[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2047":3,"related-2047":86},{"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":24,"classification":25,"search_phrases":53,"slug":56,"view_count":48,"doi":57,"paper":58,"created_at":85},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",[],{"status":26,"locked":14,"version":27,"method":28,"evidence":29},"needs_review","2026-10-22.2","rules",{"candidates":30,"agricultural_evidence":14,"reason":52},[31,39,46],{"code":32,"score":33,"supported":34,"title":35,"abstract":37,"tags":38},"I3",9,true,[36],"remote sensing",[],[21],{"code":40,"score":41,"supported":34,"title":42,"abstract":44,"tags":45},"I2",8,[43],"machine learning",[],[],{"code":47,"score":48,"supported":14,"title":49,"abstract":50,"tags":51},"H6",1,[],[],[22],"农业相关性需要复核",[54,55],"土地利用 城市热岛 遥感监测","土地利用 城市热岛","土地利用城市热岛遥感监测-2047","10.1007\u002Fs44327-026-00360-7",{"doi":57,"openalex_id":59,"authors":60,"venue":10,"cited_by_count":15,"oa_url":76,"card":77,"direction":83,"ingested_from":84},"W7211960821",[61,63,65,67,69,72,74],{"name":62,"orcid":9},"Anup Raj Timalsina",{"name":64,"orcid":9},"Saditya Baral",{"name":66,"orcid":9},"Krishna Gyawali",{"name":68,"orcid":9},"Ashish Ayer",{"name":70,"orcid":71},"Shisir Kharel","https:\u002F\u002Forcid.org\u002F0009-0006-1594-7143",{"name":73,"orcid":9},"Subash Ghimire",{"name":75,"orcid":9},"Bigyan Banjara","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs44327-026-00360-7.pdf",{"tldr":78,"method":79,"finding":80,"direction":81,"opportunity":82},"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",{"total":87,"page":48,"page_size":87,"items":88},6,[89,161,241,310,354,428],{"id":90,"title":91,"url":92,"summary":93,"summary_zh":94,"content":9,"source_name":10,"source_url":92,"published_at":95,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":96,"sources":98,"tags":100,"primary_topic":102,"secondary_topics":108,"classification":109,"search_phrases":143,"slug":146,"view_count":147,"doi":148,"paper":149,"created_at":160},2060,"Land use transformation and urban heat dynamics in India’s world heritage cities Jaipur and Ahmedabad","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs44327-026-00361-6","The rapid growth of cities as a result of urbanization has caused immense changes to land-use\u002Fland-cover (LU\u002FLC) patterns and urban thermal environments within Indian cities, especially in semi-arid regions. Urban growth dramatically affects World Heritage Cities (WHC) designated by UNESCO and has the potential to negatively affect environmental sustainability and the conservation of cultural heritage. Thus, this study presents a structured comparative narrative review of LU\u002FLC transformation and urban heat dynamics in Jaipur and Ahmedabad, India. Literature published between 2000 and 2025 was identified from major scientific literature databases and used in a comparative synthesis of urban growth patterns, research methodology, and environmental consequences. The results of the review indicate considerable growth of built-up areas of each city at the expense of agricultural land, vegetation, and open spaces. Ahmedabad had greater than 130% growth of built-up area from 1990 to 2019 while Jaipur experienced considerable directional urban expansion and increased land surface temperatures (LST). Most of the studies utilized remote sensing and GIS methods using Landsat and IRS data sets; the study of Ahmedabad represented a greater methodological advancement through use of CA-Markov and ANN urban growth model methodologies than that of Jaipur which was mostly retrospective, with few predictive modeling studies. Results of the study reveal that there is a strong connection between land use\u002Fland cover changes and the heat dynamics of an urban area, resulting in increased thermal stress, more vulnerable(e.g.) to climate change; and There will be growing pressures on these cities to sustain their cultural and architectural heritage (including all things associated with the built environment). Consequently, climate resilient and heritage sensitive urban planning strategies must be implemented in semi-arid World Heritage Cities of India.","城市化推动的城市快速扩张，使印度城市尤其是半干旱地区的土地利用\u002F土地覆盖（LU\u002FLC）格局和城市热环境发生了巨大变化。城市增长对联合国教科文组织指定的世界遗产城市（WHC）产生了显著影响，并可能对环境的可持续性和文化遗产保护造成负面影响。因此，本研究对印度斋浦尔和艾哈迈达巴德两座城市的LU\u002FLC转变与城市热动态进行了结构化的比较性叙述综述。研究从主要科学文献数据库中检索了2000年至2025年间发表的文献，并用于城市增长模式、研究方法和环境影响的比较综合。综述结果表明，两座城市的建成区均大幅扩张，侵占了农业用地、植被和开敞空间。1990年至2019年间，艾哈迈达巴德的建成区增长率超过130%，而斋浦尔则经历了显著的方向性城市扩张和地表温度（LST）升高。大多数研究采用遥感和GIS方法，使用Landsat和IRS数据集；艾哈迈达巴德的研究在方法论上更为先进，采用了CA-Markov和ANN城市增长模型方法，而斋浦尔的研究多为回顾性分析，预测建模研究较少。研究结果揭示，土地利用\u002F土地覆盖变化与城市区域的热动态之间存在密切关联，导致热胁迫加剧，使城市在气候变化面前更加脆弱；同时，这些城市在维持其文化和建筑遗产（包括与建成环境相关的所有事物）方面将面临日益增大的压力。因此，必须在印度半干旱世界遗产城市中实施气候韧性和遗产敏感的城市规划策略。","2026-09-09T00:00:00Z",{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":97},"研究印度世界遗产城市土地利用与城市热动态，属城市环境与遗产保护领域，与三农、农业信息化、智慧农业无关，不建议进入每日精选。",[99],{"name":10,"url":92},[21,22,23,101],"世界遗产城市",{"code":103,"slug":104,"name":105,"path":106,"parent_code":107},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",[],{"status":110,"locked":14,"version":27,"method":28,"evidence":111},"classified",{"candidates":112,"agricultural_evidence":34},[113,120,126,133,139],{"code":103,"score":33,"supported":34,"title":114,"abstract":115,"tags":119},[],[116,117,118],"气候变化","干旱","climate change",[],{"code":32,"score":121,"supported":34,"title":122,"abstract":123,"tags":125},7,[],[124,36],"遥感",[21],{"code":127,"score":128,"supported":34,"title":129,"abstract":130,"tags":132},"I4",3,[],[131],"GIS",[],{"code":134,"score":128,"supported":34,"title":135,"abstract":136,"tags":138},"I7",[],[137],"研究方法",[],{"code":47,"score":48,"supported":14,"title":140,"abstract":141,"tags":142},[],[],[22],[144,145],"世界遗产城市 土地利用 城市热岛 遥感监测","世界遗产城市 土地利用","世界遗产城市土地利用城市热岛遥感监测-2060",2,"10.1007\u002Fs44327-026-00361-6",{"doi":148,"openalex_id":150,"authors":151,"venue":10,"cited_by_count":15,"oa_url":92,"card":155,"direction":81,"ingested_from":84},"W7212052957",[152],{"name":153,"orcid":154},"Parag Jyoti Kashyap","https:\u002F\u002Forcid.org\u002F0000-0003-4273-841X",{"tldr":156,"method":157,"finding":158,"direction":81,"opportunity":159},"综述印度两座世界遗产城市2000-2025年土地利用变化与城市热动态。","基于Landsat\u002FIRS遥感与GIS，结合CA-Markov和ANN城市增长模","建设用地侵占农地与绿地，地表温度上升，热胁迫加剧，威胁文化遗产。","可拓展遥感与预测模型结合，研究遗产城市热环境与农业用地保护的协同规划。","2026-09-10T23:30:27.930226Z",{"id":162,"title":163,"url":164,"summary":165,"summary_zh":166,"content":9,"source_name":167,"source_url":164,"published_at":168,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":169,"score_detail":170,"sources":176,"tags":178,"primary_topic":9,"secondary_topics":182,"classification":183,"search_phrases":211,"slug":214,"view_count":15,"doi":215,"paper":216,"created_at":240},5577,"Assessing Land Use and Land Cover Change in the Jamanxim River Region (Brazilian Amazon) from 2015 to 2025 Using CBERS-4\u002FPAN Imagery","https:\u002F\u002Fdoi.org\u002F10.29150\u002Fjhrs.v16i4.269068","Land use and land cover (LULC) changes in the Amazon are among the main drivers of biodiversity loss, ecosystem degradation, and the intensification of climate change. This study aimed to analyze the multitemporal dynamics of LULC around the Jamanxim River, Pará, Brazilian Amazon, between 2015 and 2025, using high-resolution imagery from the CBERS-4\u002FPAN satellite. The study area covers 492.74 km², including part of the Jamanxim National Forest, a conservation unit under strong pressure from agricultural expansion and logging activities. The methodology included geometric correction of the images, Gram–Schmidt pansharpening, and supervised classification with the Random Forest algorithm, validated with historical imagery from Google Earth Pro. Thematic maps of land use and cover were generated, and performance metrics such as overall accuracy, omission and commission errors, and F1-scores were assessed. The results indicated a significant reduction in forest cover, from 412.28 km² in 2015 to 355.47 km² in 2025 (–13.78%). Non-vegetated areas increased by 132.47%, while agricultural areas expanded by 54.97%. The forest and water body classes showed high accuracy (F1-score > 0.95), while confusion persisted between agricultural and bare soil classes. Overall accuracy remained above 90% across all years, with the highest performance recorded in 2020 (96.31%). These results highlight the potential of CBERS-4\u002FPAN imagery for monitoring multitemporal changes in tropical forest landscapes. At the same time, they reveal the intensification of deforestation and the growing threats to conservation in the Amazon, reinforcing the importance of remote sensing for environmental management and policy support.","亚马逊地区的土地利用与土地覆盖（LULC）变化是生物多样性丧失、生态系统退化以及气候变化加剧的主要驱动因素之一。本研究旨在利用CBERS-4\u002FPAN卫星的高分辨率影像，分析2015年至2025年间巴西亚马孙州雅曼辛河（Jamanxim River）周边土地利用与土地覆盖的多时相动态。研究区面积为492.74 km²，包括雅曼辛国家森林（Jamanxim National Forest）的一部分，该保护区正面临农业扩张和采伐活动的强烈压力。研究方法包括影像几何校正、Gram–Schmidt全色锐化以及基于随机森林（Random Forest）算法的监督分类，并利用Google Earth Pro的历史影像进行验证。研究生成了土地利用与覆盖专题图，并评估了总体精度、漏分误差与错分误差以及F1分数等性能指标。结果表明，森林覆盖显著减少，从2015年的412.28 km²降至2025年的355.47 km²（–13.78%）。无植被区域增加了132.47%，农业区域扩张了54.97%。森林和水体类别表现出较高精度（F1分数 > 0.95），而农业类别与裸土类别之间仍存在混淆。各年份总体精度均保持在90%以上，其中2020年性能最高（96.31%）。这些结果凸显了CBERS-4\u002FPAN影像在监测热带森林景观多时相变化方面的潜力，同时揭示了亚马逊地区森林砍伐的加剧以及保护面临的日益增长的威胁，进一步强调了遥感在环境管理和政策支持中的重要性。","Journal of Hyperspectral Remote Sensing","2026-10-20T00:00:00Z",78,{"impact":171,"substance":172,"depth":173,"authority":174,"freshness":33,"relevant":48,"comment":175},18,22,17,12,"该研究利用CBERS-4\u002FPAN影像量化亚马逊保护区十年间森林减少13.78%，方法规范、结论可靠，对遥感监测与生态保护有参考价值，但地域性强、与国内三农直接关联有限。",[177],{"name":167,"url":164},[21,22,179,180,181],"森林保护","亚马逊","CBERS-4",[],{"status":26,"locked":14,"version":27,"method":28,"evidence":184},{"candidates":185,"agricultural_evidence":34,"reason":210},[186,192,196,200,206],{"code":47,"score":121,"supported":34,"title":187,"abstract":188,"tags":191},[],[189,190],"生物多样性","biodiversity",[22],{"code":32,"score":121,"supported":34,"title":193,"abstract":194,"tags":195},[],[124,36],[21],{"code":103,"score":87,"supported":34,"title":197,"abstract":198,"tags":199},[],[116,118],[],{"code":201,"score":128,"supported":34,"title":202,"abstract":203,"tags":205},"H2",[],[204],"soil",[],{"code":134,"score":128,"supported":34,"title":207,"abstract":208,"tags":209},[],[137],[],"多个产业证据相当，需要确认主归属",[212,213],"CBERS-4 亚马逊 土地利用","Jamanxim 森林覆盖 遥感","CBERS-4亚马逊土地利用-5577","10.29150\u002Fjhrs.v16i4.269068",{"doi":215,"openalex_id":217,"authors":218,"venue":167,"cited_by_count":15,"oa_url":164,"card":235,"direction":81,"ingested_from":84},"W7223141286",[219,222,224,227,230,233],{"name":220,"orcid":221},"Ubiratan Joaquim da Silva","https:\u002F\u002Forcid.org\u002F0000-0001-7995-6416",{"name":223,"orcid":9},"Gabriel Henrique Santos Silva",{"name":225,"orcid":226},"Juarez Antonio da Silva","https:\u002F\u002Forcid.org\u002F0000-0002-2898-0309",{"name":228,"orcid":229},"Anderson Luiz Ribeiro de Paiva","https:\u002F\u002Forcid.org\u002F0000-0003-3475-1454",{"name":231,"orcid":232},"Leidjane Maria Maciel de Oliveira","https:\u002F\u002Forcid.org\u002F0000-0003-1251-6998",{"name":234,"orcid":9},"Sylvana Melo dos Santos",{"tldr":236,"method":237,"finding":238,"direction":81,"opportunity":239},"用CBERS-4\u002FPAN影像分析亚马逊Jamanxim河流域2015-2025年土地利用变化。","几何校正、Gram-Schmidt融合、随机森林监督分类，Google Eart","森林覆盖减少13.78%，农业扩张54.97%，裸地增132.47%，总体精度超90%。","可探索中巴地球资源卫星在热带森林退化监测中的业务化应用与多源数据融合。","2026-10-22T11:20:42.901761Z",{"id":242,"title":243,"url":244,"summary":245,"summary_zh":246,"content":9,"source_name":247,"source_url":244,"published_at":168,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":248,"sources":250,"tags":252,"primary_topic":255,"secondary_topics":260,"classification":265,"search_phrases":287,"slug":290,"view_count":15,"doi":291,"paper":292,"created_at":309},5485,"Integrating machine learning and geospatial technologies for spatio-temporal analysis of urban expansion and land use transformation in Gambella Town, Ethiopia","https:\u002F\u002Fdoi.org\u002F10.1371\u002Fjournal.pone.0360419","Rapid urbanization and the associated expansion of built-up areas are transforming land resources, ecosystem functions, and the spatial structure of cities. Distinguishing urban growth, urban expansion, and urban sprawl is essential for understanding the processes and patterns of urban transformation and supporting sustainable urban planning. This study integrates machine learning and geospatial technologies to analyze spatio-temporal land use and land cover (LULC) dynamics and urban expansion in Gambella Town, Ethiopia. Multi-temporal Landsat satellite images from 2005, 2015, and 2025 were processed using remote sensing techniques and a supervised machine learning approach based on the Random Forest (RF) classifier. Six major LULC classes were identified: Agricultural Land, Bush with Grass, Built-Up Area, Bare Land, Forest Land, and Water Bodies. The classification achieved overall accuracies of 90%, 90%, and 89% for 2005, 2015, and 2025, respectively. The results revealed substantial land transformation and rapid physical urban expansion, with the built-up area increasing from 93 ha (2.0%) in 2005–1,956 ha (41.3%) in 2025. This expansion was accompanied by major transformations of surrounding land resources, particularly the conversion of Bush with Grass and other open areas to built-up land. Agricultural land increased from 634 ha (13.4%) to 1,750 ha (36.9%), whereas Bush with Grass declined from 3,375 ha (71.1%) to 652 ha (13.7%). Socio-economic information collected from 277 respondents indicated that population growth, rural–urban migration, housing demand, employment opportunities, and infrastructure development were major drivers of urban expansion. The study distinguishes urban expansion, represented by the increase in built-up land, from urban sprawl, which refers to the spatial pattern and form of outward urban development. The integration of Random Forest classification and GIS-based spatial analysis provides valuable evidence of land transformation, vegetation loss, and increasing pressure on urban services, supporting more sustainable urban planning and land management in Gambella Town.","快速城市化及与之相关的建成区扩张正在改变土地资源、生态系统功能和城市空间结构。区分城市增长（urban growth）、城市扩张（urban expansion）和城市蔓延（urban sprawl），对于理解城市转型的过程与格局、支撑可持续城市规划至关重要。本研究融合机器学习与地理空间技术，分析埃塞俄比亚甘贝拉镇（Gambella Town）土地利用与土地覆盖（LULC）的时空动态及城市扩张。采用遥感技术和基于随机森林（RF）分类器的监督机器学习方法，处理了2005年、2015年和2025年的多时相Landsat卫星影像。研究识别出六类主要LULC类型：农业用地、灌草地、建成区、裸地、林地和水体。2005年、2015年和2025年的分类总体精度分别为90%、90%和89%。结果表明，土地发生了显著转变，城市实体空间快速扩张，建成区面积从2005年的93公顷（2.0%）增至2025年的1,956公顷（41.3%）。这一扩张伴随着周边土地资源的重大转变，尤其是灌草地及其他开阔地被转化为建成用地。农业用地从634公顷（13.4%）增至1,750公顷（36.9%），而灌草地则从3,375公顷（71.1%）降至652公顷（13.7%）。从277名受访者收集的社会经济信息表明，人口增长、城乡迁移、住房需求、就业机会和基础设施建设是城市扩张的主要驱动因素。本研究将城市扩张（以建成用地增加为表征）与城市蔓延（指城市向外发展的空间格局与形态）加以区分。随机森林分类与基于GIS的空间分析的融合，为土地转变、植被丧失及城市服务压力加剧提供了有价值的证据，有助于甘贝拉镇实现更可持续的城市规划与土地管理。","PLoS ONE",{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":249},"研究聚焦埃塞俄比亚城市扩张与土地利用变化，属城市地理与遥感应用，与三农、农业信息化主题无直接关联，不建议进入每日精选。",[251],{"name":247,"url":244},[253,21,22,254],"机器学习","城市扩张",{"code":40,"slug":256,"name":257,"path":258,"parent_code":259},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[261],{"code":127,"slug":262,"name":263,"path":264,"parent_code":259},"gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",{"status":110,"locked":14,"version":27,"method":28,"evidence":266},{"candidates":267,"agricultural_evidence":34},[268,273,279,283],{"code":40,"score":269,"supported":34,"title":270,"abstract":271,"tags":272},15,[43],[253,43],[253],{"code":127,"score":33,"supported":34,"title":274,"abstract":275,"tags":278},[],[276,131,277],"空间分析","spatial analysis",[],{"code":32,"score":121,"supported":34,"title":280,"abstract":281,"tags":282},[],[124,36],[21],{"code":47,"score":48,"supported":14,"title":284,"abstract":285,"tags":286},[],[],[22],[288,289],"Gambella Town 城市扩张","Random Forest 土地利用分类","GambellaTown城市扩张-5485","10.1371\u002Fjournal.pone.0360419",{"doi":291,"openalex_id":293,"authors":294,"venue":247,"cited_by_count":15,"oa_url":244,"card":304,"direction":81,"ingested_from":84},"W7223022155",[295,298,300,302],{"name":296,"orcid":297},"Kanenus Fufa Dararo","https:\u002F\u002Forcid.org\u002F0009-0007-5747-1558",{"name":299,"orcid":9},"Goanar Jal Dup",{"name":301,"orcid":9},"Weyesa Merga Chemeda",{"name":303,"orcid":9},"Shankar Karuppannan",{"tldr":305,"method":306,"finding":307,"direction":81,"opportunity":308},"用机器学习与遥感分析埃塞俄比亚甘贝拉镇2005-2025年城市扩张与土地利用变化。","Landsat多时相影像、随机森林分类与GIS空间分析，结合277份问卷。","建成区从93公顷增至1956公顷，灌草地大幅转为建成区和农地。","可将随机森林时序分类与驱动力调查结合，扩展到更多非洲小城镇并耦合城市增长模拟。","2026-10-21T23:30:23.271293Z",{"id":311,"title":312,"url":313,"summary":314,"summary_zh":9,"content":9,"source_name":315,"source_url":313,"published_at":168,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":316,"sources":318,"tags":320,"primary_topic":9,"secondary_topics":322,"classification":323,"search_phrases":334,"slug":337,"view_count":15,"doi":338,"paper":339,"created_at":353},5484,"Spatiotemporal Dynamics and Assess of Ecological Quality in Sylhet Metropolitan Area amid Rapid Urbanization: A Remote Sensing Approach","https:\u002F\u002Fdoi.org\u002F10.21203\u002Frs.3.rs-11422676\u002Fv1","Spatiotemporal Dynamics and Assess of Ecological Quality in Sylhet Metropolitan Area amid Rapid Urbanization: A Remote Sensing Approach。Research Square","Research Square",{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":317},"研究孟加拉国锡尔赫特市生态质量，与三农及农业信息化无关，不予精选。",[319],{"name":315,"url":313},[21,22,321],"城市生态",[],{"status":26,"locked":14,"version":27,"method":28,"evidence":324},{"candidates":325,"agricultural_evidence":14,"reason":52},[326,330],{"code":32,"score":174,"supported":34,"title":327,"abstract":328,"tags":329},[36],[36],[21],{"code":47,"score":147,"supported":14,"title":331,"abstract":332,"tags":333},[],[],[22,321],[335,336],"Sylhet 城市扩张 遥感","土地利用 城市生态 遥感监测","Sylhet城市扩张遥感-5484","10.21203\u002Frs.3.rs-11422676\u002Fv1",{"doi":338,"openalex_id":340,"authors":341,"venue":315,"cited_by_count":15,"oa_url":352,"card":9,"direction":81,"ingested_from":84},"W7223013355",[342,344,346,349],{"name":343,"orcid":9},"Md Saikoth Jahan",{"name":345,"orcid":9},"Mst Arjumoni Anu",{"name":347,"orcid":348},"Md. Inzamul Haque","https:\u002F\u002Forcid.org\u002F0000-0003-1756-5561",{"name":350,"orcid":351},"Most. Atia Parvin","https:\u002F\u002Forcid.org\u002F0000-0002-2718-9259","https:\u002F\u002Fwww.researchsquare.com\u002Farticle\u002Frs-11422676\u002Flatest.pdf","2026-10-21T23:30:23.223087Z",{"id":355,"title":356,"url":357,"summary":358,"summary_zh":359,"content":9,"source_name":360,"source_url":357,"published_at":361,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":362,"sources":364,"tags":366,"primary_topic":369,"secondary_topics":373,"classification":374,"search_phrases":394,"slug":397,"view_count":15,"doi":398,"paper":399,"created_at":427},5390,"Land use and socio-economic changes caused by riverbank erosion - case study: Obrenovac municipality, Serbia","https:\u002F\u002Fdoi.org\u002F10.59954\u002Fstnv.797","Lateral bank erosion of meandering rivers represents a significant geomorphological process in the lower part of the Kolubara River Basin in Serbia. This process directly impacts land-use and agricultural productivity and lead to broader environmental, socioeconomic, and demographic changes. Historically, the Kolubara riverbanks have been intensively used for agriculture. Today, lateral channel migration and riverbank erosion, driven by the interaction between natural process and human activities, directly threaten traditionally active land plots along the river. This study investigates the long-term dynamics of riverbank erosion along the four active meanders of the Kolubara River in Obrenovac Municipality over a 97-year period (1930–2026). Main tasks include quantification of lateral channel migration and land loss rates, analysis of land-use changes within the endangered plots, and an investigation of socioeconomic consequences and demographic dynamics in affected settlements. The methodological framework integrates cadastral maps analysis, remote sensing techniques (UAVs), and GIS software, with analysis of demographic and land-use data. The results indicate that maximum lateral channel migration reached 259.3 m during the observed period, while the total land loss amounted to 17.6 ha, consisting mostly of arable land. This reduction has directly affected the local agricultural sector, resulting in an estimated land loss value of approximately 24,122 USD. Demographic analysis reveals population decline exceeding 40% in the analysed settlements (Draževac, Konatice and Poljane) between 1961 and 2022, from over 90% of the economically active population in 1961 to less than 2% in 2022. Ultimately, by highlighting the socioeconomic impacts of riverbank erosion such as irreversible agricultural land loss and rural livelihood disruption, these findings offer valuable insights for spatial planning and sustainable land management.","蜿蜒河流的侧向河岸侵蚀是塞尔维亚科卢巴拉河（Kolubara River）流域下游一个重要的地貌过程。该过程直接影响土地利用和农业生产力，并引发更广泛的环境、社会经济和人口变化。历史上，科卢巴拉河岸曾被密集用于农业生产。如今，在自然过程与人类活动相互作用驱动下的侧向河道迁移和河岸侵蚀，直接威胁着沿河传统上活跃的地块。本研究考察了奥布雷诺瓦茨市（Obrenovac Municipality）科卢巴拉河四个活跃曲流段在97年期间（1930—2026年）河岸侵蚀的长期动态。主要任务包括：量化侧向河道迁移和土地流失速率，分析受威胁地块内的土地利用变化，以及调查受影响居民点的社会经济后果和人口动态。方法框架整合了地籍图分析、遥感技术（无人机，UAVs）和GIS软件，并结合人口与土地利用数据分析。结果表明，在观测期内侧向河道迁移最大距离达259.3 m，土地流失总量为17.6 ha，其中大部分为耕地。这一减少直接影响了当地农业部门，估计土地流失价值约为24,122美元。人口分析显示，1961年至2022年间，所分析居民点（Draževac、Konatice和Poljane）的人口下降幅度超过40%，经济活动人口比例从1961年的超过90%降至2022年的不足2%。最终，通过突出河岸侵蚀的社会经济影响，如不可逆的农业土地流失和农村生计破坏，这些发现为空间规划和可持续土地管理提供了有价值的见解。","Stanovnistvo","2026-10-19T00:00:00Z",{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":363},"塞尔维亚河岸侵蚀案例研究，属地理与土地利用领域，与三农及农业信息化主题关联弱，不建议进入每日精选。",[365],{"name":360,"url":357},[21,22,367,368],"土壤侵蚀","乡村人口",{"code":32,"slug":370,"name":371,"path":372,"parent_code":259},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",[],{"status":110,"locked":14,"version":27,"method":28,"evidence":375},{"candidates":376,"agricultural_evidence":34},[377,381,385,390],{"code":32,"score":121,"supported":34,"title":378,"abstract":379,"tags":380},[],[124,36],[21],{"code":127,"score":128,"supported":34,"title":382,"abstract":383,"tags":384},[],[131],[],{"code":134,"score":128,"supported":34,"title":386,"abstract":387,"tags":389},[],[388],"methodological framework",[],{"code":47,"score":48,"supported":14,"title":391,"abstract":392,"tags":393},[],[],[22],[395,396],"Kolubara 河岸侵蚀","Obrenovac 土地利用","Kolubara河岸侵蚀-5390","10.59954\u002Fstnv.797",{"doi":398,"openalex_id":400,"authors":401,"venue":360,"cited_by_count":15,"oa_url":357,"card":421,"direction":81,"ingested_from":84},"W7222945959",[402,405,408,410,413,416,419],{"name":403,"orcid":404},"Slavoljub Dragićević","https:\u002F\u002Forcid.org\u002F0000-0002-1417-5405",{"name":406,"orcid":407},"Marko Langović","https:\u002F\u002Forcid.org\u002F0000-0002-4614-7265",{"name":409,"orcid":9},"Милан Радовић",{"name":411,"orcid":412},"Radislav Tošić","https:\u002F\u002Forcid.org\u002F0000-0002-3994-125X",{"name":414,"orcid":415},"Milica Langović","https:\u002F\u002Forcid.org\u002F0000-0002-6979-377X",{"name":417,"orcid":418},"Novica Lovrić","https:\u002F\u002Forcid.org\u002F0009-0006-0838-4370",{"name":420,"orcid":9},"Manojlović Stefan",{"tldr":422,"method":423,"finding":424,"direction":425,"opportunity":426},"研究塞尔维亚Kolubara河岸侵蚀97年间的河道迁移、耕地损失及社会经济影响。","结合地籍图、UAV遥感、GIS与人口土地利用数据分析四条曲流。","最大侧向迁移259.3米，损失17.6公顷耕地约2.4万美元，村落人口降超40%。","其他","可将河岸侵蚀遥感监测与农户生计、土地价值评估耦合，构建侵蚀风险预警与补偿决策模型。","2026-10-20T23:30:18.982943Z",{"id":429,"title":430,"url":431,"summary":432,"summary_zh":433,"content":9,"source_name":434,"source_url":431,"published_at":435,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":436,"score_detail":437,"sources":440,"tags":442,"primary_topic":445,"secondary_topics":446,"classification":447,"search_phrases":464,"slug":467,"view_count":15,"doi":468,"paper":469,"created_at":487},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":41,"substance":171,"depth":438,"authority":87,"freshness":33,"relevant":48,"comment":439},14,"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。",[441],{"name":434,"url":431},[21,22,443,444],"植被覆盖","卢旺达农业",{"code":127,"slug":262,"name":263,"path":264,"parent_code":259},[],{"status":110,"locked":14,"version":27,"method":28,"evidence":448},{"candidates":449,"agricultural_evidence":34},[450,456,460],{"code":127,"score":87,"supported":34,"title":451,"abstract":452,"tags":455},[],[453,454],"地理信息","geographic information",[],{"code":32,"score":147,"supported":14,"title":457,"abstract":458,"tags":459},[],[],[443,21],{"code":47,"score":48,"supported":14,"title":461,"abstract":462,"tags":463},[],[],[22],[465,466],"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":468,"openalex_id":470,"authors":471,"venue":434,"cited_by_count":15,"oa_url":481,"card":482,"direction":81,"ingested_from":84},"W7222817253",[472,474,476,478],{"name":473,"orcid":9},"Vincent Hategekimana",{"name":475,"orcid":9},"Richard Mind J. E.",{"name":477,"orcid":9},"Aboubakar Gasirabo",{"name":479,"orcid":480},"Didier Ntwali","https:\u002F\u002Forcid.org\u002F0000-0002-6914-6652","https:\u002F\u002Fwww.ijisrt.com\u002Fassets\u002Fupload\u002Ffiles\u002FIJISRT26OCT696.pdf",{"tldr":483,"method":484,"finding":485,"direction":81,"opportunity":486},"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"]