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^1,2,3,†

, Anna Chiara Tangari^2,*,†

, Fabio Scarciglia⁴

, Giusy Lavecchia^2,3 and Francesco Brozzetti^1,2,3

¹ Laboratory of Structural Geology, 3D Digital Cartography and Geomatics, University of Chieti-Pescara, 66100 Chieti, Italy

² Science Department, University G. d’Annunzio Chieti-Pescara, 66100 Chieti, Italy

³ CRUST—Interuniversity Center for 3D Seismotectonics with Territorial Applications, 66100 Chieti, Italy

⁴ Biology, Ecology and Earth Sciences Department (DiBEST), University of Calabria, 87036 Arcavacata di Rende, Italy

^† These authors contributed equally to this work.

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

Cited by 5 | Viewed by 1679

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 Edwards¹

, Francisco J. Gomez², Son Kim Do³

, David A. Weiss^1,4, Jude Kastens⁴

, Sagy Cohen⁵

, Hamid Moradkhani²

, Venkataraman Lakshmi³ and Xingong Li^1,*

¹ Department of Geography and Atmospheric Science, University of Kansas, Lawrence, KS 66045, USA

² Department of Civil, Construction and Environmental Engineering, Center for Complex Hydrosystems Research, University of Alabama, Tuscaloosa, AL 35487, USA

³ Department of Civil and Environmental Engineering, University of Virginia, Charlottesville, VA 22904, USA

⁴ Kansas Biological Survey, University of Kansas, Lawrence, KS 66047, USA

⁵ Department of Geography, University of Alabama, Tuscaloosa, AL 35487, USA

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

Viewed by 3065

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^1,2,*, Alexandra Jarna Ganerød^3,4, Graziella Devoli⁵, Johannes Reiche⁶

, Steinar Nordal¹ and Regula Frauenfelder⁷

¹ Department of Civil and Environmental Engineering, Norwegian University of Science and Technology, 7034 Trondheim, Norway

² Rock and Soil Mechanics Group, SINTEF, 7031 Trondheim, Norway

³ Department of Geography, Norwegian University of Science and Technology, 7049 Trondheim, Norway

⁴ Geological Survey of Norway (NGU), 7040 Trondheim, Norway

⁵ Norwegian Water Resources and Energy Directorate, 0368 Oslo, Norway

⁶ Laboratory of Geo-Information Science and Remote Sensing, Wageningen University & Research, Droevendaalsesteeg 3, 6708 PB Wageningen, The Netherlands

⁷ Norwegian Geotechnical Institute (NGI), 0806 Oslo, Norway

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

Viewed by 1744

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 Barbera¹

, Antonino Maltese^2,*

and Christian Conoscenti¹

¹ Dipartimento di Scienze della Terra e del Mare (DiSTeM), Università degli Studi di Palermo, 90123 Palermo, Italy

² Dipartimento di Ingegneria (DI), Università degli Studi di Palermo, 90128 Palermo, Italy

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

Cited by 1 | Viewed by 3085

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^1,*,†

, Margot Dominique^1,†, Andrew Hartley²

, Céline Lamarche¹

, Audric Bos¹

and Pierre Defourny¹

¹ Earth and Life Institute, Université Catholique de Louvain, 1348 Louvain-la-Neuve, Belgium

² Met Office, Exeter EX1 3PB, UK

^† These authors contributed equally to this work.

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

Viewed by 2601

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^1,*, Andrew T. Hudak²

, Jeff W. Atkins³

, Eben North Broadbent⁴

, Kody Melissa Brock⁴

, Michael J. Campbell⁵

, Nuria Sánchez-López²

, Monique Bohora Schlickmann⁴

, Francisco Mauro⁶

, Andres Susaeta⁷

, Eric Rowell⁸, Caio Hamamura⁹

, Ana Paula Dalla Corte¹⁰, Inga La Puma²

, Russell A. Parsons¹¹

, Benjamin C. Bright²

, Jason Vogel⁴, Inacio Thomaz Bueno⁴

, Gabriel Maximo da Silva¹², Carine Klauberg⁴

, Jinyi Xia⁴, Jessie F. Eastburn⁵

, Kleydson Diego Rocha⁴

and Carlos Alberto Silva⁴

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¹ College of Natural Resources, University of Idaho, Moscow, ID 83844, USA

² USDA Forest Service, Rocky Mountain Research Station, Moscow, ID 83843, USA

³ USDA Forest Service, Southern Research Station, New Ellenton, SC 29809, USA

⁴ School of Forest, Fisheries, & Geomatics Sciences, University of Florida, Gainesville, FL 32611, USA

⁵ School of Environment, Society & Sustainability, University of Utah, Salt Lake City, UT 84112, USA

⁶ Instituto Universitario de Investigación en Gestión Forestal Sostenible, University of Valladolid, 42004 Soria, Spain

⁷ Department of Forest Engineering, Resources & Management, Oregon State University, Corvallis, OR 97331, USA

⁸ Department of Atmospheric Science, Desert Research Institute, Reno, NV 89512, USA

⁹ Federal Institute of Education, Science and Technology of São Paulo, São Paulo 01109-010, SP, Brazil

¹⁰ Department of Forest Science, Federal University of Paraná, Curitiba 80060-000, PR, Brazil

¹¹ USDA Forest Service, Fire Sciences Lab, Missoula, MT 59808, USA

¹² National Institute for Space Research, São José dos Campos 12227-010, SP, Brazil

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

Cited by 1 | Viewed by 1475

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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18 pages, 3600 KB

Open AccessEditor’s ChoiceArticle

Long-Term Snow Cover Change in the Qilian Mountains (1986–2024): A High-Resolution Landsat-Based Analysis

by Enwei Huang^1,2,3, Guofeng Zhu^1,2,3,4,*

, Yuhao Wang^1,2,3

, Rui Li^1,2, Yuxin Miao^1,2,3, Xiaoyu Qi^1,2, Qingyang Wang^1,2,3, Yinying Jiao^1,2,4, Qinqin Wang^1,2,4 and Ling Zhao^1,2,4

¹ College of Geography and Environmental Science, Northwest Normal University, Lanzhou 730070, China

² Comprehensive Observatory of Ecological Environment in Shiyang River Basin, Northwest Normal University, Lanzhou 730070, China

³ Remote Sensing Application Center of Ministry of Agriculture and Rural Affairs, Lanzhou Branch, Lanzhou 730070, China

⁴ Gansu Key Laboratory of Oasis Resource Environment and Sustainable Development, Lanzhou 730070, China

Remote Sens. 2025, 17(14), 2497; https://doi.org/10.3390/rs17142497 - 18 Jul 2025

Cited by 2 | Viewed by 1271

Abstract

Snow cover, as a critical component of the cryosphere, serves as a vital water resource for arid regions in Northwest China. The Qilian Mountains (QLM), situated on the northeastern margin of the Tibetan Plateau, function as an important ecological barrier and water conservation [...] Read more.

Snow cover, as a critical component of the cryosphere, serves as a vital water resource for arid regions in Northwest China. The Qilian Mountains (QLM), situated on the northeastern margin of the Tibetan Plateau, function as an important ecological barrier and water conservation area in western China. This study presents the first high-resolution historical snow cover product developed specifically for the QLM, utilizing a multi-level snow classification algorithm tailored to the complex topography of the region. By employing Landsat satellite data from 1986–2024, we constructed a comprehensive 39-year snow cover dataset at a resolution of 30 m. A dual adaptive cloud masking strategy and spatial interpolation techniques were employed to effectively address cloud contamination and data gaps prevalent in mountainous regions. The spatiotemporal characteristics and driving mechanisms of snow cover changes in the QLM were systematically analyzed using Sen–Theil trend analysis and Mann–Kendall tests. The results reveal the following: (1) The mean annual snow cover extent in the QLM was 15.73% during 1986–2024, exhibiting a slight declining trend (−0.046% yr⁻¹), though statistically insignificant (p = 0.215); (2) The snowline showed significant upward migration, with mean elevation and minimum elevation rising at rates of 3.98 m yr⁻¹ and 2.81 m yr⁻¹, respectively; (3) Elevation-dependent variations were observed, with significant snow cover decline in high-altitude (>5000 m) and low-altitude (2000–3500 m) regions, while mid-altitude areas remained relatively stable; (4) Comparison with MODIS data demonstrated good correlation (r = 0.828) but revealed systematic differences (RMSE = 12.88%), with MODIS showing underestimation in mountainous environments (Bias: −8.06%). This study elucidates the complex response mechanisms of the QLM snow system under global warming, providing scientific evidence for regional water resource management and climate change adaptation strategies. Full article

(This article belongs to the Special Issue Application of Remote Sensing in Snow and Ice Monitoring)

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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 Lamichhane¹

, Sushant Mehan^1,*

and Kyle R. Mankin²

¹ Department of Agricultural and Biosystems Engineering, South Dakota State University, Brookings, SD 57007, USA

² Water Management and Systems Research Unit, United States Department of Agriculture-Agricultural Research Service, 2150 Centre Ave., Fort Collins, CO 80526, USA

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

Cited by 11 | Viewed by 7779

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^1,*

, Carlos A. Silva¹

, Monique B. Schlickmann¹

, Victoria M. Donovan¹

, Jeff W. Atkins², Kody M. Brock¹

, Jinyi Xia¹, Denis R. Valle¹

, Jiangxiao Qiu¹, Jason Vogel¹, Andres Susaeta³

, Ajay Sharma⁴, Carine Klauberg¹

, Midhun Mohan⁵

and Ana Paula Dalla Corte⁶

¹ School of Forest, Fisheries, and Geomatics Sciences, University of Florida, Gainesville, FL 32611, USA

² USDA Forest Service, Southern Research Station, New Ellenton, SC 29809, USA

³ Department of Forest Engineering, Resources and Management, Oregon State University, Corvallis, OR 97331, USA

⁴ College of Forestry, Wildlife and Environment, Auburn University, Auburn, AL 36849, USA

⁵ Ecoresolve, San Francisco, CA 94105, USA

⁶ Department of Forest Science, Federal University of Paraná, Curitiba 80060-000, PR, Brazil

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

Cited by 1 | Viewed by 2382

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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22 pages, 4465 KB

Open AccessEditor’s ChoiceArticle

Urban Expansion Scenario Prediction Model: Combining Multi-Source Big Data, a Graph Attention Network, a Vector Cellular Automata, and an Agent-Based Model

by Yunqi Gao¹, Dongya Liu^1,*

, Xinqi Zheng¹

, Xiaoli Wang² and Gang Ai¹

¹ School of Information Engineering, China University of Geosciences, Beijing 100083, China

² China Land Surveying and Planning Institute, Beijing 100035, China

Remote Sens. 2025, 17(13), 2272; https://doi.org/10.3390/rs17132272 - 2 Jul 2025

Cited by 2 | Viewed by 1723

Abstract

The construction of transition rules is the core and difficulty faced by the cellular automata (CA) model. Dynamic mining of transition rules can more accurately simulate urban land use change. By introducing a graph attention network (GAT) to mine CA model transition rules, [...] Read more.

The construction of transition rules is the core and difficulty faced by the cellular automata (CA) model. Dynamic mining of transition rules can more accurately simulate urban land use change. By introducing a graph attention network (GAT) to mine CA model transition rules, the temporal and spatial dynamics of the model are increased based on the construction of a real-time dynamic graph structure. At the same time, by adding an agent-based model (ABM) to the CA model, the simulation evolution of different human decision-making behaviors can be achieved. Based on this, an urban expansion scenario prediction (UESP) model has been proposed: (1) the UESP model employs a multi-head attention mechanism to dynamically capture high-order spatial dependencies, supporting the efficient processing of large-scale datasets with over 50,000 points of interest (POIs); (2) it incorporates the behaviors of agents such as residents, governments, and transportation systems to more realistically reflect human micro-level decision-making; and (3) by integrating macro-structural learning with micro-behavioral modeling, it effectively addresses the existing limitations in representing high-order spatial relationships and human decision-making processes in urban expansion simulations. Based on the policy context of the Outline of the Beijing–Tianjin–Hebei (BTH) Coordinated Development Plan, four development scenarios were designed to simulate construction land change by 2030. The results show that (1) the UESP model achieved an overall accuracy of 0.925, a Kappa coefficient of 0.878, and a FoM index of 0.048, outperforming traditional models, with the FoM being 3.5% higher; (2) through multi-scenario simulation prediction, it is found that under the scenario of ecological conservation and farmland protection, forest and grassland increase by 3142 km², and cultivated land increases by 896 km², with construction land showing a concentrated growth trend; and (3) the expansion of construction land will mainly occur at the expense of farmland, concentrated around Beijing, Tianjin, Tangshan, Shijiazhuang, and southern core cities in Hebei, forming a “core-driven, axis-extended, and cluster-expanded” spatial pattern. Full article

(This article belongs to the Special Issue Advances in Methods and Techniques for Satellite Image Processing and Analysis)

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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^1,2,3, Gang Yang^1,2,3,*

, Hao Lu^1,2,3

and Xue Zhang⁴

¹ School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China

² Hebei Key Laboratory of Smart National Park, Beijing 100083, China

³ Engineering Research Center for Forestry-Oriented Intelligent Information Processing, National Forestry and Grassland Administration, Beijing 100083, China

⁴ School of Economics, Minzu University of China, Beijing 100081, China

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

Viewed by 1624

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^1,2,†, Xin Shen^1,† and Lin Cao^1,*

¹ Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, China

² College of Information Science and Technology, Nanjing Forestry University, Nanjing 210037, China

^† These authors contributed equally to this work.

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

Viewed by 1110

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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31 pages, 6061 KB

Open AccessEditor’s ChoiceReview

A Comprehensive Review of Path-Planning Algorithms for Planetary Rover Exploration

by Qingliang Miao and Guangfei Wei^*

Deep Space Exploration Laboratory, Institute of Deep Space Sciences, Hefei 230026, China

Remote Sens. 2025, 17(11), 1924; https://doi.org/10.3390/rs17111924 - 31 May 2025

Cited by 7 | Viewed by 6027

Abstract

Path-planning algorithms for planetary rovers are critical for autonomous robotic exploration, enabling the efficient and safe traversal of complex and dynamic extraterrestrial terrains. Unlike terrestrial mobile robots, planetary rovers must navigate highly unpredictable environments influenced by diverse factors such as terrain variability, obstacles, [...] Read more.

Path-planning algorithms for planetary rovers are critical for autonomous robotic exploration, enabling the efficient and safe traversal of complex and dynamic extraterrestrial terrains. Unlike terrestrial mobile robots, planetary rovers must navigate highly unpredictable environments influenced by diverse factors such as terrain variability, obstacles, illumination conditions, and temperature fluctuations, necessitating advanced path-planning strategies to ensure mission success. This review comprehensively synthesizes recent advancements in planetary rover path-planning algorithms. First, we categorize these algorithms from a constraint-oriented perspective, distinguishing between internal rover state constraints and external environmental constraints. Next, we examine rule-based path-planning approaches, including graph search-based methods, potential field methods, sampling-based techniques, and dynamic window approaches, analyzing representative algorithms in each category. Subsequently, we explore bio-inspired path-planning methods, such as evolutionary algorithms, fuzzy computing, and machine learning-based approaches, with a particular emphasis on the latest developments and prospects of machine learning techniques in planetary rover navigation. Finally, we synthesize key insights from existing algorithms and discuss future research directions, highlighting their potential applications in planetary exploration missions. Full article

(This article belongs to the Special Issue Autonomous Space Navigation (Second Edition))

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41 pages, 4978 KB

Open AccessEditor’s ChoiceReview

Harnessing Geospatial Artificial Intelligence and Deep Learning for Landslide Inventory Mapping: Advances, Challenges, and Emerging Directions

by Xiao Chen¹

, Wenwen Li^1,*

, Chia-Yu Hsu¹

, Samantha T. Arundel²

and Bretwood Higman³

¹ School of Geographical Sciences and Urban Planning, Arizona State University, Tempe, AZ 85287, USA

² U.S. Geological Survey, Center of Excellence for Geospatial Information Science (CEGIS), Rolla, MO 65401, USA

³ Ground Truth Alaska, Seldovia, AK 99663, USA

Remote Sens. 2025, 17(11), 1856; https://doi.org/10.3390/rs17111856 - 26 May 2025

Cited by 4 | Viewed by 4038

Abstract

Recent advancements in artificial intelligence (AI) and deep learning enable more accurate, scalable, and automated mapping. This paper provides a comprehensive review of the applications of AI, particularly deep learning, in landslide inventory mapping. In addition to examining commonly used data sources and [...] Read more.

Recent advancements in artificial intelligence (AI) and deep learning enable more accurate, scalable, and automated mapping. This paper provides a comprehensive review of the applications of AI, particularly deep learning, in landslide inventory mapping. In addition to examining commonly used data sources and model architectures, we explore innovative strategies such as feature enhancement and fusion, attention-boosted techniques, and advanced learning approaches, including active learning and transfer learning, to enhance model adaptability and predictability. We also highlight the remaining challenges and potential research directions, including the estimation of more diverse variables in landslide mapping, multimodal data alignment, modeling regional variability and replicability, as well as issues related to data misinterpretation and model explainability. This review aims to serve as a useful resource for researchers and practitioners, promoting the integration of deep learning into landslide research and disaster management. Full article

(This article belongs to the Section Environmental Remote Sensing)

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16 pages, 3645 KB

Open AccessEditor’s ChoiceArticle

A Global Coseismic InSAR Dataset for Deep Learning: Automated Construction from Sentinel-1 Observations (2015–2024)

by Xu Liu^1,2

, Zhenjie Wang^1,*

, Yingfeng Zhang², Xinjian Shan² and Ziwei Liu^1,2

¹ College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, China

² State Key Laboratory of Earthquake Dynamics and Forecasting, China Earthquake Administration, Beijing 100029, China

Remote Sens. 2025, 17(11), 1832; https://doi.org/10.3390/rs17111832 - 23 May 2025

Cited by 1 | Viewed by 2858

Abstract

Interferometric synthetic aperture radar (InSAR) technology has been widely employed in the rapid monitoring of earthquakes and associated geological hazards. With the continued advancement of InSAR technology, the growing volume of satellite-acquired data has opened new avenues for applying deep learning (DL) techniques [...] Read more.

Interferometric synthetic aperture radar (InSAR) technology has been widely employed in the rapid monitoring of earthquakes and associated geological hazards. With the continued advancement of InSAR technology, the growing volume of satellite-acquired data has opened new avenues for applying deep learning (DL) techniques to the analysis of earthquake-induced surface deformation. Although DL holds great promise for processing InSAR data, its development progress has been significantly constrained by the absence of large-scale, accurately annotated datasets related to earthquake-induced deformation. To address this limitation, we propose an automated method for constructing deep learning training datasets by integrating the Global Centroid Moment Tensor (GCMT) earthquake catalog with Sentinel-1 InSAR observations. This approach reduces the inefficiencies and manual labor typically involved in InSAR data preparation, thereby significantly enhancing the efficiency and automation of constructing deep learning datasets for coseismic deformation. Using this method, we developed and publicly released a large-scale training dataset consisting of coseismic InSAR samples. The dataset contained 353 Sentinel-1 interferograms corresponding to 62 global earthquakes that occurred between 2015 and 2024. Following standardized preprocessing and data augmentation (DA), a large number of image samples were generated for model training. Multidimensional analyses of the dataset confirmed its high quality and strong representativeness, making it a valuable asset for deep learning research on coseismic deformation. The dataset construction process followed a standardized and reproducible workflow, ensuring objectivity and consistency throughout data generation. As additional coseismic InSAR observations become available, the dataset can be continuously expanded, evolving into a comprehensive, high-quality, and diverse training resource. It serves as a solid foundation for advancing deep learning applications in the field of InSAR-based coseismic deformation analysis. Full article

(This article belongs to the Special Issue Artificial Intelligence and Remote Sensing for Geohazards)

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23 pages, 4743 KB

Open AccessEditor’s ChoiceArticle

Utilizing Remote Sensing for Sponge City Development: Enhancing Flood Management and Urban Resilience in Karachi

by Asifa Iqbal^1,*

, Lubaina Soni², Ammad Waheed Qazi³

and Humaira Nazir⁴

¹ Department of Computer and Geospatial Sciences, University of Gävle, 801 76 Gävle, Sweden

² Department of Architecture and Planning, Dawood University of Engineering and Technology, Karachi 74800, Pakistan

³ Department of Environmental Science, International Islamic University Islamabad, Islamabad 44000, Pakistan

⁴ Department of Architecture and Environmental Design, Sir Syed University of Engineering and Technology, Karachi 75300, Pakistan

Remote Sens. 2025, 17(11), 1818; https://doi.org/10.3390/rs17111818 - 23 May 2025

Cited by 3 | Viewed by 5178

Abstract

Rapid urbanization in Karachi, Pakistan, has resulted in increased impervious surfaces, leading to significant challenges, such as frequent flooding, urban heat islands, and loss of vegetation. These issues pose challenges to urban resilience, livability, and sustainability, which further demand solutions that incorporate urban [...] Read more.

Rapid urbanization in Karachi, Pakistan, has resulted in increased impervious surfaces, leading to significant challenges, such as frequent flooding, urban heat islands, and loss of vegetation. These issues pose challenges to urban resilience, livability, and sustainability, which further demand solutions that incorporate urban greening and effective water management. This research uses remote sensing technologies and Geographic Information Systems (GISs), to analyze current surface treatments and their relationship to Karachi’s blue-green infrastructure. By following this approach, we evaluate flood risk and identify key flood-conditioning factors, including elevation, slope, rainfall distribution, drainage density, and land use/land cover changes. By utilizing the Analytical Hierarchy Process (AHP), we develop a flood risk assessment framework and a comprehensive flood risk map. Additionally, this research proposes an innovative Sponge City (SC) framework that integrates nature-based solutions (NBS) into urban planning, especially advocating for the establishment of green infrastructure, such as green roofs, rain gardens, and vegetated parks, to enhance water retention and drainage capacity. The findings highlight the urgent need for targeted policies and stakeholder engagement strategies to implement sustainable urban greening practices that address flooding and enhance the livability of Karachi. This work not only advances the theoretical understanding of Sponge Cities but also provides practical insights for policymakers, urban planners, and local communities facing similar sustainability challenges. Full article

(This article belongs to the Special Issue Application of Remote Sensing to Flood and Drought Analysis, Monitoring and Risk Management)

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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^1,2

, Peng Chen^1,2,*

, Siqi Zhang^1,2

, Haiqing Huang^1,2, Yuliang Pan¹ and Delu Pan^1,2

¹ Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), No. 1119, Haibin Road Nansha District, Guangzhou 511458, China

² State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, 36 Bochubeilu, Hangzhou 310012, China

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

Cited by 9 | Viewed by 5051

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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27 pages, 34152 KB

Open AccessEditor’s ChoiceReview

Retrieving Inland Water Quality Parameters via Satellite Remote Sensing: Sensor Evaluation, Atmospheric Correction, and Machine Learning Approaches

by Mohsen Ansari¹

, Anders Knudby^1,*

, Meisam Amani² and Michael Sawada¹

¹ Department of Geography, Environment and Geomatics, University of Ottawa, 75 Laurier Avenue East, Ottawa, ON K1N 6N5, Canada

² Canada Centre for Mapping and Earth Observation, Natural Resources Canada, 560 Rochester Street, Ottawa, ON K1S 4M2, Canada

Remote Sens. 2025, 17(10), 1734; https://doi.org/10.3390/rs17101734 - 15 May 2025

Cited by 5 | Viewed by 4081

Abstract

Satellite remote sensing provides a cost-effective and large-scale alternative to traditional methods for retrieving water quality parameters for inland waters. Effective water quality parameter retrieval via optical satellite remote sensing requires three key components: (1) a sensor whose measurements are sensitive to variations [...] Read more.

Satellite remote sensing provides a cost-effective and large-scale alternative to traditional methods for retrieving water quality parameters for inland waters. Effective water quality parameter retrieval via optical satellite remote sensing requires three key components: (1) a sensor whose measurements are sensitive to variations in water quality; (2) accurate atmospheric correction to eliminate the effect of absorption and scattering in the atmosphere and retrieve the water-leaving radiance/reflectance; and (3) a bio-optical model used to estimate water quality from the optical signal. This study provides a literature review and an evaluation of these three components. First, a review of decommissioned, active, and upcoming satellite sensors is presented, highlighting their advantages and limitations, and a ranking method is introduced to assess their suitability for retrieving chlorophyll-a, colored dissolved organic matter, and non-algal particles in inland waters. This ranking can aid in selecting appropriate sensors for future studies. Second, the strengths and weaknesses of atmospheric correction algorithms used over inland waters are examined. The results show that no atmospheric correction algorithm performed consistently across all conditions. However, understanding their strengths and weaknesses allows users to select the most suitable algorithm for a specific use case. Third, the challenges, limitations, and recent advances of machine learning use in bio-optical models for inland water quality parameter retrieval are discussed. Machine learning models have limitations, including low generalizability, low dimensionality, spatial/temporal autocorrelation, and information leakage. These issues highlight the importance of locally trained models, rigorous cross-validation methods, and integrating auxiliary data to enhance dimensionality. Finally, recommendations for promising research directions are provided. 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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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^1,2,3, Yanhong Wu^1,2,*, Yongkang Ren^1,2,3, Siqi Zheng⁴, Hao Chen^1,2,3

and Xuankai Teng^1,2,3

¹ Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

² International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China

³ University of Chinese Academy of Sciences, Beijing 100049, China

⁴ College of Civil Engineering, Nanjing Forestry University, Nanjing 210037, China

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

Cited by 3 | Viewed by 1545

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^1,2 and Boqiang Qin^1,2,*

¹ School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China

² State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 211135, China

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

Cited by 9 | Viewed by 5431

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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21 pages, 23238 KB

Open AccessEditor’s ChoiceArticle

Semantic and Geometric Fusion for Object-Based 3D Change Detection in LiDAR Point Clouds

by Abderrazzaq Kharroubi^1,*

, Fabio Remondino²

, Zouhair Ballouch^1,3

, Rafika Hajji³

and Roland Billen¹

¹ Spheres Research Unit, Geospatial Data Science and City Information Modeling Lab (GeoScITY), University of Liège, 4000 Liège, Belgium

² 3D Optical Metrology Unit, Bruno Kessler Foundation, 38123 Trento, Italy

³ College of Geomatic Sciences and Surveying Engineering, Hassan II IAV, Rabat 10101, Morocco

Remote Sens. 2025, 17(7), 1311; https://doi.org/10.3390/rs17071311 - 6 Apr 2025

Viewed by 2172

Abstract

Accurate three-dimensional change detection is essential for monitoring dynamic environments such as urban areas, infrastructure, and natural landscapes. Point-based methods are sensitive to noise and lack spatial coherence, while object-based approaches rely on clustering, which can miss fine-scale changes. To address these limitations, [...] Read more.

Accurate three-dimensional change detection is essential for monitoring dynamic environments such as urban areas, infrastructure, and natural landscapes. Point-based methods are sensitive to noise and lack spatial coherence, while object-based approaches rely on clustering, which can miss fine-scale changes. To address these limitations, we introduce an object-based change detection framework integrating semantic segmentation and geometric change indicators. The proposed method first classifies bi-temporal point clouds into ground, vegetation, buildings, and moving objects. A cut-pursuit clustering algorithm then segments the data into spatially coherent objects, which are matched across epochs using a nearest-neighbor search based on centroid distance. Changes are characterized by a combination of geometric features—including verticality, sphericity, omnivariance, and surface variation—and semantic information. These features are processed by a random forest classifier to assign change labels. The model is evaluated on the Urb3DCD-v2 dataset, with feature importance analysis to identify important features. Results show an 81.83% mean intersection over union. An additional ablation study without clustering reached 83.43% but was more noise-sensitive, leading to fragmented detections. The proposed method improves the efficiency, interpretability, and spatial coherence of change classification, making it well suited for large-scale monitoring applications. Full article

(This article belongs to the Special Issue Geospatial Intelligence in Remote Sensing)

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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^*

Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD)/Key Laboratory of Ecosystem Carbon Source and Sink, China Meteorological Administration (ECSS-CMA), Nanjing University of Information Science and Technology, Nanjing 210044, China

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

Cited by 4 | Viewed by 1273

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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38 pages, 7941 KB

Open AccessEditor’s ChoiceArticle

Flood Inundation Mapping Using the Google Earth Engine and HEC-RAS Under Land Use/Land Cover and Climate Changes in the Gumara Watershed, Upper Blue Nile Basin, Ethiopia

by Haile Belay^1,2

, Assefa M. Melesse^3,*

, Getachew Tegegne^3,4 and Shimelash Molla Kassaye⁵

¹ Africa Center of Excellence for Water Management, Addis Ababa University, Addis Ababa 1176, Ethiopia

² Department of Hydraulic and Water Resources Engineering, Dilla University, Dilla 419, Ethiopia

³ Department of Earth and Environment, Florida International University, Miami, FL 33199, USA

⁴ Sustainable Energy Center of Excellence, Addis Ababa Science and Technology University, Addis Ababa 16417, Ethiopia

⁵ Department of Water Resources and Irrigation Engineering, Mattu University, Mattu 318, Ethiopia

Remote Sens. 2025, 17(7), 1283; https://doi.org/10.3390/rs17071283 - 3 Apr 2025

Cited by 6 | Viewed by 7026

Abstract

Floods are among the most frequent and devastating climate-related hazards, causing significant environmental and socioeconomic impacts. This study integrates synthetic aperture radar (SAR)-based flood mapping via the Google Earth Engine (GEE) with hydraulic modeling in HEC-RAS to analyze flood dynamics downstream of the [...] Read more.

Floods are among the most frequent and devastating climate-related hazards, causing significant environmental and socioeconomic impacts. This study integrates synthetic aperture radar (SAR)-based flood mapping via the Google Earth Engine (GEE) with hydraulic modeling in HEC-RAS to analyze flood dynamics downstream of the Gumara watershed, Upper Blue Nile (UBN) Basin, Ethiopia. A change detection approach using Sentinel-1 imagery was employed to generate flood inundation maps from 2017–2021. Among these events, flood events on 22 July, 3 August, and 27 August 2019 were used to calibrate the HEC-RAS model, achieving an F-score of 0.57, an overall accuracy (OA) of 86.92%, and a kappa coefficient (K) of 0.62 across the three events. Further validation using ground control points (GCPs) resulted in an OA of 86.33% and a K of 0.72. Using the calibrated HEC-RAS model, hydraulic simulations were performed to map flood inundation for return periods of 5, 10, 25, 50, and 100 years. Additionally, flood mapping was conducted for historical (1981–2005), near-future (2031–2055), and far-future (2056–2080) periods under extreme climate scenarios. The results indicate increases of 16.48% and 27.23% in the flood inundation area in the near-future and far-future periods, respectively, under the SSP5-8.5 scenario compared with the historical period. These increases are attributed primarily to deforestation, agricultural expansion, and intensified extreme rainfall events in the upstream watershed. The comparison between SAR-based flood maps and HEC-RAS simulations highlights the advantages of integrating remote sensing and hydraulic modeling for enhanced flood risk assessment. This study provides critical insights for flood mitigation and sustainable watershed management, emphasizing the importance of incorporating current and future flood risk analyses in policy and planning efforts. Full article

(This article belongs to the Special Issue Real-Time Flood Monitoring and Prediction Using Integrative Remote Sensing and AI)

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26 pages, 7339 KB

Open AccessEditor’s ChoiceArticle

Remote Sensing Reveals Multidecadal Trends in Coral Cover at Heron Reef, Australia

by David E. Carrasco Rivera^1,*

, Faye F. Diederiks¹

, Nicholas M. Hammerman¹

, Timothy Staples²

, Eva Kovacs¹

, Kathryn Markey³

and Chris M. Roelfsema¹

¹ Marine Ecosystem Monitoring Lab, School of The Environment, University of Queensland, Brisbane, QLD 4072, Australia

² School of The Environment, University of Queensland, Brisbane, QLD 4072, Australia

³ Earth Observation Research Centre, School of The Environment, University of Queensland, Brisbane, QLD 4072, Australia

Remote Sens. 2025, 17(7), 1286; https://doi.org/10.3390/rs17071286 - 3 Apr 2025

Cited by 5 | Viewed by 3446

Abstract

Coral reefs are experiencing increasing disturbance regimes. The influence these disturbances have on coral reef health is traditionally captured through field-based monitoring, representing a very small reef area (<1%). Satellite-based observations offer the ability to up-scale the spatial extent of monitoring efforts to [...] Read more.

Coral reefs are experiencing increasing disturbance regimes. The influence these disturbances have on coral reef health is traditionally captured through field-based monitoring, representing a very small reef area (<1%). Satellite-based observations offer the ability to up-scale the spatial extent of monitoring efforts to larger reef areas, providing valuable insights into benthic trajectories through time. Our aim was to demonstrate a repeatable benthic habitat mapping approach integrating field and satellite data acquired annually over 21 years. With this dataset, we analyzed the trends in benthic composition for a shallow platform reef: Heron Reef, Australia. Annual benthic habitat maps were created for the period of 2002 to 2023, using a random forest classifier and object-based contextual editing, with annual in situ benthic data derived from geolocated photoquadrats and coincident high-spatial-resolution (2–5 m pixel size) multi-spectral satellite imagery. Field data that were not used for calibration were used to conduct accuracy assessments. The results demonstrated the capability of remote sensing to map the time series of benthic habitats with overall accuracies between 59 and 81%. We identified various ecological trajectories for the benthic types, such as decline and recovery, over time and space. These trajectories were derived from satellite data and compared with those from the field data. Remote sensing offered valuable insights at both reef and within-reef scales (i.e., geomorphic zones), complementing percentage cover data with precise surface area metrics. We demonstrated that monitoring benthic trajectories at the reef scale every 2 to 3 years effectively captured ecological trends, which is crucial for balancing resource allocation. Full article

(This article belongs to the Section Ocean Remote Sensing)

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26 pages, 10537 KB

Open AccessEditor’s ChoiceArticle

SAMNet++: A Segment Anything Model for Supervised 3D Point Cloud Semantic Segmentation

by Mohsen Shahraki^*, Ahmed Elamin and Ahmed El-Rabbany

Department of Civil Engineering, Faculty of Engineering and Architectural Science, Toronto Metropolitan University, Toronto, ON M5B 2K3, Canada

Remote Sens. 2025, 17(7), 1256; https://doi.org/10.3390/rs17071256 - 2 Apr 2025

Cited by 5 | Viewed by 5452

Abstract

Segmentation of 3D point clouds is essential for applications such as environmental monitoring and autonomous navigation, where making accurate distinctions between different classes from high-resolution 3D datasets is critical. Segmenting 3D point clouds often requires a trade-off between preserving spatial information and achieving [...] Read more.

Segmentation of 3D point clouds is essential for applications such as environmental monitoring and autonomous navigation, where making accurate distinctions between different classes from high-resolution 3D datasets is critical. Segmenting 3D point clouds often requires a trade-off between preserving spatial information and achieving computational efficiency. In this paper, we present SAMNet++, a hybrid 3D segmentation model that integrates segment anything model (SAM) and adopted PointNet++ in a sequential two-stage pipeline. Firstly, SAM performs an initial unsupervised segmentation, which is then refined using adopted PointNet++ to improve the accuracy. The key innovations of SAMNet++ include its hybrid architecture, which combines SAM’s generalization with PointNet++’s local feature extraction, and a feature refinement strategy that enhances precision while reducing computational overhead. Additionally, SAMNet++ minimizes the reliance on extensive supervised training, while maintaining high accuracy. The proposed model is tested on three urban datasets, which are collected by an unmanned aerial vehicle (UAV). The proposed SAMNet++ model demonstrates high segmentation performance, achieving accuracy, precision, recall, and F1-score values above 0.97 across all classes on our experimental datasets. Furthermore, its mean intersection over union (mIoU) of 86.93% on a public benchmark dataset signifies a more balanced and precise segmentation across all classes, surpassing previous state-of-the-art methods. In addition to its improved accuracy, SAMNet++ showcases remarkable computational efficiency, requiring almost half the processing time of standard PointNet++ and nearly one-sixteenth of the time needed by the original PointNet algorithm. Full article

(This article belongs to the Special Issue 3D Scene Reconstruction, Modeling and Analysis Using Remote Sensing)

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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 Meng¹, Guoqing Ma^1,*

, Lili Li¹ and Jingyu Li²

¹ College of Geoexploration Science and Technology, Jilin University, Changchun 130026, China

² School of Mathematics, Jilin University, Changchun 130026, China

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

Cited by 5 | Viewed by 921

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^1,2, Xia Hu^1,2,*

and Shaojie Zhao¹

¹ State Key Laboratory of Earth Surface Processes and Hazards Risk Governance, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

² School of Natural Resources, Faculty of Geographical Science, Beijing Normal University, No. 19, Xinjiekouwai Street, Haidian District, Beijing 100875, China

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

Cited by 8 | Viewed by 1645

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

Department of Natural Resources and the Environment, Eversource Energy Center, University of Connecticut, Storrs, CT 06269, USA

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

Cited by 7 | Viewed by 2671

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^1,*

, Monica Moroni²

and Soufyane Bouchelaghem¹

¹ Department of Information Engineering, Electronics and Telecommunications (DIET), Sapienza University of Rome, 00184 Rome, Italy

² Department of Civil, Building, and Environmental Engineering (DICEA), Sapienza University of Rome, 00184 Rome, Italy

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

Cited by 7 | Viewed by 3878

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^1,2, Veronica Manara^1,*

, Guglielmina Adele Diolaiuti¹

, Maurizio Maugeri¹

, Alessia Spezza^1,3

and Davide Fugazza¹

¹ Environmental Science and Policy Department, University of Milan, via Celoria 10, 20133 Milan, Italy

² Department of Physics “Aldo Pontremoli”, University of Milano, via Celoria 16, 20133 Milan, Italy

³ Environmental Sciences, Informatics and Statistics Department, Ca’ Foscari University, via Torino 155, 30172 Mestre, Italy

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

Cited by 3 | Viewed by 3027

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^1,2

, Yong Wang^1,2,*

, Jingcheng Lai^1,3

, Yunjie Lin⁴ and Leyan Shi⁵

¹ School of Ecology and Applied Meteorology, Nanjing University of Information Science and Technology, Nanjing 210044, China

² Jiangsu Key Laboratory of Agricultural Meteorology, Nanjing University of Information Science and Technology, Nanjing 210044, China

³ School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai 519082, China

⁴ School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing 210044, China

⁵ Changwang School of Honors, Nanjing University of Information Science and Technology, Nanjing 210044, China

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

Cited by 2 | Viewed by 1723

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^1,2

, Nikolaos Tziolas^2,*

, Nikolaos L. Tsakiridis¹

, José Lucas Safanelli³

, Tomislav Hengl⁴

and Jonathan Sanderman³

¹ Spectra Lab Group, Laboratory of Remote Sensing, Spectroscopy, and GIS, Department of Agriculture, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece

² Southwest Florida Research and Education Center, Department of Soil, Water and Ecosystem Sciences, Institute of Food and Agricultural Sciences, University of Florida, 2685 State Rd 29N, Immokalee, FL 34142, USA

³ Woodwell Climate Research Center, Falmouth, MA 02540, USA

⁴ OpenGeoHub Foundation, 6865 HK Doorwerth, The Netherlands

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

Cited by 5 | Viewed by 3393

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^1,†, Leizhen Liu^2,†, Shasha Liu^1,*, Zhenyu Shi¹ and Chunhong Shi¹

¹ School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, China

² College of Grassland Science and Technology, China Agricultural University, Beijing 100193, China

^† These authors contributed equally to this work.

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

Cited by 24 | Viewed by 9396

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

Earth and Space Institute, University of Maryland Baltimore County, Baltimore, MD 21228, USA

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

Cited by 5 | Viewed by 2937

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^1,*

, Russell Main¹

, Michael S. Watt²

, Mohammad-Mahdi Arpanaei³ and Taoho Patuawa⁴

¹ Scion, Rotorua 49 Sala Street, Rotorua 3046, New Zealand

² Scion, 10 Kyle St, Christchurch 8011, New Zealand

³ Department of Environmental Science and Policy, Università degli Studi di Milano, 20122 Milano, Italy

⁴ Te Iwi o Te Roroa, PO Box 6, Waimamaku 0446, New Zealand

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

Cited by 4 | Viewed by 2294

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^1,†

, Haitian Wang^1,†

, Irfan A. Iqbal²

, Yumeng Miao¹

, Hezam Albaqami³

, Hans Blom² and Ajmal Mian^1,*

¹ Department of Computer Science and Software Engineering, The University of Western Australia, 35 Stirling Hwy, Crawley, WA 6009, Australia

² Forest Products Commission of Western Australia, Level 7/233 Adelaide Terrace, Perth, WA 6000, Australia

³ Department of Computer Science and Artificial Intelligence, College of Computer Science and Engineering, University of Jeddah, Jeddah 21493, Saudi Arabia

^† These authors contributed equally to this work.

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

Cited by 6 | Viewed by 2173

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^1,2

, Dualta O’Fionnagáin^1,2

, Abosede Omowumi Babatunde³

, Micheal Geever^1,2

, Patricia Codyre^1,2

, Pearse C. Murphy^1,2

, Charles Spillane^2,4

and Aaron Golden^1,2,*

¹ School of Natural Sciences, College of Science and Engineering, University of Galway, Galway H91 TK33, Ireland

² Ryan Institute, University of Galway, Galway H91 TK33, Ireland

³ Centre for Peace and Strategic Studies, University of Ilorin, Kwara 240102, Nigeria

⁴ School of Biological and Chemical Sciences, College of Science and Engineering, University of Galway, Galway H91 TK33, Ireland

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

Cited by 6 | Viewed by 8005

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 Quattrini¹

, Simone Pesaresi^1,*

, Nicole Hofmann¹, Adriano Mancini²

and Simona Casavecchia¹

¹ Department of Agricultural, Food and Environmental Sciences, D3A, Università Politecnica delle Marche, Via Brecce Bianche 12, 60131 Ancona, Italy

² Department of Information Engineering, DII, Università Politecnica delle Marche, Via Brecce Bianche 12, 60131 Ancona, Italy

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

Cited by 3 | Viewed by 2751

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^1,*, Inacio Thomaz Bueno¹

, Denis Valle²

, William M. Hammond³, Susan J. Prichard⁴

, Andrew T. Hudak⁵, Carine Klauberg¹

, Mauro Alessandro Karasinski⁶

, Kody Melissa Brock¹

, Kleydson Diego Rocha⁷

, Jinyi Xia¹, Rodrigo Vieira Leite⁸, Pedro Higuchi⁹, Ana Carolina da Silva⁹, Gabriel Maximo da Silva¹

, Gina R. Cova⁴

and Carlos Alberto Silva¹

¹ Forest Biometrics, Remote Sensing, and Artificial Intelligence Laboratory (Silva Lab)—School of Forest, Fisheries and Geomatics Sciences, University of Florida, P.O. Box 110410, Gainesville, FL 32611, USA

² Quantitative Ecology, Conservation, and Remote Sensing Laboratory (Valle Lab)—School of Forest, Fisheries and Geomatics Sciences, University of Florida, P.O. Box 110410, Gainesville, FL 32611, USA

³ Plant Ecophysiology Laboratory (Ecophys Lab)—Agronomy Department, University of Florida, Gainesville, FL 32611, USA

⁴ School of Environmental and Forest Sciences, University of Washington, Seattle, WA 98195, USA

⁵ USDA Forest Service, Rocky Mountain Research Station, Moscow, ID 83843, USA

⁶ BIOFIX Research Center—Department of Forest Engineering, Federal University of Parana, Curitiba 80210-170, Brazil

⁷ Global Forest Dynamics Lab—School of Forest, Fisheries, and Geomatics Sciences, University of Florida, Gainesville, FL 32611, USA

⁸ NASA Goddard Space Flight Center Greenbelt, Greenbelt, MD 20771, USA

⁹ Forest Engineering Department, Santa Catarina State University, Av Luiz de Camões, 2090 Conta Dinheiro, Lages 88520-000, Brazil

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

Cited by 2 | Viewed by 3961

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 Hartmann¹

, Mathieu Gravey²

, Timothy David Price³

, Wiebe Nijland³

and Steven Michael de Jong^3,*

¹ Royal Netherlands Aerospace Centre (NLR), Anthony Fokkerweg 2, 1059 CM Amsterdam, The Netherlands

² Institute for Interdisciplinary Mountain Research, Austrian Academy of Sciences, 6020 Innsbruck, Austria

³ Faculty of Geosciences, Utrecht University, Princetonlaan 8A, 3584 CW Utrecht, The Netherlands

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

Cited by 5 | Viewed by 3382

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^1,2, Rong Tang³, Ge Zhang^1,2, Jiacong Xue^1,2, Baolin Xue^1,2

and Yuntao Wang^1,2,*

¹ Innovation Research Center of Satellite Application, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

² State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China

³ China Institute of Water Resources and Hydropower Research, Beijing 100038, China

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

Cited by 4 | Viewed by 1409

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^1,2,*

, Gabriel Walton¹, Paul Santi¹

and Carlos Luza³

¹ Department of Geology and Geological Engineering, Colorado School of Mines, Golden, CO 80401, USA

² National Institute for Modeling Biological Systems, University of Tennessee, Knoxville, TN 37996, USA

³ Department of Geology, Geophysics and Mines, Universidad Nacional de San Agustín, Arequipa 04000, Peru

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

Cited by 10 | Viewed by 4423

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 Wang¹, Caili Zhou¹, Jiaqi Shi²

, Fei Lin^3,4,5

, Yucheng Li¹, Yimin Hu^3,4,5 and Xuesheng Zhang^1,*

¹ School of Resources and Environmental Engineering, Anhui University, Hefei 230601, China

² Nanjing Institute of Environmental Sciences of the Ministry of Ecology and Environment, Nanjing 210042, China

³ Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China

⁴ Intelligent Agriculture Engineering Laboratory of Anhui Province, Hefei 230031, China

⁵ Hefei Institutes of Collaborative and Innovation for Intelligent Agriculture, Hefei 231131, China

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

Cited by 9 | Viewed by 2487

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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23 pages, 10008 KB

Open AccessEditor’s ChoiceReview

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

by Shuanggen Jin^1,2

, Xuyang Meng¹, Gino Dardanelli^3,*

and Yunlong Zhu¹

¹ School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454003, China

² Shanghai Astronomical Observatory, Chinese Academy of Sciences, Shanghai 200030, China

³ Department of Engineering, University of Palermo, Viale delle Scienze, 90128 Palermo, Italy

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

Cited by 7 | Viewed by 3685

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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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^1,*, Édson Luis Bolfe^1,2

, Taya Cristo Parreiras²

, Jayme Garcia Arnal Barbedo¹

, Thiago Teixeira Santos¹

and Luciano Gebler³

¹ Brazilian Agricultural Research Corporation, Embrapa Agricultura Digital, Campinas 13083-886, São Paulo, Brazil

² Institute of Geosciences, State University of Campinas (Unicamp), Campinas 13083-970, São Paulo, Brazil

³ Brazilian Agricultural Research Corporation, Embrapa Uva e Vinho, Vacaria 95200-970, Rio Grande do Sul, Brazil

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

Cited by 4 | Viewed by 5341

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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16 pages, 9121 KB

Open AccessEditor’s ChoiceTechnical Note

A Benchmark Dataset for Aircraft Detection in Optical Remote Sensing Imagery

by Jianming Hu¹

, Xiyang Zhi^1,*

, Bingxian Zhang², Tianjun Shi¹, Qi Cui¹ and Xiaogang Sun³

¹ Research Center for Space Optical Engineering, Harbin Institute of Technology, Harbin 150001, China

² Beijing Institute of Space Mechanics and Electricity, Beijing 100076, China

³ School of Instrumentation Science and Engineering, Harbin Institute of Technology, Harbin 150001, China

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

Cited by 1 | Viewed by 4207

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^1,2,*

, Hyovin Kwak², Boitumelo Ruf²

and Martin Weinmann¹

¹ Institute of Photogrammetry and Remote Sensing, Karlsruhe Institute of Technology (KIT), 76131 Karlsruhe, Germany

² Fraunhofer IOSB, 76131 Karlsruhe, Germany

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

Cited by 2 | Viewed by 5864

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^1,2,†

, Chendi Zhang^2,3,†

, David Puhl⁴

, Xiao Pan^5,6

, Marwan A. Hassan^4,*

, Stephen Bird⁷, Kejun Yang² and Yang Zhao⁸

¹ Changjiang River Scientific Research Institute, Wuhan 430010, China

² State Key Laboratory of Hydraulics and Mountain River Engineering, Sichuan University, Chengdu 610065, China

³ Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences (CAS), Beijing 100101, China

⁴ Department of Geography, The University of British Columbia, Vancouver, BC V6T 1Z2, Canada

⁵ Department of Civil Engineering, The University of British Columbia, Vancouver, BC V6T 1Z4, Canada

⁶ Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong

⁷ Fluvial Systems Research Inc., White Rock, BC V4B 0A7, Canada

⁸ Sichuan Zipingpu Development Co., Ltd., Chengdu 610091, China

^† These authors contributed equally to this work.

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

Cited by 2 | Viewed by 2310

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 Zuo¹

, Yuan Yang¹

, Rugui Yao^1,*

, Ye Fan^1,2 and Lu Li³

¹ School of Electronics and Information, Northwestern Polytechnical University, Xi’an 710129, China

² Research and Development Institute of Northwestern Polytechnical University in Shenzhen, Shenzhen 518057, China

³ China Electronics Technology Corporation 29th Research Institute, Chengdu 610036, China

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

Cited by 7 | Viewed by 3934

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^1,*

, Rolando Chacón¹

, Necati Catbas²

, Belén Riveiro³

and Daniel Tonelli⁴

¹ Department of Civil and Environmental Engineering, UPC-BarcelonaTech, 08034 Barcelona, Spain

² Department of Civil, Environmental and Construction Engineering, University of Central Florida, Orlando, FL 32816, USA

³ CINTECX, GeoTECH Group, Campus Universitario de Vigo, Universidade de Vigo, As Lagoas, Marcosende, 36310 Vigo, Spain

⁴ Department of Civil, Environmental and Mechanical Engineering, University of Trento, 38123 Trento, Italy

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

Cited by 4 | Viewed by 4640

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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