, type I Chebyshev filter for the GNSS-TEC variation selection problem in this paper. We carried out the analysis based on both model and experimental data. Modeling was based on simple analytical models as well as the International Reference Ionosphere. Analysis of TEC variations of 2–10 min, 10–20 min, and 20–60 min under insufficient detrending conditions showed that the higher errors appear for the longer periods (20–60 min). For the detrending problem, the smoothing cubic spline provided the best results among the algorithms discussed in this paper. The spline detrending featured the minimal value of the mean bias error (MBE) and the root-mean-square error (RMSE), as well as high correlation coefficient. The 6th-order polynomial also produced good results. Spline detrending does not introduce a RMSE more than 0.25 TECU and MBE > 0.35 TECU for IRI trends, while, for the 6th-order polynomial, these errors can exceed 1 TECU in some cases. However, in 95% of observations the RMSE and MBE do not exceed 0.05 TECU. For the variation selection, the centered moving average filter showed the best performance among the algorithms discussed in this paper. Full article
(This article belongs to the Section Atmosphere Remote Sensing)
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Article
Sun-Angle Effects on Remote-Sensing Phenology Observed and Modelled Using Himawari-8
Remote Sens. 2020, 12(8), 1339; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12081339 - 23 Apr 2020
Cited by 5 | Viewed by 2091
Abstract
Satellite remote sensing of vegetation at regional to global scales is undertaken at considerable variations in solar zenith angle (SZA) across space and time, yet the extent to which these SZA variations matter for the retrieval of phenology remains largely unknown. Here we [...] Read more.
Satellite remote sensing of vegetation at regional to global scales is undertaken at considerable variations in solar zenith angle (SZA) across space and time, yet the extent to which these SZA variations matter for the retrieval of phenology remains largely unknown. Here we examined the effect of seasonal and spatial variations in SZA on retrieving vegetation phenology from time series of the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) across a study area in southeastern Australia encompassing forest, woodland, and grassland sites. The vegetation indices (VI) data span two years and are from the Advanced Himawari Imager (AHI), which is onboard the Japanese Himawari-8 geostationary satellite. The semi-empirical RossThick-LiSparse-Reciprocal (RTLSR) bidirectional reflectance distribution function (BRDF) model was inverted for each spectral band on a daily basis using 10-minute reflectances acquired by H-8 AHI at different sun-view geometries for each site. The inverted RTLSR model was then used to forward calculate surface reflectance at three constant SZAs (20°, 40°, 60°) and one seasonally varying SZA (local solar noon), all normalised to nadir view. Time series of NDVI and EVI adjusted to different SZAs at nadir view were then computed, from which phenological metrics such as start and end of growing season were retrieved. Results showed that NDVI sensitivity to SZA was on average nearly five times greater than EVI sensitivity. VI sensitivity to SZA also varied among sites (biome types) and phenological stages, with NDVI sensitivity being higher during the minimum greenness period than during the peak greenness period. Seasonal SZA variations altered the temporal profiles of both NDVI and EVI, with more pronounced differences in magnitude among NDVI time series normalised to different SZAs. When using VI time series that allowed SZA to vary at local solar noon, the uncertainties in estimating start, peak, end, and length of growing season introduced by local solar noon varying SZA VI time series, were 7.5, 3.7, 6.5, and 11.3 days for NDVI, and 10.4, 11.9, 6.5, and 8.4 days for EVI respectively, compared to VI time series normalised to a constant SZA. Furthermore, the stronger SZA dependency of NDVI compared with EVI, resulted in up to two times higher uncertainty in estimating annual integrated VI, a commonly used remote-sensing proxy for vegetation productivity. Since commonly used satellite products are not generally normalised to a constant sun-angle across space and time, future studies to assess the sun-angle effects on satellite applications in agriculture, ecology, environment, and carbon science are urgently needed. Measurements taken by new-generation geostationary (GEO) satellites offer an important opportunity to refine this assessment at finer temporal scales. In addition, studies are needed to evaluate the suitability of different BRDF models for normalising sun-angle across a broad spectrum of vegetation structure, phenological stages and geographic locations. Only through continuous investigations on how sun-angle variations affect spatiotemporal vegetation dynamics and what is the best strategy to deal with it, can we achieve a more quantitative remote sensing of true signals of vegetation change across the entire globe and through time. Full article
(This article belongs to the Special Issue Earth Monitoring from A New Generation of Geostationary Satellites)
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Article
SI-Traceability and Measurement Uncertainty of the Atmospheric Infrared Sounder Version 5 Level 1B Radiances
Remote Sens. 2020, 12(8), 1338; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12081338 - 23 Apr 2020
Cited by 4 | Viewed by 1186
Abstract
The Atmospheric Infrared Sounder (AIRS) on the EOS Aqua Spacecraft was launched on 4 May 2002. The AIRS is designed to measure atmospheric temperature and water vapor profiles and has demonstrated exceptional radiometric and spectral accuracy and stability in orbit. The International System [...] Read more.
The Atmospheric Infrared Sounder (AIRS) on the EOS Aqua Spacecraft was launched on 4 May 2002. The AIRS is designed to measure atmospheric temperature and water vapor profiles and has demonstrated exceptional radiometric and spectral accuracy and stability in orbit. The International System of Units (SI)-traceability of the derived radiances is achieved by transferring the calibration from the Large Area Blackbody (LABB) with SI traceable temperature sensors, to the On-Board Calibrator (OBC) blackbody during preflight testing. The AIRS views the OBC blackbody and four full aperture space views every scan. A recent analysis of pre-flight and on-board data has improved our understanding of the measurement uncertainty of the Version 5 AIRS L1B radiance product. For temperatures greater than 260 K, the measurement uncertainty is better than 250 mK 1-sigma for most channels. SI-traceability and quantification of the radiometric measurement uncertainty is critical to reducing biases in reanalysis products and radiative transfer models (RTMs) that use AIRS data, as well as establishing the suitability of AIRS as a benchmark for radiances established in the early 2000s. Full article
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Article
Combining Fractional Cover Images with One-Class Classifiers Enables Near Real-Time Monitoring of Fallows in the Northern Grains Region of Australia
Remote Sens. 2020, 12(8), 1337; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12081337 - 23 Apr 2020
Cited by 1 | Viewed by 1015
Abstract
Fallows are widespread in dryland cropping systems. However, timely information about their spatial extent and location remains scarce. To overcome this lack of information, we propose to classify fractional cover data from Sentinel-2 with biased support vector machines. Fractional cover images describe the [...] Read more.
Fallows are widespread in dryland cropping systems. However, timely information about their spatial extent and location remains scarce. To overcome this lack of information, we propose to classify fractional cover data from Sentinel-2 with biased support vector machines. Fractional cover images describe the land surface in intuitive, biophysical terms, which reduces the spectral variability within the fallow class. Biased support vector machines are a type of one-class classifiers that require labelled data for the class of interest and unlabelled data for the other classes. They allow us to extrapolate in-situ observations collected during flowering to the rest of the growing season to generate large training data sets, thereby reducing the data collection requirements. We tested this approach to monitor fallows in the northern grains region of Australia and showed that the seasonal fallow extent can be mapped with >92% accuracy both during the summer and winter seasons. The summer fallow extent can be accurately mapped as early as mid-December (1–4 months before harvest). The winter fallow extent can be accurately mapped from mid-August (2–4 months before harvest). Our method also detected emergence dates successfully, indicating the near real-time accuracy of our method. We estimated that the extent of fallow fields across the northern grains region of Australia ranged between 50% in winter 2017 and 85% in winter 2019. Our method is scalable, sensor independent and economical to run. As such, it lays the foundations for reconstructing and monitoring the cropping dynamics in Australia. Full article
(This article belongs to the Special Issue Digital Agriculture with Remote Sensing)
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Article
Near Real-Time Monitoring of the Christmas 2018 Etna Eruption Using SEVIRI and Products Validation
Remote Sens. 2020, 12(8), 1336; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12081336 - 23 Apr 2020
Cited by 12 | Viewed by 1405
Abstract
On the morning of 24 December 2018, an eruptive event occurred at Etna, which was followed the next day by a strong sequence of shallow earthquakes. The eruptive episode lasted until 30 December, ranging from moderate strombolian to lava fountain activity coupled with [...] Read more.
On the morning of 24 December 2018, an eruptive event occurred at Etna, which was followed the next day by a strong sequence of shallow earthquakes. The eruptive episode lasted until 30 December, ranging from moderate strombolian to lava fountain activity coupled with vigorous ash/gas emissions and a lava flow effusion toward the eastern volcano flank of Valle del Bove. In this work, the data collected from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) instruments on board the Meteosat Second Generation (MSG) geostationary satellite are used to characterize the Etna activity by estimating the proximal and distal eruption parameters in near real time. The inversion of data indicates the onset of eruption on 24 December at 11:15 UTC, a maximum Time Average Discharge Rate (TADR) of 8.3 m3/s, a cumulative lava volume emitted of 0.5 Mm3, and a Volcanic Plume Top Height (VPTH) that reached a maximum altitude of 8 km above sea level (asl). The volcanic cloud ash and SO2 result totally collocated, with an ash amount generally lower than SO2 except on 24 December during the climax phase. A total amount of about 100 and 35 kt of SO2 and ash respectively was emitted during the entire eruptive period, while the SO2 fluxes reached peaks of more than 600 kg/s, with a mean value of about 185 kg/s. The SEVIRI VPTH, ash/SO2 masses, and flux time series have been compared with the results obtained from the ground-based visible (VIS) cameras and FLux Automatic MEasurements (FLAME) networks, and the satellite images collected by the MODerate resolution Imaging Spectroradiometer (MODIS) instruments on board the Terra and Aqua- polar satellites. The analysis indicates good agreement between SEVIRI, VIS camera, and MODIS retrievals with VPTH, ash, and SO2 estimations all within measurement errors. The SEVIRI and FLAME SO2 flux retrievals show significant discrepancies due to the presence of volcanic ash and a gap of data on the FLAME network. The results obtained in this study show the ability of geostationary satellite systems to characterize eruptive events from the source to the atmosphere in near real time during the day and night, thus offering a powerful tool to mitigate volcanic risk on both local population and airspace and to give insight on volcanic processes. Full article
(This article belongs to the Special Issue Convective and Volcanic Clouds (CVC))
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Article
Chlorophyll a Concentration Distribution on the Mainland Coast of the Gulf of California, Mexico
Remote Sens. 2020, 12(8), 1335; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12081335 - 23 Apr 2020
Cited by 2 | Viewed by 1424
Abstract
Coastal zones are important areas for the development of diverse ecosystems. The analysis of chlorophyll a (Chl a), as an indicator of primary production in these regions, is crucial for the quantification of phytoplankton biomass, which is considered the main food chain [...] Read more.
Coastal zones are important areas for the development of diverse ecosystems. The analysis of chlorophyll a (Chl a), as an indicator of primary production in these regions, is crucial for the quantification of phytoplankton biomass, which is considered the main food chain base in the oceans and an indicator of the trophic state index. This variable is greatly important for the analysis of the oceanographic variability, and it is crucial for determining the tendencies of change in these areas with the objective of determining the effects on the ecosystem and the population dynamics of marine resources. In this study, we analysed the Chl a concentration distribution on the mainland coast of the Gulf of California based on the monthly data from July 2002 to July 2019, obtained from remote sensing (Moderate-Resolution Imaging Spectroradiometer Aqua (MODIS-Aqua) with a 9 km resolution). The results showed a clear distribution pattern of Chl a observed along this area with the maximum levels in March and minimum levels in August. A four-region characterisation on this area was used to make a comparison of the Chl a concentrations during warm and cold periods. The majority of the results were statistically significant. The spectral analysis in each of the four regions analysed in this study determined the following variation frequencies: annual, semi-annual, seasonal, and inter-annual; the last was related to the macroscale climatological phenomena El Niño-La Niña affecting the variability of the Chl a concentration in the study region. Full article
(This article belongs to the Special Issue Remote Sensing of Ocean Primary Production)
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Article
Comparison of Machine Learning Methods for Estimating Mangrove Above-Ground Biomass Using Multiple Source Remote Sensing Data in the Red River Delta Biosphere Reserve, Vietnam
Remote Sens. 2020, 12(8), 1334; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12081334 - 23 Apr 2020
Cited by 18 | Viewed by 4314
Abstract
This study proposes a hybrid intelligence approach based on an extreme gradient boosting regression and genetic algorithm, namely, the XGBR-GA model, incorporating Sentinel-2, Sentinel-1, and ALOS-2 PALSAR-2 data to estimate the mangrove above-ground biomass (AGB), including small and shrub mangrove patches in the [...] Read more.
This study proposes a hybrid intelligence approach based on an extreme gradient boosting regression and genetic algorithm, namely, the XGBR-GA model, incorporating Sentinel-2, Sentinel-1, and ALOS-2 PALSAR-2 data to estimate the mangrove above-ground biomass (AGB), including small and shrub mangrove patches in the Red River Delta biosphere reserve across the northern coast of Vietnam. We used the novel extreme gradient boosting decision tree (XGBR) technique together with genetic algorithm (GA) optimization for feature selection to construct and verify a mangrove AGB model using data from a field survey of 105 sampling plots conducted in November and December of 2018 and incorporated the dual polarimetric (HH and HV) data of the ALOS-2 PALSAR-2 L-band and the Sentinel-2 multispectral data combined with Sentinel-1 (C-band VV and VH) data. We employed the root-mean-square error (RMSE) and coefficient of determination (R2) to evaluate the performance of the proposed model. The capability of the XGBR-GA model was assessed via a comparison with other machine-learning (ML) techniques, i.e., the CatBoost regression (CBR), gradient boosted regression tree (GBRT), support vector regression (SVR), and random forest regression (RFR) models. The XGBR-GA model yielded a promising result (R2 = 0.683, RMSE = 25.08 Mg·ha−1) and outperformed the four other ML models. The XGBR-GA model retrieved a mangrove AGB ranging from 17 Mg·ha−1 to 142 Mg·ha−1 (with an average of 72.47 Mg·ha−1). Therefore, multisource optical and synthetic aperture radar (SAR) combined with the XGBR-GA model can be used to estimate the mangrove AGB in North Vietnam. The effectiveness of the proposed method needs to be further tested and compared to other mangrove ecosystems in the tropics. Full article
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Article
Tree, Shrub, and Grass Classification Using Only RGB Images
Remote Sens. 2020, 12(8), 1333; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12081333 - 23 Apr 2020
Cited by 12 | Viewed by 1480
Abstract
In this work, a semantic segmentation-based deep learning method, DeepLabV3+, is applied to classify three vegetation land covers, which are tree, shrub, and grass using only three band color (RGB) images. DeepLabV3+’s detection performance has been studied on low and high resolution datasets [...] Read more.
In this work, a semantic segmentation-based deep learning method, DeepLabV3+, is applied to classify three vegetation land covers, which are tree, shrub, and grass using only three band color (RGB) images. DeepLabV3+’s detection performance has been studied on low and high resolution datasets that both contain tree, shrub, and grass and some other land cover types. The two datasets are heavily imbalanced where shrub pixels are much fewer than tree and grass pixels. A simple weighting strategy known as median frequency weighting was incorporated into DeepLabV3+ to mitigate the data imbalance issue, which originally used uniform weights. The tree, shrub, grass classification performances are compared when all land cover types are included in the classification and also when classification is limited to the three vegetation classes with both uniform and median frequency weights. Among the three vegetation types, shrub is found to be the most challenging one to classify correctly whereas correct classification accuracy was highest for tree. It is observed that even though the median frequency weighting did not improve the overall accuracy, it resulted in better classification accuracy for the underrepresented classes such as shrub in our case and it also significantly increased the average class accuracy. The classification performance and computation time comparison of DeepLabV3+ with two other pixel-based classification methods on sampled pixels of the three vegetation classes showed that DeepLabV3+ achieves significantly higher accuracy than these methods with a trade-off for longer model training time. Full article
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Article
Revealing the Fingerprint of Climate Change in Interannual NDVI Variability among Biomes in Inner Mongolia, China
Remote Sens. 2020, 12(8), 1332; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12081332 - 23 Apr 2020
Cited by 9 | Viewed by 1028
Abstract
An understanding of the response of interannual vegetation variations to climate change is critical for the future projection of ecosystem processes and developing effective coping strategies. In this study, the spatial pattern of interannual variability in the growing season normalized difference vegetation index [...] Read more.
An understanding of the response of interannual vegetation variations to climate change is critical for the future projection of ecosystem processes and developing effective coping strategies. In this study, the spatial pattern of interannual variability in the growing season normalized difference vegetation index (NDVI) for different biomes and its relationships with climate variables were investigated in Inner Mongolia during 1982–2015 by jointly using linear regression, geographical detector, and geographically weighted regression methodologies. The result showed that the greatest variability of the growing season NDVI occurred in typical steppe and desert steppe, with forest and desert most stable. The interannual variability of NDVI differed monthly among biomes, showing a time gradient of the largest variation from northeast to southwest. NDVI interannual variability was significantly related to that of the corresponding temperature and precipitation for each biome, characterized by an obvious spatial heterogeneity and time lag effect marked in the later period of the growing season. Additionally, the large slope of NDVI variation to temperature for desert implied that desert tended to amplify temperature variations, whereas other biomes displayed a capacity to buffer climate fluctuations. These findings highlight the relationships between vegetation variability and climate variability, which could be used to support the adaptive management of vegetation resources in the context of climate change. Full article
(This article belongs to the Special Issue Ecosystem Services with Remote Sensing)
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Article
Evaluation of Global Solar Irradiance Estimates from GL1.2 Satellite-Based Model over Brazil Using an Extended Radiometric Network
Remote Sens. 2020, 12(8), 1331; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12081331 - 23 Apr 2020
Cited by 1 | Viewed by 856
Abstract
The GL (GLobal radiation) physical model was developed to compute global solar irradiance at ground level from (VIS) visible channel imagery of geostationary satellites. Currently, its version 1.2 (GL1.2) runs at Brazilian Center for Weather Forecast and Climate Studies/National Institute for Space Research [...] Read more.
The GL (GLobal radiation) physical model was developed to compute global solar irradiance at ground level from (VIS) visible channel imagery of geostationary satellites. Currently, its version 1.2 (GL1.2) runs at Brazilian Center for Weather Forecast and Climate Studies/National Institute for Space Research (CPTEC/INPE) based on GOES-East VIS imagery. This study presents an extensive validation of GL1.2 global solar irradiance estimates using ground-based measurements from 409 stations belonging to the Brazilian National Institute of Meteorology (INMET) over Brazil for the year 2016. The INMET reasonably dense network allows characterizing the spatial distribution of GL1.2 data uncertainties. It is found that the GL1.2 estimates have a tendency to overestimate the ground data, but the magnitude varies according to region. On a daily basis, the best performances are observed for the Northeast, Southeast, and South regions, with a mean bias error (MBE) between 2.5 and 4.9 W m−2 (1.2% and 2.1%) and a root mean square error (RMSE) between 21.1 and 26.7 W m−2 (10.8% and 11.8%). However, larger differences occur in the North and Midwest regions, with MBE between 12.7 and 23.5 W m−2 (5.9% and 11.7%) and RMSE between 27 and 33.4 W m−2 (12.7% and 16.7%). These errors are most likely due to the simplified assumptions adopted by the GL1.2 algorithm for clear sky reflectance (Rmin) and aerosols as well as the uncertainty of the water vapor data. Further improvements in determining these parameters are needed. Additionally, the results also indicate that the GL1.2 operational product can help to improve the quality control of radiometric data from a large network, such as INMET's. Overall, the GL1.2 data are suitable for use in various regional applications. Full article
(This article belongs to the Special Issue Satellite Images for Assessing Solar Radiation at Surface)
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Communication
Earth Observation Contribution to Cultural Heritage Disaster Risk Management: Case Study of Eastern Mediterranean Open Air Archaeological Monuments and Sites
Remote Sens. 2020, 12(8), 1330; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12081330 - 22 Apr 2020
Cited by 8 | Viewed by 1757
Abstract
Disaster risk management (DRM) for cultural heritage is a complex task that requires multidisciplinary cooperation. This short communication underlines the critical role of satellite remote sensing (also known as earth observation) in DRM in dealing with various hazards for cultural heritage sites and [...] Read more.
Disaster risk management (DRM) for cultural heritage is a complex task that requires multidisciplinary cooperation. This short communication underlines the critical role of satellite remote sensing (also known as earth observation) in DRM in dealing with various hazards for cultural heritage sites and monuments. Here, satellite observation potential is linked with the different methodological steps of the DRM cycle. This is achieved through a short presentation of recent paradigms retrieved from research studies and the Scopus scientific repository. The communication focuses on the Eastern Mediterranean region, an area with an indisputable wealth of archaeological sites. Regarding the cultural heritage type, this article considers relevant satellite observation studies implemented in open-air archaeological monuments and sites. The necessity of this communication article emerged while trying to bring together earth observation means, cultural heritage needs, and DRM procedures. Full article
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Article
Unraveling Spatial and Temporal Heterogeneities of Very Slow Rock-Slope Deformations with Targeted DInSAR Analyses
Remote Sens. 2020, 12(8), 1329; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12081329 - 22 Apr 2020
Cited by 5 | Viewed by 1233
Abstract
Spaceborne radar interferometry is a powerful tool to characterize landslides at local and regional scales. However, its application to very slow rock slope deformations in alpine environments (displacement rates < 5 cm/year) remains challenging, mainly due to low signal to noise ratio, atmospheric [...] Read more.
Spaceborne radar interferometry is a powerful tool to characterize landslides at local and regional scales. However, its application to very slow rock slope deformations in alpine environments (displacement rates < 5 cm/year) remains challenging, mainly due to low signal to noise ratio, atmospheric disturbances, snow cover effects, and complexities resulting from heterogeneous displacement in space and time. Here we combine SqueeSARTM data, targeted multi-temporal baseline DInSAR, GPS data, and detailed field morpho-structural mapping, to unravel the kinematics, internal segmentation, and style of activity of the Mt. Mater deep-seated gravitational slope deformation (DSGSD) in Valle Spluga (Italy). We retrieve slope kinematics by performing 2D decomposition (2D InSAR) of SqueeSARTM products derived from Sentinel-1 data acquired in ascending and descending orbits. To achieve a spatially-distributed characterization of DSGSD displacement patterns and activity, we process Sentinel-1 A/B images (2016-2019) with increasing temporal baselines (ranging from 24-days to 1-year) and generate several multi-temporal interferograms. Unwrapped displacement maps are validated using ground-based GPS data. Interferograms derived with different temporal baselines reveal a strong kinematic and morpho-structural heterogeneity and outline nested rockslides and active sectors, that arise from the background displacement signal of the main DSGSD. Seasonal interferograms, supported by GPS displacement measurements, reveal non-linear displacement trends suggesting a complex response of different slope sectors to rainfall and snowmelt. Our analyses clearly outline a composite slope instability with different nested sectors possibly undergoing different evolutionary trends towards failure. The results herein outline the potential of a targeted use of DInSAR for the detailed investigation of very slow rock slope deformations in different geological and geomorphological settings. Full article
(This article belongs to the Special Issue Leveraging on SAR Imagery for Landslide Detection and Monitoring)
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Article
Exploring the Combined Effect of Urbanization and Climate Variability on Urban Vegetation: A Multi-Perspective Study Based on More than 3000 Cities in China
Remote Sens. 2020, 12(8), 1328; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12081328 - 22 Apr 2020
Cited by 4 | Viewed by 958
Abstract
More than 3000 cities in China were used to study the effect of urbanization and local climate variability on urban vegetation across different geographical and urbanization conditions. The national scale estimation shows that China’s urban vegetation depicts a trend of degradation from 2000 [...] Read more.
More than 3000 cities in China were used to study the effect of urbanization and local climate variability on urban vegetation across different geographical and urbanization conditions. The national scale estimation shows that China’s urban vegetation depicts a trend of degradation from 2000 to 2015, especially in developed areas such as the Yangtze River Delta. According to the panel models, the increase of precipitation (PREC), solar radiation (SRAD), air temperature (TEMP), and specific humidity (SHUM) all enhance urban vegetation, while nighttime light intensity (NLI), population density (POPDEN), and fractal dimension (FRAC) do the opposite. The effects change along the East–West gradient; the influences of PREC and SHUM become greater, while those of TEMP, SRAD, NLI, AREA, and FRAC become smaller. PREC, SHUM, and SRAD play the most important roles in Northeast, Central, and North China, respectively. The role of FRAC and NLI in East China is much greater than in other regions. POPDEN remains influential across all altitudes, while FRAC affects only low-altitude cities. NLI plays a greater role in larger cities, while FRAC and POPDEN are the opposite. In cities outside of the five major urban agglomerations, PREC has a great influence while the key factors are more diversified inside. Full article
(This article belongs to the Special Issue Ecosystem Services with Remote Sensing)
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Article
Antarctic Supraglacial Lake Identification Using Landsat-8 Image Classification
Remote Sens. 2020, 12(8), 1327; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12081327 - 22 Apr 2020
Cited by 1 | Viewed by 1747
Abstract
Surface meltwater generated on ice shelves fringing the Antarctic Ice Sheet can drive ice-shelf collapse, leading to ice sheet mass loss and contributing to global sea level rise. A quantitative assessment of supraglacial lake evolution is required to understand the influence of Antarctic [...] Read more.
Surface meltwater generated on ice shelves fringing the Antarctic Ice Sheet can drive ice-shelf collapse, leading to ice sheet mass loss and contributing to global sea level rise. A quantitative assessment of supraglacial lake evolution is required to understand the influence of Antarctic surface meltwater on ice-sheet and ice-shelf stability. Cloud computing platforms have made the required remote sensing analysis computationally trivial, yet a careful evaluation of image processing techniques for pan-Antarctic lake mapping has yet to be performed. This work paves the way for automating lake identification at a continental scale throughout the satellite observational record via a thorough methodological analysis. We deploy a suite of different trained supervised classifiers to map and quantify supraglacial lake areas from multispectral Landsat-8 scenes, using training data generated via manual interpretation of the results from k-means clustering. Best results are obtained using training datasets that comprise spectrally diverse unsupervised clusters from multiple regions and that include rock and cloud shadow classes. We successfully apply our trained supervised classifiers across two ice shelves with different supraglacial lake characteristics above a threshold sun elevation of 20°, achieving classification accuracies of over 90% when compared to manually generated validation datasets. The application of our trained classifiers produces a seasonal pattern of lake evolution. Cloud shadowed areas hinder large-scale application of our classifiers, as in previous work. Our results show that caution is required before deploying ‘off the shelf’ algorithms for lake mapping in Antarctica, and suggest that careful scrutiny of training data and desired output classes is essential for accurate results. Our supervised classification technique provides an alternative and independent method of lake identification to inform the development of a continent-wide supraglacial lake mapping product. Full article
(This article belongs to the Section Environmental Remote Sensing)
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Article
Mapping of Maximum and Minimum Inundation Extents in the Amazon Basin 2014–2017 with ALOS-2 PALSAR-2 ScanSAR Time-Series Data
Remote Sens. 2020, 12(8), 1326; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12081326 - 22 Apr 2020
Cited by 3 | Viewed by 1066
Abstract
Seasonal inundation is an important effect that governs the distribution of ecosystems in the tropics. In the Amazon Basin, the seasonal flood pulse causes a difference in high and low water levels that can exceed 15 m. The associated flood duration and extent [...] Read more.
Seasonal inundation is an important effect that governs the distribution of ecosystems in the tropics. In the Amazon Basin, the seasonal flood pulse causes a difference in high and low water levels that can exceed 15 m. The associated flood duration and extent play an important role in land-atmosphere carbon exchange and affect the ecosystem’s carbon pool that originates from organic matter transported from upland and flooded forests. Studies of wetlands inundation across the Amazon Basin have utilized dual season mosaics from JERS-1 and wide-swath ScanSAR data from ALOS PALSAR to characterize inundation across the basin. This study builds upon past efforts with JERS-1 and ALOS PALSAR and uses ALOS-2 PALSAR-2 ScanSAR data to generate annual maximum and minimum inundation extent maps over the full Amazon Basin for the period spanning November 2014–October 2017. The study uses decision tree classification to create a maximum and a minimum inundation extent map for each year over this time period. The results show that a generalized algorithm that fits the entire basin has an 86% overall accuracy compared with a classification made for a local region from the same PALSAR-2 datasets. Comparisons with previous full-basin inundation maps by other L-band radars shows similar results for inundated areas during maximum inundation. The maps derived previously from JERS-1 and ALOS PALSAR show 7.3% and 6.9% inundated vegetation, respectively, and this study using PALSAR-2 shows values ranging between 5.5% and 7.0% across the three study years. Comparisons between the stage data across the basin and acquisition dates/periods for JERS-1 and PALSAR-2 show that the sensors capture the nature of the maximum and minimum flooding across the basins but have not successfully captured the exact maximum and minimum flood levels that have been recorded in the stage data. The inundation maps are publicly available under a Creative Commons (CC BY 4.0) licensefrom the Alaska Satellite Facility. Full article
(This article belongs to the Section Environmental Remote Sensing)
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Article
Detection of Magnesite and Associated Gangue Minerals using Hyperspectral Remote Sensing—A Laboratory Approach
Remote Sens. 2020, 12(8), 1325; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12081325 - 22 Apr 2020
Cited by 4 | Viewed by 1208
Abstract
This study introduced a detection method for magnesite and associated gangue minerals, including dolomite, calcite, and talc, based on mineralogical, chemical, and hyperspectral analyses using hand samples from thirteen different source locations and Specim hyperspectral short wave infrared (SWIR) hyperspectral images. Band ratio [...] Read more.
This study introduced a detection method for magnesite and associated gangue minerals, including dolomite, calcite, and talc, based on mineralogical, chemical, and hyperspectral analyses using hand samples from thirteen different source locations and Specim hyperspectral short wave infrared (SWIR) hyperspectral images. Band ratio methods and logistic regression models were developed based on the spectral bands selected by the random forest algorithm. The mineralogical analysis revealed the heterogeneity of mineral composition for naturally occurring samples, showing various carbonate and silicate minerals as accessory minerals. The Mg and Ca composition of magnesite and dolomite varied significantly, inferring the mixture of minerals. The spectral characteristics of magnesite and associated gangue minerals showed major absorption features of the target minerals mixed with the absorption features of accessory carbonate minerals and talc affected by mineral composition. The spectral characteristics of magnesite and dolomite showed a systematic shift of the Mg-OH absorption features toward a shorter wavelength with an increased Mg content. The spectral bands identified by the random forest algorithm for detecting magnesite and gangue minerals were mainly associated with spectral features manifested by Mg-OH, CO3, and OH. A two-step band ratio classification method achieved an overall accuracy of 92% and 55.2%. The classification models developed by logistic regression models showed a significantly higher accuracy of 98~99.9% for training samples and 82–99.8% for validation samples. Because the samples were collected from heterogeneous sites all over the world, we believe that the results and the approach to band selection and logistic regression developed in this study can be generalized to other case studies of magnesite exploration. Full article
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Article
Superpixel-Based Mixed Noise Estimation for Hyperspectral Images Using Multiple Linear Regression
Remote Sens. 2020, 12(8), 1324; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12081324 - 22 Apr 2020
Cited by 2 | Viewed by 898
Abstract
HSIs (hyperspectral images) obtained by new-generation hyperspectral sensors contain both electronic noise and photon noise with comparable power. Therefore, both the SI (signal-independent) component and the SD (signal-dependent) component have to be considered. In this paper, a superpixel-based noise estimation algorithm using MLR [...] Read more.
HSIs (hyperspectral images) obtained by new-generation hyperspectral sensors contain both electronic noise and photon noise with comparable power. Therefore, both the SI (signal-independent) component and the SD (signal-dependent) component have to be considered. In this paper, a superpixel-based noise estimation algorithm using MLR (multiple linear regression) is proposed for the above mixed noise to estimate the noise standard deviation of both SI component and SD component. First, superpixel segmentation is performed on the first principal component obtained by MNF (minimum noise fraction)-based dimensionality reduction to generate non-overlapping regions with similar pixels. Then, MLR is performed to remove the spectral correlation, and a system of linear equations with respect to noise variances is established according to the local sample statistics calculated within each superpixel. By solving the equations in terms of the least-squares method, the noise variances are determined. The experimental results show that the proposed algorithm provides more accurate local sample statistics, and yields a more accurate noise estimation than the other state-of-the-art algorithms for simulated HSIs. The results of the real-life data also verify the effectiveness of the proposed algorithm. Full article
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