Remote Sensing

36 pages, 51143 KB

Open AccessEditor’s ChoiceArticle

UAV-PPK Photogrammetry, GIS, and Soil Analysis to Estimate Long-Term Slip Rates on Active Faults in a Seismic Gap of Northern Calabria (Southern Italy)

by Daniele Cirillo, Anna Chiara Tangari, Fabio Scarciglia, Giusy Lavecchia and Francesco Brozzetti

Remote Sens. 2025, 17(19), 3366; https://doi.org/10.3390/rs17193366 - 5 Oct 2025

Cited by 2 | Viewed by 1101

Abstract

The study of faults in seismic gap areas is essential for assessing the potential for future seismic activity and developing strategies to mitigate its impact. In this research, we employed a combination of geomorphological analysis, aerophotogrammetry, high-resolution topography, and soil analysis to estimate [...] Read more.

The study of faults in seismic gap areas is essential for assessing the potential for future seismic activity and developing strategies to mitigate its impact. In this research, we employed a combination of geomorphological analysis, aerophotogrammetry, high-resolution topography, and soil analysis to estimate the age of tectonically exposed fault surfaces in a seismic gap area. Our focus was on the Piano delle Rose Fault in the northern Calabria region, (southern Italy), which is a significant regional tectonic structure associated with seismic hazards. We conducted a field survey to carry out structural and pedological observations and collect soil samples from the fault surface. These samples were analyzed to estimate the fault’s age based on their features and degree of pedogenic development. Additionally, we used high-resolution topography and aerophotogrammetry to create a detailed 3D model of the fault surface, allowing us to identify features such as fault scarps and offsets. Our results indicate recent activity on the fault surface, suggesting that the Piano delle Rose Fault may pose a significant seismic hazard. Soil analysis suggests that the onset of the fault surface is relatively young, estimated in an interval time from 450,000 to ~ 300,000 years old. Considering these age constraints, the long-term slip rates are estimated to range between ~0.12 mm/yr and ~0.33 mm/yr, which are values comparable with those of many other well-known active faults of the Apennines extensional belt. Analyses of key fault exposures document cumulative displacements up to 21 m. These values yield long-term slip rates ranging from ~0.2 mm/yr (100,000 years) to ~1.0 mm/yr (~20,000 years LGM), indicating persistent Late Quaternary activity. A second exposure records ~0.6 m of displacement in very young soils, confirming surface faulting during recent times and suggesting that the fault is potentially capable of generating ground-rupturing earthquakes. High-resolution topography and aerophotogrammetry analyses show evidence of ongoing tectonic deformation, indicating that the area is susceptible to future seismic activity and corresponding risk. Our study highlights the importance of integrating multiple techniques for examining fault surfaces in seismic gap areas. By combining geomorphological analysis, aerophotogrammetry, high-resolution topography, and soil analysis, we gain a comprehensive understanding of the structure and behavior of faults. This approach can help assess the potential for future seismic activity and develop strategies for mitigating its impact. Full article

(This article belongs to the Special Issue Multiplatform Remote Sensing Techniques for Active Tectonics, Seismotectonics, and Volcanic Hazard Assessment (Second Edition))

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24 pages, 7126 KB

Open AccessEditor’s ChoiceArticle

FLDSensing: Remote Sensing Flood Inundation Mapping with FLDPLN

by Jackson Edwards, Francisco J. Gomez, Son Kim Do, David A. Weiss, Jude Kastens, Sagy Cohen, Hamid Moradkhani, Venkataraman Lakshmi and Xingong Li

Remote Sens. 2025, 17(19), 3362; https://doi.org/10.3390/rs17193362 - 4 Oct 2025

Viewed by 2335

Abstract

Flood inundation mapping (FIM), which is essential for effective disaster response and management, requires rapid and accurate delineation of flood extent and depth. Remote sensing FIM, especially using satellite imagery, offers certain capabilities and advantages, but also faces challenges such as cloud and [...] Read more.

Flood inundation mapping (FIM), which is essential for effective disaster response and management, requires rapid and accurate delineation of flood extent and depth. Remote sensing FIM, especially using satellite imagery, offers certain capabilities and advantages, but also faces challenges such as cloud and canopy obstructions and flood depth estimation. This research developed a novel hybrid approach, named FLDSensing, which combines remote sensing imagery with the FLDPLN (pronounced “floodplain”) flood inundation model, to improve remote sensing FIM in both inundation extent and depth estimation. The method first identifies clean flood edge pixels (i.e., floodwater pixels next to bare ground), which, combined with the FLDPLN library, are used to estimate the water stages at certain stream pixels. Water stage is further interpolated and smoothed at additional stream pixels, which is then used with an FLDPLN library to generate flood extent and depth maps. The method was applied over the Verdigris River in Kansas to map the flood event that occurred in late May 2019, where Sentinel-2 imagery was used to generate remote sensing FIM and to identify clean water-edge pixels. The results show a significant improvement in FIM accuracy when compared to a HEC-RAS 2D (Version 6.5) benchmark, with the metrics of CSI/POD/FAR/F1-scores reaching 0.89/0.98/0.09/0.94 from 0.55/0.56/0.03/0.71 using remote sensing alone. The method also performed favorably against several existing hybrid approaches, including FLEXTH and FwDET 2.1. This study demonstrates that integrating remote sensing imagery with the FLDPLN model, which uniquely estimates stream stage through floodwater-edges, offers a more effective hybrid approach to enhancing remote sensing-based FIM. Full article

(This article belongs to the Special Issue Multi-Source Remote Sensing Data in Hydrology and Water Management)

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36 pages, 9276 KB

Open AccessEditor’s ChoiceArticle

Understanding Landslide Expression in SAR Backscatter Data: Global Study and Disaster Response Application

by Erin Lindsay, Alexandra Jarna Ganerød, Graziella Devoli, Johannes Reiche, Steinar Nordal and Regula Frauenfelder

Remote Sens. 2025, 17(19), 3313; https://doi.org/10.3390/rs17193313 - 27 Sep 2025

Viewed by 1334

Abstract

Cloud cover can delay landslide detection in optical satellite imagery for weeks, complicating disaster response. Synthetic Aperture Radar (SAR) backscatter imagery, which is widely used for monitoring floods and avalanches, remains underutilised for landslide detection due to a limited understanding of landslide signatures [...] Read more.

Cloud cover can delay landslide detection in optical satellite imagery for weeks, complicating disaster response. Synthetic Aperture Radar (SAR) backscatter imagery, which is widely used for monitoring floods and avalanches, remains underutilised for landslide detection due to a limited understanding of landslide signatures in SAR data. We developed a conceptual model of landslide expression in SAR backscatter (σ°) change images through iterative investigation of over 1000 landslides across 30 diverse study areas. Using multi-temporal composites and dense time series Sentinel-1 C-band SAR data, we identified characteristic patterns linked to land cover, terrain, and landslide material. The results showed either increased or decreased backscatter depending on environmental conditions, with reduced visibility in urban or mixed vegetation areas. Detection was also hindered by geometric distortions and snow cover. The diversity of landslide expression illustrates the need to consider local variability and multi-track (ascending and descending) satellite data in designing representative training datasets for automated detection models. The conceptual model was applied to three recent disaster events using the first post-event Sentinel-1 image, successfully identifying previously unknown landslides before optical imagery became available in two cases. This study provides a theoretical foundation for interpreting landslides in SAR imagery and demonstrates its utility for rapid landslide detection. The findings support further exploration of rapid landslides in SAR backscatter data and future development of automated detection models, offering a valuable tool for disaster response. Full article

(This article belongs to the Special Issue Advances in Synthetic Aperture Radar Data Processing and Application (Second Edition))

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28 pages, 4355 KB

Open AccessEditor’s ChoiceArticle

Automated Dating of Recent Landslides Using Sentinel-2 and Sentinel-1 on Google Earth Engine

by Liborio Barbera, Antonino Maltese and Christian Conoscenti

Remote Sens. 2025, 17(19), 3270; https://doi.org/10.3390/rs17193270 - 23 Sep 2025

Viewed by 2347

Abstract

Landslides are complex phenomena controlled by natural and anthropogenic factors. In recent years, the need to understand their dynamics has driven the development of methodologies for improving risk monitoring and mitigation. In this context, landslide occurrence dating helps identify triggering causes and critical [...] Read more.

Landslides are complex phenomena controlled by natural and anthropogenic factors. In recent years, the need to understand their dynamics has driven the development of methodologies for improving risk monitoring and mitigation. In this context, landslide occurrence dating helps identify triggering causes and critical thresholds. This study introduces a fully automated and objective methodology, implemented on the Google Earth Engine platform, which allows access to and processing of large volumes of satellite data online, speeding up analyses and facilitating method sharing. The procedure exploits the complementarity between changes in vegetation cover detected through vegetation indices and changes in radar backscattering, intending to narrow the time window in which the landslide occurred. In 45 out of 46 cases analyzed, the time interval of landslide occurrence could be correctly identified, with a mean temporal window of approximately 8 days (range—3–12 days), confirming the robustness of the approach across different geomorphological settings and landslide types. The complete automation of the workflow is among the most innovative aspects of the methodology, as it allows the script to be directly and consistently applied to a wide range of recent and vegetated landslides with sizes larger than about 10 Sentinel-2 pixels without requiring additional manual procedures. Full article

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19 pages, 11289 KB

Open AccessEditor’s ChoiceArticle

Land Cover Types Drive the Surface Temperature for Upscaling Surface Urban Heat Islands with Daylight Images

by Julien Radoux, Margot Dominique, Andrew Hartley, Céline Lamarche, Audric Bos and Pierre Defourny

Remote Sens. 2025, 17(16), 2815; https://doi.org/10.3390/rs17162815 - 14 Aug 2025

Viewed by 1915

Abstract

The widespread availability and spatial coverage of land surface temperature (LST) estimates from space often result in LST being used as a proxy for near-surface air temperature in order to characterize the urban heat island (UHI) effect. High-spatial-resolution satellite-based LST estimates from sensors [...] Read more.

The widespread availability and spatial coverage of land surface temperature (LST) estimates from space often result in LST being used as a proxy for near-surface air temperature in order to characterize the urban heat island (UHI) effect. High-spatial-resolution satellite-based LST estimates from sensors such as Landsat-8 provide the spatial and thematic details necessary to understand the potential effects of urban greening measures to mitigate the increased frequency and intensity of heatwaves that are projected to occur as a result of human-induced climate change. Here, we investigate the influence of land cover on Surface Urban Heat Island (SUHI) observations of LST using a technique to reduce the spatial spread of the per-pixel temperature observation. Additionally, using land cover-based linear mixture models, we downscale the surface temperature to a 2 m spatial resolution. We find a mean difference in LST, compared to the city average, of +8.94 °C (+/−1.87 °C at 95% CI) for built-up cover type, compared to a difference of −7.42 °C (+/−0.8 °C) for broadleaf trees. This highlights the potential benefits of creating urban green spaces for mitigating the UHI amplification of extreme heatwaves. Furthermore, we highlight the need for improved observations of night-time temperatures, e.g., from forthcoming missions such as TRISHNA, in order to fully capture the diurnal variability of land surface temperature and energy fluxes. Full article

(This article belongs to the Special Issue Monitoring and Managing Environmental Sustainability Using Remote Sensing)

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31 pages, 5985 KB

Open AccessEditor’s ChoiceArticle

Comparing Terrestrial and Mobile Laser Scanning Approaches for Multi-Layer Fuel Load Prediction in the Western United States

by Eugênia Kelly Luciano Batista, Andrew T. Hudak, Jeff W. Atkins, Eben North Broadbent, Kody Melissa Brock, Michael J. Campbell, Nuria Sánchez-López, Monique Bohora Schlickmann, Francisco Mauro, Andres Susaeta, Eric Rowell, Caio Hamamura, Ana Paula Dalla Corte, Inga La Puma, Russell A. Parsons, Benjamin C. Bright, Jason Vogel, Inacio Thomaz Bueno, Gabriel Maximo da Silva, Carine Klauberg, Jinyi Xia, Jessie F. Eastburn, Kleydson Diego Rocha and Carlos Alberto Silva Show full author list Hide full author list

Remote Sens. 2025, 17(16), 2757; https://doi.org/10.3390/rs17162757 - 8 Aug 2025

Viewed by 1202

Abstract

Effective estimation of fuel load is critical for mitigating wildfire risks. Here, we evaluate the performance of mobile laser scanning (MLS) and terrestrial laser scanning (TLS) to estimate fuel loads across multiple vegetation layers. Data were collected in two forest regions: the North [...] Read more.

Effective estimation of fuel load is critical for mitigating wildfire risks. Here, we evaluate the performance of mobile laser scanning (MLS) and terrestrial laser scanning (TLS) to estimate fuel loads across multiple vegetation layers. Data were collected in two forest regions: the North Kaibab (NK) Plateau in Arizona and Monroe Mountain (MM) in Utah. We used random forest models to predict vegetation attributes, evaluating the performance of full models and transferred models using R², RMSE, and bias. The MLS consistently outperformed the TLS system, particularly for canopy-related attributes and woody biomass components. However, the TLS system showed potential for capturing canopy structure attributes, while offering advantages like operational simplicity, low equipment demands, and ease of deployment in the field, making it a cost-effective alternative for managers without access to more complex and expensive mobile or airborne systems. Our results show that model transferability between NK and MM is highly variable depending on the fuel attributes. Attributes related to canopy biomass showed better transferability, with small losses in predictive accuracy when models were transferred between the two sites. Conversely, surface fuel attributes showed more significant challenges for model transferability, given the difficulty of laser penetration in the lower vegetation layers. In general, models trained in NK and validated in MM consistently outperformed those trained in MM and transferred to NK. This may suggest that the NK plots captured a broader complexity of vegetation structure and environmental conditions from which models learned better and were able to generalize to MM. This study highlights the potential of ground-based LiDAR technologies in providing detailed information and important insights into fire risk and forest structure. Full article

(This article belongs to the Section Forest Remote Sensing)

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36 pages, 2263 KB

Open AccessEditor’s ChoiceReview

Soil Moisture Prediction Using Remote Sensing and Machine Learning Algorithms: A Review on Progress, Challenges, and Opportunities

by Manoj Lamichhane, Sushant Mehan and Kyle R. Mankin

Remote Sens. 2025, 17(14), 2397; https://doi.org/10.3390/rs17142397 - 11 Jul 2025

Cited by 5 | Viewed by 6310

Abstract

Machine learning (ML) has gained significant attention for unraveling the complex, nonlinear relationships between soil moisture (SM) and various predictive variables, including remote sensing (RS; reflectance, brightness temperature, backscatter coefficients) and biophysical (topographic, soil, vegetation, and weather) variables. We reviewed the literature to [...] Read more.

Machine learning (ML) has gained significant attention for unraveling the complex, nonlinear relationships between soil moisture (SM) and various predictive variables, including remote sensing (RS; reflectance, brightness temperature, backscatter coefficients) and biophysical (topographic, soil, vegetation, and weather) variables. We reviewed the literature to extract and synthesize ML algorithms, reliable input features, and challenges in SM estimation using RS data. We analyzed results from 144 articles published from 2010 to 2024. Random forest (40 out of 67 studies), support vector regressor (13 out of 39 studies), and artificial neural networks (12 out of 27 studies) often outperformed other algorithms to estimate SM using RS datasets. Multi-source RS data often outperformed single-source data in SM estimation. Satellite-derived features, such as vegetation indices and backscattering coefficients, provided critical information on surface SM (SSM) variability to estimate SSM. For root zone SM estimation, soil properties and SSM generally were more reliable predictors than surface information derived solely from RS. Two recent advances—the use of semi-empirical models and L-band SAR to mitigate vegetation effects, and transfer learning to improve model transferability—have shown promise in addressing key challenges in SM estimation. Full article

(This article belongs to the Special Issue Remote Sensing for Soil and Water Conservation and Sustainable Development in the Context of Climate Change)

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31 pages, 6764 KB

Open AccessEditor’s ChoiceArticle

Upscaling Frameworks Drive Prediction Accuracy and Uncertainty When Mapping Aboveground Biomass Density from the Synergism of Spaceborne LiDAR, SAR, and Passive Optical Data

by Inacio T. Bueno, Carlos A. Silva, Monique B. Schlickmann, Victoria M. Donovan, Jeff W. Atkins, Kody M. Brock, Jinyi Xia, Denis R. Valle, Jiangxiao Qiu, Jason Vogel, Andres Susaeta, Ajay Sharma, Carine Klauberg, Midhun Mohan and Ana Paula Dalla Corte

Remote Sens. 2025, 17(14), 2340; https://doi.org/10.3390/rs17142340 - 8 Jul 2025

Viewed by 1987

Abstract

Accurate mapping of aboveground biomass density (AGBD) is vital for ecological research and carbon cycle monitoring. Integrating multi-source remote sensing data offers significant potential to enhance the accuracy and coverage of AGBD estimates. This study evaluated three upscaling frameworks for integrating GEDI LiDAR, [...] Read more.

Accurate mapping of aboveground biomass density (AGBD) is vital for ecological research and carbon cycle monitoring. Integrating multi-source remote sensing data offers significant potential to enhance the accuracy and coverage of AGBD estimates. This study evaluated three upscaling frameworks for integrating GEDI LiDAR, SAR, and optical satellite data to create wall-to-wall AGBD maps. The frameworks tested in this paper were: (1) a single-step approach using optical imagery, (2) a two-stage approach with GEDI-derived variables, and (3) a three-stage approach combining imagery and in situ-derived allometries. Internal validation showed that framework 1 achieved the lowest root mean square difference (%RMSD) of 53.3% and highest coefficient of determination (R²) of 0.53. An independent external validation of the AGBD map was performed using in situ observations, also revealing that framework 1 was the most accurate (%RMSD = 39.3% and R² = 0.93), while frameworks 2 and 3 were less accurate (%RMSD = 54.7, 44.7 and R² = 0.95, 0.90, respectively). Herein, we show that upscaling frameworks significantly impacted AGBD map uncertainty and the magnitude of estimate differences. Our findings suggest that upscaling framework 1 based on a single step approach was the most effective for capturing detailed AGBD variations, while careful consideration of model sensitivity and map uncertainties is essential for reliable AGBD estimation. This study provides valuable insights for advancing forest AGBD monitoring and highlights the potential for further enhancements in remote sensing methodologies. Full article

(This article belongs to the Section Forest Remote Sensing)

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29 pages, 3799 KB

Open AccessEditor’s ChoiceArticle

Forest Three-Dimensional Reconstruction Method Based on High-Resolution Remote Sensing Image Using Tree Crown Segmentation and Individual Tree Parameter Extraction Model

by Guangsen Ma, Gang Yang, Hao Lu and Xue Zhang

Remote Sens. 2025, 17(13), 2179; https://doi.org/10.3390/rs17132179 - 25 Jun 2025

Viewed by 1292

Abstract

Efficient and accurate acquisition of tree distribution and three-dimensional geometric information in forest scenes, along with three-dimensional reconstructions of entire forest environments, hold significant application value in precision forestry and forestry digital twins. However, due to complex vegetation structures, fine geometric details, and [...] Read more.

Efficient and accurate acquisition of tree distribution and three-dimensional geometric information in forest scenes, along with three-dimensional reconstructions of entire forest environments, hold significant application value in precision forestry and forestry digital twins. However, due to complex vegetation structures, fine geometric details, and severe occlusions in forest environments, existing methods—whether vision-based or LiDAR-based—still face challenges such as high data acquisition costs, feature extraction difficulties, and limited reconstruction accuracy. This study focuses on reconstructing tree distribution and extracting key individual tree parameters, and it proposes a forest 3D reconstruction framework based on high-resolution remote sensing images. Firstly, an optimized Mask R-CNN model was employed to segment individual tree crowns and extract distribution information. Then, a Tree Parameter and Reconstruction Network (TPRN) was constructed to directly estimate key structural parameters (height, DBH etc.) from crown images and generate tree 3D models. Subsequently, the 3D forest scene could be reconstructed by combining the distribution information and tree 3D models. In addition, to address the data scarcity, a hybrid training strategy integrating virtual and real data was proposed for crown segmentation and individual tree parameter estimation. Experimental results demonstrated that the proposed method could reconstruct an entire forest scene within seconds while accurately preserving tree distribution and individual tree attributes. In two real-world plots, the tree counting accuracy exceeded 90%, with an average tree localization error under 0.2 m. The TPRN achieved parameter extraction accuracies of 92.7% and 96% for tree height, and 95.4% and 94.1% for DBH. Furthermore, the generated individual tree models achieved average Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) scores of 11.24 and 0.53, respectively, validating the quality of the reconstruction. This approach enables fast and effective large-scale forest scene reconstruction using only a single remote sensing image as input, demonstrating significant potential for applications in both dynamic forest resource monitoring and forestry-oriented digital twin systems. Full article

(This article belongs to the Special Issue Digital Modeling for Sustainable Forest Management)

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34 pages, 12128 KB

Open AccessEditor’s ChoiceArticle

A Novel Supervoxel-Based NE-PC Model for Separating Wood and Leaf Components from Terrestrial Laser Scanning Data

by Shengqin Gong, Xin Shen and Lin Cao

Remote Sens. 2025, 17(12), 1978; https://doi.org/10.3390/rs17121978 - 6 Jun 2025

Viewed by 986

Abstract

The precise extractions of tree components such as wood (i.e., trunk and branches) and leaves are fundamental prerequisites for obtaining the key attributes of trees, which will provide significant benefits for ecological and physiological studies and forest applications. Terrestrial laser scanning technology offers [...] Read more.

The precise extractions of tree components such as wood (i.e., trunk and branches) and leaves are fundamental prerequisites for obtaining the key attributes of trees, which will provide significant benefits for ecological and physiological studies and forest applications. Terrestrial laser scanning technology offers an efficient means for acquiring three-dimensional information on tree attributes, and has marked potential for extracting the detailed tree attributes of tree components. However, previous studies on wood–leaf separation exhibited limitations in unsupervised adaptability and robustness to complex tree architectures, while demonstrating inadequate performance in fine branch detection. This study proposes a novel unsupervised model (NE-PC) that synergizes geometric features with graph-based path analysis to achieve accurate wood–leaf classification without training samples or empirical parameter tuning. First, the boundary-preserved supervoxel segmentation (BPSS) algorithm was adapted to generate supervoxels for calculating geometric features and representative points for constructing the undirected graph. Second, a node expansion (NE) approach was proposed, with nodes with similar curvature and verticality expanded into wood nodes to avoid the omission of trunk points in path frequency detection. Third, a path concatenation (PC) approach was developed, which involves detecting salient features of nodes along the same path to improve the detection of tiny branches that are often deficient during path retracing. Tested on multi-station TLS point clouds from trees with complex leaf–branch architectures, the NE-PC model achieved a 94.1% mean accuracy and a 86.7% kappa coefficient, outperforming renowned TLSeparation and LeWos (ΔOA = 2.0–29.7%, Δkappa = 6.2–53.5%). Moreover, the NE-PC model was verified in two other study areas (Plot B, Plot C), which exhibited more complex and divergent branch structure types. It achieved classification accuracies exceeding 90% (Plot B: 92.8 ± 2.3%; Plot C: 94.4 ± 0.7%) along with average kappa coefficients above 80% (Plot B: 81.3 ± 4.2%; Plot C: 81.8 ± 3.2%), demonstrating robust performance across various tree structural complexities. Full article

(This article belongs to the Section Forest Remote Sensing)

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29 pages, 1755 KB

Open AccessEditor’s ChoiceReview

A Review of Machine Learning Applications in Ocean Color Remote Sensing

by Zhenhua Zhang, Peng Chen, Siqi Zhang, Haiqing Huang, Yuliang Pan and Delu Pan

Remote Sens. 2025, 17(10), 1776; https://doi.org/10.3390/rs17101776 - 20 May 2025

Cited by 5 | Viewed by 4247

Abstract

Ocean color remote sensing technology has proven to be an indispensable tool for monitoring ocean conditions, as it has consistently provided critical data on global ocean optical properties, color, and biogeochemical parameters over several decades. With the rapid advancement of artificial intelligence, the [...] Read more.

Ocean color remote sensing technology has proven to be an indispensable tool for monitoring ocean conditions, as it has consistently provided critical data on global ocean optical properties, color, and biogeochemical parameters over several decades. With the rapid advancement of artificial intelligence, the integration of machine learning (ML) models into ocean color remote sensing has become a significant focus within the scientific community. This article provides a comprehensive review of the current status and challenges associated with ML models in ocean color remote sensing, assessing their applications in atmospheric correction, color inversion, carbon cycle analysis, and data reconstruction. This review highlights the advancements made in applying ML techniques, such as neural networks and deep learning, to improve data accuracy, enhance resolution, and enable more precise predictions of oceanic phenomena. Despite challenges such as model generalization and computational complexity, ML has significant potential for enhancing our understanding of marine ecosystems, facilitating real-time monitoring, and supporting global climate models. Full article

(This article belongs to the Section Environmental Remote Sensing)

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19 pages, 22717 KB

Open AccessEditor’s ChoiceArticle

Modeling Dynamics of Water Balance for Lakes in the Northwest Tibetan Plateau with Satellite-Based Observations

by Jiaheng Yan, Yanhong Wu, Yongkang Ren, Siqi Zheng, Hao Chen and Xuankai Teng

Remote Sens. 2025, 17(9), 1618; https://doi.org/10.3390/rs17091618 - 2 May 2025

Cited by 3 | Viewed by 1218

Abstract

The hydrological cycle in the Tibetan Plateau is experiencing notable changes in recent decades under a changing climate. The hydrological changes, however, are not well investigated due to the limitations in the availability of ground-based observations. In this study, by incorporating satellite-based observations [...] Read more.

The hydrological cycle in the Tibetan Plateau is experiencing notable changes in recent decades under a changing climate. The hydrological changes, however, are not well investigated due to the limitations in the availability of ground-based observations. In this study, by incorporating satellite-based observations into a hydrological modeling framework, seasonal and inter-annual dynamics of water balance for lakes in the northwest Tibetan Plateau are examined systematically for the period of 1990 to 2022. Satellite-based observations, including lake water area and water level, have been used to calibrate the hydrological model and to estimate lake water storage. The hydrological model performs satisfactorily, with the Nash–Sutcliffe efficiency coefficient (NSE) exceeding 0.5 for all 15 studied lakes. It is found that inflow contributes over 70% of annual water gain for most lakes, while percolation accounts for a larger portion (>60%) of total water loss than evaporation. The studied lakes have expanded substantially, with regional average increasing rates in lake level and water storage of 0.38 m/a and 3.12 × 10⁸ m³/a, respectively. Some lakes transitioned from shrinking to expanding around 1999, and expansion in most lakes has further accelerated since around 2012, primarily because of increased precipitation over the lake catchments, leading to greater inflow to the lakes. These findings provide important insights into understanding and predicting responses of lake water balance to climate change as well as for developing adaptative strategies. Full article

(This article belongs to the Special Issue Hydrological Modelling Based on Satellite Observations)

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33 pages, 3742 KB

Open AccessEditor’s ChoiceReview

Application of Optical Remote Sensing in Harmful Algal Blooms in Lakes: A Review

by Simeng Wang and Boqiang Qin

Remote Sens. 2025, 17(8), 1381; https://doi.org/10.3390/rs17081381 - 12 Apr 2025

Cited by 7 | Viewed by 4543

Abstract

Harmful algal blooms (HABs) are a critical global issue, severely impacting aquatic ecosystems, public health, and economies. Optical remote sensing (ORS) has emerged as a prominent tool for HABs monitoring, providing operational capabilities for quantifying spatiotemporal dynamics through cost-effective observation platforms. This review [...] Read more.

Harmful algal blooms (HABs) are a critical global issue, severely impacting aquatic ecosystems, public health, and economies. Optical remote sensing (ORS) has emerged as a prominent tool for HABs monitoring, providing operational capabilities for quantifying spatiotemporal dynamics through cost-effective observation platforms. This review systematically synthesizes recent advancements in ORS technologies, encompassing (1) novel sensor development, (2) advanced data analytics frameworks, and (3) the synergistic integration of multi-scale observation platforms (satellite–airborne–ground). The analysis critically evaluates (a) spectral signature identification methodologies and (b) persistent challenges including suboptimal spatiotemporal resolution, atmospheric correction uncertainties, and limited model generalizability across heterogeneous aquatic systems. Emerging technologies, including machine learning, spatial–temporal data fusion, and high-performance sensors, are explored as potential solutions to overcome these challenges. Full article

(This article belongs to the Special Issue Advancements in Environmental Remote Sensing and GIS)

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18 pages, 6034 KB

Open AccessEditor’s ChoiceArticle

How Urban Expansion and Climatic Regimes Affect Groundwater Storage in China’s Major River Basins: A Comparative Analysis of the Humid Yangtze and Semi-Arid Yellow River Basins

by Weijing Zhou and Lu Hao

Remote Sens. 2025, 17(7), 1292; https://doi.org/10.3390/rs17071292 - 4 Apr 2025

Cited by 3 | Viewed by 1107

Abstract

This study investigated and compared the spatiotemporal evolution and driving factors of groundwater storage anomalies (GWSAs) under the dual pressures of climate change and urban expansion in two contrasting river basins of China. Integrating GRACE and GLDAS data with multi-source remote sensing data [...] Read more.

This study investigated and compared the spatiotemporal evolution and driving factors of groundwater storage anomalies (GWSAs) under the dual pressures of climate change and urban expansion in two contrasting river basins of China. Integrating GRACE and GLDAS data with multi-source remote sensing data and using attribution analysis, we reveal divergent urban GWSA dynamics between the humid Yangtze River Basin (YZB) and semi-arid Yellow River Basin (YRB). The GWSAs in YZB urban grids showed a marked increasing trend at 3.47 mm/yr (p < 0.05) during 2002–2020, aligning with the upward patterns observed in agricultural land types including dryland and paddy fields, rather than exhibiting the anticipated decline. Conversely, GWSAs in YRB urban grids experienced a pronounced decline (−5.59 mm/yr, p < 0.05), exceeding those observed in adjacent dryland regions (−5.00 mm/yr). The contrasting climatic regimes form the fundamental drivers. YZB’s humid climate (1074 mm/yr mean precipitation) with balanced seasonality amplified groundwater recharge through enhanced surface runoff (+6.1%) driven by precipitation increases (+7.4 mm/yr). In contrast, semi-arid YRB’s water deficit intensified, despite marginal precipitation gains (+3.5 mm/yr), as amplified evapotranspiration (+4.1 mm/yr) exacerbated moisture scarcity. Human interventions further differentiated trajectories: YZB’s urban clusters demonstrated GWSA growth across all city types, highlighting the synergistic effects of urban expansion under humid climates through optimized drainage infrastructure and reduced evapotranspiration from impervious surfaces. Conversely, YRB’s over-exploitation due to rapid urbanization coupled with irrigation intensification drove cross-sector GWSA depletion. Quantitative attribution revealed climate change dominated YZB’s GWSA dynamics (86% contribution), while anthropogenic pressures accounted for 72% of YRB’s depletion. These findings provide critical insights for developing basin-specific management strategies, emphasizing climate-adaptive urban planning in water-rich regions versus demand-side controls in water-stressed basins. Full article

(This article belongs to the Special Issue Analyzing the Influence of Environmental Change on Water and Terrestrial Vegetation Using Satellite Data)

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20 pages, 17052 KB

Open AccessEditor’s ChoiceArticle

An Optimized Detection Approach to Subsurface Coalfield Spontaneous Combustion Areas Using Airborne Magnetic Data

by Qingfa Meng, Guoqing Ma, Lili Li and Jingyu Li

Remote Sens. 2025, 17(7), 1185; https://doi.org/10.3390/rs17071185 - 27 Mar 2025

Cited by 5 | Viewed by 738

Abstract

It is of great significance to clarify the ranges and states of subsurface coalfield spontaneous combustion areas for coal mining and disaster management. Since the spontaneous combustion of coal seams produces highly magnetic burnt rocks and high temperatures, magnetic and infrared remote sensing [...] Read more.

It is of great significance to clarify the ranges and states of subsurface coalfield spontaneous combustion areas for coal mining and disaster management. Since the spontaneous combustion of coal seams produces highly magnetic burnt rocks and high temperatures, magnetic and infrared remote sensing measurements are commonly used for detection. To infer the accurate ranges of highly magnetic burnt rocks, we propose a three-dimensional constrained magnetization vector inversion method based on coal seam information, which considers highly magnetic burnt rocks to be produced via the combustion of a coal seam and to have thermal remanence, and this method can more accurately obtain the ranges of magnetic source for deducing coalfield spontaneous combustion areas. Combined with infrared remote sensing temperature measurement data, we analyze the range, state, and future spread direction of coalfield spontaneous combustion areas in Liaoning Province, China, according to the relative positions of high-temperature areas and highly magnetic burnt rocks. Based on the inversion results, we divided the survey area into nine blocks and obtained corresponding interpretation results. The accuracy of the interpretation was verified through drilling. This provides comprehensive spontaneous combustion area information for coal mining and disaster management. Full article

(This article belongs to the Section Environmental Remote Sensing)

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19 pages, 8960 KB

Open AccessEditor’s ChoiceArticle

Changes in the Distribution of Thermokarst Lakes on the Qinghai-Tibet Plateau from 2015 to 2020

by Rongrong Wei, Xia Hu and Shaojie Zhao

Remote Sens. 2025, 17(7), 1174; https://doi.org/10.3390/rs17071174 - 26 Mar 2025

Cited by 6 | Viewed by 1325

Abstract

Thermokarst lakes are widely distributed on the Qinghai-Tibet Plateau (QTP). However, owing to the lack of high-precision remote sensing imagery and the difficulty of in situ monitoring of permafrost regions, quantifying the changes in the distribution of thermokarst lakes is challenging. In this [...] Read more.

Thermokarst lakes are widely distributed on the Qinghai-Tibet Plateau (QTP). However, owing to the lack of high-precision remote sensing imagery and the difficulty of in situ monitoring of permafrost regions, quantifying the changes in the distribution of thermokarst lakes is challenging. In this study, we used four machine learning methods—random forest (RF), gradient boosting decision tree (GBDT), classification and regression tree (CART), and support vector machine (SVM)—and combined various environmental factors to assess the distribution of thermokarst lakes from 2015 to 2020 via the Google Earth Engine (GEE). The results indicated that the RF model performed optimally in the extraction of thermokarst lakes, followed by GBDT, CART, and SVM. From 2015 to 2020, the number of thermokarst lakes increased by 52%, and the area expanded by 1.6 times. A large proportion of STK lakes (with areas less than or equal to 1000 m²) gradually developed into MTK lakes (with areas between 1000 and 10,000 m²) in the central part of the QTP. Additionally, thermokarst lakes are located primarily at elevations between 4000 and 5000 m, with slopes ranging from 0 to 5°, and the sand content is approximately 65%. The normalized difference water index (NDWI) and enhanced vegetation index (EVI) were the most favourable factors for thermokarst lake extraction. The results provide a scientific reference for the assessment and prediction of dynamic changes in thermokarst lakes on the QTP in the future, which will have important scientific significance for the studies of carbon and water processes in alpine ecosystems. Full article

(This article belongs to the Special Issue Remote Sensing of the Cryosphere (Second Edition))

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35 pages, 8703 KB

Open AccessEditor’s ChoiceArticle

Vision Transformer-Based Unhealthy Tree Crown Detection in Mixed Northeastern US Forests and Evaluation of Annotation Uncertainty

by Durga Joshi and Chandi Witharana

Remote Sens. 2025, 17(6), 1066; https://doi.org/10.3390/rs17061066 - 18 Mar 2025

Cited by 5 | Viewed by 2382

Abstract

Forest health monitoring at scale requires high-spatial-resolution remote sensing images coupled with deep learning image analysis methods. However, high-quality large-scale datasets are costly to acquire. To address this challenge, we explored the potential of freely available National Agricultural Imagery Program (NAIP) imagery. By [...] Read more.

Forest health monitoring at scale requires high-spatial-resolution remote sensing images coupled with deep learning image analysis methods. However, high-quality large-scale datasets are costly to acquire. To address this challenge, we explored the potential of freely available National Agricultural Imagery Program (NAIP) imagery. By comparing the performance of traditional convolutional neural network (CNN) models (U-Net and DeepLabv3+) with a state-of-the-art Vision Transformer (SegFormer), we aimed to determine the optimal approach for detecting unhealthy tree crowns (UTC) using a publicly available data source. Additionally, we investigated the impact of different spectral band combinations on model performance to identify the most effective configuration without incurring additional data acquisition costs. We explored various band combinations, including RGB, color infrared (CIR), vegetation indices (VIs), principal components (PC) of texture features (PCA), and spectral band with PC (RGBPC). Furthermore, we analyzed the uncertainty associated with potential subjective crown annotation and its impact on model evaluation. Our results demonstrated that the Vision Transformer-based model, SegFormer, outperforms traditional CNN-based models, particularly when trained on RGB images yielding an F1-score of 0.85. In contrast, DeepLabv3+ achieved F1-score of 0.82. Notably, PCA-based inputs yield reduced performance across all models, with U-Net producing particularly poor results (F1-score as low as 0.03). The uncertainty analysis indicated that the Intersection over Union (IoU) could fluctuate between 14.81% and 57.41%, while F1-scores ranged from 8.57% to 47.14%, reflecting the significant sensitivity of model performance to inconsistencies in ground truth annotations. In summary, this study demonstrates the feasibility of using publicly available NAIP imagery and advanced deep learning techniques to accurately detect unhealthy tree canopies. These findings highlight SegFormer’s superior ability to capture complex spatial patterns, even in relatively low-resolution (60 cm) datasets. Our findings underline the considerable influence of human annotation errors on model performance, emphasizing the need for standardized annotation guidelines and quality control measures. Full article

(This article belongs to the Special Issue Computer Vision-Based Methods and Tools in Remote Sensing)

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18 pages, 7811 KB

Open AccessEditor’s ChoiceArticle

Plastic Litter Detection in the Environment Using Hyperspectral Aerial Remote Sensing and Machine Learning

by Marco Balsi, Monica Moroni and Soufyane Bouchelaghem

Remote Sens. 2025, 17(5), 938; https://doi.org/10.3390/rs17050938 - 6 Mar 2025

Cited by 6 | Viewed by 3353

Abstract

Plastic waste has become a critical environmental issue, necessitating effective methods for detection and monitoring. This article presents a machine-learning-based methodology and embedded solution to detect plastic waste in the environment using an airborne hyperspectral sensor operating in the short-wave infrared (SWIR) band. [...] Read more.

Plastic waste has become a critical environmental issue, necessitating effective methods for detection and monitoring. This article presents a machine-learning-based methodology and embedded solution to detect plastic waste in the environment using an airborne hyperspectral sensor operating in the short-wave infrared (SWIR) band. Experimental data were obtained from drone flights in several case studies in natural and controlled environments. Data were preprocessed to simply equalize the spectra across the whole band and across different environmental conditions, and machine learning techniques were applied to detect plastics even in real-time. Several algorithms for spectrum calibration, feature selection, and classification were optimized and compared to obtain an optimal solution that has high-quality results under cross-validation. This way, deploying the system in different environments without requiring complicated manual adjustments or re-learning is possible. The results of this work prove the feasibility of the proposed plastic litter detection approach using high-definition aerial remote sensing, with high specificity to plastic polymers that are not obtained using visible and NIR data. Full article

(This article belongs to the Section Environmental Remote Sensing)

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28 pages, 8540 KB

Open AccessEditor’s ChoiceArticle

Snow Cover Variability and Trends over Karakoram, Western Himalaya and Kunlun Mountains During the MODIS Era (2001–2024)

by Cecilia Delia Almagioni, Veronica Manara, Guglielmina Adele Diolaiuti, Maurizio Maugeri, Alessia Spezza and Davide Fugazza

Remote Sens. 2025, 17(5), 914; https://doi.org/10.3390/rs17050914 - 5 Mar 2025

Cited by 3 | Viewed by 2663

Abstract

Monitoring the snow cover variability and trends is crucial due to its significant contribution to river formation and sustenance. Using gap-filled MODIS data over the 2001–2024 period, the spatial distribution and temporal evolution of three snow cover metrics were studied: number of days, [...] Read more.

Monitoring the snow cover variability and trends is crucial due to its significant contribution to river formation and sustenance. Using gap-filled MODIS data over the 2001–2024 period, the spatial distribution and temporal evolution of three snow cover metrics were studied: number of days, onset and end of the snow cover season across fourteen regions covering the Karakoram, Western Himalayas and Kunlun Mountains. The obtained signals exhibit considerable complexity, making it difficult to find a unique factor explaining their variability, even if elevation emerged as the most important one. The mean values of snow-covered days span from about 14 days in desert regions to about 184 days in the Karakoram region. Given the high interannual variability, the metrics show no significant trend across the study area, even if significant trends were identified in specific regions. The obtained results correlate well with the ERA5 and ERA5-Land values: the Taklamakan Desert and the Kunlun Mountains experienced a significant decrease in the snow cover extent possibly associated with an increase in temperature and a decline in precipitation. Similarly, the Karakoram and Western Himalayas region show a positive snow cover trend possibly associated with a stable temperature and a positive precipitation trend. Full article

(This article belongs to the Section Environmental Remote Sensing)

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21 pages, 6227 KB

Open AccessEditor’s ChoiceArticle

Evaluation of Satellite-Based Global Navigation Satellite System Reflectometry (GNSS-R) Soil Moisture Products in Complex Terrain: A Case Study of the Yunnan–Guizhou Plateau

by Yixiao Liu, Yong Wang, Jingcheng Lai, Yunjie Lin and Leyan Shi

Remote Sens. 2025, 17(5), 887; https://doi.org/10.3390/rs17050887 - 2 Mar 2025

Cited by 2 | Viewed by 1548

Abstract

Complex terrain is one of the main factors affecting the process of retrieving surface soil moisture using GNSS-R technology. This study evaluates the impact of complex terrain on surface soil moisture inversion using Cyclone Global Navigation Satellite System (CYGNSS) L3 SSM products, with [...] Read more.

Complex terrain is one of the main factors affecting the process of retrieving surface soil moisture using GNSS-R technology. This study evaluates the impact of complex terrain on surface soil moisture inversion using Cyclone Global Navigation Satellite System (CYGNSS) L3 SSM products, with Soil Moisture Active Passive (SMAP) SSM products as the true value. The errors in CYGNSS SSM are primarily attributed to med–high elevation and large relief. Compared with the Soil Moisture and Ocean Salinity (SMOS) and Advanced Microwave Scanning Radiometer 2 (AMSR2) SSM products, CYGNSS exhibits superior performance in terms of AD and RMSE (median AD = −0.10 m³/m³, RMSE = 0.14 m³/m³). The ubRMSE of CYGNSS (median ubRMSE = 0.094 m³/m³) outperforms SMOS, but is slightly worse than AMSR2, with the differences mainly observed in med–high elevation and large-relief regions. The three satellites complement each other in detecting complex terrain. CYGNSS errors (AD, RMSE) are higher in the rainy season than in the dry season, with greater discrepancies observed in large-relief, high-elevation regions compared to flatter, lower-elevation areas. This study provides the first comprehensive analysis of CYGNSS in such a complex region, offering valuable insights for improving the application of GNSS-R inversion technology. Full article

(This article belongs to the Special Issue Earth Observation Satellites for Soil Moisture Monitoring)

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23 pages, 24774 KB

Open AccessEditor’s ChoiceArticle

Large-Scale Soil Organic Carbon Estimation via a Multisource Data Fusion Approach

by Eleni Kalopesa, Nikolaos Tziolas, Nikolaos L. Tsakiridis, José Lucas Safanelli, Tomislav Hengl and Jonathan Sanderman

Remote Sens. 2025, 17(5), 771; https://doi.org/10.3390/rs17050771 - 23 Feb 2025

Cited by 4 | Viewed by 2949

Abstract

This study presents a methodological framework for predicting soil organic carbon (SOC) using laboratory spectral recordings from a handheld near-infrared (NIR, 1350–2550 nm) device combined with open geospatial data derived from remote sensing sensors related to landform, climate, and vegetation. Initial experiments proved [...] Read more.

This study presents a methodological framework for predicting soil organic carbon (SOC) using laboratory spectral recordings from a handheld near-infrared (NIR, 1350–2550 nm) device combined with open geospatial data derived from remote sensing sensors related to landform, climate, and vegetation. Initial experiments proved the superiority of convolutional neural networks (CNNs) using only spectral data captured by the low-cost spectral devices reaching an

R^{2}

of 0.62, RMSE of 0.31 log-SOC, and an RPIQ of 1.87. Furthermore, the incorporation of geo-covariates with Neo-Spectra data substantially enhanced predictive capabilities, outperforming existing approaches. Although the CNN-derived spectral features had the greatest contribution to the model, the geo-covariates that were most informative to the model were primarily the rainfall data, the valley bottom flatness, and the snow probability. The results demonstrate that hybrid modeling approaches, particularly using CNNs to preprocess all features and fit prediction models with Extreme Gradient Boosting trees, CNN-XGBoost, significantly outperformed traditional machine learning methods, with a notable RMSE reduction, reaching an

R^{2}

of 0.72, and an RPIQ of 2.17. The findings of this study highlight the effectiveness of multimodal data integration and hybrid models in enhancing predictive accuracy for SOC assessments. Finally, the application of interpretable techniques elucidated the contributions of various climatic and topographical factors to predictions, as well as spectral information, underscoring the complex interactions affecting SOC variability. Full article

(This article belongs to the Special Issue Recent Advances in Remote Sensing of Soil Science)

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25 pages, 1644 KB

Open AccessEditor’s ChoiceReview

The Application of Remote Sensing Technology in Inland Water Quality Monitoring and Water Environment Science: Recent Progress and Perspectives

by Lei Chen, Leizhen Liu, Shasha Liu, Zhenyu Shi and Chunhong Shi

Remote Sens. 2025, 17(4), 667; https://doi.org/10.3390/rs17040667 - 16 Feb 2025

Cited by 19 | Viewed by 8211

Abstract

Due to its long-term and high-frequency observation capabilities, remote sensing is widely recognized as an indispensable and preferred technology for large-scale and cross-regional water quality monitoring. This paper comprehensively reviews the recent progress of remote sensing for water environment monitoring, predominantly focusing on [...] Read more.

Due to its long-term and high-frequency observation capabilities, remote sensing is widely recognized as an indispensable and preferred technology for large-scale and cross-regional water quality monitoring. This paper comprehensively reviews the recent progress of remote sensing for water environment monitoring, predominantly focusing on remote sensing data sources, inversion indices, and inversion models. Specifically, we summarize the inversion methods for commonly monitored water quality parameters, including optically active constituents (such as chlorophyll-a, colored dissolved organic matter, total suspended solids, and water clarity) and non-optically active constituents (including total nitrogen, total phosphorus, and chemical oxygen demand). Furthermore, the applications of remote sensing in the field of environmental sciences such as spatiotemporal evolution and driver factor analysis of water quality, carbon budget research, and pollution source identification are also systematically reviewed. Finally, we propose that atmospheric correction algorithm improvement, multi-source data fusion, and high-precision large-scale inversion algorithms should be further developed to reduce the current dependence on empirical observation algorithms in remote sensing and overcome the limitations imposed by temporal and spatial scales and that more inversion models for non-optically active parameters should be explored to realize accurate remote sensing monitoring of these components in the future. This review not only enhances our understanding of the critical role of remote sensing in inland water quality monitoring but also provides a scientific basis for water environment management. Full article

(This article belongs to the Special Issue River and Lake Dynamic Monitoring and Ecological Assessment Based on Remote Sensing)

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23 pages, 2910 KB

Open AccessEditor’s ChoiceArticle

Key Governance Practices That Facilitate the Use of Remote Sensing Information for Wildfire Management: A Case Study in Spain

by Ana I. Prados and Mackenzie Allen

Remote Sens. 2025, 17(4), 649; https://doi.org/10.3390/rs17040649 - 14 Feb 2025

Cited by 5 | Viewed by 2629

Abstract

We present results from a comprehensive analysis on the use of Earth Observations (EO) in Spain for wildfire risk management. Our findings are based on interviews with scientists, firefighters, forest engineers, and other professionals from government and private sector organizations in nine autonomous [...] Read more.

We present results from a comprehensive analysis on the use of Earth Observations (EO) in Spain for wildfire risk management. Our findings are based on interviews with scientists, firefighters, forest engineers, and other professionals from government and private sector organizations in nine autonomous regions in Spain. Our aim is to identify the key governance practices facilitating or hindering the use of remote sensing (RS) information and to provide recommendations for improving their integration into landscape management and fire suppression activities to reduce wildfire risk. We share several case studies detailing activities and institutional arrangements facilitating the translation of satellite science and research into decision-making environments, with a focus on how this knowledge flows among the various stakeholder categories. Among the barriers faced by fire management teams in Spain, we identified institutional silos, lack of technical skills in satellite data processing and analysis, and the evolving acceptance of satellite data by decision makers. Full article

(This article belongs to the Section Environmental Remote Sensing)

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16 pages, 2818 KB

Open AccessEditor’s ChoiceArticle

Early Detection of Water Stress in Kauri Seedlings Using Multitemporal Hyperspectral Indices and Inverted Plant Traits

by Mark Jayson B. Felix, Russell Main, Michael S. Watt, Mohammad-Mahdi Arpanaei and Taoho Patuawa

Remote Sens. 2025, 17(3), 463; https://doi.org/10.3390/rs17030463 - 29 Jan 2025

Cited by 4 | Viewed by 2071

Abstract

Global climate variability is projected to result in more frequent and severe droughts, which can have adverse effects on New Zealand’s endemic tree species such as the iconic kauri (Agathis australis). Several studies have investigated the physiological response of kauri to [...] Read more.

Global climate variability is projected to result in more frequent and severe droughts, which can have adverse effects on New Zealand’s endemic tree species such as the iconic kauri (Agathis australis). Several studies have investigated the physiological response of kauri to medium- and long-term water stress; however, no research has used hyperspectral technology for the early detection and characterization of water stress in this species. In this study, physiological (stomatal conductance (g_s), assimilation rate (A), equivalent water thickness (EWT)) and leaf-level hyperspectral measurements were recorded over a ten-week period on 100 potted kauri seedlings subjected to control (well-watered) and drought treatments. In addition, plant functional traits (PTs) were retrieved from spectral reflectance data via inversion of the PROSPECT-D radiative transfer model. These data were used to (i) identify key PTs and narrow-band hyperspectral indices (NBHIs) associated with the expression of water stress and (ii) develop classification models based on single-date and multitemporal datasets for the early detection of water stress. A significant decline in soil water content and physiological responses (g_s and A) occurred among the trees in the drought treatment in weeks 2 and 4, respectively. Although no significant treatment differences (p > 0.05) were observed in EWT across the whole duration of the experiment, lower mean values in the drought treatment were apparent from week 4 onwards. In contrast, several spectral bands and NBHIs exhibited significant differences the week after water was withheld. The number and category of significant NBHIs varied up to week 4, after which a substantial increase in the number of significant indices was observed until week 10. However, despite this increase, the single-date models did not show good model performance (F1 score > 0.70) until weeks 9 and 10. In contrast, when multitemporal datasets were used, the classification performance ranged from good to outstanding from weeks 2 to 10. This improvement was largely due to the enhanced temporal and feature representation in the multitemporal models. Among the input NBHIs, water indices emerged as the most important predictors, followed by photochemical indices. Furthermore, a comparison of inverted and measured EWT showed good correspondence (mean absolute percentage error (MAPE) = 8.49%, root mean squared error (RMSE) = 0.0026 g/cm²), highlighting the potential use of radiative transfer modelling for high-throughput drought monitoring. Future research is recommended to scale these measurements to the canopy level, which could prove valuable in detecting and characterizing drought stress at a larger scale. Full article

(This article belongs to the Section Environmental Remote Sensing)

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24 pages, 2886 KB

Open AccessEditor’s ChoiceArticle

Forest Stem Extraction and Modeling (FoSEM): A LiDAR-Based Framework for Accurate Tree Stem Extraction and Modeling in Radiata Pine Plantations

by Muhammad Ibrahim, Haitian Wang, Irfan A. Iqbal, Yumeng Miao, Hezam Albaqami, Hans Blom and Ajmal Mian

Remote Sens. 2025, 17(3), 445; https://doi.org/10.3390/rs17030445 - 28 Jan 2025

Cited by 5 | Viewed by 1922

Abstract

Accurate characterization of tree stems is critical for assessing commercial forest health, estimating merchantable timber volume, and informing sustainable value management strategies. Conventional ground-based manual measurements, although precise, are labor-intensive and impractical at large scales, while remote sensing approaches using satellite or UAV [...] Read more.

Accurate characterization of tree stems is critical for assessing commercial forest health, estimating merchantable timber volume, and informing sustainable value management strategies. Conventional ground-based manual measurements, although precise, are labor-intensive and impractical at large scales, while remote sensing approaches using satellite or UAV imagery often lack the spatial resolution needed to capture individual tree attributes in complex forest environments. To address these challenges, this study provides a significant contribution by introducing a large-scale dataset encompassing 40 plots in Western Australia (WA) with varying tree densities, derived from Hovermap LiDAR acquisitions and destructive sampling. The dataset includes parameters such as plot and tree identifiers, DBH, tree height, stem length, section lengths, and detailed diameter measurements (e.g., DiaMin, DiaMax, DiaMean) across various heights, enabling precise ground-truth calibration and validation. Based on this dataset, we present the Forest Stem Extraction and Modeling (FoSEM) framework, a LiDAR-driven methodology that efficiently and reliably models individual tree stems from dense 3D point clouds. FoSEM integrates ground segmentation, height normalization, and K-means clustering at a predefined elevation to isolate stem cores. It then applies circle fitting to capture cross-sectional geometry and employs MLESAC-based cylinder fitting for robust stem delineation. Experimental evaluations conducted across various radiata pine plots of varying complexity demonstrate that FoSEM consistently achieves high accuracy, with a DBH RMSE of 1.19 cm (rRMSE = 4.67%) and a height RMSE of 1.00 m (rRMSE = 4.24%). These results surpass those of existing methods and highlight FoSEM’s adaptability to heterogeneous stand conditions. By providing both a robust method and an extensive dataset, this work advances the state of the art in LiDAR-based forest inventory, enabling more efficient and accurate tree-level assessments in support of sustainable forest management. Full article

(This article belongs to the Special Issue New Insight into Point Cloud Data Processing)

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21 pages, 8197 KB

Open AccessEditor’s ChoiceArticle

Quantifying the Impact of Crude Oil Spills on the Mangrove Ecosystem in the Niger Delta Using AI and Earth Observation

by Jemima O’Farrell, Dualta O’Fionnagáin, Abosede Omowumi Babatunde, Micheal Geever, Patricia Codyre, Pearse C. Murphy, Charles Spillane and Aaron Golden

Remote Sens. 2025, 17(3), 358; https://doi.org/10.3390/rs17030358 - 22 Jan 2025

Cited by 5 | Viewed by 7177

Abstract

The extraction, processing and transport of crude oil in the Niger Delta region of Nigeria has long been associated with collateral environmental damage to the largest mangrove ecosystem in Africa. Oil pollution is impacting not only one of the planet’s most ecologically diverse [...] Read more.

The extraction, processing and transport of crude oil in the Niger Delta region of Nigeria has long been associated with collateral environmental damage to the largest mangrove ecosystem in Africa. Oil pollution is impacting not only one of the planet’s most ecologically diverse regions but also the health, livelihoods, and social cohesion of the Delta region inhabitants. Quantifying and directly associating localised oil pollution events to specific petrochemical infrastructure is complicated by the difficulty of monitoring such vast and complex terrain, with documented concerns regarding the thoroughness and impartiality of reported oil pollution events. Earth Observation (EO) offers a means to deliver such a monitoring and assessment capability using Normalised Difference Vegetation Index (NDVI) measurements as a proxy for mangrove biomass health. However, the utility of EO can be impacted by persistent cloud cover in such regions. To overcome such challenges here, we present a workflow that leverages EO-derived high-resolution (10 m) synthetic aperture radar data from the Sentinel-1 satellite constellation combined with machine learning to conduct observations of the spatial land cover changes associated with oil pollution-induced mangrove mortality proximal to pipeline networks in a 9000 km² region of Rivers State located near Port Harcourt. Our analysis identified significant deforestation from 2016–2024, with an estimated mangrove mortality rate of 5644 hectares/year. Using our empirically derived Pipeline Impact Indicator (PII), we mapped the oil pipeline network to 1 km resolution, highlighting specific pipeline locations in need of immediate intervention and restoration, and identified several new pipeline sites showing evidence of significant oil spill damage that have yet to be formally reported. Our findings emphasise the critical need for the continuous and comprehensive monitoring of oil extractive regions using satellite remote sensing to support decision-making and policies to mitigate environmental and societal damage from pipeline oil spills, particularly in ecologically vulnerable regions such as the Niger Delta. Full article

(This article belongs to the Special Issue Remote Sensing for Oil and Gas Development, Production and Monitoring)

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19 pages, 7245 KB

Open AccessEditor’s ChoiceArticle

Integrating Drone Truthing and Functional Classification of Remote Sensing Time Series for Supervised Vegetation Mapping

by Giacomo Quattrini, Simone Pesaresi, Nicole Hofmann, Adriano Mancini and Simona Casavecchia

Remote Sens. 2025, 17(2), 330; https://doi.org/10.3390/rs17020330 - 18 Jan 2025

Cited by 3 | Viewed by 2424

Abstract

Accurate vegetation mapping is essential for monitoring biodiversity and managing habitats, particularly in the context of increasing environmental pressures and conservation needs. Ground truthing plays a crucial role in ensuring the accuracy of supervised remote sensing maps, as it provides the high-quality reference [...] Read more.

Accurate vegetation mapping is essential for monitoring biodiversity and managing habitats, particularly in the context of increasing environmental pressures and conservation needs. Ground truthing plays a crucial role in ensuring the accuracy of supervised remote sensing maps, as it provides the high-quality reference data needed for model training and validation. However, traditional ground truthing methods are labor-intensive, time-consuming and restricted in spatial coverage, posing challenges for large-scale or complex landscapes. The advent of drone technology offers an efficient and cost-effective solution to these limitations, enabling the rapid collection of high-resolution imagery even in remote or inaccessible areas. This study proposes an approach to enhance the efficiency of supervised vegetation mapping in complex landscapes, integrating Multivariate Functional Principal Component Analysis (MFPCA) applied to the Sentinel-2 time series with drone-based ground truthing. Unlike traditional ground truthing activities, drone truthing enabled the generation of large, spatially balanced reference datasets, which are critical for machine learning classification systems. These datasets improved classification accuracy by ensuring a comprehensive representation of vegetation spectral variability, enabling the classifier to identify the key phenological patterns that best characterize and distinguish different vegetation types across the landscape. The proposed methodology achieves a classification accuracy of 92.59%, significantly exceeding the commonly reported thresholds for habitat mapping. This approach, characterized by its efficiency, repeatability and adaptability, aligns seamlessly with key environmental monitoring and conservation policies, such as the Habitats Directive. By integrating advanced remote sensing with drone-based technologies, it offers a scalable and cost-effective solution to the challenges of biodiversity monitoring, enabling timely updates and supporting effective habitat management in diverse and complex environments. Full article

(This article belongs to the Special Issue Monitoring and Managing Environmental Sustainability Using Remote Sensing)

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32 pages, 6342 KB

Open AccessEditor’s ChoiceArticle

Statewide Forest Canopy Cover Mapping of Florida Using Synergistic Integration of Spaceborne LiDAR, SAR, and Optical Imagery

by Monique Bohora Schlickmann, Inacio Thomaz Bueno, Denis Valle, William M. Hammond, Susan J. Prichard, Andrew T. Hudak, Carine Klauberg, Mauro Alessandro Karasinski, Kody Melissa Brock, Kleydson Diego Rocha, Jinyi Xia, Rodrigo Vieira Leite, Pedro Higuchi, Ana Carolina da Silva, Gabriel Maximo da Silva, Gina R. Cova and Carlos Alberto Silva

Remote Sens. 2025, 17(2), 320; https://doi.org/10.3390/rs17020320 - 17 Jan 2025

Cited by 2 | Viewed by 3645

Abstract

Southern U.S. forests are essential for carbon storage and timber production but are increasingly impacted by natural disturbances, highlighting the need to understand their dynamics and recovery. Canopy cover is a key indicator of forest health and resilience. Advances in remote sensing, such [...] Read more.

Southern U.S. forests are essential for carbon storage and timber production but are increasingly impacted by natural disturbances, highlighting the need to understand their dynamics and recovery. Canopy cover is a key indicator of forest health and resilience. Advances in remote sensing, such as NASA’s GEDI spaceborne LiDAR, enable more precise mapping of canopy cover. Although GEDI provides accurate data, its limited spatial coverage restricts large-scale assessments. To address this, we combined GEDI with Synthetic Aperture Radar (SAR), and optical imagery (Sentinel-1 GRD and Landsat–Sentinel Harmonized (HLS)) data to create a comprehensive canopy cover map for Florida. Using a random forest algorithm, our model achieved an R² of 0.69, RMSD of 0.17, and MD of 0.001, based on out-of-bag samples for internal validation. Geographic coordinates and the red spectral channel emerged as the most influential predictors. External validation with airborne laser scanning (ALS) data across three sites yielded an R² of 0.70, RMSD of 0.29, and MD of −0.22, confirming the model’s accuracy and robustness in unseen areas. Statewide analysis showed lower canopy cover in southern versus northern Florida, with wetland forests exhibiting higher cover than upland sites. This study demonstrates the potential of integrating multiple remote sensing datasets to produce accurate vegetation maps, supporting forest management and sustainability efforts in Florida. Full article

(This article belongs to the Section Environmental Remote Sensing)

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29 pages, 19709 KB

Open AccessEditor’s ChoiceArticle

Surveying Nearshore Bathymetry Using Multispectral and Hyperspectral Satellite Imagery and Machine Learning

by David Hartmann, Mathieu Gravey, Timothy David Price, Wiebe Nijland and Steven Michael de Jong

Remote Sens. 2025, 17(2), 291; https://doi.org/10.3390/rs17020291 - 15 Jan 2025

Cited by 4 | Viewed by 2933

Abstract

Nearshore bathymetric data are essential for assessing coastal hazards, studying benthic habitats and for coastal engineering. Traditional bathymetry mapping techniques of ship-sounding and airborne LiDAR are laborious, expensive and not always efficient. Multispectral and hyperspectral remote sensing, in combination with machine learning techniques, [...] Read more.

Nearshore bathymetric data are essential for assessing coastal hazards, studying benthic habitats and for coastal engineering. Traditional bathymetry mapping techniques of ship-sounding and airborne LiDAR are laborious, expensive and not always efficient. Multispectral and hyperspectral remote sensing, in combination with machine learning techniques, are gaining interest. Here, the nearshore bathymetry of southwest Puerto Rico is estimated with multispectral Sentinel-2 and hyperspectral PRISMA imagery using conventional spectral band ratio models and more advanced XGBoost models and convolutional neural networks. The U-Net, trained on 49 Sentinel-2 images, and the 2D-3D CNN, trained on PRISMA imagery, had a Mean Absolute Error (MAE) of approximately 1 m for depths up to 20 m and were superior to band ratio models by ~40%. Problems with underprediction remain for turbid waters. Sentinel-2 showed higher performance than PRISMA up to 20 m (~18% lower MAE), attributed to training with a larger number of images and employing an ensemble prediction, while PRISMA outperformed Sentinel-2 for depths between 25 m and 30 m (~19% lower MAE). Sentinel-2 imagery is recommended over PRISMA imagery for estimating shallow bathymetry given its similar performance, much higher image availability and easier handling. Future studies are recommended to train neural networks with images from various regions to increase generalization and method portability. Models are preferably trained by area-segregated splits to ensure independence between the training and testing set. Using a random train test split for bathymetry is not recommended due to spatial autocorrelation of sea depth, resulting in data leakage. This study demonstrates the high potential of machine learning models for assessing the bathymetry of optically shallow waters using optical satellite imagery. Full article

(This article belongs to the Section Environmental Remote Sensing)

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23 pages, 7550 KB

Open AccessEditor’s ChoiceArticle

Spatiotemporal Changes in Evapotranspiration and Its Influencing Factors in the Jiziwan Region of the Yellow River from 1982 to 2018

by Wenting Liu, Rong Tang, Ge Zhang, Jiacong Xue, Baolin Xue and Yuntao Wang

Remote Sens. 2025, 17(2), 252; https://doi.org/10.3390/rs17020252 - 12 Jan 2025

Cited by 4 | Viewed by 1278

Abstract

Evapotranspiration (ET) is a critical process in the interaction between the terrestrial climate system and vegetation. In recent years, ET has undergone significant changes in the Jiziwan region of the Yellow River Basin, primarily due to the implementation of ecological restoration programs and [...] Read more.

Evapotranspiration (ET) is a critical process in the interaction between the terrestrial climate system and vegetation. In recent years, ET has undergone significant changes in the Jiziwan region of the Yellow River Basin, primarily due to the implementation of ecological restoration programs and the dual impacts of climate change. As a result, hydrological cycle processes have been profoundly affected, making it crucial to accurately capture trends in ET and its components, as well as to identify the key drivers of these changes. In this study, we first systematically analyzed the dynamic evolution of ET and its components in the Jiziwan of the Yellow River area between 1982 and 2018 from the perspective of land use change. To achieve accurate ET simulations, we introduced a multiple linear regression algorithm and quantitatively evaluated the specific contributions of five climate factors, including precipitation, temperature, wind speed, specific humidity, and radiation, as well as the normalized difference vegetation index (NDVI), a vegetation factor, to ET and its components. On this basis, we explored the combined influence mechanism of climate change and vegetation change on ET in detail. The results revealed that the structure of ET in the Jiziwan of the Yellow River area has changed significantly and that vegetation evapotranspiration has gradually replaced soil evaporation, occupies a dominant position, and has become the main component of ET in this area. Among the many factors affecting ET, the contribution of climate change is the most significant, with an average contribution rate of approximately 59%. Moreover, the influence of human activities on total ET and its components is also high. The factors that had the greatest impact on total ET, soil evaporation, and vegetation transpiration were precipitation, radiation, and the NDVI, respectively. In terms of spatial distribution, the eastern part of Jiziwan was more significantly affected by environmental changes, and the trends of the ET changes were more dramatic. This study not only enhances our scientific understanding of the changes in ET and their driving mechanisms in the Jiziwan area of the Yellow River but also provides a solid scientific foundation for the development of water resource management and ecological restoration strategies in the region. Full article

(This article belongs to the Special Issue Quantitative Remote Sensing of Vegetation and Its Applications)

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23 pages, 8216 KB

Open AccessFeature PaperEditor’s ChoiceArticle

Random Cross-Validation Produces Biased Assessment of Machine Learning Performance in Regional Landslide Susceptibility Prediction

by Chandan Kumar, Gabriel Walton, Paul Santi and Carlos Luza

Remote Sens. 2025, 17(2), 213; https://doi.org/10.3390/rs17020213 - 9 Jan 2025

Cited by 5 | Viewed by 3605

Abstract

Machine learning (ML) models are extensively used in spatial predictive modeling, including landslide susceptibility prediction. The performance statistics of these models are vital for assessing their reliability, which is typically obtained using the random cross-validation (R-CV) method. However, R-CV has a major drawback, [...] Read more.

Machine learning (ML) models are extensively used in spatial predictive modeling, including landslide susceptibility prediction. The performance statistics of these models are vital for assessing their reliability, which is typically obtained using the random cross-validation (R-CV) method. However, R-CV has a major drawback, i.e., it ignores the spatial autocorrelation (SAC) inherent in spatial datasets when partitioning the training and testing sets. We assessed the impact of SAC at three crucial phases of ML modeling: hyperparameter tuning, performance evaluation, and learning curve analysis. As an alternative to R-CV, we used spatial cross-validation (S-CV). This method considers SAC when partitioning the training and testing subsets. This experiment was conducted on regional landslide susceptibility prediction using different ML models: logistic regression (LR), k-nearest neighbor (KNN), linear discriminant analysis (LDA), artificial neural networks (ANN), support vector machine (SVM), random forest (RF), and C5.0. The experimental results showed that R-CV often produces optimistic performance estimates, e.g., 6–18% higher than those obtained using the S-CV. R-CV also occasionally fails to reveal the true importance of the hyperparameters of models such as SVM and ANN. Additionally, R-CV falsely portrays a considerable improvement in model performance as the number of variables increases. However, this was not the case when the models were evaluated using S-CV. The impact of SAC was more noticeable in complex models such as SVM, RF, and C5.0 (except for ANN) than in simple models such as LDA and LR (except for KNN). Overall, we recommend S-CV over R-CV for a reliable assessment of ML model performance in large-scale LSM. Full article

(This article belongs to the Special Issue Assessing Natural Hazards through Advanced Machine Learning Methods and Remote Sensing Technology: 3rd Edition)

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24 pages, 11292 KB

Open AccessEditor’s ChoiceArticle

Inversion of Water Quality Parameters from UAV Hyperspectral Data Based on Intelligent Algorithm Optimized Backpropagation Neural Networks of a Small Rural River

by Manqi Wang, Caili Zhou, Jiaqi Shi, Fei Lin, Yucheng Li, Yimin Hu and Xuesheng Zhang

Remote Sens. 2025, 17(1), 119; https://doi.org/10.3390/rs17010119 - 2 Jan 2025

Cited by 8 | Viewed by 2150

Abstract

The continuous and effective monitoring of the water quality of small rural rivers is crucial for rural sustainable development. In this work, machine learning models were established to predict the water quality of a typical small rural river based on a small quantity [...] Read more.

The continuous and effective monitoring of the water quality of small rural rivers is crucial for rural sustainable development. In this work, machine learning models were established to predict the water quality of a typical small rural river based on a small quantity of measured water quality data and UAV hyperspectral images. Firstly, the spectral data were preprocessed using fractional order derivation (FOD), standard normal variate (SNV), and normalization (Norm) to enhance the spectral response characteristics of the water quality parameters. Second, a method combining the Pearson’s correlation coefficient and the variance inflation factor (PCC–VIF) was utilized to decrease the dimensionality of features and improve the quality of the input data. Again, based on the screened features, a back-propagation neural network (BPNN) model optimized using a mixture of the genetic algorithm (GA) and the particle swarm optimization (PSO) algorithm was established as a means of estimating water quality parameter concentrations. To intuitively evaluate the performance of the hybrid optimization algorithm, its prediction accuracy is compared with that of conventional machine learning algorithms (Random Forest, CatBoost, XGBoost, BPNN, GA–BPNN and PSO–BPNN). The results show that the GA–PSO–BPNN model for turbidity (TUB), ammonia nitrogen (NH₃-N), total nitrogen (TN), and total phosphorus (TP) prediction exhibited optimal accuracy with coefficients of determination (R²) of 0.770, 0.804, 0.754, and 0.808, respectively. Meanwhile, the model also demonstrated good robustness and generalization ability for data from different periods. In addition, we used this method to visualize the water quality parameters in the study area. This work provides a new approach to the refined monitoring of water quality in small rural rivers. Full article

(This article belongs to the Special Issue Impacts of Climate Change on Water Cycle and Terrestrial Ecosystems by Remote Sensing)

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29 pages, 5124 KB

Open AccessEditor’s ChoiceReview

Combination of Remote Sensing and Artificial Intelligence in Fruit Growing: Progress, Challenges, and Potential Applications

by Danielle Elis Garcia Furuya, Édson Luis Bolfe, Taya Cristo Parreiras, Jayme Garcia Arnal Barbedo, Thiago Teixeira Santos and Luciano Gebler

Remote Sens. 2024, 16(24), 4805; https://doi.org/10.3390/rs16244805 - 23 Dec 2024

Cited by 4 | Viewed by 4881

Abstract

Fruit growing is important in the global agricultural economy, contributing significantly to food security, job creation, and rural development. With the advancement of technologies, mapping fruits using remote sensing and machine learning (ML) and deep learning (DL) techniques has become an essential tool [...] Read more.

Fruit growing is important in the global agricultural economy, contributing significantly to food security, job creation, and rural development. With the advancement of technologies, mapping fruits using remote sensing and machine learning (ML) and deep learning (DL) techniques has become an essential tool to optimize production, monitor crop health, and predict harvests with greater accuracy. This study was developed in four main stages. In the first stage, a comprehensive review of the existing literature was made from July 2018 (first article found) to June 2024, totaling 117 articles. In the second stage, a general analysis of the data obtained was made, such as the identification of the most studied fruits with the techniques of interest. In the third stage, a more in-depth analysis was made focusing on apples and grapes, with 27 and 30 articles, respectively. The analysis included the use of remote sensing (orbital and proximal) imagery and ML/DL algorithms to map crop areas, detect diseases, and monitor crop development, among other analyses. The fourth stage shows the data’s potential application in a Southern Brazilian region, known for apple and grape production. This study demonstrates how the integration of modern technologies can transform fruit farming, promoting more sustainable and efficient agriculture through remote sensing and artificial intelligence technologies. Full article

(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)

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23 pages, 10008 KB

Open AccessEditor’s ChoiceReview

Multi-Global Navigation Satellite System for Earth Observation: Recent Developments and New Progress

by Shuanggen Jin, Xuyang Meng, Gino Dardanelli and Yunlong Zhu

Remote Sens. 2024, 16(24), 4800; https://doi.org/10.3390/rs16244800 - 23 Dec 2024

Cited by 5 | Viewed by 3265

Abstract

The Global Navigation Satellite System (GNSS) has made important progress in Earth observation and applications. With the successful design of the BeiDou Navigation Satellite System (BDS), four global navigation satellite systems are available worldwide, together with Galileo, GLONASS, and GPS. These systems have [...] Read more.

The Global Navigation Satellite System (GNSS) has made important progress in Earth observation and applications. With the successful design of the BeiDou Navigation Satellite System (BDS), four global navigation satellite systems are available worldwide, together with Galileo, GLONASS, and GPS. These systems have been widely employed in positioning, navigation, and timing (PNT). Furthermore, GNSS refraction, reflection, and scattering signals can remotely sense the Earth’s surface and atmosphere with powerful implications for environmental remote sensing. In this paper, the recent developments and new application progress of multi-GNSS in Earth observation are presented and reviewed, including the methods of BDS/GNSS for Earth observations, GNSS navigation and positioning performance (e.g., GNSS-PPP and GNSS-NRTK), GNSS ionospheric modelling and space weather monitoring, GNSS meteorology, and GNSS-reflectometry and its applications. For instance, the static Precise Point Positioning (PPP) precision of most MGEX stations was improved by 35.1%, 18.7%, and 8.7% in the east, north, and upward directions, respectively, with PPP ambiguity resolution (AR) based on factor graph optimization. A two-layer ionospheric model was constructed using IGS station data through three-dimensional ionospheric model constraints and TEC accuracy was increased by about 20–27% with the GIM model. Ten-minute water level change with centimeter-level accuracy was estimated with ground-based multiple GNSS-R data based on a weighted iterative least-squares method. Furthermore, a cyclone and its positions were detected by utilizing the GNSS-reflectometry from the space-borne Cyclone GNSS (CYGNSS) mission. Over the years, GNSS has become a dominant technology among Earth observation with powerful applications, not only for conventional positioning, navigation and timing techniques, but also for integrated remote sensing solutions, such as monitoring typhoons, river water level changes, geological geohazard warnings, low-altitude UAV navigation, etc., due to its high performance, low cost, all time and all weather. Full article

(This article belongs to the Special Issue State of the Art of GNSS and SAR/InSAR Techniques for Geomatic Applications)

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16 pages, 9121 KB

Open AccessEditor’s ChoiceTechnical Note

A Benchmark Dataset for Aircraft Detection in Optical Remote Sensing Imagery

by Jianming Hu, Xiyang Zhi, Bingxian Zhang, Tianjun Shi, Qi Cui and Xiaogang Sun

Remote Sens. 2024, 16(24), 4699; https://doi.org/10.3390/rs16244699 - 17 Dec 2024

Cited by 1 | Viewed by 3727

Abstract

The problem is that existing aircraft detection datasets rarely simultaneously consider the diversity of target features and the complexity of environmental factors, which has become an important factor restricting the effectiveness and reliability of aircraft detection algorithms. Although a large amount of research [...] Read more.

The problem is that existing aircraft detection datasets rarely simultaneously consider the diversity of target features and the complexity of environmental factors, which has become an important factor restricting the effectiveness and reliability of aircraft detection algorithms. Although a large amount of research has been devoted to breaking through few-sample-driven aircraft detection technology, most algorithms still struggle to effectively solve the problems of missed target detection and false alarms caused by numerous environmental interferences in bird-eye optical remote sensing scenes. To further aircraft detection research, we have established a new dataset, Aircraft Detection in Complex Optical Scene (ADCOS), sourced from various platforms including Google Earth, Microsoft Map, Worldview-3, Pleiades, Ikonos, Orbview-3, and Jilin-1 satellites. It integrates 3903 meticulously chosen images of over 400 famous airports worldwide, containing 33,831 annotated instances employing the oriented bounding box (OBB) format. Notably, this dataset encompasses a wide range of various targets characteristics including multi-scale, multi-direction, multi-type, multi-state, and dense arrangement, along with complex relationships between targets and backgrounds like cluttered backgrounds, low contrast, shadows, and occlusion interference conditions. Furthermore, we evaluated nine representative detection algorithms on the ADCOS dataset, establishing a performance benchmark for subsequent algorithm optimization. The latest dataset will soon be available on the Github website. Full article

(This article belongs to the Section Earth Observation Data)

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18 pages, 5411 KB

Open AccessEditor’s ChoiceArticle

Leveraging Neural Radiance Fields for Large-Scale 3D Reconstruction from Aerial Imagery

by Max Hermann, Hyovin Kwak, Boitumelo Ruf and Martin Weinmann

Remote Sens. 2024, 16(24), 4655; https://doi.org/10.3390/rs16244655 - 12 Dec 2024

Cited by 2 | Viewed by 5150

Abstract

Since conventional photogrammetric approaches struggle with with low-texture, reflective, and transparent regions, this study explores the application of Neural Radiance Fields (NeRFs) for large-scale 3D reconstruction of outdoor scenes, since NeRF-based methods have recently shown very impressive results in these areas. We evaluate [...] Read more.

Since conventional photogrammetric approaches struggle with with low-texture, reflective, and transparent regions, this study explores the application of Neural Radiance Fields (NeRFs) for large-scale 3D reconstruction of outdoor scenes, since NeRF-based methods have recently shown very impressive results in these areas. We evaluate three approaches: Mega-NeRF, Block-NeRF, and Direct Voxel Grid Optimization, focusing on their accuracy and completeness compared to ground truth point clouds. In addition, we analyze the effects of using multiple sub-modules, estimating the visibility by an additional neural network and varying the density threshold for the extraction of the point cloud. For performance evaluation, we use benchmark datasets that correspond to the setting off standard flight campaigns and therefore typically have nadir camera perspective and relatively little image overlap, which can be challenging for NeRF-based approaches that are typically trained with significantly more images and varying camera angles. We show that despite lower quality compared to classic photogrammetric approaches, NeRF-based reconstructions provide visually convincing results in challenging areas. Furthermore, our study shows that in particular increasing the number of sub-modules and predicting the visibility using an additional neural network improves the quality of the resulting reconstructions significantly. Full article

(This article belongs to the Special Issue Deep Learning Applications of 3D Reconstruction and Visualization from Remote Sensing Imagery)

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20 pages, 4062 KB

Open AccessEditor’s ChoiceArticle

A CNN-Based Framework for Automatic Extraction of High-Resolution River Bankfull Width

by Wenqi Li, Chendi Zhang, David Puhl, Xiao Pan, Marwan A. Hassan, Stephen Bird, Kejun Yang and Yang Zhao

Remote Sens. 2024, 16(23), 4614; https://doi.org/10.3390/rs16234614 - 9 Dec 2024

Cited by 2 | Viewed by 2156

Abstract

River width is a crucial parameter that correlates and reflects the hydrological, geomorphological, and ecological characteristics of the channel. However, the width data with high spatial resolution is limited owing to the difficulties in extracting channel width under complex and variable riverine surroundings. [...] Read more.

River width is a crucial parameter that correlates and reflects the hydrological, geomorphological, and ecological characteristics of the channel. However, the width data with high spatial resolution is limited owing to the difficulties in extracting channel width under complex and variable riverine surroundings. To address this issue, we aimed to develop an automatic framework specifically for delineating river channels and measuring the bankfull widths at small spatial intervals along the channel. The DeepLabV3+ Convolutional Neural Network (CNN) model was employed to accurately delineate channel boundaries and a Voronoi Diagram approach was complemented as the river width algorithm (RWA) to calculate river bankfull widths. The CNN model was trained by images across four river types and performed well with all the evaluating metrics (mIoU, Accuracy, F1-score, and Recall) higher than 0.97, referring to the accuracy over 97% in prediction. The RWA outperformed other existing river width calculation methods by showing lower errors. The application of the framework in the Lillooet River, Canada, presented the capacity of this methodology to obtain detailed distributions of hydraulic and hydrological parameters, including flow resistance, flow energy, and sediment transport capacity, based on high-resolution channel widths. Our work highlights the significant potential of the newly developed framework in acquiring high-resolution channel width information and characterizing fluvial dynamics based on these widths along river channels, which contributes to facilitating cost-effective integrated river management. Full article

(This article belongs to the Special Issue Remote Sensing in Geomatics (Second Edition))

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20 pages, 3750 KB

Open AccessEditor’s ChoiceArticle

An Automatic Modulation Recognition Algorithm Based on Time–Frequency Features and Deep Learning with Fading Channels

by Xiaoya Zuo, Yuan Yang, Rugui Yao, Ye Fan and Lu Li

Remote Sens. 2024, 16(23), 4550; https://doi.org/10.3390/rs16234550 - 4 Dec 2024

Cited by 6 | Viewed by 3539

Abstract

Automatic modulation recognition (AMR) stands as a crucial core technology within the realm of signal processing and perception, playing a significant part in harsh electromagnetic environments. The time–frequency image (TFI) of communication signals can manifest modulation characteristics and serve as a foundation for [...] Read more.

Automatic modulation recognition (AMR) stands as a crucial core technology within the realm of signal processing and perception, playing a significant part in harsh electromagnetic environments. The time–frequency image (TFI) of communication signals can manifest modulation characteristics and serve as a foundation for signal modulation recognition and classification. However, under the influence of the electromagnetic environment, communication signals are exposed to varying degrees of interference, which poses a challenge to the recognition of modulation types. Taking into account the effects of interference and channel fading, this paper introduces a communication signal modulation recognition algorithm based on deep learning (DL) and time–frequency analysis. This approach employs short-time Fourier transform (STFT) to generate time–frequency diagrams from time-domain signals. Subsequently, it binarizes the image and feeds it as input data to the neural network. Our research presents a composite deep convolutional neural network (CNN) architecture known as the composite dense-residual neural network (CDRNN). This architecture focuses on enhancing the feature extraction and identification, aiming to achieve accurate recognition of modulation types in harsh electromagnetic environments. Finally, simulation results validate that the proposed deep learning algorithm holds remarkable advantages in boosting the accuracy of modulation type recognition with better adaptability. The algorithm shows better performance even in harsh electromagnetic environments. When the signal-to-noise ratio (SNR) is 18 dB, the recognition accuracy can reach 92.1%. Full article

(This article belongs to the Special Issue Signal Processing Theory and Methods in Remote Sensing (Second Edition))

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51 pages, 19385 KB

Open AccessEditor’s ChoiceReview

Remote Sensing in Bridge Digitalization: A Review

by Joan R. Casas, Rolando Chacón, Necati Catbas, Belén Riveiro and Daniel Tonelli

Remote Sens. 2024, 16(23), 4438; https://doi.org/10.3390/rs16234438 - 27 Nov 2024

Cited by 3 | Viewed by 4022

Abstract

A review of the application of remote sensing technologies in the SHM and management of existing bridges is presented, showing their capabilities and advantages, as well as the main drawbacks when specifically applied to bridge assets. The main sensing technologies used as corresponding [...] Read more.

A review of the application of remote sensing technologies in the SHM and management of existing bridges is presented, showing their capabilities and advantages, as well as the main drawbacks when specifically applied to bridge assets. The main sensing technologies used as corresponding platforms are discussed. This is complemented by the presentation of five case studies emphasizing the wide field of application in several bridge typologies and the justification for the selection of the optimal techniques depending on the objectives of the monitoring and assessment of a particular bridge. The review shows the potentiality of remote sensing technologies in the decision-making process regarding optimal interventions in bridge management. The data gathered by them are the mandatory precursors for determining the relevant performance indicators needed for the quality control of these important infrastructure assets. Full article

(This article belongs to the Special Issue Structural Health Monitoring and Damage Assessment by Advanced Remote Sensing Techniques and Methods)

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17 pages, 12379 KB

Open AccessEditor’s ChoiceArticle

Artificial-Intelligence-Based Classification to Unveil Geodynamic Processes in the Eastern Alps

by Christian Bignami, Alessandro Pignatelli, Giulia Romoli and Carlo Doglioni

Remote Sens. 2024, 16(23), 4364; https://doi.org/10.3390/rs16234364 - 22 Nov 2024

Viewed by 1286

Abstract

InSAR has emerged as a leading technique for studying and monitoring ground movements over large areas and across various geodynamic environments. Recent advancements in SAR sensor technology have enabled the acquisition of dense spatial datasets, providing substantial information at regional and national scales. [...] Read more.

InSAR has emerged as a leading technique for studying and monitoring ground movements over large areas and across various geodynamic environments. Recent advancements in SAR sensor technology have enabled the acquisition of dense spatial datasets, providing substantial information at regional and national scales. Despite these improvements, classifying and interpreting such vast datasets remains a significant challenge. InSAR analysts and geologists frequently have to manually analyze the time series from Persistent Scatterer Interferometry (PSI) to model the complexity of geological and tectonic phenomena. This process is time-consuming and impractical for large-scale monitoring. Utilizing Artificial Intelligence (AI) to classify and detect deformation processes presents a promising solution. In this study, vertical ground deformation time series from northeastern Italy were obtained from the European Ground Motion Service and classified by experts into different deformation categories. Convolutional and pre-trained neural networks were then trained and tested using both numerical time-series data and trend images. The application of the best performing trained network to test data showed an accuracy of

83 %

. Such a result demonstrates that neural networks can successfully identify areas experiencing distinct geodynamic processes, emphasizing the potential of AI to improve PSI data interpretation. Full article

(This article belongs to the Special Issue Signal Processing and Machine Learning for Space Geodesy Applications)

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22 pages, 2412 KB

Open AccessEditor’s ChoiceReview

Remote Sensing Technologies Using UAVs for Pest and Disease Monitoring: A Review Centered on Date Palm Trees

by Bashar Alsadik, Florian J. Ellsäßer, Muheeb Awawdeh, Abdulla Al-Rawabdeh, Lubna Almahasneh, Sander Oude Elberink, Doaa Abuhamoor and Yolla Al Asmar

Remote Sens. 2024, 16(23), 4371; https://doi.org/10.3390/rs16234371 - 22 Nov 2024

Cited by 7 | Viewed by 7644

Abstract

This review is aimed at exploring the use of remote sensing technology with a focus on Unmanned Aerial Vehicles (UAVs) in monitoring and management of palm pests and diseases with a special focus on date palms. It highlights the most common sensor types, [...] Read more.

This review is aimed at exploring the use of remote sensing technology with a focus on Unmanned Aerial Vehicles (UAVs) in monitoring and management of palm pests and diseases with a special focus on date palms. It highlights the most common sensor types, ranging from passive sensors such as RGB, multispectral, hyperspectral, and thermal as well as active sensors such as light detection and ranging (LiDAR), expounding on their unique functions and gains as far as the detection of pest infestation and disease symptoms is concerned. Indices derived from UAV multispectral and hyperspectral sensors are used to assess their usefulness in vegetation health monitoring and plant physiological changes. Other UAVs are equipped with thermal sensors to identify water stress and temperature anomalies associated with the presence of pests and diseases. Furthermore, the review discusses how LiDAR technology can be used to capture detailed 3D canopy structures as well as volume changes that may occur during the progressing stages of a date palm infection. Besides, the paper examines how machine learning algorithms have been incorporated into remote sensing technologies to ensure high accuracy levels in detecting diseases or pests. This paper aims to present a comprehensive outline for future research focusing on modern methodologies, technological improvements, and direction for the efficient application of UAV-based remote sensing in managing palm tree pests and diseases. Full article

(This article belongs to the Special Issue State-of-the-Art on Satellite and UAV Remote Sensing in Geoscience Research)

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24 pages, 14942 KB

Open AccessEditor’s ChoiceArticle

The Ground-Penetrating Radar Image Matching Method Based on Central Dense Structure Context Features

by Jie Xu, Qifeng Lai, Dongyan Wei, Xinchun Ji, Ge Shen and Hong Yuan

Remote Sens. 2024, 16(22), 4291; https://doi.org/10.3390/rs16224291 - 18 Nov 2024

Cited by 6 | Viewed by 2615

Abstract

Subsurface structural distribution can be detected using Ground-Penetrating Radar (GPR). The distribution can be considered as road fingerprints for vehicle positioning. Similar to the principle of visual image matching for localization, the position coordinates of the vehicle can be calculated by matching real-time [...] Read more.

Subsurface structural distribution can be detected using Ground-Penetrating Radar (GPR). The distribution can be considered as road fingerprints for vehicle positioning. Similar to the principle of visual image matching for localization, the position coordinates of the vehicle can be calculated by matching real-time GPR images with pre-constructed reference GPR images. However, GPR images, due to their low resolution, cannot extract well-defined geometric features such as corners and lines. Thus, traditional visual image processing algorithms perform inadequately when applied to GPR image matching. To address this issue, this paper innovatively proposes a GPR image matching and localization method based on a novel feature descriptor, termed as central dense structure context (CDSC) features. The algorithm utilizes the strip-like elements in GPR images to improve the accuracy of GPR image matching. First, a CDSC feature descriptor is designed. By applying threshold segmentation and extremum point extraction to the GPR image, stratified strip-like elements and pseudo-corner points are obtained. The pseudo-corner points are treated as the centers, and the surrounding strip-like elements are described in context to form the GPR feature descriptors. Then, based on the feature description method, feature descriptors for both the real-time image and the reference image are calculated separately. By searching for the nearest matching point pairs and removing erroneous pairs, GPR image matching and localization are achieved. The proposed algorithm was evaluated on datasets collected from urban roads and railway tracks, achieving localization errors of 0.06 m (RMSE) and 1.22 m (RMSE), respectively. Compared to the traditional Speeded Up Robust Features (SURF) visual image matching algorithm, localization errors were reduced by 86.6% and 95.7% in urban road and railway track scenarios, respectively. Full article

(This article belongs to the Special Issue Advanced Ground-Penetrating Radar (GPR) Technologies and Applications)

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22 pages, 8045 KB

Open AccessEditor’s ChoiceArticle

A GIS Plugin for the Assessment of Deformations in Existing Bridge Portfolios via MTInSAR Data

by Mirko Calò, Sergio Ruggieri, Andrea Nettis and Giuseppina Uva

Remote Sens. 2024, 16(22), 4293; https://doi.org/10.3390/rs16224293 - 18 Nov 2024

Cited by 14 | Viewed by 1459

Abstract

The paper presents a GIS plugin, named Bridge Assessment System via MTInSAR (BAS-MTInSAR), aimed at assessing deformations in existing simply supported concrete girder bridges through Multi-Temporal Interferometry Synthetic Aperture Radar (MTInSAR). Existing bridges require continuous maintenance to ensure functionality toward external effects undermining [...] Read more.

The paper presents a GIS plugin, named Bridge Assessment System via MTInSAR (BAS-MTInSAR), aimed at assessing deformations in existing simply supported concrete girder bridges through Multi-Temporal Interferometry Synthetic Aperture Radar (MTInSAR). Existing bridges require continuous maintenance to ensure functionality toward external effects undermining the safety of these structures, such as aging, material degradation, and environmental factors. Although effective and standardized methodologies exist (e.g., structural monitoring, periodic onsite inspections), new emerging technologies could be employed to provide time- and cost-effective information on the current state of structures and to drive prompt interventions to mitigate risk. One example is represented by MTInSAR data, which can provide near-continuous information about structural displacements over time. To easily manage these data, the paper presents BAS-MTInSAR. The tool allows users to insert information of the focused bridge (displacement time series, structural information, temperature data) and, through a user-friendly GUI, observe the occurrence of abnormal deformations. In addition, the tool implements a procedure of multisource data management and defines proper thresholds to assess bridge behavior against current code prescriptions. BAS-MTInSAR is fully described throughout the text and was tested on a real case study, showing the main potentialities of the tool in managing bridge portfolios. Full article

(This article belongs to the Special Issue Structural Health Monitoring and Damage Assessment by Advanced Remote Sensing Techniques and Methods)

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19 pages, 19888 KB

Open AccessEditor’s ChoiceArticle

Improvement of Coal Mining-Induced Subsidence-Affected (MISA) Zone Irregular Boundary Delineation by MT-InSAR Techniques, UAV Photogrammetry, and Field Investigation

by Linan Liu, Nengxiong Xu, Wendy Zhou, Yan Qin and Shilong Luan

Remote Sens. 2024, 16(22), 4221; https://doi.org/10.3390/rs16224221 - 12 Nov 2024

Cited by 4 | Viewed by 2279

Abstract

Coal mining-induced ground subsidence is a severe hazard that can damage property, infrastructure, and the environment in the vicinity when the deformation is not negligible. The boundary of a mining-induced subsidence-affected zone refers to the area beyond which the ground subsidence is less [...] Read more.

Coal mining-induced ground subsidence is a severe hazard that can damage property, infrastructure, and the environment in the vicinity when the deformation is not negligible. The boundary of a mining-induced subsidence-affected zone refers to the area beyond which the ground subsidence is less concerned. Accurately measuring mining-induced ground deformation is essential for delineating the irregular boundary of the impacted area. This study employs multitemporal interferometric synthetic aperture radar (MT-InSAR) techniques, including differential InSAR (DInSAR), InSAR stacking, and interferometric point target analysis (IPTA), to analyze coal mine subsidence and delineate the boundaries of the mining-impacted zones. DInSAR accurately reconstructs, locates, and detects the trend in mining-induced subsidence and correlates well with documented mining operations. The InSAR stacking method maps the spatial variation of the ground’s average line-of-sight (LOS) velocity over the mining area, delineating the boundary of the impacted zone. IPTA analysis combining multilook and single-pixel phases achieves millimeter-level surface measurement above tunnel alignments and measures unevenly distributed deformation fields. This study considers an average of 4 cm per year of surface deformation in the LOS direction as the subsidence threshold value for delineating the boundary of the mining-induced subsidence-affected (MISA) zone during the active coal mining stage. Interestingly, there are twin transportation tunnels near the mining area. The twin tunnels completed before the coal mining activities started were functioning well, but damage was observed after the mining began. Our study reveals the tunnels are located within the InSAR-derived MISA zone, although the tunnels approach the MISA boundary. As direct signs of subsidence, ground fissures have been identified near the tunnels via field investigations and UAV photogrammetry. Furthermore, the derived distribution of ground fissures validates and verifies InSAR measurements. The integrated approach of MT-InSAR, UVA photogrammetry, and field investigation developed in this study can be applied to delineate the irregular boundary of the MISA zone and study the accumulating effects of mining-induced subsidence on the performance of infrastructure in areas proximate to coal mining activities. Full article

(This article belongs to the Special Issue Remote Sensing for Sustainability and Durability of Transportation Infrastructures)

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19 pages, 2630 KB

Open AccessEditor’s ChoiceArticle

Enhancing Long-Term Robustness of Inter-Space Laser Links in Space Gravitational Wave Detection: An Adaptive Weight Optimization Method for Multi-Attitude Sensors Data Fusion

by Zhao Cui, Xue Wang, Jinke Yang, Haoqi Shi, Bo Liang, Xingguang Qian, Zongjin Ye, Jianjun Jia, Yikun Wang and Jianyu Wang

Remote Sens. 2024, 16(22), 4179; https://doi.org/10.3390/rs16224179 - 8 Nov 2024

Cited by 1 | Viewed by 1084

Abstract

The stable and high-precision acquisition of attitude data is crucial for sustaining the long-term robustness of laser links to detect gravitational waves in space. We introduce an effective method that utilizes an adaptive weight optimization approach for the fusion of attitude data obtained [...] Read more.

The stable and high-precision acquisition of attitude data is crucial for sustaining the long-term robustness of laser links to detect gravitational waves in space. We introduce an effective method that utilizes an adaptive weight optimization approach for the fusion of attitude data obtained from charge-coupled device (CCD) spot-positioning-based attitude measurements, differential power sensing (DPS), and differential wavefront sensing (DWS). This approach aims to obtain more robust and lower-noise-level attitude data. A system is designed based on the Michelson interferometer for link simulations; validation experiments are also conducted. The experimental results demonstrate that the fused data exhibit higher robustness. Even in the case of a single sensor failure, valid attitude data can still be obtained. Additionally, the fused data have lower noise levels, with root mean square errors of 9.5%, 37.4%, and 93.4% for the single CCD, DPS, and DWS noise errors, respectively. Full article

(This article belongs to the Special Issue Advances in Multiple Sensor Fusion and Classification for Object Detection and Tracking)

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18 pages, 6544 KB

Open AccessEditor’s ChoiceArticle

Remote Inspection of Bridges with the Integration of Scanning Total Station and Unmanned Aerial Vehicle Data

by Piotr Olaszek, Edgar Maciejewski, Anna Rakoczy, Rafael Cabral, Ricardo Santos and Diogo Ribeiro

Remote Sens. 2024, 16(22), 4176; https://doi.org/10.3390/rs16224176 - 8 Nov 2024

Cited by 5 | Viewed by 2631

Abstract

Remote visual inspections are valuable tools for maintaining bridges in safe operation. In the case of old structures with incomplete documentation, the verification of dimensions is also an essential aspect. This paper presents an attempt to use a Scanning Total Station (STS) and [...] Read more.

Remote visual inspections are valuable tools for maintaining bridges in safe operation. In the case of old structures with incomplete documentation, the verification of dimensions is also an essential aspect. This paper presents an attempt to use a Scanning Total Station (STS) and Unmanned Aerial Vehicle (UAV) for the inspection and inventory of bridge dimensions. The STS’s measurements are conducted by applying two methods: the direct method using a total station (TS) and advanced geometric analyses of the collected point cloud. The UAV’s measurements use a Structure from Motion (SfM) method. Verification tests were conducted on a steel truss railway bridge over the largest river in Poland. The measurements concerned both the basic dimensions of the bridge and the details of a selected truss connection. The STS identified a significant deviation in the actual geometry of the measured connection and the design documentation. The UAV’s inspection confirmed these findings. The integration of STS and UAV technologies has demonstrated significant advantages, including STS’s high accuracy in direct measurements, with deviations within acceptable engineering tolerances (below a few mm), and the UAV’s efficiency in covering large areas, achieving over 90% compliance with reference dimensions. This combined approach not only reduces operating costs and enhances safety by minimizing the need for heavy machinery or scaffolding but also provides a more comprehensive understanding of the structural condition. Full article

(This article belongs to the Special Issue Structural Health Monitoring and Damage Assessment by Advanced Remote Sensing Techniques and Methods)

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28 pages, 6037 KB

Open AccessFeature PaperEditor’s ChoiceArticle

Statistical and Independent Component Analysis of Sentinel-1 InSAR Time Series to Assess Land Subsidence Trends

by Celina Anael Farías, Michelle Lenardón Sánchez, Roberta Bonì and Francesca Cigna

Remote Sens. 2024, 16(21), 4066; https://doi.org/10.3390/rs16214066 - 31 Oct 2024

Cited by 7 | Viewed by 4841

Abstract

Advanced statistics can enable the detailed characterization of ground deformation time series, which is a fundamental step for thoroughly understanding the phenomena of land subsidence and their main drivers. This study presents a novel methodological approach based on pre-existing open-access statistical tools to [...] Read more.

Advanced statistics can enable the detailed characterization of ground deformation time series, which is a fundamental step for thoroughly understanding the phenomena of land subsidence and their main drivers. This study presents a novel methodological approach based on pre-existing open-access statistical tools to exploit satellite differential interferometric synthetic aperture radar (DInSAR) data to investigate land subsidence processes, using European Ground Motion Service (EGMS) Sentinel-1 DInSAR 2018−2022 datasets. The workflow involves the implementation of Persistent Scatterers (PS) time series classification through the PS-Time tool, deformation signal decomposition via independent component analysis (ICA), and drivers’ investigation through spatio-temporal correlation with geospatial and monitoring data. Subsidence time series at the three demonstration sites of Bologna, Ravenna and Carpi (Po Plain, Italy) were classified into linear and nonlinear (quadratic, discontinuous, uncorrelated) categories, and the mixed deformation signal of each PS was decomposed into independent components, allowing the identification of new spatial clusters with linear, accelerating/decelerating, and seasonal trends. The relationship between the different independent components and DInSAR-derived displacement velocity, acceleration, and seasonality was also analyzed via regression analysis. Correlation with geological and groundwater monitoring data supported the investigation of the relationship between the observed deformation and subsidence drivers, such as aquifer resource exploitation, local geological setting, and gas extraction/reinjection. Full article

(This article belongs to the Special Issue Monitoring Geohazard from Synthetic Aperture Radar Interferometry)

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32 pages, 7202 KB

Open AccessEditor’s ChoiceArticle

Learn from Simulations, Adapt to Observations: Super-Resolution of Isoprene Emissions via Unpaired Domain Adaptation

by Antonio Giganti, Sara Mandelli, Paolo Bestagini and Stefano Tubaro

Remote Sens. 2024, 16(21), 3963; https://doi.org/10.3390/rs16213963 - 24 Oct 2024

Cited by 5 | Viewed by 17945

Abstract

Plants emit biogenic volatile organic compounds (BVOCs), such as isoprene, significantly influencing atmospheric chemistry and climate. BVOC emissions estimated from bottom-up (BU) approaches (derived from numerical simulations) usually exhibit denser and more detailed spatial information compared to those estimated through top-down (TD) approaches [...] Read more.

Plants emit biogenic volatile organic compounds (BVOCs), such as isoprene, significantly influencing atmospheric chemistry and climate. BVOC emissions estimated from bottom-up (BU) approaches (derived from numerical simulations) usually exhibit denser and more detailed spatial information compared to those estimated through top-down (TD) approaches (derived from satellite observations). Moreover, numerically simulated emissions are typically easier to obtain, even if they are less reliable than satellite acquisitions, which, being derived from actual measurements, are considered a more trustworthy instrument for performing chemistry and climate investigations. Given the coarseness and relative lack of satellite-derived measurements, fine-grained numerically simulated emissions could be exploited to enhance them. However, simulated (BU) and observed (TD) emissions usually differ regarding value range and spatiotemporal resolution. In this work, we present a novel deep learning (DL)-based approach to increase the spatial resolution of satellite-derived isoprene emissions, investigating the adoption of efficient domain adaptation (DA) techniques to bridge the gap between numerically simulated emissions and satellite-derived emissions, avoiding the need for retraining a specific super-resolution (SR) algorithm on them. For this, we propose a methodology based on the cycle generative adversarial network (CycleGAN) architecture, which has been extensively used for adapting natural images (like digital photographs) of different domains. In our work, we depart from the standard CycleGAN framework, proposing additional loss terms that allow for better DA and emissions’ SR. We extensively demonstrate the proposed method’s effectiveness and robustness in restoring fine-grained patterns of observed isoprene emissions. Moreover, we compare different setups and validate our approach using different emission inventories from both domains. Eventually, we show that the proposed DA strategy paves the way towards robust SR solutions even in the case of spatial resolution mismatch between the training and testing domains and in the case of unknown testing data. Full article

(This article belongs to the Special Issue Advances in Satellite Image Analysis and Applications for Earth Observation)

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18 pages, 6507 KB

Open AccessEditor’s ChoiceArticle

Estimation of PM_2.5 Using Multi-Angle Polarized TOA Reflectance Data from the GF-5B Satellite

by Ruijie Zhang, Hui Chen, Ruizhi Chen, Chunyan Zhou, Qing Li, Huizhen Xie and Zhongting Wang

Remote Sens. 2024, 16(21), 3944; https://doi.org/10.3390/rs16213944 - 23 Oct 2024

Cited by 5 | Viewed by 1766

Abstract

The use of satellite data to estimate PM_2.5 is an appropriate approach for long-term, substantial monitoring and assessment. To estimate PM_2.5, the majority of the algorithms now in use utilize the top-of-atmosphere (TOA) reflectance or aerosol optical depth (AOD) derived [...] Read more.

The use of satellite data to estimate PM_2.5 is an appropriate approach for long-term, substantial monitoring and assessment. To estimate PM_2.5, the majority of the algorithms now in use utilize the top-of-atmosphere (TOA) reflectance or aerosol optical depth (AOD) derived from scalar satellite data. However, there is relatively little research on the retrieval of PM_2.5 using multi-angle polarized data. With its directional polarimetric camera (DPC), the Chinese new-generation satellite Gaofen 5B (henceforth referred to as GF-5B) offers a unique opportunity to close this gap in multi-angle polarized observation data. In this research, we utilized TOA data from the DPC payload and applied the gradient boosting machine method to simulate the impact of the observation angle, wavelength, and polarization information on the accuracy of PM_2.5 retrieval. We identified the optimal conditions for the effective estimation of PM_2.5. The quantitative results indicated that, under these optimal conditions, the PM_2.5 concentrations retrieved by GF-5B showed a strong correlation with the ground-based data, achieving an R² of 0.9272 and an RMSE of 7.38 µg·m⁻³. By contrast, Himawari-8’s retrieval accuracy under similar data conditions consisted of an R² of 0.9099 and RMSE of 7.42 µg·m⁻³, indicating that GF-5B offers higher accuracy. Furthermore, the retrieval results in this study demonstrated an R² of 0.81 when compared to the CHAP dataset, confirming the feasibility and effectiveness of the use of GF-5B for PM_2.5 retrieval and providing support for PM_2.5 estimation through multi-angle polarized data. Full article

(This article belongs to the Special Issue Trend, Progress and Application of Remote Sensing for Atmospheric Environment and Climate Change)

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21 pages, 7709 KB

Open AccessEditor’s ChoiceArticle

Impacts of GCP Distributions on UAV-PPK Photogrammetry at Sermeq Avannarleq Glacier, Greenland

by Haiyan Zhao, Gang Li, Zhuoqi Chen, Shuhang Zhang, Baogang Zhang and Xiao Cheng

Remote Sens. 2024, 16(21), 3934; https://doi.org/10.3390/rs16213934 - 22 Oct 2024

Cited by 5 | Viewed by 2577

Abstract

Real-Time/Post-Processing Kinematic (RTK/PPK) technology has been widely applied in Unmanned Aerial Vehicle (UAV) photogrammetry in glaciological research. Considering that ground control points (GCPs) cannot be set on glaciers, evaluating the impacts of one-sided distribution is essential. In this study, 8571 images were captured [...] Read more.

Real-Time/Post-Processing Kinematic (RTK/PPK) technology has been widely applied in Unmanned Aerial Vehicle (UAV) photogrammetry in glaciological research. Considering that ground control points (GCPs) cannot be set on glaciers, evaluating the impacts of one-sided distribution is essential. In this study, 8571 images were captured at Sermeq Avannarleq glacier in western Greenland from 4 August 2021 to the 6th, covering approximately 85 km², with the furthest distance being 13.22 km away from the coastline. Benefited by the meandering coastline, 11 roving stations roughly uniformly distributed on bare rock were surveyed with the RTK technique. PPK-geotagged images were processed in Agisoft Metashape Professional to derive the DSMs, utilizing eight different configurations of GCP distributions that gradually extended longitudinally (along the glacier flow direction) to the upper part of the glacier. The accuracy of DSMs was evaluated by referring to the validation points (VPs) that were not employed in the Bundle Block Adjustment (BBA). The results indicated that the RMSE values of the easting, northing, and height of the reconstruction model georeferenced by only PPK geotagging (no GCPs applied) were 0.038 m, 0.031 m, and 0.146 m, respectively. Applying four GCPs located at one side of the region but with both longitudinal and lateral distribution improved the RMSE values in easting, northing, and vertical to 0.037 m, 0.031 m, and 0.081 m, respectively, and these values were stable even when distributing four GCPs evenly or when increasing the number of GCPs to eleven. Moreover, the cross-validation with ICESat-2 and ArcticDEM performed only at an off-glacier region also suggested that vertical accuracy shows significant improvements for every configuration of GCPs compared to the reconstruction model optimized only by PPK, but such improvements were not obvious if the number of GCPs exceeded four. Moreover, no elevation ramps appeared in the UAV DSM, even for the GCP configuration with only two GCPs distributed at the terminus. Therefore, combining PPK with only a few GCPs but distributing in both directions of the surveying region can offer a viable solution for obtaining glacier DSMs at the coastline with decimeter-level accuracy. Full article

(This article belongs to the Special Issue Earth Observation of Glacier and Snow Cover Mapping in Cold Regions)

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