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Item Decadal Dynamics of Nighttime Urban Heat Island in Coimbatore: A Spatio-Temporal Investigation of Thermal Clustering and Intensification(Czech Society for Landscape Ecology, 2026-02-14) Gadekar, Kajesh; Mathew, Aneesh; Sarwesh, P; Naresh, C.This study presents a comprehensive spatio-temporal analysis of nighttime Land Surface Temperature (LST) and Urban Heat Island Intensity (UHII) in Coimbatore from 2001 to 2022, highlighting statistically significant warming trends and intensifying urban heat island effects. Urban areas experienced a notable nighttime LST increase from 21.4 °C in 2001 to 23.7 °C in 2019, compared to a rural rise from 20.5 °C to 22.5 °C. The average urban–rural LST differential (~1 °C) widened post-2016, aligning with the recorded peak LST of 26.8 °C. The minimum LST dropped to 8.5 °C in 2001, indicating a reduction in cold extremes. Kendall’s tau analysis confirmed a stronger warming trend in urban areas (τ = 0.593) than rural zones (τ = 0.429). Seasonal UHII analysis showed progressive winter intensification post-2012, while summer UHII peaked in 2013 and 2015, then dipped post-2016 before rising again in 2022. Mann-Kendall tests confirmed statistically significant increasing trends in winter UHII, urban LST, and rural LST, with urban LST exhibiting the steepest rise. Spatial autocorrelation analysis using Moran’s Index revealed intensifying clustering of high LST zones: the annual Moran’s Index increased from 0.797 (2001) to 0.857 (2022), with z-scores rising from 42.253 to 45.445. Winter showed the most pronounced clustering, with Moran’s Index jumping from 0.812 to 0.903 and z-scores reaching 47.848 by 2022. Hotspots with 99 % confidence levels were primarily urban, expanding over time with temperatures between 24.8 °C and 26.7 °C, while cold spots (99 % CL) remained stable in rural areas. These findings confirm the persistent and intensifying nature of UHI in Coimbatore, driven by urban expansion, declining vegetation, and increased impervious surfaces. This study fills a critical research gap by providing one of the first long-term assessments of nighttime UHI intensity in a mid-sized Indian city, thereby contributing to the broader understanding of urban thermal dynamics beyond metropolitan regions. The study underscores the urgent need for spatially informed interventions, such as urban greening, reflective materials, and climate-sensitive planning, to mitigate urban thermal stress and enhance resilience in rapidly growing cities.Item Machine learning–based prioritization of sub-watersheds for soil erosion management: A case study of the Bardha watershed(Elsevier Inc, 2026-03-26) Shekar, Padala Raja; Mathew, Aneesh; Arun, P.S.; Prasanth, M. Surya; Alshehri, FahadSoil erosion is a major environmental concern that affects land productivity and water quality. Although soil erosion is a serious global environmental challenge, understanding its potential influence in the Bardha Watershed is important due to its topographical characteristics and the dependence of local communities on land resources for agriculture. Morphometric analysis helps assess a watershed's physical characteristics to understand its erosion potential. In this study, sub-watersheds were delineated using the shuttle radar topography mission (SRTM) digital elevation model (DEM) to accurately derive drainage and terrain characteristics. To enhance the precision of sub-watershed prioritization, morphometric analysis is combined with multi-criteria decisionmaking (MCDM) techniques. This research ranks sub-watersheds in the Bardha watershed in Chhattisgarh using morphometric parameters in combination with four MCDM approaches: additive ratio assessment (ARAS), multiobjective optimization by ratio analysis (MOORA), visekriterijumsko kompromisno rangiranje (VIKOR) and simple additive weighting (SAW). The criteria weights for these MCDM methods are determined using the criteria importance through intercriteria correlation (CRITIC) method. Furthermore, the novelty of this study lies in the integration of machine learning (ML) techniques, specifically support vector machine (SVM) and random forest (RF). By combining the outputs of all six methods, the study developed a unified priority map, which was subsequently classified into high, medium, and low priority zones. The study found that sub-watershed 3 (SW3) and SW4 fall into the common high-priority category; SW2, SW6, and SW7 into the medium category; and SW1 and SW5 into the low-priority group. This integrated method makes decision-making stronger by letting planners focus on high-priority sub-watersheds for strategic development, conservation, and optimal land management. This study aligns with SDG 15 by addressing land degradation through the identification and management of soil erosion-prone areas.