[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2047":3},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":18,"tags":20,"view_count":15,"doi":24,"paper":25,"created_at":52},2047,"Assessing urban heat island patterns in Bharatpur metropolitan city of Nepal using multi-temporal remote sensing and machine learning","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs44327-026-00360-7","Rapid urbanization in Nepal’s Terai is driving Land Use and Land Cover (LULC) change and intensifying the Urban Heat Island (UHI) effect. This study quantifies UHI dynamics in Bharatpur Metropolitan City (BMC), Nepal, across six reference years from 2000 to 2024 using multi-temporal Landsat data in Google Earth Engine (GEE). LULC was classified using Random Forest (RF), achieving Overall Accuracy of 89.00–95.50% and Kappa coefficients of 0.853–0.940. Land Surface Temperature (LST) was derived from thermal infrared bands and validated against Department of Hydrology and Meteorology (DHM) ground stations; UHI intensity was computed by pixel-wise normalization; and the Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), and Normalized Difference Moisture Index (NDMI) were correlated with LST and UHI by LULC class using a stratified sample of 800 pixels per class. A Cellular Automata-Markov model with an RF-derived suitability surface projected 2030 LULC (Figure of Merit = 0.302); RF regression predicted the standardized 2030 thermal field (R 2 = 0.578, RMSE = 2.07 °C); and 2030 UHI was derived via Urban Thermal Field Variance Index (UTFVI) zoning and Getis-Ord Gi* hotspot analysis. Built-up area expanded from 21.63% in 2000 to 45.11% in 2024 (+ 23.48 percentage points), while mean LST ranged from 30.46 °C to 37.72 °C and peaked at 57.75 °C in 2010. NDMI was the dominant thermal correlate (r = − 0.84, p \u003C 0.001), significantly exceeding NDVI in built-up (r = − 0.64) and vegetated (r = − 0.46) areas, while NDBI correlated most strongly with warming (r = +0.81). By 2030, built-up area is projected to reach 56.21% (+ 11.10 percentage points from 2024), and the statistically significant heat-hotspot area is projected to expand by 12.63 percentage points (36.69% to 49.32%), even as peak-to-baseline UHI intensity narrows from +3.41 °C to +1.62 °C, indicating spatial expansion and thermal homogenization of the heat island rather than simple intensification. The UTFVI-classified heat-affected area is projected to grow from 38.84% to 48.95% of the study area over the same period. These quantitative findings provide an evidence base for urban planning in Bharatpur and comparable fast-urbanizing South Asian Terai cities.","尼泊尔特莱地区快速城市化正驱动土地利用与土地覆盖（LULC）变化，并加剧城市热岛（UHI）效应。本研究利用Google Earth Engine（GEE）平台的多时相Landsat数据，量化了尼泊尔巴拉特普尔大都市（BMC）2000年至2024年六个参考年份的UHI动态。采用随机森林（RF）进行LULC分类，总体精度达89.00%–95.50%，Kappa系数为0.853–0.940。地表温度（LST）由热红外波段反演，并利用水文与气象局（DHM）地面站点数据进行验证；UHI强度通过逐像元归一化计算；采用分层抽样方法，每类选取800个像元，分析归一化植被指数（NDVI）、归一化建筑指数（NDBI）和归一化湿度指数（NDMI）与LST及UHI的相关性。结合RF衍生的适宜性表面，利用元胞自动机-马尔可夫模型预测2030年LULC（品质因数=0.302）；RF回归预测标准化2030年热场（R²=0.578，RMSE=2.07 °C）；并通过城市热场变异指数（UTFVI）分区和Getis-Ord Gi*热点分析推导2030年UHI。建设用地面积从2000年的21.63%扩张至2024年的45.11%（+23.48个百分点），平均LST介于30.46 °C至37.72 °C之间，2010年峰值达57.75 °C。NDMI是主导热相关因子（r=−0.84，p\u003C0.001），在建设用地（r=−0.64）和植被区（r=−0.46）中均显著超过NDVI，而NDBI与升温的相关性最强（r=+0.81）。至2030年，建设用地面积预计达56.21%（较2024年增加11.10个百分点），统计显著的热点区域预计扩大12.63个百分点（从36.69%增至49.32%），尽管峰值与基线UHI强度从+3.41 °C收窄至+1.62 °C，表明热岛呈空间扩张与热均质化趋势，而非单纯强度增加。同期，UTFVI分类的热影响区域预计从研究区的38.84%增长至48.95%。这些定量研究结果为巴拉特普尔及类似快速城市化的南亚特莱城市提供了城市规划的证据基础。",null,"Discover Cities","2026-09-08T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"研究对象为尼泊尔城市热岛与土地利用变化，属城市遥感领域，与三农、农业信息化、智慧农业无直接关联，不建议进入每日精选。",[19],{"name":10,"url":6},[21,22,23],"遥感监测","土地利用","城市热岛","10.1007\u002Fs44327-026-00360-7",{"doi":24,"openalex_id":26,"authors":27,"venue":10,"cited_by_count":15,"oa_url":43,"card":44,"direction":50,"ingested_from":51},"W7211960821",[28,30,32,34,36,39,41],{"name":29,"orcid":9},"Anup Raj Timalsina",{"name":31,"orcid":9},"Saditya Baral",{"name":33,"orcid":9},"Krishna Gyawali",{"name":35,"orcid":9},"Ashish Ayer",{"name":37,"orcid":38},"Shisir Kharel","https:\u002F\u002Forcid.org\u002F0009-0006-1594-7143",{"name":40,"orcid":9},"Subash Ghimire",{"name":42,"orcid":9},"Bigyan Banjara","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs44327-026-00360-7.pdf",{"tldr":45,"method":46,"finding":47,"direction":48,"opportunity":49},"用多时相遥感和机器学习评估尼泊尔巴拉特普尔市2000-2024年城市热岛动态并预测2030年情景。","Landsat数据、GEE、随机森林分类与回归、CA-Markov、UTFVI和","建成区从21.63%增至45.11%，热岛呈空间扩张与热均质化，2030年热点区将扩大12.63个百","农业遥感与作物表型","可借鉴其多时相遥感与机器学习耦合框架，研究快速城镇化区农业用地热环境变化及作物热胁迫风险。","智慧农业 \u002F 农业物联网","openalex","2026-09-10T23:30:11.964119Z"]