Environmental Challenges最新文献

{"title":"Streamflow forecasting using machine learning for flood management and mitigation in the White Volta basin of Ghana","authors":"JosephineThywill Katsekpor ,&nbsp;Klaus Greve ,&nbsp;Edmund I. Yamba","doi":"10.1016/j.envc.2025.101181","DOIUrl":"10.1016/j.envc.2025.101181","url":null,"abstract":"<div><div>Floods are a major threat to livelihoods and infrastructure in the White Volta basin of Ghana. Providing accurate streamflow information is essential for flood management and mitigation. This study, for the first time, used machine learning algorithms, specifically the Long Short-Term Memory (LSTM) and Random Forest (RF), trained on rainfall, temperature, soil moisture, and evapotranspiration data to predict streamflow at 1, 5, and 10-day intervals in the White Volta basin. The study further used these models (RF and LSTM) to forecast future streamflow using CMIP6 SSP5–8.5 scenario data. The model’s output was mainly evaluated using Mean Absolute Error, Mean Bias Error, and Kling-Gupta Efficiency. The result showed high variability in the streamflow, and both models performed well in capturing these variabilities. LSTM performed better in capturing peak flows, whereas RF provided stable long-term predictions for up to 10 days. The future predictions also showed high variability in streamflow, suggesting an increased risk of floods and droughts in the White Volta basin. Given that these models can capture the timings of streamflow (seasonal patterns and peaks), they are well-positioned to provide accurate and reliable forecasts to support effective flood risk management and mitigation in the basin. These models can be extended to similar basins, offering a replicable and sustainable framework for proactive flood early warnings.</div></div>","PeriodicalId":34794,"journal":{"name":"Environmental Challenges","volume":"20 ","pages":"Article 101181"},"PeriodicalIF":0.0,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144071721","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":"Surveying urban anglers with community partner input to assess contamination risk and inform environmental management","authors":"Madeleine Meadows-McDonnell ,&nbsp;Franco N. Gigliotti ,&nbsp;Jessica I. Espinosa ,&nbsp;Raul D. Flamenco","doi":"10.1016/j.envc.2025.101179","DOIUrl":"10.1016/j.envc.2025.101179","url":null,"abstract":"<div><div>Fishing for food in urban areas is common across the world, but the health risks of consuming contaminated fish and shellfish are often understudied and underreported, as are the demographics of urban angling communities. We worked with community partners from governmental and local organizations in New Haven, Connecticut, USA to better understand the behaviors, perceptions, and values of urban anglers, and to identify contaminant risks and risk mitigation strategies. With community partner input, we designed and disseminated a survey to urban anglers in the city. Results indicate that fishing for food is prevalent among anglers in New Haven, that anglers target common game species for consumption, and that anglers are generally concerned about the health risks and contributing factors of fish contamination. With the survey results in mind, we identified mitigation actions with community partners that could abate adverse health risks to New Haven anglers. Overall, the strategies that most community partners agreed would be both effective and feasible included updating long-form advisories, improving and increasing the signage around known popular fishing locations (especially those close to point-source pollution sites), hosting community-led educational opportunities for anglers, and expanding water quality and fish contaminant monitoring.</div></div>","PeriodicalId":34794,"journal":{"name":"Environmental Challenges","volume":"20 ","pages":"Article 101179"},"PeriodicalIF":0.0,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144090052","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 analysis of thermal islands in a semi-arid city: A case study of Kermanshah, Iran using machine learning and remote sensing","authors":"Peyman Karami ,&nbsp;Seyed-Mohsen Mousavi","doi":"10.1016/j.envc.2025.101174","DOIUrl":"10.1016/j.envc.2025.101174","url":null,"abstract":"<div><div>Studying urban land use/land cover (LULC) and land surface temperature (LST), and assessing their changes, is crucial for understanding and mitigating the environmental and climatic impacts on cities in semi-arid regions, where water scarcity and heat stress exacerbate urban sustainability challenges. In this study, Landsat 8 data from 2013 to 2023 were used to assess changes in Kermanshah city. LST was extracted using a Mono-Window algorithm (MWA) for each year. Following Intensity-Hue-Saturation (IHS) pan-sharpening, LULCs were classified into five categories: built-up areas, vacant land, green spaces, water bodies, and transportation infrastructure, using training samples and machine learning methods. Cold Islands (<em>CIs</em>) and Hot Islands (HIs) were identified for each image using LST and Getis-Ord Gi analysis, and their spatio-temporal changes were evaluated with the Kappa index and landscape metrics. The sample size for assessing the impact of environmental parameters on LST variations was determined using the Cochran formula. Topographic, topoclimate, and biophysical variables, along with machine learning methods and the chi-square test, were employed to model and evaluate LST variation across different LULCs.</div><div>The results demonstrate a significant increase in HIs, particularly in the city's periphery, driven by rapid urbanization, impervious surface expansion, and reduced green cover. Key environmental factors, including elevation, built-up areas, and green space density, critically influenced LST variations. Although green spaces expanded in some areas, they were insufficient to counteract the intensifying HI effect. The analysis revealed a 65 % spatial consistency in HI patterns over the decade, highlighting persistent thermal hotspots.</div><div>The findings underscore the urgent need for sustainable urban planning, prioritizing green infrastructure and resilient design strategies to mitigate HI effects, reduce energy consumption, and improve urban livability. Future studies could focus on the impact of building heights and the expansion of urban green spaces on thermal islands.</div></div>","PeriodicalId":34794,"journal":{"name":"Environmental Challenges","volume":"20 ","pages":"Article 101174"},"PeriodicalIF":0.0,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144071781","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 Geological and Seismic Hazards of Malili-Matano Region, East Luwu Regency, Sulawesi: A preliminary study for CCS and Strategic Infrastructure Planning","authors":"Asdani Soehaimi ,&nbsp;Tatang Padmawidjaja ,&nbsp;Subagio Subagio ,&nbsp;Ibrahim Mandi ,&nbsp;Adrin Tohari ,&nbsp;Suharsono Suharsono ,&nbsp;David Menier ,&nbsp;Manoj Mathew ,&nbsp;Mu Ramkumar ,&nbsp;Franto Novico","doi":"10.1016/j.envc.2025.101177","DOIUrl":"10.1016/j.envc.2025.101177","url":null,"abstract":"<div><div>Understanding the seismotectonic characteristics and seismic hazards of the Malili-Matano Region (MMR) in Sulawesi is crucial due to its proximity to active faults, including the Matano Fault Zone (MFZ) and surrounding fault systems. These geological conditions pose significant risks to infrastructure development, particularly Carbon Capture and Storage (CCS) facilities<strong>,</strong> which require a thorough assessment of seismic hazards. This study integrates seismotectonic mapping and Probabilistic Seismic Hazard Analysis (PSHA) to evaluate earthquake risks, with a particular focus on spectral acceleration (PSA) values that influence structural resilience.</div><div>The results indicate that MMR exhibits complex fault interactions<strong>,</strong> leading to elevated Peak Ground Acceleration (PGA<strong>)</strong> and Spectral Acceleration (PSA) values, with PSA Ss = 1.10 g at 0.2 s and S1 = 0.55 g at 1 s for Site Class SB under a 2 % probability of exceedance in 50 years (2500-year return period). These seismic hazard estimates suggest that structural design in the region must adhere to Seismic Design Category D standards, including reinforced foundations and real-time ground motion monitoring to enhance CCS infrastructure safety. The study underscores the importance of continuous seismic monitoring, hazard mitigation strategies, and risk communication for infrastructure resilience in seismically active environments. The findings contribute to a refined understanding of seismotectonic behavior in MMR and its implications for CCS site selection and long-term sustainability.</div></div>","PeriodicalId":34794,"journal":{"name":"Environmental Challenges","volume":"19 ","pages":"Article 101177"},"PeriodicalIF":0.0,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143929357","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":"Investigating soil erosion processes from source-to-sink to prioritise erosion hotspots in the Ethiopian highlands","authors":"Haftu Y. Hadush ,&nbsp;Berhane Grum ,&nbsp;Jantiene Baartman ,&nbsp;Kebede Wolka ,&nbsp;Niguse Abebe ,&nbsp;Ton Hoitink ,&nbsp;Martine van der Ploeg","doi":"10.1016/j.envc.2025.101173","DOIUrl":"10.1016/j.envc.2025.101173","url":null,"abstract":"<div><div>Addressing the adverse impacts of soil erosion through effective soil conservation measures (SDG15) requires a thorough understanding of the erosion processes involved. However, existing erosion prediction models are often compared solely to sediment outflow at catchment outlets without explicitly assessing these various processes. In such cases, the observed and simulated rates at the outlet may align well without effectively showing the spatial sediment redistribution patterns in the catchment. Moreover, data availability is often limited for remote catchments. This study combined the less data-intensive Unit Stream Power Erosion Deposition (USPED) model with gully erosion indices to analyse sediment redistribution and to identify erosion hotspots in a data-scarce catchment in the Ethiopian highland. The model performance was evaluated spatially at three scales, each characterised by distinct erosion processes. The main findings indicate that the model displayed overall agreement across the three scales (R² = 0.63, NSE = 0.40, KGE = 0.47, Pbias = 5 %, RMSE = 1.39 t ha⁻¹ yr⁻¹). The catchment experienced an average soil erosion of 32.3 t ha⁻¹ yr⁻¹ from 2000 to 2023, resulting in a total annual loss of 0.32 million tons. The most erosion-prone areas, which comprise just 18 % of the catchment area, contributed approximately 69 % of the total soil erosion. The observed erosion processes vary by scale, emphasising the need for scale-aware modelling, with distinct erosion processes involved. In conclusion, the USPED model, combined with gully erosion indices, effectively captures the dominant erosion processes at various scales and identifies hotspots for targeted conservation amid land use changes.</div></div>","PeriodicalId":34794,"journal":{"name":"Environmental Challenges","volume":"20 ","pages":"Article 101173"},"PeriodicalIF":0.0,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143932131","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":"Climate change and variability as drivers of vegetation dynamics in Bontioli Natural Reserve, West African drylands","authors":"Issaka Abdou Razakou Kiribou ,&nbsp;Theodore Nikiema ,&nbsp;Kangbéni Dimobe ,&nbsp;Benewinde Jean-Bosco Zoungrana ,&nbsp;Valentin Ouedraogo ,&nbsp;Huiyi Yang ,&nbsp;Truly Santika ,&nbsp;Sintayehu W. Dejene","doi":"10.1016/j.envc.2025.101175","DOIUrl":"10.1016/j.envc.2025.101175","url":null,"abstract":"<div><div>Climate change significantly impacts vegetation dynamics and plant productivity by affecting temperature, rainfall and seasonal patterns. In dryland ecosystems such as the West African savanna, these changes accelerate biodiversity loss, and reduces carbon sequestration capacity. This study examines vegetation response to climate variability in the Bontioli Natural Reserve (BNR), within the West African savanna. Using the Normalized Difference Vegetation Index (NDVI) retrieved from Landsat imagery via the Google Earth Engine platform, coupled with high-resolution bioclimatic data from the climatologies at high resolution for the earth’s land surface areas (CHELSA) we analysed the vegetation changes over the past three decades (1993 to 2023) employing Generalize Additive Modelling (GAM). Results indicate significant shifts in vegetation dynamics at a rate of 0.051 ± 0.043/year, with vegetation decline primarily associated with increased temperatures and reduced rainfall. The model (R² = 0.836, <em>p</em> &lt; 0.001, deviance explained= 0.935, RMSE= 0.073) underscores that rising temperature and prolonged droughts are key drivers of vegetation stress, while intermittent rainfall events trigger only temporary recovery phases. Vegetation productivity peaks were recorded at rainfall levels around 250–300 mm, and temperatures between 26 and 28 °C. Spatial analysis of NDVI with fractional vegetation cover (FVC) highlights an ongoing decline in dense vegetation across the BNR, emphazing the severe consequences of climate change. These findings revealed the critical role protected areas play in mitigating climate impacts by maintaining ecological integrity and promoting vegetation resilience. A safeguarding measure with climate smart policies can play a crucial role in improving the vegetation resilience to climate change effects. Conservationists and policymakers should focus on integrative adaptive long-term climate-resilient plant species. Addressing these challenges directly supports the achievement of SDG 13 and 15 across the broader West African Savanna region.</div></div>","PeriodicalId":34794,"journal":{"name":"Environmental Challenges","volume":"20 ","pages":"Article 101175"},"PeriodicalIF":0.0,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143932132","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":"Managing Indonesian coral reefs: Integration of stressors in Marine Protected Area (MPA) management plans","authors":"Agustin Capriati ,&nbsp;Ingrid A. van de Leemput ,&nbsp;Estradivari ,&nbsp;Yvonne Kunz ,&nbsp;Tries B. Razak ,&nbsp;Rili Djohani ,&nbsp;Hesti Widodo ,&nbsp;Handoko Adi Susanto ,&nbsp;Ririn Widiastutik ,&nbsp;Purwanto ,&nbsp;Leontine E. Becking","doi":"10.1016/j.envc.2025.101178","DOIUrl":"10.1016/j.envc.2025.101178","url":null,"abstract":"<div><div>Indonesia is recognized as a biodiversity hotspot within the Coral Triangle and is rapidly expanding its network of Marine Protected Areas (MPAs). MPAs are critical tools for conserving coral reefs, and MPA management plans serve as the foundational guidelines for conservation. Their effectiveness depends partly on how adequately coral reefs' stressors are addressed and integrated into actionable mitigation strategies. This study assessed the inclusion of stressors in current government-issued Indonesian MPA management plans. We analyzed the inclusion of stressor words within the comprehensive management plans and reviewed the action plan. By 2022, only 20 % of Indonesian MPAs had comprehensive management plans, comprising an introduction, zoning plan, and action plan. We found that most plans address stressors related to fishing. In contrast, less than one-third of the plans address land-based stressors, with nutrient pollution and plastic waste largely overlooked. While climate change was identified in about half of the plans, specific climate change impacts, such as rising sea surface temperature, were identified in only very few plans. Most management plans were broad, non-specific, and highly similar across locations, with stressors identified in the introduction rarely integrated into zoning and action plan sections, which may limit site-specific conservation efforts. Nevertheless, some plans showed a more targeted approach by addressing local stressors and proposing actionable responses. This study highlights the need for more site-specific and adaptive MPA plans. It offers a checklist to assess stressors in future Indonesian MPA management plan development, guiding increased responsiveness to evolving environmental challenges.</div></div>","PeriodicalId":34794,"journal":{"name":"Environmental Challenges","volume":"20 ","pages":"Article 101178"},"PeriodicalIF":0.0,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144071783","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":"Integrated assessment of human health risks from groundwater pollutants in Nubian Aquifer, Sudan: Combining source apportionment and probabilistic analysis","authors":"Musaab A.A. Mohammed ,&nbsp;Norbert P. Szabó ,&nbsp;Elamin D. Suliman ,&nbsp;Magboul M.S. Siddig ,&nbsp;Mohammed N.M. Hassan ,&nbsp;Péter Szűcs","doi":"10.1016/j.envc.2025.101176","DOIUrl":"10.1016/j.envc.2025.101176","url":null,"abstract":"<div><div>Groundwater contamination is a significant global challenge, particularly in arid and semi-arid regions where groundwater is a primary source for drinking and irrigation. This contamination is closely associated with human health risks, potentially leading to severe diseases and long-term health consequences. In this study, the groundwater quality of the Nubian Aquifer System (NAS) in the Shendi area, Sudan, is assessed to evaluate health risks linked to nitrogen compounds (NO₂, NO₃, NH₃) and fluoride (F). The analysis integrates self-organizing maps (SOM), principal component analysis (PCA), and Monte Carlo-based health risk simulations. SOM analysis revealed distinct clustering patterns in groundwater samples, identifying three major hydrochemical trends. PCA indicated that elevated NO₃ concentrations were localized, primarily associated with agricultural runoff, while NO₂ and NH₃ reflected pollution from both agriculture and wastewater. High fluoride concentrations were linked to geogenic sources, particularly water-rock interactions with fluorine-bearing minerals. The Monte Carlo simulation assessed probabilistic health risks, revealing higher mean hazard quotients (HQs) and hazard index (HI) values for children compared to adults. Children’s mean HI of 1.06 significantly exceeds the safe threshold, indicating potential non-carcinogenic health hazards. Sobol sensitivity analysis identified the most influential parameters in shaping health risks, including average exposure time, body weight, and exposure duration, with strong parameter interactions amplifying these effects. Among contaminants, NO₃ and F contributed the most to cumulative HI values. These findings underscore the urgent need for targeted interventions, such as advanced water treatment, stricter pollution controls, and public health awareness programs to mitigate groundwater contamination and protect vulnerable populations.</div></div>","PeriodicalId":34794,"journal":{"name":"Environmental Challenges","volume":"20 ","pages":"Article 101176"},"PeriodicalIF":0.0,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143932130","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":"Decoding dynamic landslide hazard processes for a massive refugee camp in Bangladesh","authors":"Dewan Mohammad Enamul Haque ,&nbsp;Ritu Roy ,&nbsp;Sumya Tasnim ,&nbsp;Shamima Ferdousi Sifa ,&nbsp;Suniti Karunatillake ,&nbsp;A.S.M. Maksud Kamal ,&nbsp;Juan M. Lorenzo","doi":"10.1016/j.envc.2025.101172","DOIUrl":"10.1016/j.envc.2025.101172","url":null,"abstract":"<div><div>Landslides disrupt human ecology worldwide, making practical hazard assessments essential for saving lives and assets. Dynamic assessments, which capture temporal changes in susceptibility, remain rare due to limited multi-temporal landslide inventories. This study addresses this gap by implementing a slope unit (SU)-based dynamic landslide hazard assessment for the Kutupalong Rohingya Refugee Camp (KTP), a region undergoing rapid environmental and landscape changes due to a massive human influx. Here, we decode dynamic landslide hazard processes by employing a Generalized Additive Model (GAM), a flexible statistical method, to explore how landslides (dependent variables) are linked to independent variables like continuous factors (e.g., slope, soil depth, rainfall) and categorical factors (e.g., soil type, landcover change category). We produced multi-temporal inventories to represent conditions before (2018 and prior) and after (2021 and prior) the establishment of refugee settlements. Anthropogenic modifications, such as distance from roads, land-use/NDVI changes, and rainfall, are treated as dynamic factors, while other factors are considered static predisposing conditions. Our GAM approach performs better than standard machine learning (ML) techniques (e.g., Random Forest, Support Vector Machine, Neural Networks), achieving an overall ROC-AUC of 0.84 and a mean cross-validated AUC of 0.81, compared to AUC (0.64-0.74) for ML models. We also performed uncertainty quantification and repeated random simulations (Monte Carlo simulations) to identify slope units with increased, decreased, or unchanged susceptibility. Priority SUs requiring immediate risk reduction measures are flagged, offering actionable insights for local authorities. Our research findings advance landslide hazard assessments by integrating time-varying dynamic processes with a slope units-based approach and facilitating risk mitigation at KTP.</div></div>","PeriodicalId":34794,"journal":{"name":"Environmental Challenges","volume":"19 ","pages":"Article 101172"},"PeriodicalIF":0.0,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143907757","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 assessment of environmental change in Kushtia, Bangladesh (1998–2023) using remote sensing-based environmental indices","authors":"Md. Anik Hossain ,&nbsp;Md. Rahedul Islam ,&nbsp;Tamanna Yesmin ,&nbsp;Rabeya Sultana ,&nbsp;Md. Kamrul Hossain ,&nbsp;Rafiquel Islam","doi":"10.1016/j.envc.2025.101167","DOIUrl":"10.1016/j.envc.2025.101167","url":null,"abstract":"<div><div>Bangladesh’s rapidly evolving environment, influenced by land use and climate dynamics, requires effective monitoring systems. This study examines spatiotemporal environmental changes in Kushtia from 1998 to 2023, utilizing NDVI, NDWI, NDMI, and LST derived from multi-spectral satellite imagery. A PCA-based weighted overlay method integrates these indices into a composite index to evaluate each year's overall environmental condition. Satellite images were processed in ENVI 5.3 and analyzed in ArcGIS 10.8, while PCA determined the weights of the indices. The weighted indices were overlaid in ArcGIS Pro 3.01, and image differencing was applied to detect environmental changes over time. The results represent a decline in the highest NDVI value from 0.72 in 1998 to 0.47 in 2023, reflecting an 18.23 % decrease in vegetated area coverage, while water availability (NDWI) and moisture content (NDMI) declined by 57.64 % and 47.01 %, respectively. Daulatpur experienced the highest reductions, with NDWI decreasing by 72.89 % and NDMI decreasing by 83.25 %, followed by Kumarkhali, which experienced a 51.16 % decline in NDWI. LST remained stable at 32.84 °C in 2023, with weak correlations to vegetation and water indices (R² &lt; 0.13), indicating non-climatic drivers. PCA results suggest that PC1 (55.6 %) and PC2 (38.58 %) collectively account for 94.2 % of the environmental variation, with NDWI (weight: 0.312) and NDVI (weight: 0.298) as the dominant factors. PCA-weighted analysis reveals that degraded areas have shown improvement, while high-performing zones have experienced a decline. Environmental change was more significant from 1998 to 2010 than from 2010 to 2023, with notable degradation and recovery in Mirpur and Daulatpur. The findings emphasize the importance of adequate water governance, afforestation, and adaptive policies in addressing vegetation health and water stress.</div></div>","PeriodicalId":34794,"journal":{"name":"Environmental Challenges","volume":"19 ","pages":"Article 101167"},"PeriodicalIF":0.0,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143882858","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}