Remote Sensing Applications-Society and Environment最新文献

{"title":"Spatiotemporal dynamics of NO2 concentration data (2018–2025) in Casablanca-Settat region, Morocco: A satellite-based assessment for urban air quality management","authors":"Abdelhalim Miftah","doi":"10.1016/j.rsase.2026.101870","DOIUrl":"10.1016/j.rsase.2026.101870","url":null,"abstract":"<div><div>The Nitrogen dioxide (NO₂) is one of the main pollutant gases in the atmosphere with known harmful effects on human health, caused by rapid urbanization, traffic, and industrialization. In the fast-growing urban areas of North Africa, NO₂ spatiotemporal variability analysis is hampered by the sparse distribution of ground stations. In neighboring Morocco, particularly in the Casablanca-Settat region, research that utilizes satellite data analysis in combination with geospatial statistical analysis is also limited. The purpose of this study is to fill the scientific gap in spatial variability analysis of NO₂ concentrations from 2018 to 2025 in a specific region in Casablanca-Settat in Morocco. The NO₂ concentration in the tropospheric region using the Sentinel-5P (TROPOMI) satellite sensor was analyzed using geospatial analysis tools. The hotspots and cold spots of NO₂ were determined using the Getis-Ord Gi∗ statistic at a statistically significant confidence level. The annual average NO₂ concentration varied between 6.3 μg/m³ in rural areas in southern provinces to 11.3 μg/m³ in Casablanca, showing sharp differences between the urban, industrial areas, and rural areas. The cities of Casablanca, Mediouna, and the Nouaceur province showed higher NO₂ concentrations than other cities in any country in the world; they often exceeded the annual average recommended by the World Health Organization at 10 μg/m³. However, southern provinces, such as Settat, Sidi Bennour, and El Jadida, had smaller NO₂ concentrations that were less variable. A marked decrease in NO₂ was observed during the COVID-19 lockdown period, followed by a stabilized period in 2022–2023 and a decline in NO₂ thereafter. The NO₂ hotspots were in the Casablanca-Beth Mohammedia-Mediouna-Berrechid region at a 99 % confidence level, which was a hotspot region, but in southern areas, it was a cold spot region.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"41 ","pages":"Article 101870"},"PeriodicalIF":4.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145977874","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":"Random forest-based species distribution modeling reveals intensifying multi-species invasion risks of alien plants in Ethiopia under climate change","authors":"Kalid Hassen Yasin ,&nbsp;Diriba Tulu ,&nbsp;Tadele Bedo Gelete ,&nbsp;Beyan Ahmed Yuya ,&nbsp;Anteneh Derribew Iguala ,&nbsp;Kiya Adare Tadesse ,&nbsp;Erana Kebede","doi":"10.1016/j.rsase.2026.101869","DOIUrl":"10.1016/j.rsase.2026.101869","url":null,"abstract":"<div><div>Invasive alien plant species (IAPS) are increasingly threatening biodiversity conservation during rapid climate change, especially in vulnerable regions, such as Ethiopia. This study aimed to model current and future habitat suitability for four major IAPS (<em>Prosopis juliflora</em>, <em>Parthenium hysterophorus</em>, <em>Lantana camara</em>, and <em>Acacia</em> spp.) and assess multi-species invasion risks across Ethiopia's 136 protected areas (PAs), using Random Forest (RF)–based species distribution modeling (SDM). The analysis integrated 27 environmental predictors with downscaled climate projections from the Coupled Model Intercomparison Project Phase 6 (CMIP6), under Shared Socioeconomic Pathway (SSP) 2-4.5 (moderate) and SSP5-8.5 (high-emission) scenarios. Findings revealed distinct ecological niches among the four major species, with <em>P. hysterophorus</em> currently exhibiting the largest area of suitable habitat (175,743 km²). All species demonstrated significant expansion potential under climate change, with <em>P. juliflora</em> showing the most dramatic relative increase (up to 106.25 % by 2070 under SSP5-8.5). Niche overlap analysis indicated high environmental similarity between <em>L. camara</em> and <em>P. hysterophorus</em> (Hellinger's <em>I</em> = 0.8826), suggesting potential for co-occurrence in invasion hotspots concentrated in the southwestern highlands and Rift Valley. Most critically, the PA vulnerability assessment revealed that smaller protected areas (&lt;100 km²) face significantly higher potential invasion pressure (mean suitable area: 41.5 %) than larger ones (≥5000 km²: 25.3 %), with <em>P. hysterophorus</em> posing the broadest spatial threat affecting 37 PAs with &gt;50 % habitat suitability. Future projections indicate 206–247 % increases in invasion threats across PAs by mid-century, with eastern and southeastern conservation landscapes facing a disproportionate rise in risk. These findings present the first national, evidence-based spatial prioritization framework for climate-adaptive IAPS management in Ethiopia, demonstrating that conservation planning should integrate dynamic invasion risk assessments to safeguard biodiversity amid global climate change.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"41 ","pages":"Article 101869"},"PeriodicalIF":4.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926032","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":"Transferability of spatial and temporal learning models for winter wheat mapping in data-scarce environments: A case study in Armenia","authors":"Ashutosh Tiwari ,&nbsp;Lei Zhao ,&nbsp;Sayantan Sarkar ,&nbsp;Vardan Urutyan ,&nbsp;Uday Santhosh Raju Vysyaraju ,&nbsp;Sejeong Moon ,&nbsp;Benjamin Ghansah ,&nbsp;Garnik Sevoyan ,&nbsp;Juan Landivar ,&nbsp;Yuri Calil ,&nbsp;Mahendra Bhandari","doi":"10.1016/j.rsase.2026.101885","DOIUrl":"10.1016/j.rsase.2026.101885","url":null,"abstract":"<div><div>Winter wheat constitutes a key component of Armenia's agricultural economy and national food security. Accurate and timely identification and mapping of winter wheat fields can support informed policymaking, infrastructure planning, resource allocation, and crop monitoring. However, large-scale crop mapping using traditional field-based surveys is labor-intensive, spatially limited, and often impractical. In this study, we present a framework for large-scale winter wheat field mapping in Armenia under limited ground truth availability using multi-source satellite images. We evaluate the possibility of cross-region generalization through image-based semantic segmentation models (3D Unet and SegNet) and pointwise classification models (Random Forest, one-dimensional convolutional neural network, and long short-term memory (LSTM)) on multi-temporal data using Sentinel-2 and PlanetScope images. We train these models using large, well-labeled winter wheat datasets from United States counties and a limited set of known winter wheat fields in Armenia, and subsequently transfer and test across five independent test regions in Armenia. Models trained on Sentinel-2 imagery generalize well within the United States, achieving test accuracy and F1-scores of 0.96 and 0.86, respectively. However, their performance degrades when transferred to fragmented agricultural landscapes in Armenia, particularly for image-based semantic segmentation models. In contrast, the LSTM-based temporal model demonstrates superior transferability, achieving accuracy and F1-scores of 0.96 and 0.96, respectively, while effectively suppressing non-wheat features and identifying both known and previously unmapped winter wheat fields. Using the best-performing model, we generate provincial-scale winter wheat maps for Shirak Province for the years 2023, 2024, and 2025, estimating cultivated areas of 25,641 ha, 21,088 ha (approximately 3 % higher than the USDA Foreign Agricultural Service estimate), and 28,909 ha, respectively. These results highlight the potential of temporal machine learning models for scalable winter wheat mapping in data-scarce regions, offering a practical pathway toward nationwide crop inventory generation and agricultural decision support.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"41 ","pages":"Article 101885"},"PeriodicalIF":4.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146022715","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":"After the flames and smoke, then what? Spatial analysis and heterogeneity modeling of bushfire effects on vegetation health and air quality in Ghana","authors":"Stephen Biliyitorb Liwur ,&nbsp;Abdul Rashid Adam ,&nbsp;Jacob Nchagmado Tagnan","doi":"10.1016/j.rsase.2025.101854","DOIUrl":"10.1016/j.rsase.2025.101854","url":null,"abstract":"<div><div>Bush-burning plays a key role in shaping ecosystems and supporting livelihoods, yet its intensification under human pressures has transformed the practice from a regenerative agent into a major environmental hazard. In Ghana's savannah zones, bushfire is used for land clearing, pasture management, and pest control, but also drives deforestation, soil degradation, and air pollution. However, extant studies mainly focused on its ecological and agricultural impacts, with limited attention to atmospheric and public health implications. This study addresses this gap by examining the spatiotemporal dynamics of bushfires, vegetation change, and air quality. We employ multiscale geographic weighted regression (MGWR), land use and land cover classification (LULC), normalized burn ratio (NBR), and normalized difference vegetation index (NDVI) to examine the spatiotemporal dynamics of bushfires, vegetation health, and air quality across Ghana's savannah zones (2019/2020 and 2024/2025). The findings point out that burn severity affected more than 60 % of the landscape, peaking between December and February. Correspondingly, NDVI declined with over 70 % showing vegetation stress. Similarly, air pollution intensified: PM_2.5 from 28 to 35 μg/m³, CO from 0.4 to 0.6 μg/m³, and SO₂ from 3 to 5 ppbv. MGWR revealed weakening NDVI–NBR associations (median β = 0.62; Adj. R² = 0.51) and spatial clustering in pollutant emissions (Residual Moran's I ≈ −0.03-0.04; p &lt; 0.01). These findings emphasize that recurrent bush burning is intensifying vegetation degradation and air pollution--a trajectory of deteriorating ecological health with significant public health implications. Therefore, there is a critical need for spatial heterogeneity-vulnerability mapping for early-warning systems, land-use planning, and public health interventions.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"41 ","pages":"Article 101854"},"PeriodicalIF":4.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884535","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":"Assessing the diurnal relationship between urban morphology and land surface temperature in a highly dense city: A case study in Hong Kong","authors":"Sze Ping Hui ,&nbsp;Yu Liu ,&nbsp;Pir Mohammad ,&nbsp;Qihao Weng","doi":"10.1016/j.rsase.2025.101858","DOIUrl":"10.1016/j.rsase.2025.101858","url":null,"abstract":"<div><div>Urban expansion and the resulting three-dimensional building patterns have significant ecological impacts, particularly the urban heat island (UHI) effect. However, the influence of urban morphology on the thermal environment of a dense city is still not clear. This study investigates the diurnal and seasonal relationships between two-dimensional (2D) and three-dimensional (3D) urban morphology indicators with the remotely sensed land surface temperature (LST) in Hong Kong, a city characterized by extreme urban density and significant vertical development. Using a boosted regression tree (BRT) model, we analyze the spatial variation of LST during both hot and cold months, in daytime and nighttime, and evaluate the marginal impacts of selected 2D/3D indicators on LST. The results reveal pronounced spatial heterogeneity in LST, with high daytime temperatures concentrated in industrial areas and nighttime peaks shifting to commercial and residential zones. The relationships between urban form indicators and LST are complex and nonlinear, differing significantly between day and night. Among all indicators, the sky view factor (SVF) is identified as the most influential, with its marginal effect on LST shifting from positive to negative after a threshold of 0.75 in January daytime and 0.76 in January nighttime. Whereas in July, the SVF threshold decreases to 0.71 in daytime and 0.62 in nighttime. The marginal effect of building height shows a similar trend with 30 m threshold in January in both daytime and nighttime, while 21.57 m in daytime and 20.31 m in nighttime in July, proving a significant cooling benefit in daytime. This study provides urban planners with evidence-based guidelines for optimizing building design and layout to mitigate UHI, potentially through manipulating factors like SVF and building height. These insights can inform sustainable urban development strategies in highly dense cities worldwide.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"41 ","pages":"Article 101858"},"PeriodicalIF":4.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926178","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":"Wavelet-enhanced Mamba-like multi-scale linear attention decoding for remote sensing cloud detection","authors":"Hui Gao ,&nbsp;Xianjun Du","doi":"10.1016/j.rsase.2026.101883","DOIUrl":"10.1016/j.rsase.2026.101883","url":null,"abstract":"<div><div>Cloud detection is a key step in remote sensing image preprocessing, but existing methods face challenges with fragmented and thin clouds. Fragmented clouds have dispersed, multi-scale features, while thin clouds are optically similar to non-cloud regions and sparsely distributed, making them hard to distinguish with local features alone. Therefore, jointly modeling global and local features is crucial. To address these issues while maintaining linear complexity, this paper proposes a Wavelet-Enhanced Multi-Scale Mamba-like Linear Attention Decoder (WMSMLAD) method. WMSMLAD consists of three components: the WMSMLA block, the Weighted Fusion (WF) module, and the Cloud Head (CH). The WMSMLA block cascades a Feature Decomposition (FD) module to extract spatial feature distributions and uses a Multi-Layer Perceptron (MLP) to handle channel interactions. The FD module suppresses irrelevant features, while the WMSMLA enhances global information through Haar wavelet transform and refines feature boundaries. It combines multi-scale convolution with a Mamba-like linear attention (MLLA) mechanism to capture multi-scale local features and global dependencies. The WF module dynamically adjusts the weight between encoding and decoding features, with the cloud mask output through the CH. Experimental results demonstrate that WMSMLAD achieves competitive performance among the compared methods, with an mIoU of 91.53 % on the MODIS dataset and a MAE of 0.0599 on the CHLandsat dataset.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"41 ","pages":"Article 101883"},"PeriodicalIF":4.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145977875","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":"Spatiotemporal dynamics of carbon, water, and energy balance in Bangladesh using multi-source remote sensing and climate data","authors":"Nur Hussain ,&nbsp;Md Saifuzzaman ,&nbsp;Didar Islam ,&nbsp;S.M. Shahriar Ahmed ,&nbsp;Md Shamim Ahamed ,&nbsp;Dara Shamsuddin","doi":"10.1016/j.rsase.2025.101847","DOIUrl":"10.1016/j.rsase.2025.101847","url":null,"abstract":"<div><div>Exploring the complex interactions between climate variables and ecosystem processes is crucial for understanding long-term environmental changes. This study examines the spatiotemporal dynamics of carbon, water and energy fluxes and their impacts on ecosystem processes in Bangladesh from 2005 to 2022 utilizing multi-source remote sensing and ground-based meteorological data. Carbon dynamics are estimated through gross primary productivity (GPP), net primary production (NPP), and ecosystem respiration (RE). Water and energy balances are derived from evapotranspiration (ET), water use efficiency (WUE), net radiation (Rn), and latent heat (LE). Our estimates indicate that GPP varied from 2351.29 g C m⁻² y⁻¹ in 2009–2178.45 g C m⁻² y⁻¹ in 2020, while NPP ranged from 1248.13 g C m⁻² y⁻¹ in 2012 to 929.46 g C m⁻² y⁻¹ in 2020, reflecting temporal variations in photosynthetic efficiency and carbon storage. The ratio of LE/Rn was found to vary from 0.72 to 1.01, with an average of 83 %, indicating that a significant portion of the radiative energy was transferred to the atmosphere as turbulent flux. Validation of LUE-based GPP compared to FLUXCOM-GPP showed a moderate correlation (R² = 0.61, p &lt; 0.005), supporting the reliability of the estimates. We also conducted multivariate regression analysis to assess the relationships between climate variables and carbon, water, and energy balance. The results indicate that photosynthetically active radiation (PAR) is the primary and dominant driver of GPP (R² = 0.97), while temperature and precipitation are key factors significantly influencing carbon uptake. This study presents a comprehensive, integrated assessment of carbon, water, and energy fluxes at the national scale across Bangladesh, emphasizing the crucial role of climate variables in shaping these fluxes and offering valuable insights for climate-resilient land management and sustainable carbon strategies in monsoon-dominated regions.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"41 ","pages":"Article 101847"},"PeriodicalIF":4.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884536","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":"Monitoring of the smouldering coal-waste dump in Chorzów (Poland) using spectral indices: A UAV- and satellite-based approach","authors":"Anna Abramowicz,&nbsp;Michał Laska,&nbsp;Ádám Nádudvari,&nbsp;Oimahmad Rahmonov","doi":"10.1016/j.rsase.2025.101865","DOIUrl":"10.1016/j.rsase.2025.101865","url":null,"abstract":"<div><div>The study aimed to evaluate the applicability of environmental indices in the monitoring of smouldering coal-waste dumps. A dump located in the Upper Silesian Coal Basin served as the research site for a multi-method analysis combining remote sensing and field-based data. Two UAV survey campaigns were conducted, capturing RGB, infrared, and multispectral imagery. These were supplemented with direct ground measurements of subsurface temperature and detailed vegetation mapping. Additionally, publicly available satellite data from the Landsat and Sentinel missions were analysed. A range of vegetation and fire-related indices (NDVI, SAVI, EVI, BAI, among others) were calculated to identify thermally active zones and assess vegetation conditions within these degraded areas. The results revealed strong seasonal variability in vegetation indices on thermally active sites, with evidence of disrupted vegetation cycles, including winter greening in moderately heated root zones – a pattern indicative of stress and degradation processes. While open-access satellite data, such as Landsat and Sentinel-2, proved useful in reconstructing the fire history of the dump, their spatial resolution was insufficient for detailed monitoring of small-scale thermal anomalies. The study highlights the diagnostic potential of UAV-based remote sensing in post-industrial environments undergoing land degradation but emphasises the importance of field validation for accurate environmental assessment.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"41 ","pages":"Article 101865"},"PeriodicalIF":4.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926023","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":"On the use of post-classification change analysis for monitoring salt marsh extent in support of carbon inventory reporting","authors":"Elisha Richardson ,&nbsp;Koreen Millard ,&nbsp;Danika van Proosdij","doi":"10.1016/j.rsase.2025.101862","DOIUrl":"10.1016/j.rsase.2025.101862","url":null,"abstract":"<div><div>Salt marshes are valuable blue carbon ecosystems that outperform terrestrial environments in their capacity to sequester carbon (per unit area). Natural salt marshes exhibit striking zonation in vegetation, which reflects their elevation in relation to tidal inundation. Natural salt marshes have been removed and degraded globally, and while the potential environmental benefits of salt marsh restoration and natural marsh conservation have been documented, many regions lack consistent mapping or monitoring efforts, partly due to their inaccessible nature and partly because of challenges in using satellite remote sensing in intertidal areas. The elevation and dominant vegetation within a salt marsh restoration site reflects in part their restoration status and maturity, which are in turn linked to their capacity for carbon storage and sequestration. Therefore, a lack of information about vegetation zonation in natural and restored salt marshes is a knowledge gap that inhibits our ability to report on any salt marsh extent changes, and subsequently their inclusion in greenhouse gas inventory reporting. Existing “global” models may not perform sufficiently in regional contexts, especially in megatidal regions such as the Bay of Fundy which experiences exceptional seasonal and tidal conditions. Demonstrated at both natural salt marshes and salt marsh restoration sites in the Bay of Fundy, this study presents a robust method for mapping of salt marshes using machine learning classification techniques. We leverage multitemporal and multisensor imagery from specific tidal stages to identify the characteristic vegetation of high elevation and low elevation salt marsh. The results indicate the importance of acquiring low tide imagery, and the inclusion of imagery from multiple seasons can improve classification accuracy. Predicted maps for 2020 and 2023 were used to produce “change data” and associated uncertainties, which may be combined with other components of activity data in carbon inventory reporting.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"41 ","pages":"Article 101862"},"PeriodicalIF":4.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926031","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":"Integrating CA–Markov–ANN for spatiotemporal prediction of land use dynamics in a fragile Himalayan watershed","authors":"Pawan Kumar Thakur ,&nbsp;Praveen Kumar Thakur ,&nbsp;Raj Kumar Verma ,&nbsp;Biswajeet Pradhan","doi":"10.1016/j.rsase.2025.101856","DOIUrl":"10.1016/j.rsase.2025.101856","url":null,"abstract":"<div><div>Predicting future Land-Use/Land-Cover (LULC) changes is essential for sustainable resource management and environmental monitoring, particularly in ecologically fragile high-altitude Himalayan watersheds like the Parbati Watershed (PW), where biodiversity, hydrology, and climate stability are increasingly threatened. However, limited studies have explored integrated, data-driven LULC modeling approaches for high-altitude Himalayan watersheds, leaving a critical gap in understanding the spatiotemporal evolution of these sensitive landscapes. This study addresses this gap by proposing a novel Cellular Automata (CA)-Markov chain (MC)-based Artificial Neural Network (CA–MC-ANN) framework, implemented through a Multi-Layer Perceptron (MLP) prediction architecture and uniquely tailored to the PW. We assess past LULC dynamics (1989–2023) and project future scenarios (2035, 2045) to understand transformations driven by intertwined anthropogenic pressures and climate change. Key findings reveal a rapidly transforming landscape where glacier/snow cover has decreased from ∼37.66 % (1989) to ∼21.61 % (2023), projected to fall to ∼13.47 % by 2045—a ∼24.19 % cumulative loss (∼-0.43 %/yr) driven by high-altitude warming. In contrast, built-up/settlement areas increased from ∼0.26 % (1989) to ∼0.80 % (2023) and are projected to reach ∼1.21 % by 2045—a ∼0.95 % cumulative gain (∼+0.02 %/yr), reflecting tourism boosts, hydroelectric projects (HEPs), and rapid infrastructure expansion. Alongside, Himalayan Moist Temperate Forests (HMTF) declined from ∼14.95 % (1989) to ∼14.06 % (2023) and are also projected to decline to ∼13.75 % by 2045—a ∼-1.20 % cumulative loss (∼-0.02 %/yr), indicating mounting pressure on natural ecosystems. These trends highlight accelerating glacial shrinkage, rapid tourism and development expansion, and land degradation. These findings underscore the urgent need for integrated spatial analysis and predictive modelling to inform regional conservation, sustainable land management, and resilient development strategies. Implementing comprehensive LULC and biodiversity impact assessments is critical for enhancing environmental resilience and ensuring long-term socio-ecological sustainability in the ecologically sensitive Himalayan region.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"41 ","pages":"Article 101856"},"PeriodicalIF":4.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926022","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}