[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4363":3,"related-4363":74},{"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,"primary_topic":9,"secondary_topics":25,"classification":26,"search_phrases":47,"slug":50,"view_count":51,"doi":52,"paper":53,"created_at":73},4363,"Spatiotemporal dynamics of urbanisation induced environmental quality in arid city using a grid-based analysis in Jodhpur India from 2001 to 2021","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs44274-026-01067-7","Rapid urbanisation in arid regions intensifies heat exposure, vegetation loss, and particulate pollution, yet medium-sized desert cities remain underrepresented in integrated environmental assessments. We quantified spatiotemporal changes in environmental quality in Jodhpur, India, from 2001 to 2021 using a novel grid-based Environmental Quality Index (EQI). Methodological innovation lies in three elements: incorporation of nighttime land surface temperature (LST) to capture urban heat retention relevant to population exposure; application of a uniform 32-grid intra-urban framework to detect micro-spatial heterogeneity; and integration of satellite-derived PM 2.5 with vegetation–thermal dynamics using a geometric mean to minimise compensatory bias. Indicators, LST (land degradation), NDVI-derived vegetation cover (ecosystem regulation), and PM 2.5 (atmospheric quality), were selected for direct biophysical relevance, long-term satellite consistency, and exposure sensitivity, rather than proxy measures such as built-up indices or population density. Annual MODIS, Landsat, and satellite model fused PM 2.5 datasets (1 km) were standardised (0–100); trends were analysed using Mann–Kendall and Sen’s slope estimators; interrelationships were assessed via multiple linear regression. Land-use classification from Landsat achieved > 85% overall accuracy. Seventy-one percent of grids showed significant nighttime warming ( p \u003C 0·05), with hotspots reaching + 1·36 °C. Core urban areas recorded vegetation decline (mean ΔVC − 0·02), whereas peri-urban grids showed moderate greening (ΔVC up to + 0·07). Mean PM 2.5 ranged 4–7 μg\u002Fm 3 and was strongly associated with vegetation loss (R 2 = 0·66) and elevated LST (R 2 = 0·49). The proportion of city area classified as poor or very poor EQI increased from 15·1% (2001) to 41·5% (2021). Vegetation decline was the dominant predictor of PM 2.5 increase (β = − 0·44; p = 0·01). These findings indicate substantial environmental deterioration and provide a scalable framework for climate-resilient urban planning in arid cities.","干旱地区快速城市化加剧了热暴露、植被丧失和颗粒物污染，但中型沙漠城市在综合环境评估中仍代表性不足。我们采用一种新颖的基于网格的环境质量指数（EQI），量化了印度焦特布尔2001年至2021年环境质量的时空变化。方法学创新体现在三个方面：纳入夜间地表温度（LST）以捕捉与人口暴露相关的城市热量滞留；应用统一的32网格城市内部框架以检测微观空间异质性；以及利用几何平均整合卫星反演的PM2.5与植被—热力动态，以最小化补偿偏差。指标选取LST（土地退化）、NDVI衍生的植被覆盖度（生态系统调节）和PM2.5（大气质量），因其具有直接的生物物理相关性、长期卫星一致性和暴露敏感性，而非建成区指数或人口密度等替代指标。年度MODIS、Landsat和卫星模型融合PM2.5数据集（1 km）经标准化处理（0–100）；趋势采用Mann–Kendall和Sen's斜率估计器分析；相互关系通过多元线性回归评估。基于Landsat的土地利用分类总体精度超过85%。71%的网格显示显著的夜间增温（p \u003C 0.05），热点区域增温达+1.36 °C。核心城区记录到植被下降（平均ΔVC −0.02），而城市边缘网格则显示中度绿化（ΔVC最高达+0.07）。PM2.5均值范围为4–7 μg\u002Fm³，与植被丧失（R² = 0.66）和LST升高（R² = 0.49）密切相关。被归类为差或极差EQI的城市面积比例从15.1%（2001年）增至41.5%（2021年）。植被下降是PM2.5增加的主导预测因子（β = −0.44；p = 0.01）。这些发现表明环境质量显著恶化，并为干旱城市的气候韧性城市规划提供了一个可扩展框架。",null,"Discover Environment","2026-10-05T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"研究印度焦特布尔干旱城市环境质量时空变化，属城市环境遥感范畴，与三农、农业信息化、数字乡村无直接关联，不建议进入每日精选。",[19],{"name":10,"url":6},[21,22,23,24],"遥感监测","城市热岛","植被覆盖","PM2.5",[],{"status":27,"locked":14,"version":28,"method":29,"evidence":30},"needs_review","2026-10-22.2","rules",{"candidates":31,"agricultural_evidence":14,"reason":46},[32,40],{"code":33,"score":34,"supported":35,"title":36,"abstract":37,"tags":39},"H7",3,true,[],[38],"干旱",[],{"code":41,"score":42,"supported":14,"title":43,"abstract":44,"tags":45},"I3",2,[],[],[23,21],"农业相关性需要复核",[48,49],"焦特布尔 环境质量指数","印度 干旱城市 城市化","焦特布尔环境质量指数-4363",1,"10.1007\u002Fs44274-026-01067-7",{"doi":52,"openalex_id":54,"authors":55,"venue":10,"cited_by_count":15,"oa_url":65,"card":66,"direction":70,"ingested_from":72},"W7220453628",[56,59,62],{"name":57,"orcid":58},"Amjed Ali","https:\u002F\u002Forcid.org\u002F0000-0002-0866-2346",{"name":60,"orcid":61},"Kheraj Kheraj","https:\u002F\u002Forcid.org\u002F0000-0002-1107-5628",{"name":63,"orcid":64},"Arshad Ahmed","https:\u002F\u002Forcid.org\u002F0000-0002-5196-928X","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs44274-026-01067-7.pdf",{"tldr":67,"method":68,"finding":69,"direction":70,"opportunity":71},"用网格化环境质量指数评估印度焦特布尔2001-2021年城市化引发的环境质量时空变化。","基于MODIS\u002FLandsat与PM2.5数据构建32格网EQI，用Mann-K","71%网格夜间显著升温，环境质量差区域从15.1%升至41.5%，植被减少是PM2.5上升主因。","农业遥感与作物表型","可将该网格化EQI框架迁移至干旱区农业景观，耦合作物物候与夜间热暴露评估。","openalex","2026-10-07T23:30:28.927907Z",{"total":75,"page":51,"page_size":75,"items":76},6,[77,147,196,266,326,390],{"id":78,"title":79,"url":80,"summary":81,"summary_zh":82,"content":9,"source_name":83,"source_url":80,"published_at":84,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":85,"score_detail":86,"sources":92,"tags":94,"primary_topic":97,"secondary_topics":103,"classification":104,"search_phrases":123,"slug":126,"view_count":15,"doi":127,"paper":128,"created_at":146},5323,"Impact of Land Use and Land Cover Change on Vegetation Cover Loss in Rwanda: Evidence from Muhoza Sector, Musanze District (2015–2025)","https:\u002F\u002Fdoi.org\u002F10.38124\u002Fijisrt\u002F26oct696","This study analyzed the impact of land use and land cover (LULC) change on vegetation cover loss in Muhoza Sector, Musanze District, Rwanda, between 2015 and 2025. A quantitative geospatial research design was adopted using multi-temporal Landsat and Sentinel-2 satellite imagery, field-based GPS validation, Geographic Information Systems, and Normalized Difference Vegetation Index (NDVI) analysis. Six land-cover classes—forestland, grassland, cropland, urbanized land, wetland, and water bodies—were mapped using supervised classification and post-classification change detection.","本研究分析了2015年至2025年间卢旺达穆桑泽区穆霍扎地区土地利用与土地覆盖（LULC）变化对植被覆盖丧失的影响。研究采用定量地理空间研究设计，运用多时相Landsat和Sentinel-2卫星影像、基于实地GPS验证、地理信息系统以及归一化植被指数（NDVI）分析。通过监督分类和分类后变化检测，对林地、草地、耕地、城市化用地、湿地和水体六类土地覆盖类型进行了制图。","International Journal of Innovative Science and Research Technology (IJISRT)","2026-10-17T00:00:00Z",55,{"impact":87,"substance":88,"depth":89,"authority":75,"freshness":90,"relevant":51,"comment":91},8,18,14,9,"基于Landsat与Sentinel-2多时相影像的卢旺达局部土地利用变化与植被损失实证研究，方法规范但属区域案例，国际参考价值有限。",[93],{"name":83,"url":80},[21,95,23,96],"土地利用","卢旺达农业",{"code":98,"slug":99,"name":100,"path":101,"parent_code":102},"I4","gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis","I",[],{"status":105,"locked":14,"version":28,"method":29,"evidence":106},"classified",{"candidates":107,"agricultural_evidence":35},[108,114,118],{"code":98,"score":75,"supported":35,"title":109,"abstract":110,"tags":113},[],[111,112],"地理信息","geographic information",[],{"code":41,"score":42,"supported":14,"title":115,"abstract":116,"tags":117},[],[],[23,21],{"code":119,"score":51,"supported":14,"title":120,"abstract":121,"tags":122},"H6",[],[],[95],[124,125],"Muhoza Sector 土地利用","卢旺达 NDVI 植被覆盖","MuhozaSector土地利用-5323","10.38124\u002Fijisrt\u002F26oct696",{"doi":127,"openalex_id":129,"authors":130,"venue":83,"cited_by_count":15,"oa_url":140,"card":141,"direction":70,"ingested_from":72},"W7222817253",[131,133,135,137],{"name":132,"orcid":9},"Vincent Hategekimana",{"name":134,"orcid":9},"Richard Mind J. E.",{"name":136,"orcid":9},"Aboubakar Gasirabo",{"name":138,"orcid":139},"Didier Ntwali","https:\u002F\u002Forcid.org\u002F0000-0002-6914-6652","https:\u002F\u002Fwww.ijisrt.com\u002Fassets\u002Fupload\u002Ffiles\u002FIJISRT26OCT696.pdf",{"tldr":142,"method":143,"finding":144,"direction":70,"opportunity":145},"分析卢旺达Muhoza地区2015–2025年土地利用变化对植被覆盖丧失的影响。","多时相Landsat与Sentinel-2影像、GPS实地验证、GIS与NDVI","六类地物制图揭示建设用地扩张等LULC变化是植被覆盖减少的主要驱动。","可结合高分辨率时序遥感与驱动因子模型，量化小农户尺度城镇化对耕地与植被的胁迫。","2026-10-19T23:30:23.040351Z",{"id":148,"title":149,"url":150,"summary":151,"summary_zh":152,"content":9,"source_name":153,"source_url":150,"published_at":84,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":154,"sources":156,"tags":158,"primary_topic":161,"secondary_topics":165,"classification":166,"search_phrases":175,"slug":178,"view_count":15,"doi":179,"paper":180,"created_at":195},5244,"Diurnal and Seasonal Surface Urban Heat Island Dynamics and Thermal Field Variance in Faridabad District, India: A MODIS-Based Assessment (2001–2025)","https:\u002F\u002Fdoi.org\u002F10.9734\u002Fajgr\u002F2026\u002Fv9i4489","Aims: To examine the spatial and temporal dynamics of land surface temperature (LST), urban–rural biophysical indices (BUI and NDVI), daily Surface Urban Heat Island Intensity (SUHII), and the Urban Thermal Field Variance Index (UTFVI) on a diurnal and seasonal basis in Faridabad District of northern India. Study Design: Multi-temporal geospatial remote sensing and statistical modelling study. Place and Duration of Study: Faridabad District (Urban Core and Rural Hinterland), Haryana, India; Summer and Winter Seasons Analysed through Six Benchmark Periods (2001, 2005, 2010, 2015, 2020, and 2025). Methodology: MODIS optical (MOD09A1) and thermal infrared (MOD11A2) composite data were analysed using Google Earth Engine at the daytime (10:30 am) and night-time (10:30 pm) overpasses. LST values were grouped into UTFVI (Urban Thermal Field Variance Index) classes. Multivariable OLS regressions and empirical biophysical sensitivities such as Vegetation Cooling Efficiency (VCE) and Impervious Heating Effect (IHE) were evaluated in Python using a stratified random grid (N = 2,500 sampling locations; 235 n 872 pixels). Results: Over the 25-year study period, the built-up gradient for winter (BUI) increased from +0.096 to +0.233 in association with agricultural changes and urban development in the region, while the vegetative deficit for winter (NDVI) increased from -0.061 to -0.136. In terms of temperature, Faridabad showed a SUHI regime during summer (ranging from to ), where the urban core nocturnal LST increased from in 2001 to in 2025, together with regional warming and compression of the urban DTR. The pre-monsoon diurnal regimes showed the SUCI phenomenon (as low as in 2005) owing to the effects of sensible heating in the desiccated fallow rural lands after harvest season. Urban Core thermal field variance showed that moderate thermal stress (UTFVI Class 3) expanded from to , while severe stress tiers (Classes 4–6) concentrated within industrial estates. Daytime vegetation provided consistent cooling (VCE up to per ), whereas nocturnal VCE exhibited temporal variability and diminished transpirational cooling following stomatal closure. Conclusion: Urban greening offers a strong mechanism for daytime heat mitigation but is insufficient to address independently nocturnal heat trapping resulting from the high thermal inertia of construction materials. The sustainable heat-mitigation strategy in industrial nodes entails a combination of urban greening and reflective cool coatings on industrial sheds and impervious surfaces.","目的：考察印度北部法里达巴德县地表温度（LST）、城乡生物物理指数（BUI和NDVI）、日间地表城市热岛强度（SUHII）以及城市热场方差指数（UTFVI）在昼夜和季节尺度上的时空动态。研究设计：多时相地理空间遥感与统计建模研究。研究地点与时长：印度哈里亚纳邦法里达巴德县（城市核心区与乡村腹地）；通过六个基准时段（2001、2005、2010、2015、2020和2025年）分析夏季与冬季。方法：利用Google Earth Engine分析MODIS光学（MOD09A1）和热红外（MOD11A2）合成数据，分别对应白天（上午10：30）和夜间（晚上10：30）过境时刻。LST值按UTFVI（城市热场方差指数）类别分组。在Python中采用分层随机网格（N=2，500个采样点；235 n 872像素）评估多变量OLS回归及经验生物物理敏感性指标，如植被冷却效率（VCE）和不透水面增温效应（IHE）。结果：在25年研究期内，冬季建成区梯度（BUI）从+0.096增至+0.233，与该地区农业变化和城市发展相关；冬季植被亏缺（NDVI）从-0.061增至-0.136。温度方面，法里达巴德在夏季呈现SUHI态势（范围为至），城市核心区夜间LST从2001年的增至2025年的，同时伴随区域增暖和城市日较差（DTR）压缩。季风前昼夜态势因收获季后干燥休耕乡村土地的感热加热效应而呈现SUCI现象（2005年低至）。城市核心区热场方差显示，中度热胁迫（UTFVI第3类）从扩大至，而严重胁迫层级（第4—6类）集中于工业园区。白天植被提供持续冷却（VCE最高达每），而夜间VCE呈现时间变异性，且气孔关闭后蒸腾冷却减弱。结论：城市绿化是白天热缓解的有力机制，但不足以独立应对建筑材料高热惯性导致的夜间热量滞留。工业节点的可持续热缓解策略需要将城市绿化与反射性","Asian Journal of Geographical Research",{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":155},"印度城市热岛遥感研究，属城市气候与环境领域，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。",[157],{"name":153,"url":150},[21,159,22,160],"地表温度","MODIS",{"code":41,"slug":162,"name":163,"path":164,"parent_code":102},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",[],{"status":105,"locked":14,"version":28,"method":29,"evidence":167},{"candidates":168,"agricultural_evidence":35},[169],{"code":41,"score":87,"supported":35,"title":170,"abstract":171,"tags":174},[],[172,173],"遥感","remote sensing",[159,21],[176,177],"Faridabad 城市热岛","MODIS 地表温度 遥感","Faridabad城市热岛-5244","10.9734\u002Fajgr\u002F2026\u002Fv9i4489",{"doi":179,"openalex_id":181,"authors":182,"venue":153,"cited_by_count":15,"oa_url":150,"card":190,"direction":70,"ingested_from":72},"W7222794736",[183,186,188],{"name":184,"orcid":185},"Abhishek Malik","https:\u002F\u002Forcid.org\u002F0009-0009-4878-0871",{"name":187,"orcid":9},"Shubham Mor",{"name":189,"orcid":9},"Gaurav Kalotra",{"tldr":191,"method":192,"finding":193,"direction":70,"opportunity":194},"基于MODIS分析印度Faridabad 2001–2025年昼夜与季节地表城市热岛及热场差异演变。","MODIS光学与热红外数据、Google Earth Engine、OLS回归及","白天绿化降温显著，但夜间建筑热惯性导致的热滞留难以单独缓解。","可结合城乡梯度与作物休耕期感热效应，研究农业用地变化对昼夜热岛的非对称贡献。","2026-10-18T23:30:31.211684Z",{"id":197,"title":198,"url":199,"summary":200,"summary_zh":201,"content":9,"source_name":202,"source_url":199,"published_at":203,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":204,"sources":206,"tags":208,"primary_topic":9,"secondary_topics":210,"classification":211,"search_phrases":222,"slug":225,"view_count":15,"doi":226,"paper":227,"created_at":265},5011,"Thirty-five years of urban expansion and thermal transformation: Integrating LULC change climate variability and population growth","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-75473-5","Abstract Rapid urban expansion can alter surface thermal conditions, yet these changes are often evaluated without accounting for climatic variability, demographic pressures, and uncertainty. This study investigated long-term land-use\u002Fland-cover (LULC) and thermal dynamics in Jhang District, Pakistan, for 1990, 2000, 2010, 2020, and 2025 using Landsat-based LULC classification, land surface temperature (LST), surface urban heat island (SUHI), Urban Thermal Field Variance Index (UTFVI), spectral indices, Getis–Ord Gi* hotspot analysis, Pearson correlation, and multiple linear regression (MLR). Landsat-derived LST was independently compared with MODIS, while ERA5-Land, CHIRPS, and GHS-POP were used to characterize climatic and demographic context. Urban land expanded from 62.67 km² (0.98%) to 341.36 km² (5.37%), with 219.53 km² of Vegetated Land converted to Urban land. Maximum LST increased from 49.09 °C to 55.01 °C, while 99% confidence hot spots expanded from 856.42 to 1,041.31 km². LST was negatively correlated with NDVI and NDMI and positively correlated with NDBI and NDBaI. MLR models explained 54.2–83.1% of spatial LST variability. MODIS comparison showed strong spatial correspondence (R 2 = 0.752–0.843) despite systematic absolute-temperature differences. Air temperature increased significantly by 0.199 °C decade⁻¹, while population rose from 1.58 to 3.44 million. The findings demonstrate that Jhang’s thermal evolution reflects interacting land-cover, climatic, and demographic pressures rather than urban growth alone.","摘要 快速的城市扩张会改变地表热环境状况，但相关评估往往未充分考虑气候变率、人口压力及不确定性。本研究以巴基斯坦章县（Jhang District）为对象，基于Landsat土地利用\u002F土地覆盖（LULC）分类、地表温度（LST）、地表城市热岛（SUHI）、城市热场变异指数（UTFVI）、光谱指数、Getis–Ord Gi*热点分析、Pearson相关分析及多元线性回归（MLR），对1990、2000、2010、2020和2025年的长期LULC与热动态进行了研究。将Landsat反演LST与MODIS数据进行了独立对比，同时利用ERA5-Land、CHIRPS和GHS-POP刻画气候与人口背景。城市用地由62.67 km²（0.98%）扩张至341.36 km²（5.37%），其中219.53 km²的植被用地转为城市用地。最高LST由49.09 °C升至55.01 °C，而99%置信度热点面积由856.42 km²扩大至1,041.31 km²。LST与NDVI和NDMI呈负相关，与NDBI和NDBaI呈正相关。MLR模型可解释54.2–83.1%的LST空间变异。与MODIS对比显示，尽管存在系统性绝对温度差异，二者仍具有较强空间一致性（R² = 0.752–0.843）。气温以0.199 °C decade⁻¹的速率显著上升，人口由158万增至344万。研究结果表明，章县的热环境演变反映的是土地覆盖、气候与人口压力的交互作用，而非仅由城市增长所致。","Scientific Reports","2026-10-13T00:00:00Z",{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":205},"研究巴基斯坦城市扩张与热环境变化，属城市遥感与气候变化领域，与三农、农业信息化、数字乡村无直接关联，不建议进入每日精选。",[207],{"name":202,"url":199},[21,159,22,209],"土地利用变化",[],{"status":27,"locked":14,"version":28,"method":29,"evidence":212},{"candidates":213,"agricultural_evidence":14,"reason":46},[214,218],{"code":41,"score":42,"supported":14,"title":215,"abstract":216,"tags":217},[],[],[159,21],{"code":119,"score":51,"supported":14,"title":219,"abstract":220,"tags":221},[],[],[209],[223,224],"Jhang District 城市扩张 地表温度","Landsat LST 城市热岛","JhangDistrict城市扩张地表温度-5011","10.1038\u002Fs41598-026-75473-5",{"doi":226,"openalex_id":228,"authors":229,"venue":202,"cited_by_count":15,"oa_url":259,"card":260,"direction":70,"ingested_from":72},"W7221689409",[230,232,234,237,240,242,245,248,251,254,257],{"name":231,"orcid":9},"Rui Li",{"name":233,"orcid":9},"Yifei Yuan",{"name":235,"orcid":236},"Zainab Tahir","https:\u002F\u002Forcid.org\u002F0000-0002-1459-3527",{"name":238,"orcid":239},"Muhammad Haseeb","https:\u002F\u002Forcid.org\u002F0000-0002-0697-6894",{"name":241,"orcid":9},"Syed Amer Mahmoood",{"name":243,"orcid":244},"Aqil Tariq","https:\u002F\u002Forcid.org\u002F0000-0003-1196-1248",{"name":246,"orcid":247},"Khadeijah Yahya Faqeih","https:\u002F\u002Forcid.org\u002F0009-0002-4475-3453",{"name":249,"orcid":250},"Somayah Moshrif Alamri","https:\u002F\u002Forcid.org\u002F0009-0003-4601-9620",{"name":252,"orcid":253},"Eman Rafi Alamery","https:\u002F\u002Forcid.org\u002F0009-0006-7052-3239",{"name":255,"orcid":256},"Bilal Kemal","https:\u002F\u002Forcid.org\u002F0009-0002-2790-3229",{"name":258,"orcid":9},"Kucher E. Dmitry","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-75473-5_reference.pdf",{"tldr":261,"method":262,"finding":263,"direction":70,"opportunity":264},"基于35年Landsat数据，分析巴基斯坦Jhang地区城市扩张与地表热环境变化及其气候人口驱动。","Landsat LULC分类、LST、SUHI、UTFVI、热点分析、相关与多元","城市面积从62.67增至341.36 km²，最高LST升5.92°C，热环境受土地覆盖、气候和人口","可引入多源遥感与机器学习，量化气候变异和人口增长对热环境的非线性贡献，并拓展至农业区热风险研究。","2026-10-15T23:30:32.222123Z",{"id":267,"title":268,"url":269,"summary":270,"summary_zh":271,"content":9,"source_name":272,"source_url":269,"published_at":273,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":274,"sources":276,"tags":278,"primary_topic":9,"secondary_topics":280,"classification":281,"search_phrases":292,"slug":295,"view_count":51,"doi":296,"paper":297,"created_at":325},4830,"Spatiotemporal dynamics and drivers of surface urban heat island intensity in Dhaka, Bangladesh using geospatial remote sensing","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs44288-026-00767-0","Abstract Rapid urbanization has a profound impact on urban thermal environments, particularly in densely populated megacities. This study aims to assess the spatiotemporal dynamics of land surface temperature (LST), surface urban heat island (SUHI) intensity, land use\u002Fland cover (LULC) changes, and their primary driving factors in Dhaka City Corporation (DCC), Bangladesh, during the pre-monsoon season from 1990 to 2024. Using multi-temporal Landsat imagery and ancillary spatial datasets on Google Earth Engine (GEE), we observed a 194.54% increase in urban land, alongside substantial declines in vegetation (45.99%) and water bodies (75.57%). These classifications were validated with strong accuracy metrics (Overall Accuracy > 92%, Kappa > 89%, K-fold > 95%). Concurrently, LST rose dramatically, with areas exceeding 40 °C expanding to 70.06% by 2024, and traditional cool zones nearly disappearing. SUHI analysis showed a growing heat island effect, with recent nocturnal SUHI values surpassing daytime levels, indicating increased nighttime heat retention. Correlation and multiple linear regression analyses (R² = 0.801) identified NDBI, population density, and nighttime light intensity as the dominant positive drivers of LST, while vegetation and water bodies offered significant cooling potential. These findings highlight the urgent need to integrate ecological benchmarks (such as the 3-30-300 rule) and Sponge City designderscorn paradigms into Dhaka’s climate-resilient urban planning framework. Ultimately, this cloud-based multi-factor modeling framework provides a highly replicable, open-access blueprint to help rapidly growing megacities worldwide diagnose thermal vulnerability and mitigate escalating heat stress.","摘要 快速城市化对城市热环境产生了深远影响，尤其是在人口密集的超大城市中。本研究旨在评估1990年至2024年季风前季节孟加拉国达卡市辖区（DCC）地表温度（LST）、地表城市热岛（SUHI）强度、土地利用\u002F土地覆盖（LULC）变化及其主要驱动因素的时空动态。利用Google Earth Engine（GEE）平台上的多时相Landsat影像和辅助空间数据集，我们观察到城市用地增加了194.54%，同时植被（45.99%）和水体（75.57%）大幅减少。这些分类结果通过了严格的精度验证（总体精度>92%，Kappa>89%，K折>95%）。与此同时，LST急剧上升，超过40 °C的区域到2024年扩大至70.06%，传统凉爽区几乎消失。SUHI分析显示热岛效应不断增强，近期夜间SUHI值超过白天水平，表明夜间热量滞留加剧。相关分析和多元线性回归分析（R² = 0.801）确定NDBI、人口密度和夜间灯光强度是LST的主要正向驱动因素，而植被和水体则提供了显著的降温潜力。这些发现凸显了将生态基准（如3-30-300规则）和海绵城市设计范式纳入达卡气候韧性城市规划框架的迫切需求。最终，这一基于云的多因素建模框架提供了一个高度可复制、开放获取的蓝图，有助于全球快速发展的超大城市诊断热脆弱性并缓解不断升级的热胁迫。","Discover Geoscience","2026-10-11T00:00:00Z",{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":275},"研究孟加拉国达卡城市热岛与地表温度遥感监测，属城市气候与环境领域，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。",[277],{"name":272,"url":269},[21,22,209,279],"孟加拉国",[],{"status":27,"locked":14,"version":28,"method":29,"evidence":282},{"candidates":283,"agricultural_evidence":14,"reason":46},[284,288],{"code":41,"score":90,"supported":35,"title":285,"abstract":286,"tags":287},[173],[],[21],{"code":119,"score":51,"supported":14,"title":289,"abstract":290,"tags":291},[],[],[209],[293,294],"达卡 城市热岛 遥感","Landsat 地表温度 城市扩张","达卡城市热岛遥感-4830","10.1007\u002Fs44288-026-00767-0",{"doi":296,"openalex_id":298,"authors":299,"venue":272,"cited_by_count":15,"oa_url":319,"card":320,"direction":70,"ingested_from":72},"W7221469610",[300,303,306,309,312,314,316],{"name":301,"orcid":302},"Shacin Chandra Saha","https:\u002F\u002Forcid.org\u002F0009-0004-4007-145X",{"name":304,"orcid":305},"Ummya Ayman Satil","https:\u002F\u002Forcid.org\u002F0009-0002-1639-7156",{"name":307,"orcid":308},"Nitun Paul","https:\u002F\u002Forcid.org\u002F0009-0008-3515-8496",{"name":310,"orcid":311},"Dipok Chandra Nath","https:\u002F\u002Forcid.org\u002F0009-0006-8250-7244",{"name":313,"orcid":9},"Farhana Rahman Nepa",{"name":315,"orcid":9},"Mir Md. Tasnim Alam",{"name":317,"orcid":318},"Sukhen Goswami","https:\u002F\u002Forcid.org\u002F0009-0007-8342-6822","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs44288-026-00767-0.pdf",{"tldr":321,"method":322,"finding":323,"direction":70,"opportunity":324},"基于Landsat与GEE分析达卡1990-2024年地表温度与热岛时空变化及驱动因素。","多时相Landsat影像、GEE云平台、LULC分类、相关与多元线性回归。","城市扩张194.54%，40°C以上区域达70.06%，NDBI、人口密度与夜间灯光是主要增温驱动。","可将该云平台多因子框架迁移至城郊农业区，探究热岛对农田蒸散与作物产量的耦合影响。","2026-10-13T23:30:25.362277Z",{"id":327,"title":328,"url":329,"summary":330,"summary_zh":331,"content":9,"source_name":332,"source_url":329,"published_at":333,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":334,"score_detail":335,"sources":339,"tags":341,"primary_topic":345,"secondary_topics":350,"classification":351,"search_phrases":369,"slug":372,"view_count":51,"doi":373,"paper":374,"created_at":389},4756,"ديناميكيات وحساسية المناخ والغطاء النباتي في العراق باستخدام بيانات رصد الأرض طويلة الأمد","https:\u002F\u002Fdoi.org\u002F10.66026\u002Fy7543b35","تؤدي تغيرات المناخ إلى تكثيف الإجهاد الحراري والمائي عبر المناطق الجافة، مما يزيد من الحاجة إلى رصد متسق مكانياً وطويل الأمد لاستجابات الغطاء النباتي. العراق معرض بشكل خاص لهذه التغيرات لأن التدرجات المناخية القوية، والجفاف المتكرر، ومحدودية توافر المياه تتزامن مع مساحات شاسعة من الأراضي المطرية والمروية. قامت هذه الدراسة بتقييم ديناميكيات المناخ والغطاء النباتي في العراق من عام 2001 إلى 2025 باستخدام مؤشر الاختلاف الغطائي النباتي الطبيعي (MODIS NDVI)، وبيانات هطول الأمطار (CHIRPS)، وبيانات درجة حرارة الهواء على ارتفاع مترين (ERA5-Land). تم تحليل متوسط هطول الأمطار للفترة من أكتوبر إلى مايو، واتجاهات مؤشر (NDVI) للفترة من يناير إلى مايو، وارتباطات المناخ مع مؤشر (NDVI) بعد إزالة الاتجاه (detrended) على شبكة مشتركة بدقة 0.1 درجة.تم بعد ذلك تصنيف البكسلات النباتية على أنها: حساسة لهطول الأمطار، أو حساسة للحرارة، أو حساسة لكليهما، أو لا تظهر علاقة متوقعة قوية، أو تظهر اتجاهاً قوياً آخر. أظهر هطول الأمطار في الموسم الرطب تدرجاً واضحاً من إجماليات عالية جداً في شمال وشمال شرق العراق إلى إجماليات منخفضة ومنخفضة جداً عبر الغرب والجنوب الغربي والجنوب. كان الاخضرار ذو الدلالة الإحصائية أكثر انتشاراً بشكل ملحوظ من الاسمرار، حيث تركزت أقوى اتجاهات مؤشر (NDVI) الإيجابية في المناطق الزراعية في الشمال والشمال الشرقي والوسط. بعد إزالة الاتجاهات الخطية، كانت ارتباطات هطول الأمطار مع مؤشر (NDVI) إيجابية في الغالب وواسعة النطاق مكانياً، في حين كانت ارتباطات درجة الحرارة مع مؤشر (NDVI) ذات دلالة سلبية بشكل رئيسي ومركّزة في شمال وشرق وجنوب شرق العراق. هيمنت حساسية هطول الأمطار على التصنيف المتكامل، بينما شكلت الحساسية المزدوجة لهطول الأمطار والحرارة مجموعات متميزة في شمال-وسط وجنوب شرق البلاد. توفر هذه النتائج خط أساس مكانياً وطنياً لتحديد مناطق الغطاء النباتي المرتبطة بتقلبات المياه والحرارة بين السنوات. وتمثل فئات الحساسية ارتباطات إحصائية وليست عزوًا سببيًا.","气候变化正在加剧干旱地区的高温与水分胁迫，从而增加了对植被响应进行空间一致、长期监测的需求。伊拉克尤其容易受到这些变化的影响，因为强烈的气候梯度、频繁的干旱以及有限的水资源可用性，与大片雨养和灌溉土地并存。本研究利用MODIS归一化植被指数（NDVI）、降雨数据（CHIRPS）以及2米高度气温数据（ERA5-Land），评估了2001年至2025年伊拉克的气候与植被动态。研究在0.1度分辨率的统一网格上，分析了10月至5月的平均降雨量、1月至5月的NDVI趋势，以及去趋势（detrended）后气候与NDVI的相关性。随后将植被像元分类为：对降雨敏感、对温度敏感、对两者均敏感、未表现出强预期关系，或表现出其他强趋势。湿季降雨呈现出从伊拉克北部和东北部极高总量向西、西南和南部低及极低总量的明显梯度。具有统计显著性的绿化现象明显比褐化更为广泛，其中最强的NDVI正趋势集中在北部、东北部和中部的农业区。去除线性趋势后，降雨与NDVI的相关性大多为正且在空间上广泛分布，而温度与NDVI的相关性主要为显著负相关，集中在伊拉克北部、东部和东南部。降雨敏感性在综合分类中占主导地位，而降雨与温度的双重敏感性在该国中北部和东南部形成了独特的集群。这些结果为识别与年际水热波动相关的植被区域提供了全国尺度的空间基线。敏感性类别代表统计关联，而非因果归因。","Journal Of Babylon Center for Humanities Studies","2026-10-09T00:00:00Z",70,{"impact":336,"substance":337,"depth":88,"authority":336,"freshness":75,"relevant":51,"comment":338},12,22,"基于MODIS、CHIRPS与ERA5-Land长时序数据评估伊拉克气候与植被敏感性，方法规范、结论有区域参考价值，但属境外基础研究，公共影响有限。",[340],{"name":332,"url":329},[342,343,21,344,23],"气候变化","NDVI","干旱区农业",{"code":33,"slug":346,"name":347,"path":348,"parent_code":349},"climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",[],{"status":105,"locked":14,"version":28,"method":29,"evidence":352},{"candidates":353,"agricultural_evidence":35},[354,358,365],{"code":33,"score":87,"supported":35,"title":355,"abstract":356,"tags":357},[],[342,38],[344,342],{"code":359,"score":75,"supported":35,"title":360,"abstract":361,"tags":364},"H3",[],[362,363],"灌溉","水资源",[],{"code":41,"score":34,"supported":14,"title":366,"abstract":367,"tags":368},[],[],[343,23,21],[370,371],"伊拉克 NDVI 植被动态","MODIS CHIRPS ERA5-Land 遥感","伊拉克NDVI植被动态-4756","10.66026\u002Fy7543b35",{"doi":373,"openalex_id":375,"authors":376,"venue":332,"cited_by_count":15,"oa_url":383,"card":384,"direction":70,"ingested_from":72},"W7221306668",[377,379,381],{"name":378,"orcid":9},"خولة‌ حمه‌رشید عمر",{"name":380,"orcid":9},"سایە دلاوەر محمد",{"name":382,"orcid":9},"Daban Kadhim Omar","https:\u002F\u002Fbcchj.uobabylon.edu.iq\u002Findex.php\u002Fbcchj\u002Farticle\u002Fdownload\u002F4819\u002F4866",{"tldr":385,"method":386,"finding":387,"direction":70,"opportunity":388},"基于2001–2025年MODIS NDVI、CHIRPS降水与ERA5-Land温度数据，评估伊拉","MODIS NDVI、CHIRPS降水、ERA5-Land温度，0.1°网格去趋","植被绿化趋势多于褐化，降水敏感性主导，温度负相关集中于北部、东部和东南部。","可结合灌溉与土地覆盖数据，区分雨养与灌溉区植被对水热胁迫的差异化响应机制。","2026-10-12T23:30:27.995466Z",{"id":391,"title":392,"url":393,"summary":394,"summary_zh":395,"content":9,"source_name":396,"source_url":393,"published_at":397,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":398,"score_detail":399,"sources":404,"tags":406,"primary_topic":408,"secondary_topics":409,"classification":410,"search_phrases":435,"slug":438,"view_count":51,"doi":439,"paper":440,"created_at":459},4678,"Remote Sensing-based Integration of Land Use\u002FLand Cover, Vegetation Cover, and Land Surface Temperature with Hydrological Implications in Tikrit City, Iraq","https:\u002F\u002Fdoi.org\u002F10.14445\u002F23488352\u002Fijce-v13i9p115","This study examines the integrated relationship among Land Use\u002FLand Cover (LULC), Normalized Difference Vegetation Index (NDVI), and Land Surface Temperature (LST), with hydrological implications, in Tikrit City, Iraq, using Landsat data acquired in 2017 and 2025. LULC, NDVI, and LST layers were prepared using the same spatial boundary, and 4124 paired sample points were extracted from identical locations to support temporal and statistical comparison. The LULC results showed a clear reduction in Soil from 70.52% in 2017 to 49.88% in 2025, whereas Vegetation increased from 25.49% to 44.12%. Built Area also increased from 3.08% to 4.90%, indicating moderate urban expansion. The dominant land-cover transition was from Soil to Vegetation, covering 66,014.66 ha. Mean NDVI increased significantly from 0.061 to 0.075, while mean LST remained statistically stable, changing only from 41.992°C to 42.031°C. However, non-parametric tests indicated that the spatial distribution of LST changed significantly. The NDVI–LST relationship was positive in both years, with Pearson correlation increasing from 0.670 in 2017 to 0.709 in 2025. Regression analysis showed that NDVI explained 44.9% and 50.2% of LST variation in 2017 and 2025, respectively. Class-based analysis indicated that the dominant heat-contributing class shifted from Soil in 2017 to Built Area in 2025. These findings show that surface thermal behavior in Tikrit City cannot be explained by vegetation cover alone, but depends on land-cover composition, mixed pixels, agricultural activity, soil exposure, surface moisture, and seasonal conditions. From a hydrological perspective, the reduction of exposed soil, expansion of vegetation, and growth of built-up areas have implications for infiltration opportunity, runoff potential, evapotranspiration tendency, and surface moisture retention.","本研究利用2017年和2025年获取的Landsat数据，考察了伊拉克提克里特市土地利用\u002F土地覆盖（LULC）、归一化植被指数（NDVI）和地表温度（LST）之间的综合关系及其水文意义。LULC、NDVI和LST图层采用相同的空间边界制备，并从相同位置提取了4124对样本点，以支持时间与统计比较。LULC结果显示，土壤占比从2017年的70.52%明显下降至2025年的49.88%，而植被占比从25.49%上升至44.12%。建成区面积也从3.08%增至4.90%，表明存在中等程度的城市扩张。主导性土地覆盖转变为由土壤转为植被，覆盖面积达66,014.66公顷。平均NDVI从0.061显著增至0.075，而平均LST在统计上保持稳定，仅从41.992°C变为42.031°C。然而，非参数检验表明LST的空间分布发生了显著变化。NDVI与LST的关系在两年中均为正相关，Pearson相关系数从2017年的0.670增至2025年的0.709。回归分析表明，NDVI在2017年和2025年分别解释了LST变异的44.9%和50.2%。基于类别的分析表明，主要的热贡献类别从2017年的土壤转变为2025年的建成区。这些发现表明，提克里特市的地表热行为不能仅由植被覆盖来解释，而是取决于土地覆盖组成、混合像元、农业活动、土壤裸露、地表湿度和季节条件。从水文角度看，裸露土壤减少、植被扩张和建成区增长对下渗机会、径流潜力、蒸散发趋势和地表水分保持具有影响。","International Journal of Civil Engineering","2026-10-10T00:00:00Z",68,{"impact":87,"substance":400,"depth":401,"authority":402,"freshness":13,"relevant":51,"comment":403},20,17,13,"基于Landsat双期影像量化伊拉克提克里特市土地利用、植被与地表温度耦合关系及其水文效应，数据扎实、结论明确，对农业遥感与土地变化研究有参考价值，但地域性强、国内关联度有限。",[405],{"name":396,"url":393},[21,95,159,23,407],"水文响应",{"code":41,"slug":162,"name":163,"path":164,"parent_code":102},[],{"status":105,"locked":14,"version":28,"method":29,"evidence":411},{"candidates":412,"agricultural_evidence":35},[413,418,425,431],{"code":41,"score":414,"supported":35,"title":415,"abstract":416,"tags":417},11,[173],[],[159,23,21],{"code":419,"score":75,"supported":35,"title":420,"abstract":421,"tags":424},"H2",[],[422,423],"土壤","soil",[],{"code":359,"score":75,"supported":35,"title":426,"abstract":427,"tags":430},[],[428,429],"蒸散发","evapotranspiration",[],{"code":119,"score":51,"supported":14,"title":432,"abstract":433,"tags":434},[],[],[95],[436,437],"Landsat 土地利用 植被覆盖 地表温度","土地利用 地表温度 植被覆盖 水文响应","Landsat土地利用植被覆盖地表温度-4678","10.14445\u002F23488352\u002Fijce-v13i9p115",{"doi":439,"openalex_id":441,"authors":442,"venue":396,"cited_by_count":15,"oa_url":453,"card":454,"direction":70,"ingested_from":72},"W7221352158",[443,445,447,450],{"name":444,"orcid":9},"Omar Taher Nafe'e",{"name":446,"orcid":9},"Dalia Shaker Mahdi",{"name":448,"orcid":449},"Lubna B. Mahmood","https:\u002F\u002Forcid.org\u002F0009-0000-4428-4891",{"name":451,"orcid":452},"Asmaa Abdul Jabbar Jamel","https:\u002F\u002Forcid.org\u002F0000-0001-7602-3426","https:\u002F\u002Fwww.internationaljournalssrg.org\u002F..\u002FIJCE\u002F2026\u002FVolume13-Issue9\u002FIJCE-V13I9P115.pdf",{"tldr":455,"method":456,"finding":457,"direction":70,"opportunity":458},"基于Landsat 2017与2025年数据，分析伊拉克提克里特市LULC、NDVI与LST的耦合关","Landsat影像、4124个配对样点、NDVI与LST反演、相关与回归分析。","植被增加、裸土减少，LST均值稳定但空间分布显著变化，NDVI-LST正相关增强，主导热贡献由裸土转","可结合土壤湿度与蒸散数据，探究NDVI-LST正相关机制及混合像元对城市农业水文过程的影响。","2026-10-11T23:30:30.165387Z"]