[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-5092":3,"related-5092":79},{"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":47,"slug":50,"view_count":42,"doi":51,"paper":52,"created_at":78},5092,"Seasonal flood inundation area and repetitive flood exposure shape the fundamental patterns of flood exposure in China from 2000 to 2020","https:\u002F\u002Fdoi.org\u002F10.1080\u002F19475705.2026.2745246","Remote sensing of floods and associated exposure is essential for understanding and managing flood risks, yet exposure differences among floods with varying frequencies remain underexplored. Using MODIS time-series data, this study mapped infrequent flood inundation area (IFIA) and seasonal flood inundation area (SFIA) across China during 2000–2020. Differences in flood extent and built-up land exposure were assessed using the Mann–Kendall test, improved Theil–Sen algorithm, and one-way analysis of variance. Cumulative flood inundation area reached 3.52 × 10⁶ km², with SFIA accounting for 40%. SFIA increased significantly (0.88%·a⁻¹), whereas IFIA declined markedly (−1.32%·a⁻¹). Built-up land exposure to SFIA reached 9.79 × 10⁵ km², representing 62% of total exposure, and increased faster (3.26%·a⁻¹) than exposure to IFIA (2.38%·a⁻¹). Among built-up lands affected by SFIA, 91% experienced repetitive flooding, contributing 99% of cumulative exposure. These findings demonstrate that seasonal and repetitive flooding shape the fundamental patterns of China’s flood exposure over the past two decades, providing insights into flood spatiotemporal dynamics and supporting risk-informed land-use planning.","洪水及其相关暴露的遥感监测对于理解和管理洪水风险至关重要，但不同频率洪水之间的暴露差异仍未得到充分探讨。本研究利用MODIS时间序列数据，绘制了2000—2020年中国低频洪水淹没面积（IFIA）和季节性洪水淹没面积（SFIA）。采用Mann–Kendall检验、改进的Theil–Sen算法和单因素方差分析评估了洪水范围与建设用地暴露的差异。累积洪水淹没面积达3.52 × 10⁶ km²，其中SFIA占40%。SFIA显著增加（0.88%·a⁻¹），而IFIA显著下降（−1.32%·a⁻¹）。建设用地对SFIA的暴露面积达9.79 × 10⁵ km²，占总暴露面积的62%，且其增长速度（3.26%·a⁻¹）快于对IFIA的暴露（2.38%·a⁻¹）。在受SFIA影响的建设用地中，91%经历了重复淹没，贡献了99%的累积暴露。这些发现表明，季节性和重复性洪水塑造了过去二十年我国洪水暴露的基本格局，为理解洪水时空动态提供了依据，并为基于风险的国土空间规划提供了支持。",null,"Geomatics Natural Hazards and Risk","2026-10-14T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"该研究聚焦全国洪水暴露遥感制图，属自然灾害与地理信息领域，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。",[19],{"name":10,"url":6},[21,22,23],"遥感监测","土地利用","洪涝风险",[],{"status":26,"locked":14,"version":27,"method":28,"evidence":29},"needs_review","2026-10-22.2","rules",{"candidates":30,"agricultural_evidence":14,"reason":46},[31,40],{"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":14,"title":43,"abstract":44,"tags":45},"H6",1,[],[],[22],"农业相关性需要复核",[48,49],"MODIS 洪水淹没 中国","季节性洪水 建成区暴露","MODIS洪水淹没中国-5092","10.1080\u002F19475705.2026.2745246",{"doi":51,"openalex_id":53,"authors":54,"venue":10,"cited_by_count":15,"oa_url":70,"card":71,"direction":75,"ingested_from":77},"W7221771962",[55,57,59,61,63,66,68],{"name":56,"orcid":9},"Hanru Shen",{"name":58,"orcid":9},"Chengjie Zhou",{"name":60,"orcid":9},"Peng Su",{"name":62,"orcid":9},"Zhenfei Cui",{"name":64,"orcid":65},"Kai Tao","https:\u002F\u002Forcid.org\u002F0000-0002-5343-1432",{"name":67,"orcid":9},"Wei Xu",{"name":69,"orcid":9},"Shiqiang Du","https:\u002F\u002Fwww.tandfonline.com\u002Fdoi\u002Fpdf\u002F10.1080\u002F19475705.2026.2745246?needAccess=true",{"tldr":72,"method":73,"finding":74,"direction":75,"opportunity":76},"基于MODIS时序数据绘制2000-2020年中国季节性\u002F偶发性洪水淹没范围，揭示暴露格局。","MODIS时序数据、Mann-Kendall检验、改进Theil-Sen与单因素","季节性洪水淹没显著增加，贡献62%建成区暴露，其中91%反复受淹。","农业遥感与作物表型","可结合耕地与作物物候，量化季节性洪水对农业暴露与产量的反复影响。","openalex","2026-10-16T23:30:28.411370Z",{"total":80,"page":42,"page_size":80,"items":81},6,[82,171,252,321,365,439],{"id":83,"title":84,"url":85,"summary":86,"summary_zh":87,"content":9,"source_name":88,"source_url":85,"published_at":89,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":90,"score_detail":91,"sources":98,"tags":100,"primary_topic":104,"secondary_topics":109,"classification":110,"search_phrases":145,"slug":148,"view_count":149,"doi":150,"paper":151,"created_at":170},4435,"Land Use Change, Flood Exposure, and Vegetation Stress in the Chi River Basin, Northeast Thailand: A Multi-Temporal Remote Sensing and Machine Learning Assessment","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fsu181910188","Climate change has intensified hydrological extremes in Southeast Asia, yet the relationships between land use change, flood exposure, and vegetation stress remain poorly understood in tropical floodplain environments. This study assessed land use dynamics, flood exposure, and vegetation stress in the Upper Chi River Basin, Northeast Thailand, using multi-temporal remote sensing datasets spanning 2015–2024, official land use data from the Land Development Department (LDD), and machine learning approaches, including Random Forest classification of binary flood occurrence, SHAP analysis, and spatial statistics. Agricultural land dominated the landscape (84.2% in 2023), with 11.0% land use change occurring between 2015 and 2023, primarily agricultural conversion from forest (38.6% of Forest Area converted) and water body (30.0% of Water Body converted). Flood frequency was highest in areas classified as Water Bodies (0.38 events) and Agricultural Areas (0.55 events), while Forest Areas showed the greatest resilience (0.05 events). Random Forest classification of flood occurrence achieved an overall accuracy of 0.920 and AUC-ROC of 0.905, with DEM emerging as the dominant predictor in both Random Forest importance (0.435) and mean absolute SHAP (0.308). Vegetation stress affected 21.1% of the basin, with 59.2% of points showing an NDVI decline greater than 0.02 and 33.3% falling below the VCI \u003C 0.4 drought threshold. Four sensitivity classes were identified: Low (66.4%), Moderate (19.2%), High (12.4%), and Very High (2.0%), with Very High Sensitivity concentrated along the Chi River corridor in low-lying areas (mean DEM = 146.0 m). Moran’s I confirmed strong spatial clustering of flood frequency (I = 0.787) and sensitivity class (I = 0.534), highlighting the need for spatial statistical approaches. The findings support elevation-based land use zoning, forest conservation, and climate-resilient agricultural practices to enhance flood resilience and contribute to Sustainable Development Goals 2 (Zero Hunger), 11 (Sustainable Cities), 13 (Climate Action), and 15 (Life on Land).","气候变化加剧了东南亚的水文极端事件，但在热带洪泛平原环境中，土地利用变化、洪水暴露与植被胁迫之间的关系仍缺乏深入认识。本研究利用2015—2024年多时相遥感数据集、泰国土地发展厅（LDD）的官方土地利用数据以及机器学习方法（包括对二元洪水发生事件的随机森林分类、SHAP分析和空间统计），评估了泰国东北部奇河上游流域的土地利用动态、洪水暴露和植被胁迫。农业用地在景观中占主导地位（2023年为84.2%），2015—2023年间土地利用变化率为11.0%，主要表现为农业用地扩张导致的林地转换（38.6%的林地面积被转换）和水体转换（30.0%的水体面积被转换）。洪水频率在水体分类区域（0.38次）和农业用地分类区域（0.55次）最高，而林地表现出最强的恢复力（0.05次）。洪水发生事件的随机森林分类总体精度达到0.920，AUC-ROC为0.905，其中DEM在随机森林重要性（0.435）和平均绝对SHAP值（0.308）中均为最主要的预测因子。植被胁迫影响了流域21.1%的区域，59.2%的样点NDVI下降幅度超过0.02，33.3%的样点低于VCI \u003C 0.4的干旱阈值。研究识别出四个敏感性等级：低敏感（66.4%）、中等敏感（19.2%）、高敏感（12.4%）和极高敏感（2.0%），其中极高敏感区集中在奇河沿岸低洼地带（平均DEM = 146.0 m）。Moran's I证实了洪水频率（I = 0.787）和敏感性等级（I = 0.534）具有强烈的空间聚集性，凸显了空间统计方法的必要性。研究结果支持基于海拔的土地利用分区、森林保护以及气候适应性农业实践，以增强洪水恢复力，并助力实现可持续发展目标2（零饥饿）、11（可持续城市）、13（气候行动）和15（陆地生物）。","Sustainability","2026-10-07T00:00:00Z",70,{"impact":92,"substance":93,"depth":94,"authority":95,"freshness":96,"relevant":42,"comment":97},8,22,18,13,9,"基于多时相遥感与机器学习的流域土地利用—洪涝—植被胁迫评估，方法扎实、数据翔实，对农业防灾与气候韧性有参考价值，但属区域性研究，公共影响有限。",[99],{"name":88,"url":85},[101,102,21,22,23,103],"数字乡村","机器学习","植被胁迫",{"code":105,"slug":106,"name":107,"path":108,"parent_code":9},"I","digital-agriculture","数字农业与前沿技术","\u002Ftopic\u002Fdigital-agriculture",[],{"status":111,"locked":14,"version":27,"method":28,"evidence":112},"partial",{"candidates":113,"agricultural_evidence":34,"reason":144},[114,121,125,135,139],{"code":115,"score":116,"supported":34,"title":117,"abstract":119,"tags":120},"I2",15,[118],"machine learning",[102,118],[102],{"code":32,"score":116,"supported":34,"title":122,"abstract":123,"tags":124},[38],[37,38],[21],{"code":126,"score":96,"supported":34,"title":127,"abstract":128,"tags":134},"H7",[],[129,130,131,132,133],"气候变化","气候适应","干旱","climate change","drought",[],{"code":41,"score":42,"supported":14,"title":136,"abstract":137,"tags":138},[],[],[22],{"code":140,"score":42,"supported":14,"title":141,"abstract":142,"tags":143},"I6",[],[],[101],"一级领域明确，二级方向需要细分",[146,147],"泰国 Chi River 流域 遥感","土地利用变化 洪涝暴露 植被胁迫","泰国ChiRiver流域遥感-4435",2,"10.3390\u002Fsu181910188",{"doi":150,"openalex_id":152,"authors":153,"venue":88,"cited_by_count":15,"oa_url":85,"card":165,"direction":75,"ingested_from":77},"W7220850940",[154,156,159,162],{"name":155,"orcid":9},"Jiradech Majandang",{"name":157,"orcid":158},"Patiwat Littidej","https:\u002F\u002Forcid.org\u002F0000-0002-1024-547X",{"name":160,"orcid":161},"Benjamabhorn Pumhirunroj","https:\u002F\u002Forcid.org\u002F0009-0009-6607-5594",{"name":163,"orcid":164},"D. C. Slack","https:\u002F\u002Forcid.org\u002F0000-0003-0324-2163",{"tldr":166,"method":167,"finding":168,"direction":75,"opportunity":169},"用多时相遥感和机器学习评估泰国栖河流域土地利用变化、洪水暴露与植被胁迫的关系。","2015–2024年多时相遥感、LDD土地利用数据、随机森林、SHAP与空间统计","农地占84.2%，洪水频发于农地和水体，21.1%流域植被受胁迫，DEM是洪水主导因子。","可结合高分辨率时序遥感与作物模型，量化洪水胁迫对热带平原农业产量的级联影响。","2026-10-08T23:30:29.519607Z",{"id":172,"title":173,"url":174,"summary":175,"summary_zh":176,"content":9,"source_name":177,"source_url":174,"published_at":178,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":179,"score_detail":180,"sources":184,"tags":186,"primary_topic":9,"secondary_topics":190,"classification":191,"search_phrases":222,"slug":225,"view_count":15,"doi":226,"paper":227,"created_at":251},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":94,"substance":93,"depth":181,"authority":182,"freshness":96,"relevant":42,"comment":183},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。",[185],{"name":177,"url":174},[21,22,187,188,189],"森林保护","亚马逊","CBERS-4",[],{"status":26,"locked":14,"version":27,"method":28,"evidence":192},{"candidates":193,"agricultural_evidence":34,"reason":221},[194,200,204,208,215],{"code":41,"score":33,"supported":34,"title":195,"abstract":196,"tags":199},[],[197,198],"生物多样性","biodiversity",[22],{"code":32,"score":33,"supported":34,"title":201,"abstract":202,"tags":203},[],[37,38],[21],{"code":126,"score":80,"supported":34,"title":205,"abstract":206,"tags":207},[],[129,132],[],{"code":209,"score":210,"supported":34,"title":211,"abstract":212,"tags":214},"H2",3,[],[213],"soil",[],{"code":216,"score":210,"supported":34,"title":217,"abstract":218,"tags":220},"I7",[],[219],"研究方法",[],"多个产业证据相当，需要确认主归属",[223,224],"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":226,"openalex_id":228,"authors":229,"venue":177,"cited_by_count":15,"oa_url":174,"card":246,"direction":75,"ingested_from":77},"W7223141286",[230,233,235,238,241,244],{"name":231,"orcid":232},"Ubiratan Joaquim da Silva","https:\u002F\u002Forcid.org\u002F0000-0001-7995-6416",{"name":234,"orcid":9},"Gabriel Henrique Santos Silva",{"name":236,"orcid":237},"Juarez Antonio da Silva","https:\u002F\u002Forcid.org\u002F0000-0002-2898-0309",{"name":239,"orcid":240},"Anderson Luiz Ribeiro de Paiva","https:\u002F\u002Forcid.org\u002F0000-0003-3475-1454",{"name":242,"orcid":243},"Leidjane Maria Maciel de Oliveira","https:\u002F\u002Forcid.org\u002F0000-0003-1251-6998",{"name":245,"orcid":9},"Sylvana Melo dos Santos",{"tldr":247,"method":248,"finding":249,"direction":75,"opportunity":250},"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":253,"title":254,"url":255,"summary":256,"summary_zh":257,"content":9,"source_name":258,"source_url":255,"published_at":178,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":259,"sources":261,"tags":263,"primary_topic":265,"secondary_topics":269,"classification":275,"search_phrases":298,"slug":301,"view_count":15,"doi":302,"paper":303,"created_at":320},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":260},"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。",[262],{"name":258,"url":255},[102,21,22,264],"城市扩张",{"code":115,"slug":266,"name":267,"path":268,"parent_code":105},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",[270],{"code":271,"slug":272,"name":273,"path":274,"parent_code":105},"I4","gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",{"status":276,"locked":14,"version":27,"method":28,"evidence":277},"classified",{"candidates":278,"agricultural_evidence":34},[279,283,290,294],{"code":115,"score":116,"supported":34,"title":280,"abstract":281,"tags":282},[118],[102,118],[102],{"code":271,"score":96,"supported":34,"title":284,"abstract":285,"tags":289},[],[286,287,288],"空间分析","GIS","spatial analysis",[],{"code":32,"score":33,"supported":34,"title":291,"abstract":292,"tags":293},[],[37,38],[21],{"code":41,"score":42,"supported":14,"title":295,"abstract":296,"tags":297},[],[],[22],[299,300],"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":302,"openalex_id":304,"authors":305,"venue":258,"cited_by_count":15,"oa_url":255,"card":315,"direction":75,"ingested_from":77},"W7223022155",[306,309,311,313],{"name":307,"orcid":308},"Kanenus Fufa Dararo","https:\u002F\u002Forcid.org\u002F0009-0007-5747-1558",{"name":310,"orcid":9},"Goanar Jal Dup",{"name":312,"orcid":9},"Weyesa Merga Chemeda",{"name":314,"orcid":9},"Shankar Karuppannan",{"tldr":316,"method":317,"finding":318,"direction":75,"opportunity":319},"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":322,"title":323,"url":324,"summary":325,"summary_zh":9,"content":9,"source_name":326,"source_url":324,"published_at":178,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":327,"sources":329,"tags":331,"primary_topic":9,"secondary_topics":333,"classification":334,"search_phrases":345,"slug":348,"view_count":15,"doi":349,"paper":350,"created_at":364},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":328},"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。",[330],{"name":326,"url":324},[21,22,332],"城市生态",[],{"status":26,"locked":14,"version":27,"method":28,"evidence":335},{"candidates":336,"agricultural_evidence":14,"reason":46},[337,341],{"code":32,"score":182,"supported":34,"title":338,"abstract":339,"tags":340},[38],[38],[21],{"code":41,"score":149,"supported":14,"title":342,"abstract":343,"tags":344},[],[],[22,332],[346,347],"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":349,"openalex_id":351,"authors":352,"venue":326,"cited_by_count":15,"oa_url":363,"card":9,"direction":75,"ingested_from":77},"W7223013355",[353,355,357,360],{"name":354,"orcid":9},"Md Saikoth Jahan",{"name":356,"orcid":9},"Mst Arjumoni Anu",{"name":358,"orcid":359},"Md. Inzamul Haque","https:\u002F\u002Forcid.org\u002F0000-0003-1756-5561",{"name":361,"orcid":362},"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":366,"title":367,"url":368,"summary":369,"summary_zh":370,"content":9,"source_name":371,"source_url":368,"published_at":372,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":373,"sources":375,"tags":377,"primary_topic":380,"secondary_topics":384,"classification":385,"search_phrases":405,"slug":408,"view_count":15,"doi":409,"paper":410,"created_at":438},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":374},"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。",[376],{"name":371,"url":368},[21,22,378,379],"土壤侵蚀","乡村人口",{"code":32,"slug":381,"name":382,"path":383,"parent_code":105},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",[],{"status":276,"locked":14,"version":27,"method":28,"evidence":386},{"candidates":387,"agricultural_evidence":34},[388,392,396,401],{"code":32,"score":33,"supported":34,"title":389,"abstract":390,"tags":391},[],[37,38],[21],{"code":271,"score":210,"supported":34,"title":393,"abstract":394,"tags":395},[],[287],[],{"code":216,"score":210,"supported":34,"title":397,"abstract":398,"tags":400},[],[399],"methodological framework",[],{"code":41,"score":42,"supported":14,"title":402,"abstract":403,"tags":404},[],[],[22],[406,407],"Kolubara 河岸侵蚀","Obrenovac 土地利用","Kolubara河岸侵蚀-5390","10.59954\u002Fstnv.797",{"doi":409,"openalex_id":411,"authors":412,"venue":371,"cited_by_count":15,"oa_url":368,"card":432,"direction":75,"ingested_from":77},"W7222945959",[413,416,419,421,424,427,430],{"name":414,"orcid":415},"Slavoljub Dragićević","https:\u002F\u002Forcid.org\u002F0000-0002-1417-5405",{"name":417,"orcid":418},"Marko Langović","https:\u002F\u002Forcid.org\u002F0000-0002-4614-7265",{"name":420,"orcid":9},"Милан Радовић",{"name":422,"orcid":423},"Radislav Tošić","https:\u002F\u002Forcid.org\u002F0000-0002-3994-125X",{"name":425,"orcid":426},"Milica Langović","https:\u002F\u002Forcid.org\u002F0000-0002-6979-377X",{"name":428,"orcid":429},"Novica Lovrić","https:\u002F\u002Forcid.org\u002F0009-0006-0838-4370",{"name":431,"orcid":9},"Manojlović Stefan",{"tldr":433,"method":434,"finding":435,"direction":436,"opportunity":437},"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":440,"title":441,"url":442,"summary":443,"summary_zh":444,"content":9,"source_name":445,"source_url":442,"published_at":446,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":447,"score_detail":448,"sources":451,"tags":453,"primary_topic":456,"secondary_topics":457,"classification":458,"search_phrases":475,"slug":478,"view_count":15,"doi":479,"paper":480,"created_at":498},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":92,"substance":94,"depth":449,"authority":80,"freshness":96,"relevant":42,"comment":450},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。",[452],{"name":445,"url":442},[21,22,454,455],"植被覆盖","卢旺达农业",{"code":271,"slug":272,"name":273,"path":274,"parent_code":105},[],{"status":276,"locked":14,"version":27,"method":28,"evidence":459},{"candidates":460,"agricultural_evidence":34},[461,467,471],{"code":271,"score":80,"supported":34,"title":462,"abstract":463,"tags":466},[],[464,465],"地理信息","geographic information",[],{"code":32,"score":149,"supported":14,"title":468,"abstract":469,"tags":470},[],[],[454,21],{"code":41,"score":42,"supported":14,"title":472,"abstract":473,"tags":474},[],[],[22],[476,477],"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":479,"openalex_id":481,"authors":482,"venue":445,"cited_by_count":15,"oa_url":492,"card":493,"direction":75,"ingested_from":77},"W7222817253",[483,485,487,489],{"name":484,"orcid":9},"Vincent Hategekimana",{"name":486,"orcid":9},"Richard Mind J. E.",{"name":488,"orcid":9},"Aboubakar Gasirabo",{"name":490,"orcid":491},"Didier Ntwali","https:\u002F\u002Forcid.org\u002F0000-0002-6914-6652","https:\u002F\u002Fwww.ijisrt.com\u002Fassets\u002Fupload\u002Ffiles\u002FIJISRT26OCT696.pdf",{"tldr":494,"method":495,"finding":496,"direction":75,"opportunity":497},"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"]