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Remote Sens., Volume 15, Issue 12 (June-2 2023) – 256 articles

Cover Story (view full-size image): This review article, with its principal basis in French research, describes data, tools and methods that use remote sensing (RS) to support the spatial predictions of soil properties, and discusses their pros and cons. The review demonstrates that RS data are frequently used in soil mapping, (i) by considering them as a substitute for analytical measurements (left part of the graph), or (ii) by considering them as covariates related to the controlling factors of soil formation and evolution used in digital soil mapping (DSM) approaches (right part of the graph). It further highlights the great potential of RS imagery to improve DSM, providing an overview of the primary challenges and prospects related to DSM and future RS sensors. The discussion opens up broad prospects for the use of RS for DSM and natural resource monitoring. View this paper
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Article
Solid Angle Geometry-Based Modeling of Volume Scattering with Application in the Adaptive Decomposition of GF-3 Data of Sea Ice in Antarctica
Remote Sens. 2023, 15(12), 3208; https://doi.org/10.3390/rs15123208 - 20 Jun 2023
Viewed by 1464
Abstract
Over the last two decades, spaceborne polarimetric synthetic aperture radar (PolSAR) has been widely used to penetrate sea ice surfaces to achieve fully polarimetric high-resolution imaging at all times of day and in a range of weather conditions. Model-based polarimetric decomposition is a [...] Read more.
Over the last two decades, spaceborne polarimetric synthetic aperture radar (PolSAR) has been widely used to penetrate sea ice surfaces to achieve fully polarimetric high-resolution imaging at all times of day and in a range of weather conditions. Model-based polarimetric decomposition is a powerful tool used to extract useful physical and geometric information about sea ice from the matrix datasets acquired by PolSAR. The volume scattering of sea ice is usually modeled as the incoherent average of scatterings of a large volume of oriented ellipsoid particles that are uniformly distributed in 3D space. This uniform spatial distribution is often approximated as a uniform orientation distribution (UOD), i.e., the particles are uniformly oriented in all directions. This is achieved in the existing literature by ensuring the canting angle φ and tilt angle τ of particles uniformly distributed in their respective ranges and introducing a factor cosτ in the ensemble average. However, we find this implementation of UOD is not always effective, while a real UOD can be realized by distributing the solid angles of particles uniformly in 3D space. By deriving the total solid angle of the canting-tilt cell spanned by particles and combining the differential relationship between solid angle and Euler angles φ and τ, a complete expression of the joint probability density function pφ,τ that can always ensure the uniform orientation of particles of sea ice is realized. By ensemble integrating the coherency matrix of φ,τ-oriented particle with pφ,τ, a generalized modeling of the volume coherency matrix of 3D uniformly oriented spheroid particles is obtained, which covers factors such as radar observation geometry, particle shape, canting geometry, tilt geometry and transmission effect in a multiplicative way. The existing volume scattering models of sea ice constitute special cases. The performance of the model in the characterization of the volume behaviors was investigated via simulations on a volume of oblate and prolate particles with the differential reflectivity ZDR, polarimetric entropy H and scattering α angle as descriptors. Based on the model, several interesting orientation geometries were also studied, including the aligned orientation, complement tilt geometry and reflection symmetry, among which the complement tilt geometry is specifically highlighted. It involves three volume models that correspond to the horizontal tilt, vertical tilt and random tilt of particles within sea ice, respectively. To match the models to PolSAR data for adaptive decomposition, two selection strategies are provided. One is based on ZDR, and the other is based on the maximum power fitting. The scattering power that reduces the rank of coherency matrix by exactly one without violating the physical realizability condition is obtained to make full use of the polarimetric scattering information. Both the models and decomposition were finally validated on the Gaofen-3 PolSAR data of a young ice area in Prydz Bay, Antarctica. The adaptive decomposition result demonstrates not only the dominant vertical tilt preference of brine inclusions within sea ice, but also the subordinate random tilt preference and non-negligible horizontal tilt preference, which are consistent with the geometric selection mechanism that the c-axes of polycrystallines within sea ice would gradually align with depth. The experiment also indicates that, compared to the strategy based on ZDR, the maximum power fitting is preferable because it is entirely driven by the model and data and is independent of any empirical thresholds. Such soft thresholding enables this strategy to adaptively estimate the negative ZDR offset introduced by the transmission effect, which provides a novel inversion of the refractive index of sea ice based on polarimetric model-based decomposition. Full article
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Article
Remote Sensing Monitoring and Analysis of Spatiotemporal Changes in China’s Anthropogenic Carbon Emissions Based on XCO2 Data
Remote Sens. 2023, 15(12), 3207; https://doi.org/10.3390/rs15123207 - 20 Jun 2023
Cited by 1 | Viewed by 704
Abstract
The monitoring and analysis of the spatiotemporal distribution of anthropogenic carbon emissions is an important part of realizing China’s regional “dual carbon” goals; that is, the aim is for carbon emissions to peak in 2030 an to achieve carbon neutrality by 2060, as [...] Read more.
The monitoring and analysis of the spatiotemporal distribution of anthropogenic carbon emissions is an important part of realizing China’s regional “dual carbon” goals; that is, the aim is for carbon emissions to peak in 2030 an to achieve carbon neutrality by 2060, as well as achieving sustainable development of the ecological environment. The column-averaged CO2 dry air mole fraction (XCO2) of greenhouse gas remote sensing satellites has been widely used to monitor anthropogenic carbon emissions. However, selecting a reasonable background region to eliminate the influence of uncertainty factors is still an important challenge to monitor anthropogenic carbon emissions by using XCO2. Aiming at the problems of the imprecise selection of background regions, this study proposes to enhance the anthropogenic carbon emission signal in the XCO2 by using the regional comparison method based on the idea of zoning. First, this study determines the background region based on the Open-Data Inventory for Anthropogenic Carbon dioxide (ODIAC) dataset and potential temperature data. Second, the average value of the XCO2 in the background area was extracted and taken as the XCO2 background. On this basis, the XCO2 anomaly (XCO2ano) was obtained by regional comparison method. Finally, the spatiotemporal variation characteristics and trends of XCO2ano were analyzed, and the correlations between the number of residential areas and fossil fuel emissions were calculated. The results of the satellite observation data experiments over China from 2010 to 2020 show that the XCO2ano and anthropogenic carbon emissions have similar spatial distribution patterns. The XCO2ano in China changed significantly and was in a positive growth trend as a whole. The XCO2ano values have a certain positive correlation with the number of residential areas and observations of fossil fuel emissions. The purpose of this research is to enhance the anthropogenic carbon emission signals in satellite observation XCO2 data by combining ODIAC data and potential temperature data, achieve the remote sensing monitoring and analysis of spatiotemporal changes in anthropogenic carbon emissions over China, and provide technical support for the policies and paths of regional carbon emission reductions and ecological environmental protection. Full article
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Article
Unsupervised Machine Learning for GNSS Reflectometry Inland Water Body Detection
Remote Sens. 2023, 15(12), 3206; https://doi.org/10.3390/rs15123206 - 20 Jun 2023
Viewed by 741
Abstract
Inland water bodies, wetlands and their dynamics have a key role in a variety of scientific, economic, and social applications. They are significant in identifying climate change, water resource management, agricultural productivity, and the modeling of land–atmosphere exchange. Changes in the extent and [...] Read more.
Inland water bodies, wetlands and their dynamics have a key role in a variety of scientific, economic, and social applications. They are significant in identifying climate change, water resource management, agricultural productivity, and the modeling of land–atmosphere exchange. Changes in the extent and position of water bodies are crucial to the ecosystems. Mapping water bodies at a global scale is a challenging task due to the global variety of terrains and water surface. However, the sensitivity of spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) to different land surface properties offers the potential to detect and monitor inland water bodies. The extensive dataset available in the Cyclone Global Navigation Satellite System (CYGNSS), launched in December 2016, is used in our investigation. Although the main mission of CYGNSS was to measure the ocean wind speed in hurricanes and tropical cyclones, we show its capability of detecting and mapping inland water bodies. Both bistatic radar cross section (BRCS) and signal-to-noise ratio (SNR) can be used to detect, identify, and map the changes in the position and extent of inland waterbodies. We exploit the potential of unsupervised machine learning algorithms, more specifically the clustering methods, K-Means, Agglomerative, and Density-based Spatial Clustering of Applications with Noise (DBSCAN), for the detection of inland waterbodies. The results are evaluated based on the Copernicus land cover classification gridded maps, at 300 m spatial resolution. The outcomes demonstrate that CYGNSS data can identify and monitor inland waterbodies and their tributaries at high temporal resolution. K-Means has the highest Accuracy (93.5%) compared to the DBSCAN (90.3%) and Agglomerative (91.6%) methods. However, the DBSCAN method has the highest Recall (83.1%) as compared to Agglomerative (82.7%) and K-Means (79.2%). The current study offers valuable insights and analysis for further investigations in the field of GNSS-R and machine learning. Full article
(This article belongs to the Special Issue Applications of GNSS Reflectometry for Earth Observation III)
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Article
Augmented GBM Nonlinear Model to Address Spectral Variability for Hyperspectral Unmixing
Remote Sens. 2023, 15(12), 3205; https://doi.org/10.3390/rs15123205 - 20 Jun 2023
Viewed by 903
Abstract
Spectral unmixing (SU) is a significant preprocessing task for handling hyperspectral images (HSI), but its process is affected by nonlinearity and spectral variability (SV). Currently, SV is considered within the framework of linear mixing models (LMM), which ignores the nonlinear effects in the [...] Read more.
Spectral unmixing (SU) is a significant preprocessing task for handling hyperspectral images (HSI), but its process is affected by nonlinearity and spectral variability (SV). Currently, SV is considered within the framework of linear mixing models (LMM), which ignores the nonlinear effects in the scene. To address that issue, we consider the effects of SV on SU while investigating the nonlinear effects of hyperspectral images. Furthermore, an augmented generalized bilinear model is proposed to address spectral variability (abbreviated AGBM-SV). First, AGBM-SV adopts a generalized bilinear model (GBM) as the basic framework to address the nonlinear effects caused by second-order scattering. Secondly, scaling factors and spectral variability dictionaries are introduced to model the variability issues caused by the illumination conditions, material intrinsic variability, and other environmental factors. Then, a data-driven learning strategy is employed to set sparse and orthogonal bases for the abundance and spectral variability dictionaries according to the distribution characteristics of real materials. Finally, the alternating direction method of multipliers (ADMM) optimization method is used to split and solve the objective function, enabling the AGBM-SV algorithm to estimate the abundance and learn the spectral variability dictionary more effectively. The experimental results demonstrate the comparative superiority of the AGBM-SV method in both qualitative and quantitative perspectives, which can effectively solve the problem of spectral variability in nonlinear mixing scenes and to improve unmixing accuracy. Full article
(This article belongs to the Special Issue Self-Supervised Learning in Remote Sensing)
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Communication
Enhanced Radar Detection in the Presence of Specular Reflection Using a Single Transmitting Antenna and Three Receiving Antennas
Remote Sens. 2023, 15(12), 3204; https://doi.org/10.3390/rs15123204 - 20 Jun 2023
Viewed by 489
Abstract
Radar target echoes undergo fading in the presence of specular reflection, which is adverse to radar detection. To address this problem, this paper proposes a radar detection method that uses a single transmitting antenna and three receiving antennas. The proposed method uses the [...] Read more.
Radar target echoes undergo fading in the presence of specular reflection, which is adverse to radar detection. To address this problem, this paper proposes a radar detection method that uses a single transmitting antenna and three receiving antennas. The proposed method uses the maximum absolute value of the difference in the radar received signal power among the three receiving antennas as the test statistic. First, the target echo in the presence of specular reflection is analyzed. Then, selection of the required number of radar antennas and the heights at which they must be situated are discussed. Subsequently, analytical expressions of the radar detection probability and the false alarm probability are derived. Finally, simulation results are presented, which show that the proposed method improves radar detection performance in the presence of specular reflection. Full article
(This article belongs to the Special Issue Theory and Applications of MIMO Radar)
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Article
Implementing Cloud Computing for the Digital Mapping of Agricultural Soil Properties from High Resolution UAV Multispectral Imagery
Remote Sens. 2023, 15(12), 3203; https://doi.org/10.3390/rs15123203 - 20 Jun 2023
Viewed by 2047
Abstract
The spatial heterogeneity of soil properties has a significant impact on crop growth, making it difficult to adopt site-specific crop management practices. Traditional laboratory-based analyses are costly, and data extrapolation for mapping soil properties using high-resolution imagery becomes a computationally expensive procedure, taking [...] Read more.
The spatial heterogeneity of soil properties has a significant impact on crop growth, making it difficult to adopt site-specific crop management practices. Traditional laboratory-based analyses are costly, and data extrapolation for mapping soil properties using high-resolution imagery becomes a computationally expensive procedure, taking days or weeks to obtain accurate results using a desktop workstation. To overcome these challenges, cloud-based solutions such as Google Earth Engine (GEE) have been used to analyze complex data with machine learning algorithms. In this study, we explored the feasibility of designing and implementing a digital soil mapping approach in the GEE platform using high-resolution reflectance imagery derived from a thermal infrared and multispectral camera Altum (MicaSense, Seattle, WA, USA). We compared a suite of multispectral-derived soil and vegetation indices with in situ measurements of physical-chemical soil properties in agricultural lands in the Peruvian Mantaro Valley. The prediction ability of several machine learning algorithms (CART, XGBoost, and Random Forest) was evaluated using R2, to select the best predicted maps (R2 > 0.80), for ten soil properties, including Lime, Clay, Sand, N, P, K, OM, Al, EC, and pH, using multispectral imagery and derived products such as spectral indices and a digital surface model (DSM). Our results indicate that the predictions based on spectral indices, most notably, SRI, GNDWI, NDWI, and ExG, in combination with CART and RF algorithms are superior to those based on individual spectral bands. Additionally, the DSM improves the model prediction accuracy, especially for K and Al. We demonstrate that high-resolution multispectral imagery processed in the GEE platform has the potential to develop soil properties prediction models essential in establishing adaptive soil monitoring programs for agricultural regions. Full article
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Article
Suppression of Mainlobe Jammers with Quadratic Element Pulse Coding in MIMO Radar
Remote Sens. 2023, 15(12), 3202; https://doi.org/10.3390/rs15123202 - 20 Jun 2023
Viewed by 481
Abstract
The problem of suppressing mainlobe deceptive jammers, which spoof radar systems by generating multiple false targets, has attracted widespread attention. To tackle this problem, in this paper, the multiple-input multiple-output (MIMO) radar system was utilized by applying a quadratic element phase code (QEPC) [...] Read more.
The problem of suppressing mainlobe deceptive jammers, which spoof radar systems by generating multiple false targets, has attracted widespread attention. To tackle this problem, in this paper, the multiple-input multiple-output (MIMO) radar system was utilized by applying a quadratic element phase code (QEPC) to the transmitted pulses of different elements. In the receiver, by utilizing the spatial frequency and Doppler frequency offset generated after decoding, the jammers were equivalently distributed in the sidelobes of the joint Doppler-transmit-receive domain and were distinguishable from the true target. Then, further spatial frequency compensation and Doppler compensation were performed to align the true target to the zero point in the transmit spatial and Doppler domains. Moreover, by designing appropriate coding coefficients, the jammers were suppressed by data-independent Doppler-transmit-receive three-dimensional beamforming. However, the beamforming performance was sensitive to angular estimation mismatches, resulting in performance degradation of jammer suppression. To this end, a center-boundary null-broadening control (CBNBC) approach was used to broaden the nulls in the equivalent beampattern by generating multiple artificial jammers with preset powers around the nulls. Thus, the false targets (FTs) with deviations were sufficiently suppressed in the broadened notches. Numerical simulations and theoretical analysis demonstrated the performance of the developed jammer suppression method. Full article
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Article
Temporal and Spatial Variations of Potential and Actual Evapotranspiration and the Driving Mechanism over Equatorial Africa Using Satellite and Reanalysis-Based Observation
Remote Sens. 2023, 15(12), 3201; https://doi.org/10.3390/rs15123201 - 20 Jun 2023
Viewed by 794
Abstract
This study investigated the actual evapotranspiration (AET) and potential evapotranspiration (PET) seasonality, trends, abrupt changes, and driving mechanisms with global sea surface temperature (SST) and atmospheric circulation patterns over Equatorial Africa (EQA) during 1980–2020. The spatiotemporal characteristics of mean ET were computed based [...] Read more.
This study investigated the actual evapotranspiration (AET) and potential evapotranspiration (PET) seasonality, trends, abrupt changes, and driving mechanisms with global sea surface temperature (SST) and atmospheric circulation patterns over Equatorial Africa (EQA) during 1980–2020. The spatiotemporal characteristics of mean ET were computed based on a 40-year average at annual and seasonal scales. The Mann-Kendall statistical test, the Sen slope test, and the Bayesian test were used to analyze trends and detect abrupt changes. The results showed that the mean annual PET (AET) for 1980–2020 was 110 (70) mm. Seasonal mean PET (AET) values were 112 (72) in summer, 110 (85) in autumn, 109 (84) in winter, and 110 (58) in spring. The MK test showed an increasing (decreasing) rate, and the Sen slope identified upward (downward) at a rate of 0.35 (0.05) mm yr−10. The PET and AET abrupt change points were observed to happen in 1995 and 2000. Both dry and wet regions showed observed weak (strong) correlation coefficient values of 0.3 (0.8) between PET/AET and climate factors, but significant spatiotemporal differences existed. Generally, air temperature, soil moisture, and relative humidity best explain ET dynamics rather than precipitation and wind speed. The regional atmospheric circulation patterns are directly linked to ET but vary significantly in space and time. From a policy perspective, these findings may have implications for future water resource management. Full article
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Article
An Assessment of Negative Samples and Model Structures in Landslide Susceptibility Characterization Based on Bayesian Network Models
Remote Sens. 2023, 15(12), 3200; https://doi.org/10.3390/rs15123200 - 20 Jun 2023
Viewed by 991
Abstract
Landslide susceptibility mapping (LSM) characterizes landslide potential, which is essential for assessing landslide risk and developing mitigation strategies. Despite the significant progress in LSM research over the past two decades, several long-standing issues, such as uncertainties related to training samples and model selection, [...] Read more.
Landslide susceptibility mapping (LSM) characterizes landslide potential, which is essential for assessing landslide risk and developing mitigation strategies. Despite the significant progress in LSM research over the past two decades, several long-standing issues, such as uncertainties related to training samples and model selection, remain inadequately addressed in the literature. In this study, we employed a physically based susceptibility model, PISA-m, to generate four different non-landslide data scenarios and combine them with mapped landslides from Magoffin County, Kentucky, for model training. We utilized two Bayesian network model structures, Naïve Bayes (NB) and Tree-Augmented Naïve Bayes (TAN), to produce LSMs based on regional geomorphic conditions. After internal validation, we evaluated the robustness and reliability of the models using an independent landslide inventory from Owsley County, Kentucky. The results revealed considerable differences between the most effective model in internal validation (AUC = 0.969), which used non-landslide samples extracted exclusively from low susceptibility areas predicted by PISA-m, and the models’ unsatisfactory performance in external validation, as manifested by the identification of only 79.1% of landslide initiation points as high susceptibility areas. The obtained results from both internal and external validation highlighted the potential overfitting problem, which has largely been overlooked by previous studies. Additionally, our findings also indicate that TAN models consistently outperformed NB models when training datasets were the same due to the ability to account for variables’ dependencies by the former. Full article
(This article belongs to the Special Issue Machine Learning and Remote Sensing for Geohazards)
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Technical Note
Region-Based Sea Ice Mapping Using Compact Polarimetric Synthetic Aperture Radar Imagery with Learned Features and Contextual Information
Remote Sens. 2023, 15(12), 3199; https://doi.org/10.3390/rs15123199 - 20 Jun 2023
Viewed by 490
Abstract
Operational sea ice maps are usually generated manually using dual-polarization (DP) synthetic aperture radar (SAR) satellite imagery, but there is strong interest in automating this process. Recently launched satellites offer compact polarimetry (CP) imagery that provides more comprehensive polarimetric information compared to DP, [...] Read more.
Operational sea ice maps are usually generated manually using dual-polarization (DP) synthetic aperture radar (SAR) satellite imagery, but there is strong interest in automating this process. Recently launched satellites offer compact polarimetry (CP) imagery that provides more comprehensive polarimetric information compared to DP, which compels the use of CP for automated classification of SAR sea ice imagery. Existing sea ice scene classification algorithms using CP imagery rely on handcrafted features, while neural networks offer the potential of features that are more discriminating. We have developed a new and effective sea ice classification algorithm that leverages the nature of CP data. First, a residual-based convolutional neural network (ResCNN) is implemented to classify each pixel. In parallel, an unsupervised segmentation is performed to generate regions based on CP statistical properties. Regions are assigned a single class label by majority voting using the ResCNN output. For testing, quad-polarimetric (QP) SAR sea ice scenes from the RADARSAT Constellation Mission (RCM) are used, and QP, DP, CP, and reconstructed QP modes are compared for classification accuracy, while also comparing them to other classification approaches. Using CP achieves an overall accuracy of 96.86%, which is comparable to QP (97.16%), and higher than reconstructed QP and DP data by about 2% and 10%, respectively. The implemented algorithm using CP imagery provides an improved option for automated sea ice mapping. Full article
(This article belongs to the Section Ocean Remote Sensing)
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Article
Shoreline Analysis and Extraction Tool (SAET): A New Tool for the Automatic Extraction of Satellite-Derived Shorelines with Subpixel Accuracy
Remote Sens. 2023, 15(12), 3198; https://doi.org/10.3390/rs15123198 - 20 Jun 2023
Viewed by 1460
Abstract
SAET (Shoreline Analysis and Extraction Tool) is a novel open-source tool to enable the completely automatic detection of shoreline position changes using the optical imagery acquired by the Sentinel-2 and Landsat 8 and 9 satellites. SAET has been developed within the ECFAS (European [...] Read more.
SAET (Shoreline Analysis and Extraction Tool) is a novel open-source tool to enable the completely automatic detection of shoreline position changes using the optical imagery acquired by the Sentinel-2 and Landsat 8 and 9 satellites. SAET has been developed within the ECFAS (European Coastal Flood Awareness System) project, which is intended to be the first European service for coastal flood forecasting, management, and recovery analysis. The tool is developed to characterise the shoreline response associated with punctual events such as coastal storms as well as any other phenomenon. For a given beach segment, SAET facilitates the selection of the satellite images closest in time to the analysed events that offer an adequate cloud coverage level for analysing the shoreline change. Subsequently, the tool automatically downloads the images from their official repositories, pre-processes them and extracts the shoreline position with sub-pixel accuracy. In order to do so, an initial approximate definition of the shoreline is carried out at the pixel level using a water index thresholding, followed by an accurate extraction operating on the shortwave infrared bands to produce a sub-pixel line in vector formats (points and lines). The tool offers different settings to be adapted to the different coastal environments and beach typologies. Its main advantages refer to its autonomy, its efficiency in extracting complete satellite scenes, its flexibility in adapting to different environments and conditions, and its high subpixel accuracy. This work presents an accuracy assessment on a long Mediterranean sandy beach of SDSs extracted from L8 and S2 imagery against coincident alongshore reference lines, showing an accuracy of about 3 m RMSE. At the same time, the work shows an example of the usage of SAET for characterising the response to Storm Gloria (January 2020) on the beaches of Valencia (E Spain). SAET provides an efficient and completely automatic workflow that leads to accurate SDSs while only relying on publicly available information. The tool appears to be a useful extraction tool for beach monitoring, both for public administrations and individual users. Full article
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Article
Winter Wheat Drought Risk Assessment by Coupling Improved Moisture-Sensitive Crop Model and Gridded Vulnerability Curve
Remote Sens. 2023, 15(12), 3197; https://doi.org/10.3390/rs15123197 - 20 Jun 2023
Viewed by 485
Abstract
The crop drought risk assessment is an important basis for mitigating the effects of drought on crops. The study of drought using crop growth models is an integral part of agricultural drought risk research. The current Decision Support System for Agrotechnology Transfer (DSSAT) [...] Read more.
The crop drought risk assessment is an important basis for mitigating the effects of drought on crops. The study of drought using crop growth models is an integral part of agricultural drought risk research. The current Decision Support System for Agrotechnology Transfer (DSSAT) model is not sufficiently sensitive to moisture parameters when performing simulations, and most studies that conduct different scenario simulations to assess crop drought vulnerability are based on the site-scale. In this paper, we improved the moisture sensitivity of the Crop Environment Resource Synthesis System (CERES)-Wheat to improve the simulation accuracy of winter wheat under water stress, and then we assessed the drought intensity in the Beijing-Tianjin-Hebei region and constructed a gridded vulnerability curve. The grid vulnerability curves (1 km × 1 km) were quantitatively characterized using key points, and the drought risk distribution and zoning of winter wheat were evaluated under different return periods. The results show that the stress mechanism of coupled water and photosynthetic behavior improved the CERES-Wheat model. The accuracy of the modified model improved in terms of the above-ground biomass and yield compared with that before the modification, with increases of 20.39% and 11.45% in accuracy, respectively. The drought hazard intensity index of winter wheat in the study area from 1970 to 2019 exhibited a trend of high in the southwest and low in the southeast. The range of the multi-year average drought hazard intensity across the region was 0.29–0.61. There were some differences in the shape and characteristic covariates of the drought vulnerability curves among the different sub-zones. In terms of the cumulative loss rates, almost the entire region had a cumulative drought loss rate of 49.00–54.00%. Overall, the drought risk index decreased from west to east and from north to south under different return periods. This quantitative evaluation of the drought hazard intensity index provides a reference for agricultural drought risk evaluation. Full article
(This article belongs to the Special Issue Crop Quantitative Monitoring with Remote Sensing)
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Article
Assessing the Magnitude of the Amazonian Forest Blowdowns and Post-Disturbance Recovery Using Landsat-8 and Time Series of PlanetScope Satellite Constellation Data
Remote Sens. 2023, 15(12), 3196; https://doi.org/10.3390/rs15123196 - 20 Jun 2023
Cited by 1 | Viewed by 3457
Abstract
Blowdown events are a major natural disturbance in the central Amazon Forest, but their impact and subsequent vegetation recovery have been poorly understood. This study aimed to track post-disturbance regeneration after blowdown events in the Amazon Forest. We analyzed 45 blowdown sites identified [...] Read more.
Blowdown events are a major natural disturbance in the central Amazon Forest, but their impact and subsequent vegetation recovery have been poorly understood. This study aimed to track post-disturbance regeneration after blowdown events in the Amazon Forest. We analyzed 45 blowdown sites identified after September 2020 at Amazonas, Mato Grosso, and Colombia jurisdictions using Landsat-8 and PlanetScope NICFI satellite imagery. Non-photosynthetic vegetation (NPV), green vegetation (GV), and shade fractions were calculated for each image and sensor using spectral mixture analysis in Google Earth Engine. The results showed that PlanetScope NICFI data provided more regular and higher-spatial-resolution observations of blowdown areas than Landsat-8, allowing for more accurate characterization of post-disturbance vegetation recovery. Specifically, NICFI data indicated that just four months after the blowdown event, nearly half of ΔNPV, which represents the difference between the NPV after blowdown and the NPV before blowdown, had disappeared. ΔNPV and GV values recovered to pre-blowdown levels after approximately 15 months of regeneration. Our findings highlight that the precise timing of blowdown detection has huge implications on quantification of the magnitude of damage. Landsat data may miss important changes in signal due to the difficulty of obtaining regular monthly observations. These findings provide valuable insights into vegetation recovery dynamics following blowdown events. Full article
(This article belongs to the Special Issue Remote Sensing of the Amazon Region)
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Article
Characteristics of Regional GPS Crustal Deformation before the 2021 Yunnan Yangbi Ms 6.4 Earthquake and Its Implications for Determining Potential Areas of Future Strong Earthquakes
Remote Sens. 2023, 15(12), 3195; https://doi.org/10.3390/rs15123195 - 20 Jun 2023
Viewed by 639
Abstract
The 2021 Yangbi Ms 6.4 earthquake in Yunnan, China, occurred in an area where the Global Positioning System (GPS) geodetic observations are particularly intensive. Based on a detailed retrospective analysis of the GPS observations of about 133 stations distributed in the proximately 400 [...] Read more.
The 2021 Yangbi Ms 6.4 earthquake in Yunnan, China, occurred in an area where the Global Positioning System (GPS) geodetic observations are particularly intensive. Based on a detailed retrospective analysis of the GPS observations of about 133 stations distributed in the proximately 400 km × 400 km region that contains the area affected by the earthquake., we obtain a high-resolution GPS velocity field and strain rate field and then derive the present-day slip rates of major faults in the region with the commonly used half-space elastic dislocation model and constraints from the GPS velocity field. Furthermore, by calculating the seismic moment accumulation and release and deficit rates in the main fault segments and combining with the distribution characteristics of small earthquakes, we evaluate the regional seismic risk. The results show that (1) there was a localized prominent strain accumulation rate around the seismogenic area of the impending Yangbi Ms 6.4 earthquake, although this was not the only area with a prominent strain rate in the whole region. (2) The seismogenic area of the earthquake was just located where the strain direction was deflected, which, together with the localized outstanding maximum shear strain and dilatation rates, provides us with important hints to determine the potential areas of future strong earthquakes. (3) Of all the seismogenic fault segments with relatively high potentials, judged using the elapsed time of historical earthquakes and effective strain accumulation rate, the middle section of the Weixi–Qiaohou fault has a higher earthquake risk than the southern section, the Midu–Binchuan section of the Chenghai fault has a higher risk than the Yongsheng section and the Jianchuan section of the Jianchuan–Qiaohou–Lijiang–Xiaojinhe fault has a higher risk than the Lijiang section. Full article
(This article belongs to the Special Issue Monitoring Subtle Ground Deformation of Geohazards from Space)
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Article
Satellite Sensed Data-Dose Response Functions: A Totally New Approach for Estimating Materials’ Deterioration from Space
Remote Sens. 2023, 15(12), 3194; https://doi.org/10.3390/rs15123194 - 20 Jun 2023
Viewed by 555
Abstract
When construction materials are exposed to the atmospheric environment, they are subject to deterioration, which varies according to the time period of exposure and the location. A tool named Dose–Response Functions (DRFs) has been developed to estimate this deterioration. DRFs use specific air [...] Read more.
When construction materials are exposed to the atmospheric environment, they are subject to deterioration, which varies according to the time period of exposure and the location. A tool named Dose–Response Functions (DRFs) has been developed to estimate this deterioration. DRFs use specific air pollutants and climatic parameters as input data. Existing DRFs in the literature use only ground-based measurements as input data. This fact constitutes a limitation for the application of this tool because it is too expensive to establish and maintain such a large network of ground-based stations for pollution monitoring. In this study, we present the development of new DRFs using only satellite data as an input named Satellite Sensed Data Dose-Response Functions (SSD-DRFs). Due to the global coverage provided by satellites, this new tool for monitoring the corrosion/soiling of materials overcomes the previous limitation because it can be applied to any area of interest. To develop SSD-DRFs, we used measurements from MODIS (Moderate Resolution Imaging Spectroradiometer) and AIRS (Atmospheric Infrared Sounder) on board Aqua and OMI (Ozone Monitoring Instrument) on Aura. According to the obtained results, SSD-DRFs were developed for the case of carbon steel, zinc, limestone and modern glass materials. SSD-DRFs are shown to produce more reliable corrosion/soiling estimates than “traditional” DRFs using ground-based data. Furthermore, research into the development of the SSD-DRFs revealed that the different corrosion mechanisms taking place on the surface of a material do not act additively with each other but rather synergistically. Full article
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Article
Exploring Deep Learning Models on GPR Data: A Comparative Study of AlexNet and VGG on a Dataset from Archaeological Sites
Remote Sens. 2023, 15(12), 3193; https://doi.org/10.3390/rs15123193 - 20 Jun 2023
Viewed by 913
Abstract
This comparative study evaluates the performance of three popular deep learning architectures, AlexNet, VGG-16, and VGG-19, on a custom-made dataset of GPR C-scans collected from several archaeological sites. The introduced dataset has 15,000 training images and 3750 test images assigned to three classes: [...] Read more.
This comparative study evaluates the performance of three popular deep learning architectures, AlexNet, VGG-16, and VGG-19, on a custom-made dataset of GPR C-scans collected from several archaeological sites. The introduced dataset has 15,000 training images and 3750 test images assigned to three classes: Anomaly, Noise, and Structure. The aim is to assess the performance of the selected architectures applied to the custom dataset and examine the potential gains of using deeper and more complex architectures. Further, this study aims to improve the training dataset using augmentation techniques. For the comparisons, learning curves, confusion matrices, precision, recall, and f1-score metrics are employed. The Grad-CAM technique is also used to gain insights into the models’ learning. The results suggest that using more convolutional layers improves overall performance. Further, augmentation techniques can also be used to increase the dataset volume without causing overfitting. In more detail, the best-obtained model was trained using VGG-19 architecture and the modified dataset, where the training samples were raised to 60,000 images through augmentation techniques. This model reached a classification accuracy of 94.12% on an evaluation set with 170 unseen data. Full article
(This article belongs to the Special Issue Application of Remote Sensing in Cultural Heritage Research II)
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Article
Applying Reconstructed Daily Water Storage and Modified Wetness Index to Flood Monitoring: A Case Study in the Yangtze River Basin
Remote Sens. 2023, 15(12), 3192; https://doi.org/10.3390/rs15123192 - 20 Jun 2023
Viewed by 766
Abstract
The terrestrial water storage anomaly (TWSA) observed by the Gravity Recovery and Climate Experiment (GRACE) satellite and its successor GRACE Follow-On (GRACE-FO) provides a new means for monitoring floods. However, due to the coarse temporal resolution of GRACE/GRACE-FO, the understanding of flood occurrence [...] Read more.
The terrestrial water storage anomaly (TWSA) observed by the Gravity Recovery and Climate Experiment (GRACE) satellite and its successor GRACE Follow-On (GRACE-FO) provides a new means for monitoring floods. However, due to the coarse temporal resolution of GRACE/GRACE-FO, the understanding of flood occurrence mechanisms and the monitoring of short-term floods are limited. This study utilizes a statistical model to reconstruct daily TWS by combining monthly GRACE observations with daily temperature and precipitation data. The reconstructed daily TWSA is utilized to monitor the catastrophic flood event that occurred in the middle and lower reaches of the Yangtze River basin in 2020. Furthermore, the study compares the reconstructed daily TWSA with the vertical displacements of eight Global Navigation Satellite System (GNSS) stations at grid scale. A modified wetness index (MWI) and a normalized daily flood potential index (NDFPI) are introduced and compared with in situ daily streamflow to assess their potential for flood monitoring and early warning. The results show that terrestrial water storage (TWS) in the study area increases from early June, reaching a peak on 19 July, and then receding till September. The reconstructed TWSA better captures the changes in water storage on a daily scale compared to monthly GRACE data. The MWI and NDFPI based on the reconstructed daily TWSA both exceed the 90th percentile 7 days earlier than the in situ streamflow, demonstrating their potential for daily flood monitoring. Collectively, these findings suggest that the reconstructed TWSA can serve as an effective tool for flood monitoring and early warning. Full article
(This article belongs to the Special Issue GRACE for Earth System Mass Change: Monitoring and Measurement)
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Article
A Framework for Retrieving Soil Organic Matter by Coupling Multi-Temporal Remote Sensing Images and Variable Selection in the Sanjiang Plain, China
Remote Sens. 2023, 15(12), 3191; https://doi.org/10.3390/rs15123191 - 20 Jun 2023
Viewed by 652
Abstract
Soil organic matter (SOM) is an important soil property for agricultural production. Rising grain demand has increased the intensity of cultivated land development in the Sanjiang Plain of China, and there is a strong demand for SOM monitoring in this region. Therefore, Baoqing [...] Read more.
Soil organic matter (SOM) is an important soil property for agricultural production. Rising grain demand has increased the intensity of cultivated land development in the Sanjiang Plain of China, and there is a strong demand for SOM monitoring in this region. Therefore, Baoqing County of the Sanjiang Plain, an important grain production area, was considered the study area. In the study, we proposed a framework for high-accuracy SOM retrieval by coupling multi-temporal remote sensing (RS) images and variable selection algorithms. A total of 73 surface soil samples (0–20 cm) were collected in 2010, and Landsat 5 images acquired during the bare soil period (April, May, and June) were selected from 2008 to 2011. Three variable selection algorithms, namely, Genetic Algorithm, Random Frog and Competitive Adaptive Reweighted Sampling (CARS), were combined with Partial Least Squares Regression (PLSR) to build SOM retrieval models on the spectral bands and indices of the images. The results using a single-date image showed that the combination of variable selection algorithms and PLSR outperformed using PLSR alone, and CARS showed the best performance (R2 = 0.34, RMSE = 15.66 g/kg) among all the algorithms. Therefore, only CARS was applied to SOM retrieval in the different year interval groups. To investigate the effect of the image acquisition time, all images were divided into various year interval groups, and the resulting images were then stacked. The results using multi-temporal images showed that the SOM retrieval accuracy improved as the year interval lengthened. The optimal result (R2 = 0.59, RMSE = 11.81 g/kg) was obtained from the 2008–2011 group, wherein the difference indices derived from the images of 2009, 2010, and 2011 dominated the selected spectral variables. Moreover, the spatial prediction of SOM based on the optimal model was consistent with the distribution of SOM. Our study suggested that the proposed framework that couples stacked multi-temporal RS images with variable selection algorithms has potential for SOM retrieval. Full article
(This article belongs to the Special Issue Remote Sensing for Soil Mapping and Monitoring)
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Article
Estimating the Evolution of a Post-Little Ice Age Deglaciated Alpine Valley through the DEM of Difference (DoD)
Remote Sens. 2023, 15(12), 3190; https://doi.org/10.3390/rs15123190 - 19 Jun 2023
Viewed by 631
Abstract
Since the end of the Little Ice Age (LIA, ~1830), the accelerated glaciers’ shrinkage along mid-latitude high mountain areas promoted a quick readjustment of geomorphological processes with the onset of the paraglacial dynamic, making proglacial areas among the most sensitive Earth’s landscapes to [...] Read more.
Since the end of the Little Ice Age (LIA, ~1830), the accelerated glaciers’ shrinkage along mid-latitude high mountain areas promoted a quick readjustment of geomorphological processes with the onset of the paraglacial dynamic, making proglacial areas among the most sensitive Earth’s landscapes to ongoing climate change. A potentially useful remote-sensing method for investigating such dynamic areas is the DEM (Digital Elevation Model) of Difference (DoD) technique, which quantifies volumetric changes in a territory between successive topographic surveys. After a detailed geomorphological analysis and comparison with historical maps of the Martello Valley (central Italian Alps), we applied the DoD for reconstructing post-LIA deglaciation dynamics and reported on the surface effects of freshly-onset paraglacial processes. The head of the valley is still glacierized, with three main ice bodies resulting from the huge reduction of a single glacier present at the apogee of the LIA. Aftermath: the glaciers lose 60% of their initial surface area, largely modifying local landforms and expanding the surface of the proglacial areas. The DoD analysis of the 2006–2015 timeframe (based on registered DEM derived from LiDAR—Light Detection and Ranging—data) highlights deep surface elevation changes ranging from +38 ± 4.01 m along the foot of rock walls, where gravitative processes increased their intensity, to −47 ± 4.01 m where the melting of buried ice caused collapses of the proglacial surface. This approach permits estimating the volume of sediments mobilized and reworked by paraglacial processes. Here, in less than 10 years, −23,675 ± 1165 m3 of sediment were removed along the proglacial area and transported down valley, highlighting the dynamicity of proglacial areas. Full article
(This article belongs to the Topic Cryosphere: Changes, Impacts and Adaptation)
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Article
A Self-Supervised Learning Approach for Extracting China Physical Urban Boundaries Based on Multi-Source Data
Remote Sens. 2023, 15(12), 3189; https://doi.org/10.3390/rs15123189 - 19 Jun 2023
Cited by 1 | Viewed by 704
Abstract
Physical urban boundaries (PUBs) are basic geographic information data for defining the spatial extent of urban landscapes with non-agricultural land and non-agricultural economic activities. Accurately mapping PUBs provides a spatiotemporal database for urban dynamic monitoring, territorial spatial planning, and ecological environment protection. However, [...] Read more.
Physical urban boundaries (PUBs) are basic geographic information data for defining the spatial extent of urban landscapes with non-agricultural land and non-agricultural economic activities. Accurately mapping PUBs provides a spatiotemporal database for urban dynamic monitoring, territorial spatial planning, and ecological environment protection. However, traditional extraction methods often have problems, such as subjective parameter settings and inconsistent cartographic scales, making it difficult to identify PUBs objectively and accurately. To address these problems, we proposed a self-supervised learning approach for PUB extraction. First, we used nighttime light and OpenStreetMap road data to map the initial urban boundary for data preparation. Then, we designed a pretext task of self-supervised learning based on an unsupervised mutation detection algorithm to automatically mine supervised information in unlabeled data, which can avoid subjective human interference. Finally, a downstream task was designed as a supervised learning task in Google Earth Engine to classify urban and non-urban areas using impervious surface density and nighttime light data, which can solve the scale inconsistency problem. Based on the proposed method, we produced a 30 m resolution China PUB dataset containing six years (i.e., 1995, 2000, 2005, 2010, 2015, and 2020). Our PUBs show good agreement with existing products and accurately describe the spatial extent of urban areas, effectively distinguishing urban and non-urban areas. Moreover, we found that the gap between the national per capita GDP and the urban per capita GDP is gradually decreasing, but regional coordinated development and intensive development still need to be strengthened. Full article
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Article
A Novel Optimization Strategy of Sidelobe Suppression for Pulse Compression Weather Radar
Remote Sens. 2023, 15(12), 3188; https://doi.org/10.3390/rs15123188 - 19 Jun 2023
Viewed by 647
Abstract
The solid-state transmitters are widely adopted for weather radars, where pulse compression is operated to provide the required sensitivity and range resolution. Therefore, effective sidelobe suppression strategies must be employed, especially for weather observation. Currently, many methods can suppress the sidelobe to a [...] Read more.
The solid-state transmitters are widely adopted for weather radars, where pulse compression is operated to provide the required sensitivity and range resolution. Therefore, effective sidelobe suppression strategies must be employed, especially for weather observation. Currently, many methods can suppress the sidelobe to a very low level in the case of point targets or uniformly distributed targets. However, in strong convection weather process, the weather echo amplitude lies in a wide dynamic range and the main lobe of weak target is prone to being contaminated by the sidelobe of strong target, causing the degradation of weather fundamental data estimation, even generating artifacts and affecting the quantitative precipitation evaluation. In this paper, we propose a novel strategy which is the further processing of a general pulse compression radar to mitigate the effects of sidelobes. The proposed method is called the predominant component extraction (PCE), in which the re-weighting processing is operated after pulse compression, and then the echo of each bin is optimized and its energy will approach the real targets in each bin. It can improve the estimation of weak signals or even eliminate the artifact at the edge of the scene. Numerical simulation experiments and real-data verifications are implemented to validate the feasibility and superiority. It is noted that the proposed method has no requirement on the transmitted waveform and can be realized only by adding a step after pulse compression in the actual system. Full article
(This article belongs to the Special Issue Processing and Application of Weather Radar Data)
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Communication
Sensitivity of Grassland Coverage to Climate across Environmental Gradients on the Qinghai-Tibet Plateau
Remote Sens. 2023, 15(12), 3187; https://doi.org/10.3390/rs15123187 - 19 Jun 2023
Viewed by 529
Abstract
Grassland cover is strongly influenced by climate change. The response of grassland cover to climate change becomes complex with background climate. There have been some advances in research on the sensitivity of grassland vegetation to climate change around the world, but the differences [...] Read more.
Grassland cover is strongly influenced by climate change. The response of grassland cover to climate change becomes complex with background climate. There have been some advances in research on the sensitivity of grassland vegetation to climate change around the world, but the differences in climate sensitivity among grassland types are still unclear in alpine grassland. Therefore, we applied MODIS NDVI data and trend analysis methods to quantify the spatial and temporal variation of grassland vegetation cover on the Qinghai-Tibet Plateau. Then, we used multiple regression models to analyze the sensitivity of fractional vegetation cover (FVC) to climatic factors (Temperature, Precipitation, Solar radiation, Palmer drought severity index) and summarized the potential mechanisms of vegetation sensitivity to different climatic gradients. Our results showed (1) a significant increasing trend in alpine desert FVC from 2000–2018 (1.12 × 10−3/a, R2 = 0.56, p < 0.001) but no significant trend in other grassland types. (2) FVC sensitivity to climatic factors varied among grassland types, especially in the alpine desert, which had over 60% of the area with positive sensitivity to temperature, precipitation and PDSI. (3) The sensitivity of grassland FVC to heat factors decreases with rising ambient temperature while the sensitivity to moisture increases. Similarly, the sensitivity to moisture decreases while the sensitivity to thermal factors increases along the moisture gradient. Furthermore, the results suggest that future climate warming will promote grassland in cold and wet areas of the Qinghai-Tibet Plateau and may suppress vegetation in warmer areas. In contrast, the response of the alpine desert to future climate is more stable. Studying the impact of climate variation at a regional scale could enhance the adaptability of vegetation in future global climates. Full article
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Article
Accuracy Assessment of Eleven Medium Resolution Global and Regional Land Cover Land Use Products: A Case Study over the Conterminous United States
Remote Sens. 2023, 15(12), 3186; https://doi.org/10.3390/rs15123186 - 19 Jun 2023
Viewed by 608
Abstract
Land cover land use (LCLU) products provide essential information for numerous environmental and human studies. Here, we assess the accuracy of eleven global and regional products over the conterminous U.S. using 25,000 high-confidence randomly distributed samples. Results show that in general, the National [...] Read more.
Land cover land use (LCLU) products provide essential information for numerous environmental and human studies. Here, we assess the accuracy of eleven global and regional products over the conterminous U.S. using 25,000 high-confidence randomly distributed samples. Results show that in general, the National Land Cover Database (NLCD) and the Land Change Monitoring, Assessment and Projection (LCMAP) outperform other multi-class products, both in terms of higher individual class accuracy and with accuracy variability across classes. More specifically, F1 accuracy comparisons between the best performing USGS and non-USGS products indicate: (i) similar performance for the water class, (ii) USGS product outperformance in the developed (+1.3%), grass/shrub (+3.2%) and tree cover (+4.2%) classes, and (iii) non-USGS product (WorldCover) gains in the cropland (+5.1%) class. The NLCD and LCMAP also outperformed specialized single-class products, such as the Hansen Global Forest Change, the Cropland Data Layer and the Global Artificial Impervious Areas, while offering comparable results to the Global Surface Water Dynamics product. Spatial visualizations also allowed accuracy comparisons across different geographic areas. In general, the NLCD and LCMAP have disagreements mainly in the middle and southeastern part of conterminous U.S. while Esri, WorldCover and Dynamic World have most errors in the western U.S. Comparisons were also undertaken on a subset of the reference data, called spatial edge samples, that identifies samples surrounded by neighboring samples of different class labels, thus excluding easy-to-classify homogenous areas. There, the WorldCover product offers higher accuracies for the highly dynamic grass/shrub (+4.4%) and cropland (+8.1%) classes when compared to the NLCD and LCMAP products. An important conclusion while looking at these challenging samples is that except for the tree class (78%), the best performing products per class range in accuracy between 55% and 70%, which suggests that there is substantial room for improvement. Full article
(This article belongs to the Section Earth Observation Data)
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Article
A Global Structure and Adaptive Weight Aware ICP Algorithm for Image Registration
Remote Sens. 2023, 15(12), 3185; https://doi.org/10.3390/rs15123185 - 19 Jun 2023
Cited by 1 | Viewed by 685
Abstract
As an important technology in 3D vision, point-cloud registration has broad development prospects in the fields of space-based remote sensing, photogrammetry, robotics, and so on. Of the available algorithms, the Iterative Closest Point (ICP) algorithm has been used as the classic algorithm for [...] Read more.
As an important technology in 3D vision, point-cloud registration has broad development prospects in the fields of space-based remote sensing, photogrammetry, robotics, and so on. Of the available algorithms, the Iterative Closest Point (ICP) algorithm has been used as the classic algorithm for solving point cloud registration. However, with the point cloud data being under the influence of noise, outliers, overlapping values, and other issues, the performance of the ICP algorithm will be affected to varying degrees. This paper proposes a global structure and adaptive weight aware ICP algorithm (GSAW-ICP) for image registration. Specifically, we first proposed a global structure mathematical model based on the reconstruction of local surfaces using both the rotation of normal vectors and the change in curvature, so as to better describe the deformation of the object. The model was optimized for the convergence strategy, so that it had a wider convergence domain and a better convergence effect than either of the original point-to-point or point-to-point constrained models. Secondly, for outliers and overlapping values, the GSAW-ICP algorithm was able to assign appropriate weights, so as to optimize both the noise and outlier interference of the overall system. Our proposed algorithm was extensively tested on noisy, anomalous, and real datasets, and the proposed method was proven to have a better performance than other state-of-the-art algorithms. Full article
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Article
A New Blind Selection Approach for Lunar Landing Zones Based on Engineering Constraints Using Sliding Window
Remote Sens. 2023, 15(12), 3184; https://doi.org/10.3390/rs15123184 - 19 Jun 2023
Viewed by 605
Abstract
Deep space exploration has risen in interest among scientists in recent years, with soft landings being one of the most straightforward ways to acquire knowledge about the Moon. In general, landing mission success depends on the selection of landing zones, and there are [...] Read more.
Deep space exploration has risen in interest among scientists in recent years, with soft landings being one of the most straightforward ways to acquire knowledge about the Moon. In general, landing mission success depends on the selection of landing zones, and there are currently few effective quantitative models that can be used to select suitable landing zones. When automatic landing zones are selected, the grid method used for data partitioning tends to miss potentially suitable landing sites between grids. Therefore, this study proposes a new engineering-constrained approach for landing zone selection using LRO LOLA-based slope data as original data based on the sliding window method, which solves the spatial omission problem of the grid method. Using the threshold ratio, mean, coefficient of variation, Moran’s I, and overall rating, this method quantifies the suitability of each sliding window. The k-means clustering algorithm is adopted to determine the suitability threshold for the overall rating. The results show that 20 of 22 lunar soft landing sites are suitable for landing. Additionally, 43 of 50 landing sites preselected by the experts (suitable landing sites considering a combination of conditions) are suitable for landing, accounting for 90.9% and 86% of the total number, respectively, for a window size of 0.5° × 0.5°. Among them, there are four soft landing sites: Surveyor 3, 6, 7, and Apollo 15, which are not suitable for landing in the evaluation results of the grid method. However, they are suitable for landing in the overall evaluation results of the sliding window method, which significantly reduces the spatial omission problem of the grid method. In addition, four candidate landing regions, including Aristarchus Crater, Marius Hills, Moscoviense Basin, and Orientale Basin, were evaluated for landing suitability using the sliding window method. The suitability of the landing area within the candidate range of small window sizes was 0.90, 0.97, 0.49, and 0.55. This indicates the capacity of the method to analyze an arbitrary range during blind landing zone selection. The results can quantify the slope suitability of the landing zones from an engineering perspective and provide different landing window options. The proposed method for selecting lunar landing zones is clearly superior to the gridding method. It enhances data processing for automatic lunar landing zone selection and progresses the selection process from qualitative to quantitative. Full article
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Article
The Transport Path and Vertical Structure of Dust Storms in East Asia and the Impacts on Cities in Northern China
Remote Sens. 2023, 15(12), 3183; https://doi.org/10.3390/rs15123183 - 19 Jun 2023
Viewed by 618
Abstract
Dust storm disasters have emerged as a significant environmental challenge in East Asia. However, relying on a single monitoring method to track dust storms presents limitations and can be variable. Therefore, it is necessary to use a combination of ground and remote sensing [...] Read more.
Dust storm disasters have emerged as a significant environmental challenge in East Asia. However, relying on a single monitoring method to track dust storms presents limitations and can be variable. Therefore, it is necessary to use a combination of ground and remote sensing monitoring methods to explore the source and impact range of dust storms in order to fully characterize them. To achieve this, we examined the sources and impact ranges of dust storms in East Asia from 1980 to 2020 using both ground station data and remote sensing data. In addition, we focused on three specific dust storm events in the region. Our results indicate that the central source areas of dust storms are located in southern Mongolia and the Taklamakan Desert in China. Dust storms are mainly transported and spread in the northwestern region, while they are relatively rare in the southeastern region. The HYSPLIT model simulations reveal that the primary source directions of dust storms in East Asia are northwest, west, and north, the region involved includes Kazakhstan, southern Mongolia, and the Taklimakan Desert in China. The vertical structure of the dust storm layer depends on the source of the dust storm and the intensity of the dust storm event. Dust grain stratification probably occurs due to differences in dust storm sources, grain size, and regularity. These findings demonstrate that a combination of ground-based and remote sensing monitoring methods is an effective approach to fully characterize dust storms and can provide more comprehensive information for dust storm studies. Full article
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Correction
Correction: Sesnie et al. In-Situ and Remote Sensing Platforms for Mapping Fine-Fuels and Fuel-Types in Sonoran Semi-Desert Grasslands. Remote Sens. 2018, 10, 1358
Remote Sens. 2023, 15(12), 3182; https://doi.org/10.3390/rs15123182 - 19 Jun 2023
Viewed by 336
Abstract
Text Correction [...] Full article
(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)
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Article
A Proposal for Automatic Coastline Extraction from Landsat 8 OLI Images Combining Modified Optimum Index Factor (MOIF) and K-Means
Remote Sens. 2023, 15(12), 3181; https://doi.org/10.3390/rs15123181 - 19 Jun 2023
Viewed by 674
Abstract
The coastal environment is a natural and economic resource of extraordinary value, but it is constantly modifying and susceptible to climate change, human activities and natural hazards. Remote sensing techniques have proved to be excellent for coastal area monitoring, but the main issue [...] Read more.
The coastal environment is a natural and economic resource of extraordinary value, but it is constantly modifying and susceptible to climate change, human activities and natural hazards. Remote sensing techniques have proved to be excellent for coastal area monitoring, but the main issue is to detect the borderline between water bodies (ocean, sea, lake or river) and land. This research aims to define a rapid and accurate methodological approach, based on the k-means algorithm, to classify the remotely sensed images in an unsupervised way to distinguish water body pixels and detect coastline. Landsat 8 Operational Land Imager (OLI) multispectral satellite images were considered. The proposal requires applying the k-means algorithm only to the most appropriate multispectral bands, rather than using the entire dataset. In fact, by using only suitable bands to detect the differences between water and no-water (vegetation and bare soil), more accurate results were obtained. For this scope, a new index based on the optimum index factor (OIF) was applied to identify the three best-performing bands for the purpose. The direct comparison between the automatically extracted coastline and the manually digitized one was used to evaluate the product accuracy. The results were very satisfactory and the combination involving bands B2 (blue), B5 (near infrared), and B6 (short-wave infrared-1) provided the best performance. Full article
(This article belongs to the Special Issue Mapping and Change Analysis Applications with Remote Sensing and GIS)
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Article
Morphology Dynamics of Ice Cover in a River Bend Revealed by the UAV-GPR and Sentinel-2
Remote Sens. 2023, 15(12), 3180; https://doi.org/10.3390/rs15123180 - 19 Jun 2023
Cited by 1 | Viewed by 444
Abstract
After the formation of the bend ice cover, the ice thickness of the bend is not uniformly distributed, and an open-water area is usually formed downstream of the bend. The spatial and temporal variation of the ice thickness in seven cross sections was [...] Read more.
After the formation of the bend ice cover, the ice thickness of the bend is not uniformly distributed, and an open-water area is usually formed downstream of the bend. The spatial and temporal variation of the ice thickness in seven cross sections was determined via Unmanned Aerial Vehicle Ground Penetrating Radar (UAV-GPR) technology and traditional borehole measurements. The plane morphology change of the open water was observed by Sentinel-2. The results show that the average dielectric permittivity of GPR was 3.231, 3.249, and 3.317 on three surveys (5 January 2022, 16 February 2022, and 25 February 2022) of the Yellow River ice growing period, respectively. The average ice thickness of the three surveys was 0.402 m, 0.509 m, and 0.633 m, respectively. The ice thickness of the concave bank was larger than that of the convex bank. The plane morphology of the open water first shrinks rapidly longitudinally and then shrinks slowly transversely. The vertical boundary of the open water was composed of two arcs, in which the slope of Arc I (close to the water surface) was steeper than that of Arc II, and the hazardous distance of the open-water boundary was 10.3 m. The increased flow mostly affected the slope change of Arc I. Finally, we discuss the variation of hummocky ice and flat ice in GPR images and the physical factors affecting GPR detection accuracy, as well as the ice-thickness variation of concave and convex banks in relation to channel curvature. Full article
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Article
First Experience with Zhuhai-1 Hyperspectral Data for Urban Dominant Tree Species Classification in Shenzhen, China
Remote Sens. 2023, 15(12), 3179; https://doi.org/10.3390/rs15123179 - 19 Jun 2023
Viewed by 717
Abstract
An accurate spatial distribution map of the urban dominant tree species is crucial for evaluating the ecosystem service value of urban forests and formulating urban sustainable development strategies. Spaceborne hyperspectral remote sensing has been utilized to distinguish tree species, but these hyperspectral data [...] Read more.
An accurate spatial distribution map of the urban dominant tree species is crucial for evaluating the ecosystem service value of urban forests and formulating urban sustainable development strategies. Spaceborne hyperspectral remote sensing has been utilized to distinguish tree species, but these hyperspectral data have a low spatial resolution (pixel size ≥ 30 m), which limits their ability to differentiate tree species in urban areas characterized by fragmented patches and robust spatial heterogeneity. Zhuhai-1 is a new hyperspectral satellite sensor with a higher spatial resolution of 10 m. This study aimed to evaluate the potential of Zhuhai-1 hyperspectral imagery for classifying the urban dominant tree species. We first extracted 32 reflectance bands and 18 vegetation indices from Zhuhai-1 hyperspectral data. We then used the random forest classifier to differentiate 28 dominant tree species in Shenzhen based on these hyperspectral features. Finally, we analyzed the effects of the classification paradigm, classifier, and species number on the classification accuracy. We found that combining the hyperspectral reflectance bands and vegetation indices could effectively distinguish the 28 dominant tree species in Shenzhen, obtaining an overall accuracy of 76.8%. Sensitivity analysis results indicated that the pixel-based classification paradigm was slightly superior to the object-based paradigm. The random forest classifier proved to be the optimal classifier for distinguishing tree species using Zhuhai-1 hyperspectral imagery. Moreover, reducing the species number could slowly improve the classification accuracy. These findings suggest that Zhuhai-1 hyperspectral data can identify the urban dominant tree species with accuracy and holds potential for application in other cities. Full article
(This article belongs to the Section Urban Remote Sensing)
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