Pub Date : 2025-12-05DOI: 10.1016/j.rsase.2025.101820
Leon Scheiber , Vera Zühlsdorff , Duong Huu Nong , Thanh Son Ngo , Nigel K. Downes , Felix Bachofer , Hong Quan Nguyen , Matthias Garschagen , Andrea Reimuth
Urban green space (UGS) contributes to sustainable and climate-resilient urban development by providing ecosystem services and enhancing public health. In rapidly urbanizing cities, UGS is compromised by expanding built infrastructure, leading to loss and fragmentation of green areas. This study employs a resource-efficient remote sensing approach for monitoring UGS dynamics in two examples of rapid urbanization, Hanoi and Ho Chi Minh City (HCMC) in Vietnam. The approach identifies UGS by applying a ground-truthed threshold to Normalized Difference Vegetation Index quartile maps (NDVI–P75) from nine years of open-access Sentinel-2 imagery before blending it with national census data. The results indicate a pronounced spatial heterogeneity in UGS distributions, with low densities in urban cores and greater availability in the peripheral districts of both metropolises. The temporal analysis shows diverging trends: while UGS areas in Hanoi are relatively stable overall but declining per capita due to ongoing urbanization, HCMC experiences a general decline in both UGS indicators. The findings emphasize the urgent need for implementing integrated UGS strategies that account for the diverse socio-economic drivers of UGS loss. By offering a robust and reproducible methodology for monitoring UGS, this research highlights the potential of remote sensing tools to inform urban planning and policy development. This approach is highly transferable to other urban contexts globally, demonstrating an effective and transparent pathway to foster climate-justice and “sustainable cities and communities” in line with the United Nations’ Sustainable Development Goal No. 11.
{"title":"Monitoring urban green space for climate-resilient development in the face of rapid urbanization: A tale of two Vietnamese cities","authors":"Leon Scheiber , Vera Zühlsdorff , Duong Huu Nong , Thanh Son Ngo , Nigel K. Downes , Felix Bachofer , Hong Quan Nguyen , Matthias Garschagen , Andrea Reimuth","doi":"10.1016/j.rsase.2025.101820","DOIUrl":"10.1016/j.rsase.2025.101820","url":null,"abstract":"<div><div>Urban green space (UGS) contributes to sustainable and climate-resilient urban development by providing ecosystem services and enhancing public health. In rapidly urbanizing cities, UGS is compromised by expanding built infrastructure, leading to loss and fragmentation of green areas. This study employs a resource-efficient remote sensing approach for monitoring UGS dynamics in two examples of rapid urbanization, Hanoi and Ho Chi Minh City (HCMC) in Vietnam. The approach identifies UGS by applying a ground-truthed threshold to Normalized Difference Vegetation Index quartile maps (NDVI–P75) from nine years of open-access Sentinel-2 imagery before blending it with national census data. The results indicate a pronounced spatial heterogeneity in UGS distributions, with low densities in urban cores and greater availability in the peripheral districts of both metropolises. The temporal analysis shows diverging trends: while UGS areas in Hanoi are relatively stable overall but declining per capita due to ongoing urbanization, HCMC experiences a general decline in both UGS indicators. The findings emphasize the urgent need for implementing integrated UGS strategies that account for the diverse socio-economic drivers of UGS loss. By offering a robust and reproducible methodology for monitoring UGS, this research highlights the potential of remote sensing tools to inform urban planning and policy development. This approach is highly transferable to other urban contexts globally, demonstrating an effective and transparent pathway to foster climate-justice and “sustainable cities and communities” in line with the United Nations’ Sustainable Development Goal No. 11.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"41 ","pages":"Article 101820"},"PeriodicalIF":4.5,"publicationDate":"2025-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145738344","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-05DOI: 10.1016/j.rsase.2025.101819
Solaiman Khan , Anes Ouadou , Xing Song , Grant J. Scott
Nitrogen dioxide (NO2) is a harmful air pollutant that can cause various health issues, including respiratory disease and lung infection. Monitoring of NO2 is primarily dependent on expensive ground-based sensor systems. This research explores the potential of integrating imagery from Sentinel-2 and Sentinel-5P to estimate high-resolution ground NO2 concentration at city and neighborhood levels. This study presents a two-stream deep learning model for NO2 estimation. The model is flexible regarding data input, allowing the use of Sentinel-2 and Sentinel-5P in combination or as single inputs from either satellite. The model performance is assessed over Chicago using Microsoft Eclipse ground sensor data aggregated in three temporal frequencies: daily, monthly, and quarterly. The experimental results demonstrate that fusing both satellite sources outperforms single-source models, achieving R2 = 0.66, MSE = 5.92, and MAE = 1.75 at the quarterly scale, compared to R2 = 0.59 for Sentinel-2 only and R2 = 0.31 for Sentinel-5P only models. The estimated NO2 is found to be most reliable at the quarterly level, followed by the monthly. Performance decreases at finer temporal scales (R2 = 0.61 daily), likely due to the short-term fluctuation of NO2 concentration. This study reinforces the application of deep learning and remote sensing for air quality monitoring, especially in the absence of expensive ground sensor-based monitoring systems.
{"title":"High-resolution ground NO2 estimation at hyperlocal level using deep learning with Sentinel-2 and Sentinel-5P data","authors":"Solaiman Khan , Anes Ouadou , Xing Song , Grant J. Scott","doi":"10.1016/j.rsase.2025.101819","DOIUrl":"10.1016/j.rsase.2025.101819","url":null,"abstract":"<div><div>Nitrogen dioxide (NO<sub>2</sub>) is a harmful air pollutant that can cause various health issues, including respiratory disease and lung infection. Monitoring of NO<sub>2</sub> is primarily dependent on expensive ground-based sensor systems. This research explores the potential of integrating imagery from Sentinel-2 and Sentinel-5P to estimate high-resolution ground NO<sub>2</sub> concentration at city and neighborhood levels. This study presents a two-stream deep learning model for NO<sub>2</sub> estimation. The model is flexible regarding data input, allowing the use of Sentinel-2 and Sentinel-5P in combination or as single inputs from either satellite. The model performance is assessed over Chicago using Microsoft Eclipse ground sensor data aggregated in three temporal frequencies: daily, monthly, and quarterly. The experimental results demonstrate that fusing both satellite sources outperforms single-source models, achieving R<sup>2</sup> = 0.66, MSE = 5.92, and MAE = 1.75 at the quarterly scale, compared to R<sup>2</sup> = 0.59 for Sentinel-2 only and R<sup>2</sup> = 0.31 for Sentinel-5P only models. The estimated NO<sub>2</sub> is found to be most reliable at the quarterly level, followed by the monthly. Performance decreases at finer temporal scales (R<sup>2</sup> = 0.61 daily), likely due to the short-term fluctuation of NO<sub>2</sub> concentration. This study reinforces the application of deep learning and remote sensing for air quality monitoring, especially in the absence of expensive ground sensor-based monitoring systems.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"41 ","pages":"Article 101819"},"PeriodicalIF":4.5,"publicationDate":"2025-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145738455","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-05DOI: 10.1016/j.rsase.2025.101818
Okikiola Michael Alegbeleye, Arjan Johan Herman Meddens, Yetunde Oladepe Rotimi, Kelechi Godwin Ibeh
Individual tree data in urban settings are used for many purposes, and gathering such information requires time and other limited resources. Additionally, the data collected are spatially and temporally sparse, especially for continuous monitoring. However, high-resolution images and deep learning can offer automated and accurate detection of trees in complex urban settings. Therefore, this study compared four popular convolutional neural network CNN-based object detection models (You Only Look Once v3, RetinanNet, Mask R-CNN, and Faster R-CNN) to map individual trees. We used high-resolution aerial imagery (∼8 cm spatial resolution), which was manually annotated to derive training (4,859) and testing (1,184) datasets. The analysis was carried out in three phases: First, we trained all the models for 20 epochs and evaluated the performance using standard metrics (Precision, Recall, and F1 score). Second, the best model was selected and retrained longer (30 epochs) with more data (5002 annotations) to develop an urban tree crown detection model for Pullman – a small-sized city in the inland northwest of the United States. Finally, we tested the reliability of the developed model under two scenarios. According to our analysis, YOLOv3 (F1 score: 69 %) outperformed Mask R-CNN (F1 score: 60 %), RetinaNet (F1 score: 57 %), and Faster R-CNN (F1 score: 52 %). Based on the evaluation metrics and visual assessment, YOLOv3 was selected to develop the final urban tree crown detector – Pullman Tree Crown Network (PTCNet), for our study area. PTCNet had precision and recall values of 78 % and 62 %, respectively. It also performed well under different tree arrangements, achieving an F1 score of over 70 %. The model was used to generate ∼12,000 individual tree locations. Subsequently, height information was extracted from a LiDAR-derived canopy height model, and a comprehensive tree inventory dataset was derived. The model and dataset are publicly available (https://github.com/Okikiola-Michael/PTCNet) for different applications, thus, contributing to open science. This study provides a straightforward and repeatable framework for researchers and managers to map urban trees with height information, which is useful for spatial and temporal tree monitoring. This study further highlights the performance of four popular models and supports the application of deep learning and aerial imagery for individual tree detection in complex urban settings.
城市环境中的单个树木数据用于许多目的，收集此类信息需要时间和其他有限的资源。此外，收集的数据在空间和时间上都是稀疏的，特别是对于连续监测而言。然而，高分辨率图像和深度学习可以在复杂的城市环境中提供自动和准确的树木检测。因此，本研究比较了四种流行的基于卷积神经网络cnn的物体检测模型（You Only Look Once v3, RetinanNet， Mask R-CNN和Faster R-CNN）来映射单个树。我们使用高分辨率航空图像（~ 8厘米空间分辨率），手动注释以获得训练（4,859）和测试（1,184）数据集。分析分三个阶段进行：首先，我们对所有模型进行了20个epoch的训练，并使用标准指标（Precision， Recall和F1分数）评估了性能。其次，选取最好的模型，用更多的数据（5002条注释）对更长的时间（30个epoch）进行再训练，开发美国西北内陆小城市Pullman的城市树冠检测模型。最后，我们在两种情况下对所建立的模型进行了可靠性测试。根据我们的分析，YOLOv3 （F1得分：69%）优于Mask R-CNN (F1得分：60%),RetinaNet （F1得分：57%）和Faster R-CNN （F1得分：52%）。基于评价指标和视觉评价，我们选择YOLOv3为我们的研究区域开发最终的城市树冠探测器——Pullman树冠网络（PTCNet）。PTCNet的查准率为78%，查全率为62%。在不同树形布置下表现良好，F1得分均在70%以上。该模型用于生成约12,000个单独的树位置。随后，利用激光雷达提取树冠高度模型的高度信息，得到一个完整的树木清查数据集。模型和数据集是公开的（https://github.com/Okikiola-Michael/PTCNet），可用于不同的应用，因此，有助于开放科学。该研究为研究人员和管理人员提供了一个简单、可重复的框架来绘制城市树木的高度信息，这对树木的时空监测是有用的。本研究进一步强调了四种流行模型的性能，并支持深度学习和航空图像在复杂城市环境中用于单个树木检测的应用。
{"title":"Urban tree crown detection based on deep learning and high-resolution aerial imagery: PTCNet for Pullman, WA, USA","authors":"Okikiola Michael Alegbeleye, Arjan Johan Herman Meddens, Yetunde Oladepe Rotimi, Kelechi Godwin Ibeh","doi":"10.1016/j.rsase.2025.101818","DOIUrl":"10.1016/j.rsase.2025.101818","url":null,"abstract":"<div><div>Individual tree data in urban settings are used for many purposes, and gathering such information requires time and other limited resources. Additionally, the data collected are spatially and temporally sparse, especially for continuous monitoring. However, high-resolution images and deep learning can offer automated and accurate detection of trees in complex urban settings. Therefore, this study compared four popular convolutional neural network CNN-based object detection models (You Only Look Once v3, RetinanNet, Mask R-CNN, and Faster R-CNN) to map individual trees. We used high-resolution aerial imagery (∼8 cm spatial resolution), which was manually annotated to derive training (4,859) and testing (1,184) datasets. The analysis was carried out in three phases: First, we trained all the models for 20 epochs and evaluated the performance using standard metrics (Precision, Recall, and F1 score). Second, the best model was selected and retrained longer (30 epochs) with more data (5002 annotations) to develop an urban tree crown detection model for Pullman – a small-sized city in the inland northwest of the United States. Finally, we tested the reliability of the developed model under two scenarios. According to our analysis, YOLOv3 (F1 score: 69 %) outperformed Mask R-CNN (F1 score: 60 %), RetinaNet (F1 score: 57 %), and Faster R-CNN (F1 score: 52 %). Based on the evaluation metrics and visual assessment, YOLOv3 was selected to develop the final urban tree crown detector – Pullman Tree Crown Network (PTCNet), for our study area. PTCNet had precision and recall values of 78 % and 62 %, respectively. It also performed well under different tree arrangements, achieving an F1 score of over 70 %. The model was used to generate ∼12,000 individual tree locations. Subsequently, height information was extracted from a LiDAR-derived canopy height model, and a comprehensive tree inventory dataset was derived. The model and dataset are publicly available (<span><span>https://github.com/Okikiola-Michael/PTCNet</span><svg><path></path></svg></span>) for different applications, thus, contributing to open science. This study provides a straightforward and repeatable framework for researchers and managers to map urban trees with height information, which is useful for spatial and temporal tree monitoring. This study further highlights the performance of four popular models and supports the application of deep learning and aerial imagery for individual tree detection in complex urban settings.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"41 ","pages":"Article 101818"},"PeriodicalIF":4.5,"publicationDate":"2025-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145738454","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Bike lanes are a critical element of urban infrastructure that promote cycling and support sustainable transportation goals. Effective planning and evaluation require comprehensive inventory datasets that both identify the locations of bike lanes and classify their types. However, existing data collection is limited by inconsistent municipal documentation practices and resource constraints. This paper introduces a computer vision–based approach for the automated detection and classification of bike lanes using publicly available multimodal imagery. Each data sample integrates two street view images, captured from opposite directions, with a corresponding satellite image, enabling complementary perspectives. This approach allows the model to reliably detect bike lane presence and distinguish between designated (marked lanes without physical barriers) and protected (lanes separated from traffic by physical barriers) types. To optimize performance, we conduct ablation experiments across three architectural dimensions: stage of modality concatenation, fusion strategy, and label structure. We also construct a training dataset using Google Street View and satellite imagery from 28 major U.S. cities to ensure broad applicability. Applying the model to over 1000 road segments in Atlanta, Georgia, we demonstrate its scalability and accuracy in a real-world urban setting. By providing an automated, transferable method for developing bike lane inventories, this research addresses a critical gap in infrastructure documentation and supports more effective planning of bicycle networks.
{"title":"Automated detection and classification of bike lanes using multimodal imagery","authors":"Seung Jae Lieu , Bon Woo Koo , Uijeong Hwang , Subhrajit Guhathakurta","doi":"10.1016/j.rsase.2025.101817","DOIUrl":"10.1016/j.rsase.2025.101817","url":null,"abstract":"<div><div>Bike lanes are a critical element of urban infrastructure that promote cycling and support sustainable transportation goals. Effective planning and evaluation require comprehensive inventory datasets that both identify the locations of bike lanes and classify their types. However, existing data collection is limited by inconsistent municipal documentation practices and resource constraints. This paper introduces a computer vision–based approach for the automated detection and classification of bike lanes using publicly available multimodal imagery. Each data sample integrates two street view images, captured from opposite directions, with a corresponding satellite image, enabling complementary perspectives. This approach allows the model to reliably detect bike lane presence and distinguish between designated (marked lanes without physical barriers) and protected (lanes separated from traffic by physical barriers) types. To optimize performance, we conduct ablation experiments across three architectural dimensions: stage of modality concatenation, fusion strategy, and label structure. We also construct a training dataset using Google Street View and satellite imagery from 28 major U.S. cities to ensure broad applicability. Applying the model to over 1000 road segments in Atlanta, Georgia, we demonstrate its scalability and accuracy in a real-world urban setting. By providing an automated, transferable method for developing bike lane inventories, this research addresses a critical gap in infrastructure documentation and supports more effective planning of bicycle networks.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"41 ","pages":"Article 101817"},"PeriodicalIF":4.5,"publicationDate":"2025-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145665528","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-02DOI: 10.1016/j.rsase.2025.101814
Anita Gautam, Bharath Haridas Aithal
Rapid urbanization fundamentally restructures metropolitan form through non-linear, scale-dependent processes that reorganize spatial hierarchy and land-use configuration. This study examines Bengaluru's morphological evolution from 2012 to 2023 using high-resolution satellite imagery, interpreted through deep learning–based classification, spatial metrics, and fractal geometry, to quantify structural and scaling transformations. The results reveal a decisive transition from fragmented, spatially spread-led expansion to a spatially integrated yet hierarchically differentiated urban system. The improvements in Patch cohesion, surface occupancy, and insignificants in landscape fragmentation and irregular edge patterns indicate urban growth in-fill (or redevelopment) and further development along corridors. The fractal dimension increased from 1.78 to 1.91, indicating an improvement in filling space through increased compactness and geometric order, whereas the multifractal spectrum increased from 1.41 to 1.92, demonstrating an increase in structural heterogeneity over a range of scales. A positive relationship (r = 0.68) between patch cohesion and fractal compactness quantitatively confirms the association of local aggregation/compactness with global geometric order. Overall, these findings illustrate the hierarchical scaling organization of urban growth where compactness and heterogeneity co-evolve through self-organizing spatial logic. By incorporating metric-based morphological analysis to fractal scaling, the framework enhances urban theory, proposing a scale-consistent account of spatial evolution. This account describes how urban systems transition from dispersed growth to geometrically ordered and hierarchically structured forms.
{"title":"Urban structural complexity in transition: Fractal analysis of deep learning-derived morphological patterns","authors":"Anita Gautam, Bharath Haridas Aithal","doi":"10.1016/j.rsase.2025.101814","DOIUrl":"10.1016/j.rsase.2025.101814","url":null,"abstract":"<div><div>Rapid urbanization fundamentally restructures metropolitan form through non-linear, scale-dependent processes that reorganize spatial hierarchy and land-use configuration. This study examines Bengaluru's morphological evolution from 2012 to 2023 using high-resolution satellite imagery, interpreted through deep learning–based classification, spatial metrics, and fractal geometry, to quantify structural and scaling transformations. The results reveal a decisive transition from fragmented, spatially spread-led expansion to a spatially integrated yet hierarchically differentiated urban system. The improvements in Patch cohesion, surface occupancy, and insignificants in landscape fragmentation and irregular edge patterns indicate urban growth in-fill (or redevelopment) and further development along corridors. The fractal dimension increased from 1.78 to 1.91, indicating an improvement in filling space through increased compactness and geometric order, whereas the multifractal spectrum increased from 1.41 to 1.92, demonstrating an increase in structural heterogeneity over a range of scales. A positive relationship (r = 0.68) between patch cohesion and fractal compactness quantitatively confirms the association of local aggregation/compactness with global geometric order. Overall, these findings illustrate the hierarchical scaling organization of urban growth where compactness and heterogeneity co-evolve through self-organizing spatial logic. By incorporating metric-based morphological analysis to fractal scaling, the framework enhances urban theory, proposing a scale-consistent account of spatial evolution. This account describes how urban systems transition from dispersed growth to geometrically ordered and hierarchically structured forms.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"41 ","pages":"Article 101814"},"PeriodicalIF":4.5,"publicationDate":"2025-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145685600","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-22DOI: 10.1016/j.rsase.2025.101804
Sahar Khoshnoud , S. Mohammad Mirmazloumi , Arsalan Ghorbanian , Hossein Mohammad Asgari , Meisam Amani
Investigating the potential for wind-induced soil erosion in arid and semi-arid regions is essential for understanding soil degradation and its associated impacts, such as agricultural productivity reduction, infrastructure damage, air quality decline, and adverse health effects. This study pioneers the integration of remote sensing data and Artificial Neural Networks (ANN) for wind erosion mapping, offering a novel approach to analyzing soil surface dynamics. ANN models were implemented to estimate aerodynamic roughness (z0) and friction velocity () using Sentinel-1 Synthetic Aperture Radar (SAR) data. These estimates were further integrated with meteorological datasets to identify areas prone to wind erosion and, subsequently, dust storms. The results indicated that wetlands, with the highest (6.98 cm) and (0.81 m/s) values have a negligible potential for wind erosion. Conversely, clay flats showed the lowest values ( = 0.89 cm, = 0.42 m/s), suggesting a higher susceptibility to wind erosion. Finally, the developed model was applied to generate wind erosion potential maps of the study area, serving as a practical asset for the identification of high-risk zones prone to erosion. This study emphasizes the importance of soil surface parameters to identify potential areas of wind erosion for developing more accurate dust emission models, which support effective management of wind erosion and mitigate the adverse effects of this environmental phenomenon. Although regionally focused, the methodology is transferable to other arid and semi-arid environments, offering valuable insights for soil conservation and land management.
{"title":"Mapping wind erosion potential using remote sensing and artificial neural networks: Insights for soil conservation in arid regions","authors":"Sahar Khoshnoud , S. Mohammad Mirmazloumi , Arsalan Ghorbanian , Hossein Mohammad Asgari , Meisam Amani","doi":"10.1016/j.rsase.2025.101804","DOIUrl":"10.1016/j.rsase.2025.101804","url":null,"abstract":"<div><div>Investigating the potential for wind-induced soil erosion in arid and semi-arid regions is essential for understanding soil degradation and its associated impacts, such as agricultural productivity reduction, infrastructure damage, air quality decline, and adverse health effects. This study pioneers the integration of remote sensing data and Artificial Neural Networks (ANN) for wind erosion mapping, offering a novel approach to analyzing soil surface dynamics. ANN models were implemented to estimate aerodynamic roughness (z<sub>0</sub>) and friction velocity (<span><math><mrow><msub><mi>u</mi><mo>∗</mo></msub></mrow></math></span>) using Sentinel-1 Synthetic Aperture Radar (SAR) data. These estimates were further integrated with meteorological datasets to identify areas prone to wind erosion and, subsequently, dust storms. The results indicated that wetlands, with the highest <span><math><mrow><msub><mi>z</mi><mn>0</mn></msub></mrow></math></span> (6.98 cm) and <span><math><mrow><msub><mi>u</mi><mo>∗</mo></msub></mrow></math></span> (0.81 m/s) values have a negligible potential for wind erosion. Conversely, clay flats showed the lowest values (<span><math><mrow><msub><mi>z</mi><mn>0</mn></msub></mrow></math></span> = 0.89 cm, <span><math><mrow><msub><mi>u</mi><mo>∗</mo></msub></mrow></math></span> = 0.42 m/s), suggesting a higher susceptibility to wind erosion. Finally, the developed model was applied to generate wind erosion potential maps of the study area, serving as a practical asset for the identification of high-risk zones prone to erosion. This study emphasizes the importance of soil surface parameters to identify potential areas of wind erosion for developing more accurate dust emission models, which support effective management of wind erosion and mitigate the adverse effects of this environmental phenomenon. Although regionally focused, the methodology is transferable to other arid and semi-arid environments, offering valuable insights for soil conservation and land management.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"41 ","pages":"Article 101804"},"PeriodicalIF":4.5,"publicationDate":"2025-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145738347","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-01DOI: 10.1016/j.rsase.2025.101784
Jiaxin Wang , Xinyi Li , Haoran Liu , Yani Wang , Xin Zhang , Donghui Song
Coastal wetlands are essential for mitigating climate change but face significant challenges in carbon storage assessment due to spatial-scale constraints and the application of oversimplified models that fail to capture complex spatiotemporal dynamics and driving mechanisms. This study addresses two key gaps in understanding carbon sink degradation in coastal land-sea interface systems: (1) the insufficient analysis of unidirectional natural factors and (2) the inability of models to capture spatial heterogeneity in carbon sink degradation. Taking Bohai Bay (China) as a case study, we developed an integrated InVEST-PLUS-GeoDetector framework to reconstruct and project the coastal wetland carbon storage evolution since 1980. Key findings include: (1) A 15.7 % net decline in carbon storage (from 58.11 ± 2.87 Tg in 1980 to 48.98 ± 2.14 Tg in 2020), driven primarily by constructed wetlands expansion encroaching on natural wetlands; (2) GeoDetector analysis identified vegetation coverage (q = 0.38), soil type (q = 0.25), distance to coastline (q = 0.24), and GDP (q = 0.18) as dominant drivers of carbon storage variation, with vegetation-soil interactions being the most influential; (3) Multi-scenario simulations revealed that wetland conservation policies could significantly increase carbon storage by 0.25 Tg by 2050 (exceeding model uncertainty), a 1.48-fold enhancement compared to the economic development scenario, attributable to the preservation of high-carbon-density natural wetlands despite their slower sequestration rates. The proposed framework effectively addresses the two key gaps by capturing key driver couplings (natural-socioeconomic) and spatial heterogeneity in carbon dynamics. Our findings advance the understanding of human-environment interactions in intensely developed coastal zones and provide practical pathways for synergizing wetland conservation and carbon sink enhancement in semi-enclosed marine systems.
{"title":"Balancing development and carbon storage: Spatiotemporal heterogeneity of Bohai Bay's coastal wetlands under socio-ecological drivers","authors":"Jiaxin Wang , Xinyi Li , Haoran Liu , Yani Wang , Xin Zhang , Donghui Song","doi":"10.1016/j.rsase.2025.101784","DOIUrl":"10.1016/j.rsase.2025.101784","url":null,"abstract":"<div><div>Coastal wetlands are essential for mitigating climate change but face significant challenges in carbon storage assessment due to spatial-scale constraints and the application of oversimplified models that fail to capture complex spatiotemporal dynamics and driving mechanisms. This study addresses two key gaps in understanding carbon sink degradation in coastal land-sea interface systems: (1) the insufficient analysis of unidirectional natural factors and (2) the inability of models to capture spatial heterogeneity in carbon sink degradation. Taking Bohai Bay (China) as a case study, we developed an integrated InVEST-PLUS-GeoDetector framework to reconstruct and project the coastal wetland carbon storage evolution since 1980. Key findings include: (1) A 15.7 % net decline in carbon storage (from 58.11 ± 2.87 Tg in 1980 to 48.98 ± 2.14 Tg in 2020), driven primarily by constructed wetlands expansion encroaching on natural wetlands; (2) GeoDetector analysis identified vegetation coverage (q = 0.38), soil type (q = 0.25), distance to coastline (q = 0.24), and GDP (q = 0.18) as dominant drivers of carbon storage variation, with vegetation-soil interactions being the most influential; (3) Multi-scenario simulations revealed that wetland conservation policies could significantly increase carbon storage by 0.25 Tg by 2050 (exceeding model uncertainty), a 1.48-fold enhancement compared to the economic development scenario, attributable to the preservation of high-carbon-density natural wetlands despite their slower sequestration rates. The proposed framework effectively addresses the two key gaps by capturing key driver couplings (natural-socioeconomic) and spatial heterogeneity in carbon dynamics. Our findings advance the understanding of human-environment interactions in intensely developed coastal zones and provide practical pathways for synergizing wetland conservation and carbon sink enhancement in semi-enclosed marine systems.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"40 ","pages":"Article 101784"},"PeriodicalIF":4.5,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145474525","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-01DOI: 10.1016/j.rsase.2025.101810
Yuanrong He , Xiajing Meng , Liheng Zhang , Lefan Wang , Tianqi Yang , Guoliang Yun
Urban agglomerations (UAs) serve as key units for new-type urbanization, where spatial heterogeneity and development disparities significantly impact regional coordination efforts. Despite abundant literature on UAs, comparative analyses of long-term urbanization patterns across multiple UAs and their differentiated driving mechanisms remain insufficiently explored, especially regarding the driving mechanisms of development disparities within and between UAs. This study employs dynamic time warping (DTW), the Dagum Gini coefficient, and partial least squares (PLS) regression to analyze four UAs. Results show significant spatial-temporal heterogeneity: (1) High-urbanization areas cluster in established metropolises, while low-level regions concentrate in Beijing-Tianjin-Hebei (BTH) and Chengdu-Chongqing (CY). Most cities (72.15 %) exhibit “Recent Urban Growth,” with BTH dominated by “Constant Urban Growth” and metropolises showing “Early Urban Growth”. (2) Overall urbanization disparities declined with the development gap narrowing by 35.56 % over 30 years (from 0.503 to 0.324), driven by inter-regional unbalance but shifted to intra-regional density gaps recently. (3) Drivers vary regionally: resource endowment amplifies disparities, while population agglomeration mitigates them; technological innovation increases disparities in the Pearl River Delta (PRD, 1.038) but reduces them elsewhere (−0.329 to −0.208). The study emphasizes stage-specific and region-specific effects of factors, advocating tailored sustainable urbanization strategies to address each UA's developmental characteristics.
{"title":"Assessing urbanization differentiation and socioeconomic drivers in China's four major urban agglomerations based on nighttime light data (1992–2021)","authors":"Yuanrong He , Xiajing Meng , Liheng Zhang , Lefan Wang , Tianqi Yang , Guoliang Yun","doi":"10.1016/j.rsase.2025.101810","DOIUrl":"10.1016/j.rsase.2025.101810","url":null,"abstract":"<div><div>Urban agglomerations (UAs) serve as key units for new-type urbanization, where spatial heterogeneity and development disparities significantly impact regional coordination efforts. Despite abundant literature on UAs, comparative analyses of long-term urbanization patterns across multiple UAs and their differentiated driving mechanisms remain insufficiently explored, especially regarding the driving mechanisms of development disparities within and between UAs. This study employs dynamic time warping (DTW), the Dagum Gini coefficient, and partial least squares (PLS) regression to analyze four UAs. Results show significant spatial-temporal heterogeneity: (1) High-urbanization areas cluster in established metropolises, while low-level regions concentrate in Beijing-Tianjin-Hebei (BTH) and Chengdu-Chongqing (CY). Most cities (72.15 %) exhibit “Recent Urban Growth,” with BTH dominated by “Constant Urban Growth” and metropolises showing “Early Urban Growth”. (2) Overall urbanization disparities declined with the development gap narrowing by 35.56 % over 30 years (from 0.503 to 0.324), driven by inter-regional unbalance but shifted to intra-regional density gaps recently. (3) Drivers vary regionally: resource endowment amplifies disparities, while population agglomeration mitigates them; technological innovation increases disparities in the Pearl River Delta (PRD, 1.038) but reduces them elsewhere (−0.329 to −0.208). The study emphasizes stage-specific and region-specific effects of factors, advocating tailored sustainable urbanization strategies to address each UA's developmental characteristics.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"40 ","pages":"Article 101810"},"PeriodicalIF":4.5,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145623635","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With its complex hydrological dynamics and high diversity of habitats, the Pantanal, a large South American wetland, is increasingly threatened by anthropogenic activities and climate change. In such vulnerable ecosystems, radar altimetry is a key source of Water Surface Elevation (WSE) measurements and, thus, fundamental for monitoring these often remote and poorly gauged environments. This study assesses the potential of the Sentinel-3 and Sentinel-6 missions to monitor hydrological dynamics in the Brazilian Pantanal (May 2016 to March 2024). We first evaluated the agreement between radar altimetry and in situ WSE and then demonstrated the contribution of these data to characterizing three distinct hydrological features across this large tropical wetland. Our results showed strong agreement between altimetry and in situ water levels, with correlation coefficients (R) greater than 0.85 and Root Mean Square Errors (RMSE) below 0.4 m in most cases. Using this extensive radar altimetry network, we demonstrated backwater flooding on the Miranda River, which experiences two annual flood cycles driven by local precipitation and the Paraguay River’s flood pulse. This dynamic was disrupted by recent megadroughts. We also detected significant WSE declines in the shallow lakes of the Nhecolândia region, directly linked to the megadroughts, and revealed along-channel variations in seasonal water level patterns across the ungauged Taquari megafan, a distributive fluvial system likely subject to the combined pressures of upland agriculture and climatic extremes. These findings underscore the high potential of radar altimetry for monitoring and understanding complex hydrologic dynamics in vulnerable ecosystems like the Pantanal.
{"title":"Monitoring water surface elevation dynamics in the Brazilian Pantanal wetland using radar altimetry","authors":"Uelison Mateus Ribeiro , Samuel Corgne , Manuela Grippa , Félix Girard , Sly Wongchuig , Carolina Joana da Silva , Vitor Matheus Bacani , Mauro Henrique Soares da Silva , Frederico Gradella , Damien Arvor","doi":"10.1016/j.rsase.2025.101805","DOIUrl":"10.1016/j.rsase.2025.101805","url":null,"abstract":"<div><div>With its complex hydrological dynamics and high diversity of habitats, the Pantanal, a large South American wetland, is increasingly threatened by anthropogenic activities and climate change. In such vulnerable ecosystems, radar altimetry is a key source of Water Surface Elevation (WSE) measurements and, thus, fundamental for monitoring these often remote and poorly gauged environments. This study assesses the potential of the Sentinel-3 and Sentinel-6 missions to monitor hydrological dynamics in the Brazilian Pantanal (May 2016 to March 2024). We first evaluated the agreement between radar altimetry and in situ WSE and then demonstrated the contribution of these data to characterizing three distinct hydrological features across this large tropical wetland. Our results showed strong agreement between altimetry and in situ water levels, with correlation coefficients (R) greater than 0.85 and Root Mean Square Errors (RMSE) below 0.4 m in most cases. Using this extensive radar altimetry network, we demonstrated backwater flooding on the Miranda River, which experiences two annual flood cycles driven by local precipitation and the Paraguay River’s flood pulse. This dynamic was disrupted by recent megadroughts. We also detected significant WSE declines in the shallow lakes of the Nhecolândia region, directly linked to the megadroughts, and revealed along-channel variations in seasonal water level patterns across the ungauged Taquari megafan, a distributive fluvial system likely subject to the combined pressures of upland agriculture and climatic extremes. These findings underscore the high potential of radar altimetry for monitoring and understanding complex hydrologic dynamics in vulnerable ecosystems like the Pantanal.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"40 ","pages":"Article 101805"},"PeriodicalIF":4.5,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145623738","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Predicting winter wheat yields is necessary for sustainable global farming and ensuring food supply. This study introduced a multi–task transfer learning framework based on a hybrid 3D–ResNet–BiLSTM architecture to predict county–level winter wheat yields in the U.S. using multi–source remote sensing (RS) data. The primary goal was to investigate how the choice of source crop (i.e., corn, soybean, or their combination) influences transfer learning performance for winter wheat yield prediction. To achieve this, two–stage modeling framework was used. First, a multi–task 3D–ResNet–BiLSTM model (3D–ResNet–BiLSTM–MT) was trained on corn and soybean yield data from 2016 to 2020, leveraging their overlapping growing seasons to capture shared spatio–temporal representations. Second, a fine–tuned transfer model (Transfer–3D–ResNet–BiLSTM) was developed using limited winter wheat data (2018-2020). During fine–tuning, the feature extraction layers were frozen, reducing trainable parameters by ∼50 % and enhancing robustness under data–scarce conditions. The models integrated multi–source inputs from Sentinel–1/2 imagery, Daymet weather variables, and SoilGrids, and were evaluated on independent test data (2021- 2022). The multi–task model efficiently predicted corn and soybean yields, achieving an R2 of 0.78. For winter wheat, the corn–based transfer model achieved the highest performance (RMSE = 9.63, MAE = 7.61, MAPE = 13.23, R2 = 0.75), followed by the soybean–based model (R2 = 0.69). In contrast, the shared corn–soybean model (the best–performing model trained specifically on both crops using 3D–ResNet–BiLSTM–MT) underperformed (R2 = 0.63), while the baseline wheat–only model without transfer learning showed the weakest performance (RMSE = 12.24, R2 = 0.60). Overall, the source-specific transfer models (corn- and soybean-based) outperformed both the wheat-only deep learning baseline and conventional machine learning models (RF, SVM, XGBoost, and LightGBM), demonstrating the strong generalization ability and data efficiency of deep transfer learning for yield prediction. These findings highlight the importance of source crop selection and show that cross–crop transfer learning is a practical, data–efficient, and generalizable approach for yield prediction, especially valuable where labeled data are scarce.
{"title":"A Multi–Task 3D–ResNet–BiLSTM transfer learning approach for winter wheat yield prediction using multi–source remote sensing data: Evaluating the impact of source crop selection (corn and soybean) in transfer learning","authors":"Mahdiyeh Fathi , Reza Shah–Hosseini , Hossein Arefi , Armin Moghimi","doi":"10.1016/j.rsase.2025.101766","DOIUrl":"10.1016/j.rsase.2025.101766","url":null,"abstract":"<div><div>Predicting winter wheat yields is necessary for sustainable global farming and ensuring food supply. This study introduced a multi–task transfer learning framework based on a hybrid 3D–ResNet–BiLSTM architecture to predict county–level winter wheat yields in the U.S. using multi–source remote sensing (RS) data. The primary goal was to investigate how the choice of source crop (i.e., corn, soybean, or their combination) influences transfer learning performance for winter wheat yield prediction. To achieve this, two–stage modeling framework was used. First, a multi–task 3D–ResNet–BiLSTM model (3D–ResNet–BiLSTM–MT) was trained on corn and soybean yield data from 2016 to 2020, leveraging their overlapping growing seasons to capture shared spatio–temporal representations. Second, a fine–tuned transfer model (Transfer–3D–ResNet–BiLSTM) was developed using limited winter wheat data (2018-2020). During fine–tuning, the feature extraction layers were frozen, reducing trainable parameters by ∼50 % and enhancing robustness under data–scarce conditions. The models integrated multi–source inputs from Sentinel–1/2 imagery, Daymet weather variables, and SoilGrids, and were evaluated on independent test data (2021- 2022). The multi–task model efficiently predicted corn and soybean yields, achieving an R<sup>2</sup> of 0.78. For winter wheat, the corn–based transfer model achieved the highest performance (RMSE = 9.63, MAE = 7.61, MAPE = 13.23, R<sup>2</sup> = 0.75), followed by the soybean–based model (R<sup>2</sup> = 0.69). In contrast, the shared corn–soybean model (the best–performing model trained specifically on both crops using 3D–ResNet–BiLSTM–MT) underperformed (R<sup>2</sup> = 0.63), while the baseline wheat–only model without transfer learning showed the weakest performance (RMSE = 12.24, R<sup>2</sup> = 0.60). Overall, the source-specific transfer models (corn- and soybean-based) outperformed both the wheat-only deep learning baseline and conventional machine learning models (RF, SVM, XGBoost, and LightGBM), demonstrating the strong generalization ability and data efficiency of deep transfer learning for yield prediction. These findings highlight the importance of source crop selection and show that cross–crop transfer learning is a practical, data–efficient, and generalizable approach for yield prediction, especially valuable where labeled data are scarce.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"40 ","pages":"Article 101766"},"PeriodicalIF":4.5,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145417690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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