[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3342":3,"related-3342":109},{"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":23,"tags":25,"primary_topic":31,"secondary_topics":37,"classification":38,"search_phrases":76,"slug":79,"view_count":45,"doi":80,"paper":81,"created_at":108},3342,"Impact of agricultural greening on food production resilience-empirical evidence from China","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1938875","The Food Production System is generally confronted with external uncertain shocks and rigid constraints from ecological factors. How to strengthen the system’s adaptability and resilience through green transition pathways is a crucial research topic in the field of modern agricultural governance. With the provincial panel data from China, the entropy method is adopted in this paper to measure agricultural greening and food production resilience, and then the two-way fixed-effect model, the moderating-effect model, the threshold-effect model and spatial econometric models are applied for empirical testing. According to the results, the degree of agricultural greening has promoted food production resilience; at the same time, regions show heterogeneity across geographical regions and grain functional zones. Green finance and rural scientific and technological input will also be effective driving forces. There is a single threshold for economic development level; after exceeding it, the marginal promoting effect on resilience weakens, but remains significantly positive. Food production resilience also exhibits significant spatial dependence, and agricultural greening generates positive spillover effects on neighboring provinces under the adjacency-based spatial specification. This paper will provide new ideas for the theory of resilience enhancement and offer some references to help optimize the distribution of agricultural green factors and foster long-term stable grain production.","粮食生产系统普遍面临外部不确定冲击与生态因素的刚性约束。如何通过绿色转型路径增强系统的适应性与韧性，是现代农业农村治理领域的重要研究课题。本文基于中国省级面板数据，采用熵值法测度农业绿色化与粮食生产韧性，进而运用双向固定效应模型、调节效应模型、门槛效应模型及空间计量模型进行实证检验。结果表明，农业绿色化程度提升了粮食生产韧性；同时，不同地理区域与粮食功能区呈现出异质性。绿色金融与农村科技投入也是有效的驱动力量。经济发展水平存在单一门槛，跨越门槛后对韧性的边际促进效应减弱，但仍显著为正。粮食生产韧性还表现出显著的空间依赖性，在邻接空间设定下，农业绿色化对邻近省份产生正向溢出效应。本文将为韧性提升理论提供新思路，并为优化农业绿色要素配置、促进粮食长期稳定生产提供一定参考。",null,"Frontiers in Sustainable Food Systems","2026-09-23T00:00:00Z","论文",10,false,79,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,13,8,1,"基于中国省级面板数据的实证研究，方法扎实、结论有政策参考价值，但属学术论文而非产业级事件，适合进入主题聚合而非头条精选。",[24],{"name":10,"url":6},[26,27,28,29,30],"农业绿色发展","粮食生产韧性","农业科技投入","绿色金融","空间溢出效应",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"I4","gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis","I",[],{"status":39,"locked":14,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":46},[44,51,56,61,66,71],{"code":32,"score":45,"supported":46,"title":47,"abstract":48,"tags":50},4,true,[],[49],"空间计量",[30],{"code":52,"score":21,"supported":14,"title":53,"abstract":54,"tags":55},"A8",[],[],[27],{"code":57,"score":21,"supported":14,"title":58,"abstract":59,"tags":60},"G3",[],[],[29],{"code":62,"score":21,"supported":14,"title":63,"abstract":64,"tags":65},"H4",[],[],[26],{"code":67,"score":21,"supported":14,"title":68,"abstract":69,"tags":70},"H8",[],[],[26],{"code":72,"score":21,"supported":14,"title":73,"abstract":74,"tags":75},"J2",[],[],[28],[77,78],"农业绿色化 粮食生产韧性","绿色金融 农村科技投入","农业绿色化粮食生产韧性-3342","10.3389\u002Ffsufs.2026.1938875",{"doi":80,"openalex_id":82,"authors":83,"venue":10,"cited_by_count":100,"oa_url":6,"card":101,"direction":105,"ingested_from":107},"W7214113879",[84,86,89,91,93,95,98],{"name":85,"orcid":9},"Huimin Shao",{"name":87,"orcid":88},"Wenjuan Zhang","https:\u002F\u002Forcid.org\u002F0000-0001-5062-1292",{"name":90,"orcid":9},"Qiong Peng",{"name":92,"orcid":9},"Haichuan Liu",{"name":94,"orcid":9},"Liangyan Lu",{"name":96,"orcid":97},"Mcxin Tee","https:\u002F\u002Forcid.org\u002F0000-0001-7990-8377",{"name":99,"orcid":9},"Qing Jin",0,{"tldr":102,"method":103,"finding":104,"direction":105,"opportunity":106},"基于中国省级面板数据，实证检验农业绿色化对粮食生产韧性的提升作用。","熵值法测度指标，双向固定效应、调节效应、门槛效应与空间计量模型。","农业绿色化显著提升粮食生产韧性，存在区域异质性、经济门槛及正向空间溢出。","农业绿色发展与碳","可探究绿色金融与科技投入的协同机制，及跨区域溢出路径的微观落地。","openalex","2026-09-24T23:30:07.857044Z",{"total":110,"page":21,"page_size":110,"items":111},6,[112,167,230,309,377,440],{"id":113,"title":114,"url":115,"summary":116,"summary_zh":9,"content":9,"source_name":117,"source_url":9,"published_at":118,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":119,"score_detail":120,"sources":123,"tags":125,"primary_topic":129,"secondary_topics":130,"classification":131,"search_phrases":152,"slug":155,"view_count":45,"doi":156,"paper":157,"created_at":166},4153,"中国农业农村数字化绿色化协同发展的时空格局及其增收效应","https:\u002F\u002Fcjournal.hep.com.cn\u002F1007-4333\u002FCN\u002F10.11841\u002Fj.issn.1007-4333.2026.10.06","蔡雪玲（中国农业大学学报2026年第10期，兰州财经大学会计学院）。文章立足于农业农村「生产-管理-生态-创新-生活」数绿协同的内在逻辑构建测度指标体系，采用熵值法、改进的耦合协调度模型、Kernel密度估计与空间Markov链转移矩阵测算农业农村数绿协同发展水平并刻画其时空演进特征，构建固定效应模型与空间计量模型检验农业农村数绿协同发展对农村居民收入的影响关系与空间溢出效应。结果表明：全国及各省份农业农村数绿协同发展水平缓慢上升，尚处于基本协同阶段。","中国农业大学学报2026年第10期","2026-10-01T00:00:00Z",80,{"impact":17,"substance":18,"depth":17,"authority":121,"freshness":20,"relevant":21,"comment":122},14,"核心期刊论文，构建数绿协同测度体系并实证检验其增收效应与空间溢出，方法规范、结论有政策参考价值，但属学术研究而非产业动态，适合主题聚合而非每日精选头条。",[124],{"name":117,"url":115},[126,26,127,128,30],"数字乡村","农村居民收入","耦合协调度",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":132},{"candidates":133,"agricultural_evidence":46},[134,139,143,147],{"code":32,"score":135,"supported":46,"title":136,"abstract":137,"tags":138},5,[],[49],[30,128],{"code":62,"score":21,"supported":14,"title":140,"abstract":141,"tags":142},[],[],[26],{"code":67,"score":21,"supported":14,"title":144,"abstract":145,"tags":146},[],[],[26],{"code":148,"score":21,"supported":14,"title":149,"abstract":150,"tags":151},"I6",[],[],[126],[153,154],"农业农村 数字化绿色化 协同发展","农村居民收入 空间溢出效应","农业农村数字化绿色化协同发展-4153","10.11841\u002Fj.issn.1007-4333.2026.10.06",{"doi":156,"openalex_id":9,"authors":158,"venue":9,"cited_by_count":100,"oa_url":9,"card":159,"direction":163,"ingested_from":165},[],{"tldr":160,"method":161,"finding":162,"direction":163,"opportunity":164},"测度中国农业农村数字化绿色化协同水平并检验其时空格局与增收效应。","熵值法、耦合协调度模型、Kernel密度、空间Markov链与空间计量模型。","全国及各省数绿协同水平缓慢上升，仍处基本协同阶段，且对农村居民收入有影响与空间溢出。","数字乡村与农业信息化","可深入探究数绿协同增收效应的区域异质性与微观农户传导机制，识别关键短板维度。","agent","2026-10-04T00:09:09.731896Z",{"id":168,"title":169,"url":170,"summary":171,"summary_zh":9,"content":9,"source_name":172,"source_url":9,"published_at":173,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":174,"score_detail":175,"sources":179,"tags":181,"primary_topic":186,"secondary_topics":187,"classification":188,"search_phrases":218,"slug":221,"view_count":135,"doi":9,"paper":222,"created_at":229},3320,"人工智能对农业新质生产力的驱动机制与效应——基于278个地级市面板数据（2010—2024）","http:\u002F\u002Fwww.qikanvip.com\u002Fqkml\u002F165477.html","《农业经济与管理》2026年第03期。钟文、郑林泽、严芝清（江西理工大学）研究表明：人工智能技术创新对农业新质生产力具有显著直接提升效应。机制检验表明，人工智能主要通过强化农业科技创新能力、激发农村地区创业活力、深化农业生产数字化水平三条核心路径驱动农业新质生产力发展。异质性分析表明，人工智能的促进作用具有普遍性但存在显著梯度差异，受区域资源禀赋、发展阶段及农业生产模式多样性深刻影响。空间计量检验进一步发现，人工智能技术创新存在显著正向空间溢出效应，但农村人力资本水平构成关键调节变量，其不足将显著制约溢出效能的有效释放。","《农业经济与管理》2026年第03期","2026-09-17T00:00:00Z",86,{"impact":18,"substance":176,"depth":177,"authority":121,"freshness":20,"relevant":21,"comment":178},23,19,"基于278个地级市面板数据的实证研究，方法规范、结论有新意，对农业AI政策制定具参考价值，值得进入每日精选。",[180],{"name":172,"url":170},[182,183,184,30,185],"农业人工智能","农业新质生产力","农业数字化","农村创业",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":189},{"candidates":190,"agricultural_evidence":46},[191,195,200,204,209,214],{"code":32,"score":45,"supported":46,"title":192,"abstract":193,"tags":194},[],[49],[30],{"code":196,"score":21,"supported":14,"title":197,"abstract":198,"tags":199},"G4",[],[],[185],{"code":36,"score":21,"supported":14,"title":201,"abstract":202,"tags":203},[],[],[184],{"code":205,"score":21,"supported":14,"title":206,"abstract":207,"tags":208},"I1",[],[],[184],{"code":210,"score":21,"supported":14,"title":211,"abstract":212,"tags":213},"I2",[],[],[182],{"code":72,"score":21,"supported":14,"title":215,"abstract":216,"tags":217},[],[],[183],[219,220],"人工智能 农业新质生产力 地级市面板","农业新质生产力 农业人工智能 空间溢出效应 农业数字化","人工智能农业新质生产力地级市面板-3320",{"doi":9,"openalex_id":9,"authors":223,"venue":9,"cited_by_count":100,"oa_url":9,"card":224,"direction":163,"ingested_from":165},[],{"tldr":225,"method":226,"finding":227,"direction":163,"opportunity":228},"基于278个地级市面板数据，实证检验人工智能对农业新质生产力的驱动机制与空间溢出效应。","278个地级市2010—2024年面板数据，机制检验、异质性分析与空间计量模型。","人工智能显著直接提升农业新质生产力，经三条路径驱动，并具正向空间溢出，但受农村人力资本调节制约。","可深入探究农村人力资本门槛下AI空间溢出的非线性衰减机制及跨区域协同提升路径。","2026-09-24T00:04:02.275354Z",{"id":231,"title":232,"url":233,"summary":234,"summary_zh":235,"content":9,"source_name":236,"source_url":233,"published_at":237,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":238,"score_detail":239,"sources":243,"tags":245,"primary_topic":251,"secondary_topics":252,"classification":253,"search_phrases":289,"slug":292,"view_count":100,"doi":293,"paper":294,"created_at":308},5572,"Potential erosion risk assessment of Kanak stream basin (Sivas, Sarkisla) with the ICONA model","https:\u002F\u002Fdoi.org\u002F10.51753\u002Fflsrt.1828829","The rapid development of technology following the Industrial Revolution has increased human domination over nature. Erosion, which plays a critical role in the process of natural shaping of the earth, can accelerate over time because of improper utilization of the natural environment by humans. The main objective of this study is to determine the erosion susceptibility of the Kanak Creek Basin, one of the sub-basins in the upper reaches of the Kizilirmak River, using the ICONA model, whose main parameters are slope, lithology, land use and vegetation cover, and Geographic Information Systems (GIS) and Remote Sensing techniques, and to list the measures that can be taken against erosion. According to the results of the ICONA model potential erosion level map, 48% of the study area has low erosion susceptibility. These areas largely correspond to the vicinity of Sama Mountain, which has volcanic rocks resistant to erosion, and the base of Sarkisla Plain where the slope is very low. The plain floor is largely covered with agricultural lands. The expansion of irrigated agriculture in this region is one of the main measures that can be taken against erosion. Erosion susceptibility increases around Turna Mountain on the northern border of the basin and Cal Mountain in the south due to both the increase in slope and the lack of resistance of the rocks in the region to erosion, and corresponds to 30% of the study area. In 22% of the basin, susceptibility to erosion is at a medium level. Most of these areas consist of natural meadows and pastures. It is essential to take measures against overgrazing in these areas to prevent the complete loss of the already sparse vegetation cover. Planning land use in the basin in a sustainable manner, arranging fields and layers perpendicular to the slope, expanding irrigated agriculture, and preventing overgrazing are of vital importance in terms of combating soil erosion.","工业革命以来技术的迅猛发展增强了人类对自然的支配能力。侵蚀在地球自然塑造过程中起着关键作用，而人类对自然环境的不当利用会加速侵蚀进程。本研究的主要目标是利用ICONA模型（其主要参数为坡度、岩性、土地利用和植被覆盖）以及地理信息系统（GIS）和遥感技术，确定克孜勒河上游子流域之一卡纳克溪流域的侵蚀敏感性，并列出可采取的侵蚀防治措施。根据ICONA模型潜在侵蚀等级图的结果，研究区48%的区域侵蚀敏感性较低。这些区域主要对应于具有抗侵蚀火山岩的萨马山附近以及坡度极低的萨尔基什拉平原底部。平原底部大部分为农业用地覆盖。在该地区扩大灌溉农业是防治侵蚀的主要措施之一。在流域北部边界的图尔纳山和南部的恰尔山周围，由于坡度增大以及该地区岩石抗侵蚀能力不足，侵蚀敏感性增加，占研究区面积的30%。流域22%的区域侵蚀敏感性为中等水平。这些区域大部分为天然草甸和牧场。在这些地区采取措施防止过度放牧至关重要，以避免本已稀疏的植被覆盖完全丧失。以可持续方式规划流域土地利用、沿垂直于坡向布置田块和土层、扩大灌溉农业以及防止过度放牧，对防治土壤侵蚀具有至关重要的意义。","Frontiers in Life Sciences and Related Technologies","2026-10-21T00:00:00Z",61,{"impact":20,"substance":17,"depth":121,"authority":240,"freshness":241,"relevant":21,"comment":242},12,9,"该研究以ICONA模型结合GIS与遥感评估土耳其小流域侵蚀风险，方法规范、结论具体，但属区域性案例，公共影响有限，可作为专业参考而非每日精选。",[244],{"name":236,"url":233},[246,247,248,249,250],"遥感","土地利用","土壤侵蚀","GIS","水土保持",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":254},{"candidates":255,"agricultural_evidence":46},[256,262,269,276,283],{"code":32,"score":13,"supported":46,"title":257,"abstract":258,"tags":261},[],[259,249,260],"地理信息","geographic information",[249],{"code":263,"score":264,"supported":46,"title":265,"abstract":266,"tags":268},"I3",7,[],[246,267],"remote sensing",[246],{"code":270,"score":110,"supported":46,"title":271,"abstract":272,"tags":275},"H2",[],[273,274],"土壤","soil",[],{"code":277,"score":278,"supported":46,"title":279,"abstract":280,"tags":282},"H3",3,[],[281],"灌溉",[],{"code":284,"score":285,"supported":14,"title":286,"abstract":287,"tags":288},"H6",2,[],[],[247,250],[290,291],"ICONA 模型 侵蚀风险","Kanak 流域 侵蚀","ICONA模型侵蚀风险-5572","10.51753\u002Fflsrt.1828829",{"doi":293,"openalex_id":295,"authors":296,"venue":236,"cited_by_count":100,"oa_url":9,"card":302,"direction":307,"ingested_from":107},"W7223117733",[297,299],{"name":298,"orcid":9},"MERİÇ KURT",{"name":300,"orcid":301},"Celal ŞENOL","https:\u002F\u002Forcid.org\u002F0000-0003-0857-866X",{"tldr":303,"method":304,"finding":305,"direction":105,"opportunity":306},"用ICONA模型评估土耳其Kanak流域潜在侵蚀风险并列出防治措施。","ICONA模型结合GIS与遥感，输入坡度、岩性、土地利用与植被覆盖。","48%区域低侵蚀风险，30%高敏感，22%中等，主因坡度与岩性。","可将ICONA与遥感时序数据结合，动态监测流域侵蚀并评估农业管理措施效果。","农业遥感与作物表型","2026-10-22T11:20:39.475037Z",{"id":310,"title":311,"url":312,"summary":313,"summary_zh":314,"content":9,"source_name":315,"source_url":312,"published_at":316,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":100,"score_detail":317,"sources":319,"tags":321,"primary_topic":325,"secondary_topics":329,"classification":331,"search_phrases":354,"slug":357,"view_count":100,"doi":358,"paper":359,"created_at":376},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","2026-10-20T00:00:00Z",{"impact":100,"substance":100,"depth":100,"authority":100,"freshness":100,"relevant":100,"comment":318},"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。",[320],{"name":315,"url":312},[322,323,247,324],"机器学习","遥感监测","城市扩张",{"code":210,"slug":326,"name":327,"path":328,"parent_code":36},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",[330],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":332},{"candidates":333,"agricultural_evidence":46},[334,340,346,350],{"code":210,"score":335,"supported":46,"title":336,"abstract":338,"tags":339},15,[337],"machine learning",[322,337],[322],{"code":32,"score":241,"supported":46,"title":341,"abstract":342,"tags":345},[],[343,249,344],"空间分析","spatial analysis",[],{"code":263,"score":264,"supported":46,"title":347,"abstract":348,"tags":349},[],[246,267],[323],{"code":284,"score":21,"supported":14,"title":351,"abstract":352,"tags":353},[],[],[247],[355,356],"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":358,"openalex_id":360,"authors":361,"venue":315,"cited_by_count":100,"oa_url":312,"card":371,"direction":307,"ingested_from":107},"W7223022155",[362,365,367,369],{"name":363,"orcid":364},"Kanenus Fufa Dararo","https:\u002F\u002Forcid.org\u002F0009-0007-5747-1558",{"name":366,"orcid":9},"Goanar Jal Dup",{"name":368,"orcid":9},"Weyesa Merga Chemeda",{"name":370,"orcid":9},"Shankar Karuppannan",{"tldr":372,"method":373,"finding":374,"direction":307,"opportunity":375},"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":378,"title":379,"url":380,"summary":381,"summary_zh":382,"content":9,"source_name":383,"source_url":380,"published_at":384,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":100,"score_detail":385,"sources":387,"tags":389,"primary_topic":393,"secondary_topics":397,"classification":405,"search_phrases":422,"slug":425,"view_count":100,"doi":426,"paper":427,"created_at":439},5326,"Flood Hazard and Vulnerability Assessment of Chitrakoot District, Uttar Pradesh: An Integrated Geographical, GIS and Remote Sensing Approach","https:\u002F\u002Fdoi.org\u002F10.36948\u002Fijfmr.2026.v08i05.89708","Flood is one of the most important hydro-meteorological hazards affecting human settlements, agriculture, infrastructure and regional economies. Chitrakoot district of Uttar Pradesh presents a distinctive case of hydro-climatic vulnerability because it belongs to the drought-prone Bundelkhand region but simultaneously experiences recurrent riverine and flash-flood conditions during intense monsoon rainfall. The present study assesses the spatial characteristics and determinants of flood hazard and vulnerability in Chitrakoot district using an integrated geographical, GIS and remote sensing approach. The analysis incorporates the Digital Elevation Model (DEM), drainage network, physiography, river proximity, historical flood information, satellite-based flood observations and socio-economic exposure. The district's DEM indicates a marked elevation gradient, approximately from 66 m to 451 m, with relatively higher terrain in the southern part and lower terrain towards the northern Yamuna valley. This topographic configuration facilitates rapid surface runoff from elevated southern terrain towards comparatively low-lying riverine areas. The drainage system is dominated by the Yamuna and Mandakini\u002FPaisuni systems along with Baghain, Gunta, Bardaha and smaller tributaries. The Uttar Pradesh Flood Hazard Atlas identifies 5,114 ha of flood-hazard area in Chitrakoot for 1998–2020, of which 4,129 ha (81%) falls in the very-low hazard category and 985 ha (19%) in the low-hazard category. However, the historical classification does not fully represent the potential consequences of extreme rainfall events. The August 2026 flood provides compelling evidence: the Mandakini at Chitrakoot reached a reported new high flood level of 145.60 m on 29 August 2026, approximately 2.95 m above the previous reported high level, while the water level rose by about 7.5 m within six hours. The study concludes that flood risk in Chitrakoot is spatially concentrated around river corridors, low-elevation areas, floodplains, agricultural land, settlements and tourism infrastructure. A shift from conventional relief-oriented flood management towards integrated floodplain regulation, watershed management, GIS-based risk zonation, real-time monitoring and community-based preparedness is therefore essential.","洪水是影响人类住区、农业、基础设施和区域经济的最重要的水文气象灾害之一。北方邦吉德古德（Chitrakoot）县在水文气候脆弱性方面呈现出独特案例，因为它属于易受干旱影响的本德尔坎德（Bundelkhand）地区，但同时在强季风降雨期间反复遭遇河流洪水和山洪状况。本研究采用地理学、地理信息系统（GIS）和遥感相结合的综合方法，评估吉德古德县洪水灾害与脆弱性的空间特征及其决定因素。分析纳入了数字高程模型（DEM）、河网、地貌、距河流远近、历史洪水信息、基于卫星的洪水观测以及社会经济暴露度。该县的DEM显示出明显的高程梯度，大约从66米到451米，南部地形相对较高，向北部的亚穆纳（Yamuna）河谷地势降低。这种地形格局促使地表径流从较高的南部地形快速流向相对低洼的河流区域。排水系统以亚穆纳河和曼达基尼\u002F派苏尼（Mandakini\u002FPaisuni）水系为主，另有巴盖恩（Baghain）、贡塔（Gunta）、巴尔达哈（Bardaha）及较小支流。北方邦洪水灾害图集确认，1998—2020年吉德古德县洪水灾害面积为5，114公顷，其中4，129公顷（81%）属于极低灾害类别，985公顷（19%）属于低灾害类别。然而，历史分类并不能充分反映极端降雨事件的潜在后果。2026年8月的洪水提供了有力证据：据报道，2026年8月29日吉德古德的曼达基尼河达到145.60米的新高洪水位，比此前报告的高水位高出约2.95米，而水位在六小时内上升了约7.5米。研究得出结论，吉德古德的洪水风险在空间上集中于河流廊道、低海拔地区、洪泛平原、农业用地、住区和旅游基础设施周围。因此，必须从传统的以救济为导向的洪水管理转向洪泛平原综合调控、流域管理、基于GIS的风险区划、实时监测和社区为基础的备灾。","International Journal For Multidisciplinary Research","2026-10-17T00:00:00Z",{"impact":100,"substance":100,"depth":100,"authority":100,"freshness":100,"relevant":100,"comment":386},"该研究聚焦印度北方邦的洪水灾害评估，虽涉及GIS与遥感方法，但与三农、农业信息化、智慧农业等主题无直接关联，不建议进入每日精选。",[388],{"name":383,"url":380},[390,323,391,392],"防灾减灾","洪水风险评估","GIS应用",{"code":263,"slug":394,"name":395,"path":396,"parent_code":36},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",[398,399],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"code":400,"slug":401,"name":402,"path":403,"parent_code":404},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",{"status":39,"locked":14,"version":40,"method":41,"evidence":406},{"candidates":407,"agricultural_evidence":46},[408,412,416],{"code":263,"score":335,"supported":46,"title":409,"abstract":410,"tags":411},[267],[246,267],[323],{"code":32,"score":121,"supported":46,"title":413,"abstract":414,"tags":415},[249],[259,249],[],{"code":400,"score":20,"supported":46,"title":417,"abstract":418,"tags":421},[],[419,420],"干旱","drought",[391,390],[423,424],"Chitrakoot 洪水风险 GIS","Yamuna 洪水脆弱性 遥感","Chitrakoot洪水风险GIS-5326","10.36948\u002Fijfmr.2026.v08i05.89708",{"doi":426,"openalex_id":428,"authors":429,"venue":383,"cited_by_count":100,"oa_url":433,"card":434,"direction":307,"ingested_from":107},"W7222885988",[430],{"name":431,"orcid":432},"Birendra Yadav","https:\u002F\u002Forcid.org\u002F0009-0007-0864-474X","https:\u002F\u002Fwww.ijfmr.com\u002Fpapers\u002F2026\u002F5\u002F89708.pdf",{"tldr":435,"method":436,"finding":437,"direction":307,"opportunity":438},"用GIS与遥感综合评估Chitrakoot区洪水灾害与脆弱性空间分布。","DEM、河网、历史洪水与卫星观测及社会经济数据叠加分析。","洪水风险集中于河谷低地、农田与居民点，历史分级低估极端降雨风险。","可结合时序遥感与水文模型，构建极端降雨情景下的农田洪涝风险动态制图与预警。","2026-10-19T23:30:24.356743Z",{"id":441,"title":442,"url":443,"summary":444,"summary_zh":445,"content":9,"source_name":446,"source_url":443,"published_at":384,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":447,"score_detail":448,"sources":450,"tags":452,"primary_topic":455,"secondary_topics":456,"classification":457,"search_phrases":472,"slug":475,"view_count":100,"doi":476,"paper":477,"created_at":495},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)",55,{"impact":20,"substance":17,"depth":121,"authority":110,"freshness":241,"relevant":21,"comment":449},"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。",[451],{"name":446,"url":443},[323,247,453,454],"植被覆盖","卢旺达农业",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":458},{"candidates":459,"agricultural_evidence":46},[460,464,468],{"code":32,"score":110,"supported":46,"title":461,"abstract":462,"tags":463},[],[259,260],[],{"code":263,"score":285,"supported":14,"title":465,"abstract":466,"tags":467},[],[],[453,323],{"code":284,"score":21,"supported":14,"title":469,"abstract":470,"tags":471},[],[],[247],[473,474],"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":476,"openalex_id":478,"authors":479,"venue":446,"cited_by_count":100,"oa_url":489,"card":490,"direction":307,"ingested_from":107},"W7222817253",[480,482,484,486],{"name":481,"orcid":9},"Vincent Hategekimana",{"name":483,"orcid":9},"Richard Mind J. E.",{"name":485,"orcid":9},"Aboubakar Gasirabo",{"name":487,"orcid":488},"Didier Ntwali","https:\u002F\u002Forcid.org\u002F0000-0002-6914-6652","https:\u002F\u002Fwww.ijisrt.com\u002Fassets\u002Fupload\u002Ffiles\u002FIJISRT26OCT696.pdf",{"tldr":491,"method":492,"finding":493,"direction":307,"opportunity":494},"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"]