[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4314":3,"related-4314":88},{"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":55,"slug":58,"view_count":15,"doi":59,"paper":60,"created_at":87},4314,"Spatiotemporal assessment of urban environmental livability in Dhaka (2004–2024)","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-69920-6","Rapid and largely unplanned urbanization has significantly reshaped Dhaka over the past two decades. This transformation has accelerated environmental degradation, reduced green and blue spaces, and undermined urban livability in one of the world's most densely populated megacities. Despite these growing environmental challenges, comprehensive long-term spatial assessments that integrate remote sensing and multi-criteria decision-making approaches remain scarce. This study assesses the spatiotemporal dynamics of urban environmental livability in Dhaka for 2004, 2014, and 2024 using the Urban Environmental Livability Index (UELI). Six indicators, including the Normalized Difference Vegetation Index (NDVI), Wetness, Index-based Built-up Index (IBI), Night Light Index (NLI), Urban Heat Island Ecological Intensity Index (UHIEII), and Air Pollution Index (API), were derived from multi-sensor remote sensing datasets in Google Earth Engine (GEE). Principal Component Analysis (PCA) and the Fuzzy Analytic Hierarchy Process (FAHP) were employed to determine indicator weights, while Global Moran’s I and Local Indicators of Spatial Association (LISA) were used to analyze the spatial distribution and clustering of UELI. The findings reveal a substantial decline in environmental livability, with livable areas decreasing from 30.53% in 2004 to 26.87% in 2014 and further to 20.75% in 2024. In contrast, unlivable areas expanded from 15.65% to 17.84% and 30.80% over the same period. Notably, approximately 24.69% of Dhaka’s total area transitioned to an unlivable state between 2004 and 2024. One-at-a-Time (OAT) sensitivity analysis and Partial Rank Correlation Coefficient (PRCC) analysis with a 95% confidence interval confirmed the robustness of the UELI framework and highlighted the dynamic nature of urban-environment interactions. Spatial analysis further identified the central and southwestern parts of Dhaka as the most affected areas, exhibiting the greatest deterioration in environmental livability and ecological conditions. The study provides robust empirical evidence to support sustainable urban planning, informed policy formulation, and effective urban ecological risk mitigation in rapidly urbanizing cities.","过去二十年间，快速且基本无序的城市化显著重塑了达卡的城市格局。这一转变加速了环境退化，减少了绿色和蓝色空间，并削弱了这座全球人口最稠密的特大城市之一的城市宜居性。尽管环境挑战日益加剧，但整合遥感与多准则决策方法的综合性长期空间评估仍然匮乏。本研究利用城市环境宜居性指数（UELI），评估了达卡2004年、2014年和2024年城市环境宜居性的时空动态。研究基于Google Earth Engine（GEE）平台的多传感器遥感数据集，提取了六个指标，包括归一化植被指数（NDVI）、湿度、基于指数的建成区指数（IBI）、夜间灯光指数（NLI）、城市热岛生态强度指数（UHIEII）和空气污染指数（API）。采用主成分分析（PCA）和模糊层次分析法（FAHP）确定指标权重，并运用全局莫兰指数（Global Moran's I）和局部空间关联指标（LISA）分析UELI的空间分布与集聚特征。研究结果表明，环境宜居性大幅下降，宜居区域从2004年的30.53%降至2014年的26.87%，并进一步降至2024年的20.75%。相比之下，不宜居区域在同一时期从15.65%扩大至17.84%和30.80%。值得注意的是，2004年至2024年间，达卡约24.69%的总面积转变为不宜居状态。单因子逐一（OAT）敏感性分析和95%置信区间下的偏秩相关系数（PRCC）分析证实了UELI框架的稳健性，并凸显了城市-环境相互作用的动态特征。空间分析进一步识别出达卡中部和西南部为受影响最严重的区域，其环境宜居性和生态状况退化最为显著。本研究为快速城市化城市的可持续城市规划、科学政策制定和有效的城市生态风险缓解提供了有力的实证依据。",null,"Scientific Reports","2026-10-04T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"研究达卡城市环境宜居性时空变化，属城市遥感与城市规划领域，与三农、农业信息化、数字乡村主题无关，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","城市宜居性",[],{"status":26,"locked":14,"version":27,"method":28,"evidence":29},"needs_review","2026-10-22.2","rules",{"candidates":30,"agricultural_evidence":14,"reason":54},[31,40,48],{"code":32,"score":33,"supported":34,"title":35,"abstract":36,"tags":39},"I3",7,true,[],[37,38],"遥感","remote sensing",[21],{"code":41,"score":42,"supported":34,"title":43,"abstract":44,"tags":47},"I4",6,[],[45,46],"空间分析","spatial analysis",[],{"code":49,"score":50,"supported":14,"title":51,"abstract":52,"tags":53},"H6",1,[],[],[22],"农业相关性需要复核",[56,57],"达卡 城市环境宜居性 遥感","城市宜居性 土地利用 遥感监测","达卡城市环境宜居性遥感-4314","10.1038\u002Fs41598-026-69920-6",{"doi":59,"openalex_id":61,"authors":62,"venue":10,"cited_by_count":15,"oa_url":79,"card":80,"direction":84,"ingested_from":86},"W7219630361",[63,65,68,70,72,74,76],{"name":64,"orcid":9},"Khandoker Mariatul Islam",{"name":66,"orcid":67},"Md. Mahmudul Hasan","https:\u002F\u002Forcid.org\u002F0009-0004-5235-4131",{"name":69,"orcid":9},"Afroja Khanam Sonia",{"name":71,"orcid":9},"Mahzabin Akhter",{"name":73,"orcid":9},"Barbara Sneha Gomes",{"name":75,"orcid":9},"Most. Mitu Akter",{"name":77,"orcid":78},"N. M. Refat Nasher","https:\u002F\u002Forcid.org\u002F0000-0003-2974-4970","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-69920-6_reference.pdf",{"tldr":81,"method":82,"finding":83,"direction":84,"opportunity":85},"基于多源遥感与多准则决策评估达卡2004–2024年城市环境宜居性时空变化。","GEE多传感器遥感提取六项指标，PCA与FAHP赋权，Moran's I\u002FLIS","宜居区从30.53%降至20.75%，不宜居区升至30.80%，中部和西南部退化最严重。","农业遥感与作物表型","可将UELI框架迁移至城乡交错带农业区，耦合耕地撂荒与生态宜居性做长时序评估。","openalex","2026-10-06T23:30:21.652983Z",{"total":42,"page":50,"page_size":42,"items":89},[90,177,248,293,367,428],{"id":91,"title":92,"url":93,"summary":94,"summary_zh":95,"content":9,"source_name":96,"source_url":93,"published_at":97,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":98,"score_detail":99,"sources":106,"tags":108,"primary_topic":9,"secondary_topics":112,"classification":113,"search_phrases":147,"slug":150,"view_count":15,"doi":151,"paper":152,"created_at":176},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":100,"substance":101,"depth":102,"authority":103,"freshness":104,"relevant":50,"comment":105},18,22,17,12,9,"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。",[107],{"name":96,"url":93},[21,22,109,110,111],"森林保护","亚马逊","CBERS-4",[],{"status":26,"locked":14,"version":27,"method":28,"evidence":114},{"candidates":115,"agricultural_evidence":34,"reason":146},[116,122,126,133,140],{"code":49,"score":33,"supported":34,"title":117,"abstract":118,"tags":121},[],[119,120],"生物多样性","biodiversity",[22],{"code":32,"score":33,"supported":34,"title":123,"abstract":124,"tags":125},[],[37,38],[21],{"code":127,"score":42,"supported":34,"title":128,"abstract":129,"tags":132},"H7",[],[130,131],"气候变化","climate change",[],{"code":134,"score":135,"supported":34,"title":136,"abstract":137,"tags":139},"H2",3,[],[138],"soil",[],{"code":141,"score":135,"supported":34,"title":142,"abstract":143,"tags":145},"I7",[],[144],"研究方法",[],"多个产业证据相当，需要确认主归属",[148,149],"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":151,"openalex_id":153,"authors":154,"venue":96,"cited_by_count":15,"oa_url":93,"card":171,"direction":84,"ingested_from":86},"W7223141286",[155,158,160,163,166,169],{"name":156,"orcid":157},"Ubiratan Joaquim da Silva","https:\u002F\u002Forcid.org\u002F0000-0001-7995-6416",{"name":159,"orcid":9},"Gabriel Henrique Santos Silva",{"name":161,"orcid":162},"Juarez Antonio da Silva","https:\u002F\u002Forcid.org\u002F0000-0002-2898-0309",{"name":164,"orcid":165},"Anderson Luiz Ribeiro de Paiva","https:\u002F\u002Forcid.org\u002F0000-0003-3475-1454",{"name":167,"orcid":168},"Leidjane Maria Maciel de Oliveira","https:\u002F\u002Forcid.org\u002F0000-0003-1251-6998",{"name":170,"orcid":9},"Sylvana Melo dos Santos",{"tldr":172,"method":173,"finding":174,"direction":84,"opportunity":175},"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":178,"title":179,"url":180,"summary":181,"summary_zh":182,"content":9,"source_name":183,"source_url":180,"published_at":97,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":184,"sources":186,"tags":188,"primary_topic":191,"secondary_topics":197,"classification":202,"search_phrases":225,"slug":228,"view_count":15,"doi":229,"paper":230,"created_at":247},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":185},"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。",[187],{"name":183,"url":180},[189,21,22,190],"机器学习","城市扩张",{"code":192,"slug":193,"name":194,"path":195,"parent_code":196},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[198],{"code":41,"slug":199,"name":200,"path":201,"parent_code":196},"gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",{"status":203,"locked":14,"version":27,"method":28,"evidence":204},"classified",{"candidates":205,"agricultural_evidence":34},[206,212,217,221],{"code":192,"score":207,"supported":34,"title":208,"abstract":210,"tags":211},15,[209],"machine learning",[189,209],[189],{"code":41,"score":104,"supported":34,"title":213,"abstract":214,"tags":216},[],[45,215,46],"GIS",[],{"code":32,"score":33,"supported":34,"title":218,"abstract":219,"tags":220},[],[37,38],[21],{"code":49,"score":50,"supported":14,"title":222,"abstract":223,"tags":224},[],[],[22],[226,227],"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":229,"openalex_id":231,"authors":232,"venue":183,"cited_by_count":15,"oa_url":180,"card":242,"direction":84,"ingested_from":86},"W7223022155",[233,236,238,240],{"name":234,"orcid":235},"Kanenus Fufa Dararo","https:\u002F\u002Forcid.org\u002F0009-0007-5747-1558",{"name":237,"orcid":9},"Goanar Jal Dup",{"name":239,"orcid":9},"Weyesa Merga Chemeda",{"name":241,"orcid":9},"Shankar Karuppannan",{"tldr":243,"method":244,"finding":245,"direction":84,"opportunity":246},"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":249,"title":250,"url":251,"summary":252,"summary_zh":9,"content":9,"source_name":253,"source_url":251,"published_at":97,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":254,"sources":256,"tags":258,"primary_topic":9,"secondary_topics":260,"classification":261,"search_phrases":273,"slug":276,"view_count":15,"doi":277,"paper":278,"created_at":292},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":255},"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。",[257],{"name":253,"url":251},[21,22,259],"城市生态",[],{"status":26,"locked":14,"version":27,"method":28,"evidence":262},{"candidates":263,"agricultural_evidence":14,"reason":54},[264,268],{"code":32,"score":103,"supported":34,"title":265,"abstract":266,"tags":267},[38],[38],[21],{"code":49,"score":269,"supported":14,"title":270,"abstract":271,"tags":272},2,[],[],[22,259],[274,275],"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":277,"openalex_id":279,"authors":280,"venue":253,"cited_by_count":15,"oa_url":291,"card":9,"direction":84,"ingested_from":86},"W7223013355",[281,283,285,288],{"name":282,"orcid":9},"Md Saikoth Jahan",{"name":284,"orcid":9},"Mst Arjumoni Anu",{"name":286,"orcid":287},"Md. Inzamul Haque","https:\u002F\u002Forcid.org\u002F0000-0003-1756-5561",{"name":289,"orcid":290},"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":294,"title":295,"url":296,"summary":297,"summary_zh":298,"content":9,"source_name":299,"source_url":296,"published_at":300,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":301,"sources":303,"tags":305,"primary_topic":308,"secondary_topics":312,"classification":313,"search_phrases":333,"slug":336,"view_count":15,"doi":337,"paper":338,"created_at":366},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":302},"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。",[304],{"name":299,"url":296},[21,22,306,307],"土壤侵蚀","乡村人口",{"code":32,"slug":309,"name":310,"path":311,"parent_code":196},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",[],{"status":203,"locked":14,"version":27,"method":28,"evidence":314},{"candidates":315,"agricultural_evidence":34},[316,320,324,329],{"code":32,"score":33,"supported":34,"title":317,"abstract":318,"tags":319},[],[37,38],[21],{"code":41,"score":135,"supported":34,"title":321,"abstract":322,"tags":323},[],[215],[],{"code":141,"score":135,"supported":34,"title":325,"abstract":326,"tags":328},[],[327],"methodological framework",[],{"code":49,"score":50,"supported":14,"title":330,"abstract":331,"tags":332},[],[],[22],[334,335],"Kolubara 河岸侵蚀","Obrenovac 土地利用","Kolubara河岸侵蚀-5390","10.59954\u002Fstnv.797",{"doi":337,"openalex_id":339,"authors":340,"venue":299,"cited_by_count":15,"oa_url":296,"card":360,"direction":84,"ingested_from":86},"W7222945959",[341,344,347,349,352,355,358],{"name":342,"orcid":343},"Slavoljub Dragićević","https:\u002F\u002Forcid.org\u002F0000-0002-1417-5405",{"name":345,"orcid":346},"Marko Langović","https:\u002F\u002Forcid.org\u002F0000-0002-4614-7265",{"name":348,"orcid":9},"Милан Радовић",{"name":350,"orcid":351},"Radislav Tošić","https:\u002F\u002Forcid.org\u002F0000-0002-3994-125X",{"name":353,"orcid":354},"Milica Langović","https:\u002F\u002Forcid.org\u002F0000-0002-6979-377X",{"name":356,"orcid":357},"Novica Lovrić","https:\u002F\u002Forcid.org\u002F0009-0006-0838-4370",{"name":359,"orcid":9},"Manojlović Stefan",{"tldr":361,"method":362,"finding":363,"direction":364,"opportunity":365},"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":368,"title":369,"url":370,"summary":371,"summary_zh":372,"content":9,"source_name":373,"source_url":370,"published_at":374,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":375,"score_detail":376,"sources":380,"tags":382,"primary_topic":385,"secondary_topics":386,"classification":387,"search_phrases":404,"slug":407,"view_count":15,"doi":408,"paper":409,"created_at":427},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":377,"substance":100,"depth":378,"authority":42,"freshness":104,"relevant":50,"comment":379},8,14,"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。",[381],{"name":373,"url":370},[21,22,383,384],"植被覆盖","卢旺达农业",{"code":41,"slug":199,"name":200,"path":201,"parent_code":196},[],{"status":203,"locked":14,"version":27,"method":28,"evidence":388},{"candidates":389,"agricultural_evidence":34},[390,396,400],{"code":41,"score":42,"supported":34,"title":391,"abstract":392,"tags":395},[],[393,394],"地理信息","geographic information",[],{"code":32,"score":269,"supported":14,"title":397,"abstract":398,"tags":399},[],[],[383,21],{"code":49,"score":50,"supported":14,"title":401,"abstract":402,"tags":403},[],[],[22],[405,406],"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":408,"openalex_id":410,"authors":411,"venue":373,"cited_by_count":15,"oa_url":421,"card":422,"direction":84,"ingested_from":86},"W7222817253",[412,414,416,418],{"name":413,"orcid":9},"Vincent Hategekimana",{"name":415,"orcid":9},"Richard Mind J. E.",{"name":417,"orcid":9},"Aboubakar Gasirabo",{"name":419,"orcid":420},"Didier Ntwali","https:\u002F\u002Forcid.org\u002F0000-0002-6914-6652","https:\u002F\u002Fwww.ijisrt.com\u002Fassets\u002Fupload\u002Ffiles\u002FIJISRT26OCT696.pdf",{"tldr":423,"method":424,"finding":425,"direction":84,"opportunity":426},"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":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":439,"tags":441,"primary_topic":9,"secondary_topics":445,"classification":446,"search_phrases":459,"slug":462,"view_count":15,"doi":463,"paper":464,"created_at":475},5182,"Land Use Land Cover (LULC) Change Detection in Delhi NCT Using Multi-Sensor Satellite Data and Google Earth Engine","https:\u002F\u002Fdoi.org\u002F10.21275\u002Fsr26903183702","Land Use Land Cover (LULC) change detection is essential for understanding the dynamics of human modified landscapes and for supporting effective land management and urban planning. This study analyses LULC change across the Delhi National Capital Territory over a fifteen-year period, from 2010 to 2025, to assess urban sprawl and its environmental impact in one of India's fastest growing metropolitan regions. Multi temporal Landsat imagery was used as the primary data source, processed on Google Earth Engine, the cloud-based platform that enabled efficient handling and visualisation of large geospatial datasets. Five classes, built up area, agricultural land, vegetation, water, and barren land, were mapped and monitored, and classification accuracy was assessed for both dates. The Normalised Difference Vegetation Index was additionally employed to track vegetation vigour and ecological change. Results reveal a marked expansion of built-up areas alongside a substantial decline in agricultural and vegetated land, driven by peri urban growth along the northwestern and southwestern fringe. NDVI analysis shows rising greenness across the outer belt, reflecting more intensive cultivation on land that remains under crops, a pattern that complements rather than contradicts the measured loss of vegetated area. Combining classification based and an index-based approach provides a robust, scalable framework for LULC monitoring. By revealing where and how urban growth is reshaping the landscape, the study offers a transferable basis for environmental assessment, resource management, and sustainable urban planning in rapidly urbanising regions worldwide.","土地利用\u002F土地覆盖（LULC）变化检测对于理解人类改造景观的动态以及支撑有效的土地管理和城市规划至关重要。本研究分析了德里国家首都辖区在2010年至2025年十五年间的土地利用\u002F土地覆盖变化，以评估印度增长最快的大都市区之一的城市蔓延及其环境影响。研究以多时相Landsat影像作为主要数据源，在Google Earth Engine这一基于云的平台上进行处理，该平台能够高效处理和可视化大型地理空间数据集。研究对建成区、农业用地、植被、水体与裸地五个类别进行了制图和监测，并对两个时相的分类精度进行了评估。此外，研究采用归一化植被指数（NDVI）追踪植被长势与生态变化。结果表明，建成区显著扩张，农业用地和植被用地大幅减少，这一变化主要由西北部和西南部边缘的近郊增长所驱动。NDVI分析显示外围地带的绿度上升，反映出仍处于耕作状态的土地上集约化种植程度提高，这一格局与所测得的植被面积损失相辅相成而非相互矛盾。将基于分类的方法与基于指数的方法相结合，为土地利用\u002F土地覆盖监测提供了一个稳健且可扩展的框架。通过揭示城市增长在何处以及如何重塑景观，本研究为全球快速城市化地区的环境评估、资源管理和可持续城市规划提供了可迁移的基础。","International Journal of Science and Research (IJSR)","2026-10-15T00:00:00Z",62,{"impact":103,"substance":100,"depth":207,"authority":377,"freshness":104,"relevant":50,"comment":438},"方法可迁移的遥感LULC变化检测研究，对快速城市化地区土地管理有参考价值，但属区域案例、非三农核心议题。",[440],{"name":434,"url":431},[442,21,22,443,444],"NDVI","Google Earth Engine","城市化",[],{"status":447,"locked":14,"version":27,"method":28,"evidence":448},"unclassified",{"candidates":449,"agricultural_evidence":34,"reason":458},[450,454],{"code":32,"score":135,"supported":14,"title":451,"abstract":452,"tags":453},[],[],[443,442,21],{"code":49,"score":50,"supported":14,"title":455,"abstract":456,"tags":457},[],[],[22],"缺少足够的主题证据",[460,461],"德里 土地利用 遥感","Google Earth Engine 城市扩张","德里土地利用遥感-5182","10.21275\u002Fsr26903183702",{"doi":463,"openalex_id":465,"authors":466,"venue":434,"cited_by_count":15,"oa_url":469,"card":470,"direction":84,"ingested_from":86},"W7222341300",[467],{"name":468,"orcid":9},"Neil Rohatgi","https:\u002F\u002Fwww.ijsr.net\u002Farchive\u002Fv15i10\u002FSR26903183702.pdf",{"tldr":471,"method":472,"finding":473,"direction":84,"opportunity":474},"基于多时相Landsat与GEE分析德里国家首都辖区2010-2025年土地利用变化。","多时相Landsat影像、Google Earth Engine平台、五类分类与","建设用地显著扩张，农业与植被用地大幅减少，城郊西北和西南边缘增长驱动。","可结合分类与指数方法构建可迁移框架，拓展至城乡交错带耕地保护与可持续规划研究。","2026-10-17T23:30:31.767409Z"]