Remote Sensing Applications-Society and Environment最新文献

{"title":"Mapping shrub and tree encroachment in Canadian Prairies using stacking ensemble and Sentinel-1/2 imagery","authors":"Yihan Pu,&nbsp;Irini Soubry,&nbsp;Xulin Guo","doi":"10.1016/j.rsase.2026.102041","DOIUrl":"10.1016/j.rsase.2026.102041","url":null,"abstract":"<div><div>Woody plant encroachment (WPE) threatens grassland ecosystems across the Canadian Prairies, causing grassland biodiversity loss with substantial economic impacts due to reduced forage production. While remote sensing offers scalable monitoring capabilities, existing approaches lack frameworks for distinguishing shrub and tree encroachment and often require extensive ground truth data. This study developed an ensemble machine learning framework integrating Sentinel-1 SAR and Sentinel-2 optical imagery with UAV-derived training data to map fractional shrub and tree cover across Saskatchewan's Aspen Parkland and Moist Mixed Grassland ecoregions, SK. A stacking ensemble combining Random Forest, Support Vector Machine, XGBoost, and Artificial Neural Network models with Ridge regression meta-learning outperformed individual algorithms, achieving mean R² values of 0.65 for shrub and 0.68 for tree cover prediction. Multi-scale training incorporating features at 10, 30, 50, and 70 m resolution improved performance by 15% for shrub and 24% for tree compared to single-scale approaches. Feature importance analysis revealed that shrub detection relied primarily on red-edge bands and moisture indices, while tree detection depended heavily on SAR backscatters. Quantile histogram matching enabled successful model transfer from Foam Lake Community pasture to Aberdeen Community Pasture, with resulting maps indicating that total WPE exceeded 50% in both study areas, with shrubs occupying 23.7% (Foam Lake) and 18.5% (Aberdeen) of both regions at rates higher than 5% shrub cover. The present framework provides a scalable, cost-effective approach for operational woody encroachment monitoring, enabling early detection and targeted functional management interventions to preserve grassland ecosystems.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"42 ","pages":"Article 102041"},"PeriodicalIF":4.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147803463","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}

{"title":"Digital elevation model and SAR imagery — A marriage for effectual flood detection using novel lightweight ensemble neural networks","authors":"Jayasree Thazhath Veedu,&nbsp;Rajesh Reghunadhan","doi":"10.1016/j.rsase.2026.102031","DOIUrl":"10.1016/j.rsase.2026.102031","url":null,"abstract":"<div><div>Floods are among the most devastating natural disasters, causing significant loss of life and damage to essential infrastructure. The most effective way to mitigate flood impact is through early prediction and efficient post-flood management. However, one of the main challenges in flood monitoring is the lack of accurate and timely data, often due to adverse weather conditions. Synthetic Aperture Radar (SAR) imagery has provided a partial solution, as it can capture information regardless of weather or illumination. However, analyzing SAR images solely based on backscattering coefficients presents difficulties, especially in complex terrains affected by distortions. To address this, integrating Digital Elevation Models (DEMs) can enhance flood classification by accurately capturing terrain slopes. In this study, we propose a novel lightweight terrain-aware ensemble NN model that integrates Sentinel-1 VV and VH polarization bands with the Local Positive Neighborhood Averaged (LPNA-corrected) Copernicus DEM and terrain slope to enhance flood classification performance. The results demonstrate a relative improvement in classification accuracy, highlighting the effectiveness of multi-source data fusion with hierarchical NN for flood mapping.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"42 ","pages":"Article 102031"},"PeriodicalIF":4.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147802875","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}

{"title":"Fine-scale thermal anomaly mapping in peri-urban landscapes using HotSat-1","authors":"Can Trong Nguyen","doi":"10.1016/j.rsase.2026.102017","DOIUrl":"10.1016/j.rsase.2026.102017","url":null,"abstract":"<div><div>Fine-scale monitoring of thermal environments is essential for assessing urban heat islands and heat-related risks at local scale. Conventional thermal sensors, such as Landsat and MODIS, provide valuable long-term records but are limited by coarse spatial resolution. HotSat-1, launched in June 2023, offers a promising opportunity to monitor detailed patterns of urban thermal environments at very high resolution (3.5 m). This study evaluates its potential by comparing its thermal anomalies, represented by standardized thermal anomaly (as known as z-score), with Landsat-based temperature data in detecting thermal anomalies and hotspots across heterogeneous landscapes. HotSat-1 reveals pronounced fine-scale thermal heterogeneity both between and within land use categories, with statistically significant differences that are often unresolved by coarser sensors. Cross-sensor comparisons show that HotSat-1 and Landsat consistently identify major thermal coolspots and hotspots (<em>r =</em> 0.58, <em>p &lt; 0001</em>). However, Landsat-based data exhibits reduced sensitivity to extreme and localized thermal anomalies, particularly over small, linear, and fragmented features. Due to coarser spatial resolution, Landsat tends to detect larger and more contiguous hotspot patches, thereby potentially leading to the misclassification of thermal hotspots over cooler vegetated surfaces. This research initially presents the strong potential of HotSat-1 high-resolution thermal imagery to enhance the detection of fine-scale and localized urban heat anomalies for local-scale assessments. However, further work is necessary to address uncertainties and limitations before broader applications can be fully realized.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"42 ","pages":"Article 102017"},"PeriodicalIF":4.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147710211","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}

{"title":"Urban built-up health and morphological liveability assessment for climate-resilient and sustainable development in coastal megacities","authors":"Dyutisree Halder,&nbsp;R.D. Garg","doi":"10.1016/j.rsase.2026.102015","DOIUrl":"10.1016/j.rsase.2026.102015","url":null,"abstract":"<div><div>As climate change intensifies, understanding the spatial morphology of megacities is crucial for developing sustainable adaptation and mitigation strategies. Previous studies often overlooked the integration of multi-scale urban indicators and qualitative human-centric factors in geospatial assessments. This study introduces a novel framework for assessing Urban Built-up Health (UBH), reinforcing climate resilience and integrating equitable novel urban morphological indicators to evaluate liveability in developing coastal megacities. Using Mumbai and Kolkata as case studies, we propose a non-linear multivariate analysis examining spatial structures, urban sprawl, and resource allocation to understand their role in shaping liveability. Utilizing robust indicators such as built-up/population density distance metrics, land surface temperature (LST) with integrated proximity variables based on walkability, accessibility, and blue-green infrastructures (BGI), a novel conceptualization of the Morphological Liveability Index (<span><math><mrow><msub><mi>M</mi><mrow><mi>L</mi><mi>I</mi></mrow></msub></mrow></math></span>) is proposed. Comparative results indicate that Mumbai (2.98) demonstrates slightly better UBH than Kolkata (2.90), reinforcing the need for climate-sensitive urban planning. Results underscore the importance of efficient resource distribution, including natural assets (vegetation, water bodies) and human-made infrastructure (transport, utilities), to enhance climate resilience in fast-growing urban centers. The proposed morphological indicators offer a well-defined framework for quantifying urban spatial structure and liveability, enhancing transparency in crisis situations and supporting urban resilience. The need for urban design strategies that incorporate climate-sensitive policies, nature-based solutions is crucial to mitigate the impacts of environmental degradation. Overall, findings contribute to liveability research by offering a novel framework for implementing climate-adaptive policies to promote sustainable development and equitable urban growth.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"42 ","pages":"Article 102015"},"PeriodicalIF":4.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147710289","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}

{"title":"Assessment of dust storm hazardousness in Iran using remotely sensed aerosol loading and hydroclimatic data","authors":"Masoud Soleimani ,&nbsp;Ali Darvishi Boloorani ,&nbsp;Najmeh Neysani Samany ,&nbsp;Mohammad Reza Ghayamghamian ,&nbsp;Ali Al-Hemoud","doi":"10.1016/j.rsase.2026.102033","DOIUrl":"10.1016/j.rsase.2026.102033","url":null,"abstract":"<div><h3>Purpose</h3><div>This study aims to assess the long-term satellite-based dynamics of dust hazardousness in Iran, a country significantly affected by dust storms, to enhance the understanding of the spatiotemporal patterns of this destructive environmental phenomenon. The insights gained will contribute to the development of effective disaster risk reduction and management strategies.</div></div><div><h3>Methods</h3><div>Using daily MODIS Aerosol Optical Depth (AOD) products, data-driven thresholds were applied to derive three dust dynamic indicators—frequency, intensity, and continuity. These were then integrated with monthly hydroclimatic indicators, including precipitation, air temperature, soil moisture, wind speed, drought, and vegetation cover, for the period 2000 to 2024. This integration enabled the generation of annual dust storm hazardousness maps using the IsoData unsupervised clustering method.</div></div><div><h3>Results</h3><div>Spatial statistical analysis reveals a relatively stable country-scale dust intensity and continuity, while dust frequency exhibits a pronounced increasing trend, emerging as the primary driver of hazard intensification. High and Very High hazardousness levels consistently dominate southern, southwestern, and southeastern Iran, with a noticeable spatial expansion toward central and eastern regions after 2020. Transition analysis indicates a shift from largely stable conditions in the early 2000s to widespread intensification of hazardousness in recent years. Furthermore, population exposure analysis shows that 38% of Iran’s population has been annually exposed to High dust hazardousness levels, on average, with a significant increase in exposure to Very High levels during the last decade.</div></div><div><h3>Conclusion</h3><div>In recent decades, dust has emerged as a widespread and persistent hazard in Iran, particularly in densely populated areas, highlighting the necessity for integrated and region-specific management strategies.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"42 ","pages":"Article 102033"},"PeriodicalIF":4.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147858926","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}

{"title":"A globally applicable framework for earthquake precursor identification using gravity gradient tensor anomalies from satellite gravimetry","authors":"Sarfaraz Newaz ,&nbsp;Jannatun Noor ,&nbsp;A.B.M. Alim Al Islam","doi":"10.1016/j.rsase.2026.102000","DOIUrl":"10.1016/j.rsase.2026.102000","url":null,"abstract":"<div><div>Earthquakes remain one of the most unpredictable natural disasters, yet forecasting such events is critical for mitigating their devastating impact. In this study, we introduce a novel framework for earthquake forecasting by analyzing satellite remote sensing data. Specifically, we use Gravity Gradient Tensor (GGT) anomalies derived from the GRACE satellite mission as a prospective pre-seismic precursor. Unlike traditional forecasting methods that are often limited to point-specific epicentral data or complex machine learning ‘black-box’ models, our proposed framework offers the distinct advantage of physical interpretability and continuous global scalability. Our work directly addresses a key debate in the literature by demonstrating the statistical significance of these anomalies, thereby moving beyond previous isolated case studies. Our proposed method is physically interpretable and operates on a continuous, global scale.</div><div>By analyzing historical GRACE data from 2002 to 2016, we developed a unique threshold-based methodology to extract GGT anomalies across five distinct tectonic regions: Japan, Sumatra, Chile, China, and the India–Nepal belt. The framework employs robust statistical filtering to minimize noise and relies on a regionalized approach to establish a dynamic, data-driven threshold. Our results demonstrate high predictive accuracy, with a regional performance of up to 96% true positives in a highly seismic region and as low as 0% false positives. Our method achieves an average true prediction rate of 91% and an average false alarm rate of 13% across all regions. Quantitative comparisons confirm that our model provides superior reliability and significantly lower false alarm rates compared to conventional machine learning classifiers. This research confirms the potential of GGT anomalies as a valuable precursor and establishes a scalable, data-driven framework for global earthquake forecasting, highlighting a new frontier for satellite geodesy applications in solid Earth science.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"42 ","pages":"Article 102000"},"PeriodicalIF":4.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147657707","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}

{"title":"Observed amplification of flood risk in Pakistan driven by changes in inundation extent and human settlement","authors":"Ho-Minh-Tam Nguyen ,&nbsp;Young-Joo Kwon ,&nbsp;Dasom Lee ,&nbsp;Tomohiro Tanaka ,&nbsp;Sunmin Kim ,&nbsp;Mustajab Ali ,&nbsp;Hyungjun Kim","doi":"10.1016/j.rsase.2026.102025","DOIUrl":"10.1016/j.rsase.2026.102025","url":null,"abstract":"<div><div>Pakistan, with its long history of flooding and rapid urban expansion, has suffered severe consequences for both its population and economy, exemplified by the catastrophic flood of 2022. Understanding evolving flood patterns and their impacts is critical, particularly in the context of climate change. This study introduces the Flood-focused Extent of Water – Pakistan (FEW-Pakistan), a high-resolution, long-term flood dataset spanning 35 years (1988–2022). The dataset was developed using Landsat satellite imagery and a newly devised flood detection methodology that integrates the Normalized Difference Water Index and the Normalized Difference Snow Index. Further, Global Human Settlement Layer data has been integrated to quantify the human impacts of flooding over time. Our analysis reveals a clear trend of intensifying flood risks and damage over the past decade, with the proportion of affected urban populations annually rising from 10.5% to 13.4%. In contrast, rural areas experienced smaller increases in flood exposure, with peaks of 8.3%. Approximately 70% of the variability in flood exposure is attributable to the compound effects of urban population growth and expansion of inundated area. These findings underscore the escalating challenges posed by flooding in rapidly urbanizing regions and the urgent need for targeted adaptation and mitigation strategies.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"42 ","pages":"Article 102025"},"PeriodicalIF":4.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147750206","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}

{"title":"Analysis of storm surge dynamics using numerical modeling & geospatial techniques: A case study of Cyclone Dana","authors":"Ankush Charavande ,&nbsp;Chandra Mohan Bhatt","doi":"10.1016/j.rsase.2026.102048","DOIUrl":"10.1016/j.rsase.2026.102048","url":null,"abstract":"<div><div>This study simulates storm surge dynamics and coastal inundation from Cyclone Dana, which struck Odisha between Bhitarkanika and Dhamra Port on October 25, 2024. The ADCIRC model was implemented with a 2D unstructured mesh using GEBCO bathymetry, SRTM DEM, MODIS LULC, and IMD cyclone track data. The methodology involved a 30-day tidal spin-up achieving 0.968 correlation with observed water levels, followed by cyclone simulation using the Holland parametric wind model (NWS-8) with atmospheric forcing. Two model configurations were compared: a standard implementation and an LULC-optimized version incorporating MODIS-derived Manning's roughness coefficients and surface roughness length to enable spatially variable friction parameterization. Validation against Survey of India tide gauges showed the optimized model significantly outperformed the standard configuration, with correlations improving from 0.832 to 0.907 at Paradip and 0.865 to 0.903 at Haldia. LULC integration enhanced wind field representation, capturing localized velocity variations (13.49 m/s vs. 2.36 m/s overland) resulting from surface roughness heterogeneity. Sentinel-1 SAR imagery validated spatial inundation extent with strong performance metrics (POD = 1.0, FAR = 0.27, CSI = 0.72). Peak surge Level ranged from 1.5 to 1.75 m, demonstrating negative surge dynamics left-of-track (Paradip) and positive surge right-of-track (Haldia). The validated framework demonstrates ADCIRC's operational potential for real-time coastal flood forecasting along vulnerable Bay of Bengal coastlines, providing critical lead time for evacuation planning and emergency resource deployment.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"42 ","pages":"Article 102048"},"PeriodicalIF":4.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147957106","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}

{"title":"Scatterometer and multispectral-based image pansharpening for improved spatial resolution snow cover maps","authors":"Rajinder Kaur ,&nbsp;Sartajvir Singh ,&nbsp;Ganesh Kumar Sethi","doi":"10.1016/j.rsase.2026.102044","DOIUrl":"10.1016/j.rsase.2026.102044","url":null,"abstract":"<div><div>Snow cover monitoring in the Western Himalayan region is essential for understanding regional climate variability, hydrological processes, and water resource management. This study investigates changes in snow cover using the integration of optical and microwave satellite datasets. Data obtained from MODIS (Moderate Resolution Imaging Spectroradiometer) and SCATSAT-1 (Scatterometer Satellite-1) were combined to improve the reliability of snow mapping in complex mountainous terrain. To enhance spatial detail and reduce the limitations of individual datasets, five pan-sharpening techniques—Brovey Transform (BT), Gram–Schmidt (GS), Intensity Hue Saturation (IHS), Nearest Neighbor Diffusion (NND), and Principal Component Analysis (PCA)—were applied before classification. Snow and non-snow areas were identified using the Support Vector Machine. The integration of microwave backscatter with optical reflectance improved the discrimination between snow and non-snow surfaces and enhanced the spatial clarity of the imagery. The results demonstrate that pan-sharpened images provide better boundary delineation and more reliable classification compared to optical data alone. Change detection analysis highlights variations in snow distribution across the Himalayan region. The findings confirm that the fusion of microwave and optical satellite data significantly improves snow cover mapping, particularly under cloud cover, and provides a reliable approach for monitoring snow dynamics in mountainous regions.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"42 ","pages":"Article 102044"},"PeriodicalIF":4.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147956590","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}

{"title":"Tipping points in climate-driven variables of ecosystem resilience across Africa's Niger River Basin","authors":"Chukwuka Ogbue ,&nbsp;Xinwen Xu ,&nbsp;Emeka Igboeli ,&nbsp;Yongdong Wang ,&nbsp;Ibrahim Yahaya ,&nbsp;Chukwuemeka Nnoli ,&nbsp;Alphonse Kayiranga ,&nbsp;Yeneayehu Fenetahun ,&nbsp;Chukwuedozie Ajaero ,&nbsp;Yuan You","doi":"10.1016/j.rsase.2026.102065","DOIUrl":"10.1016/j.rsase.2026.102065","url":null,"abstract":"<div><div>Vegetation resilience to climate stress is a critical determinant of ecosystem stability, particularly in hydroclimatically variable regions. This study assesses vegetation resilience across the Niger River Basin using a multi-indicator framework grounded in critical slowing down theory. Satellite-derived NDVI, EVI, and CASA-based NPP (2000–2024) were analyzed to quantify resilience via lag-1 autocorrelation (AC1) and recovery rates derived from detrended and deseasonalized time series. Results reveal consistent early-warning signals of resilience loss under increasing hydroclimatic stress, with rising AC1 and declining recovery rates along gradients of aridity, rainfall variability, and vapour pressure deficit (VPD). Nonlinear responses indicate potential resilience thresholds, with functional dynamics (NPP) responding earlier (≈1.0–1.19 aridity; ≈1.39–1.50 kPa VPD) than structural indicators (NDVI/EVI: ≈1.25–1.30; ≈1.50–1.57 kPa). Land-cover analysis shows heterogeneous responses, with forests exhibiting high baseline resilience but sharp declines near thresholds, while grasslands and shrublands show gradual, stress-adapted dynamics. These findings highlight the importance of integrating structural and functional indicators for early detection of ecosystem degradation. The identified threshold ranges provide a basis for targeted monitoring and climate adaptation strategies in vulnerable transition zones, supporting the development of early warning systems in data-scarce regions.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"42 ","pages":"Article 102065"},"PeriodicalIF":4.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147956591","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}