[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2532":3,"related-2532":90},{"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":24,"tags":26,"primary_topic":32,"secondary_topics":38,"classification":39,"search_phrases":70,"slug":73,"view_count":55,"doi":74,"paper":75,"created_at":89},2532,"Remote Sensing-Based Spatiotemporal Assessment of Ecological Quality using RSEI from 2016 to 2025: Gala Lake Basin, Türkiye","https:\u002F\u002Fdoi.org\u002F10.35229\u002Fjaes.1965400","Wetland-agricultural basins are among the most sensitive landscapes to ecological change because vegetation dynamics, surface moisture, bare soil exposure, and thermal stress interact within the same hydrological system. This study assessed the spatiotemporal ecological quality of the Gala Lake Basin, Türkiye, from 2016 to 2025 using the Remote Sensing Ecological Index (RSEI). The basin was selected because it contains Gala Lake, associated wetlands, agricultural lands, and seasonally variable water surfaces, making it a representative and environmentally important wetland-agricultural landscape in northwestern Türkiye. Landsat 8 OLI\u002FTIRS and Landsat 9 OLI-2\u002FTIRS-2 images were processed in Google Earth Engine to derive NDVI, WET, NDBSI, and LST, representing greenness, wetness, dryness, and heat, respectively. MNDWI was used to mask open-water areas before RSEI construction. The four indicators were normalized and integrated using PCA, and annual RSEI maps were classified into five ecological quality levels. The results revealed that ecological quality in the basin showed strong interannual variability rather than a uniform trend. The mean RSEI reached its lowest level in 2022 and its highest in 2025. Class-based analysis showed a clear reduction in low-ecological-quality areas and an expansion of moderate-ecological-quality areas by the end of the study period. PCA results confirmed that PC1 captured the dominant ecological gradient in all years, supporting its use for annual RSEI construction. This study demonstrates that RSEI provides a robust and reproducible framework for monitoring ecological quality in wetland-agricultural basins and offers baseline information for long-term environmental management of the Gala Lake Basin.","湿地-农业流域是对生态变化最为敏感的景观之一，因为植被动态、地表湿度、裸土暴露和热胁迫在同一水文系统内相互作用。本研究利用遥感生态指数（RSEI）评估了土耳其加拉湖流域2016年至2025年的时空生态质量。选择该流域是因为其包含加拉湖、相关湿地、农业用地和季节性变化的水面，使其成为土耳其西北部具有代表性和环境重要性的湿地-农业景观。在Google Earth Engine中处理Landsat 8 OLI\u002FTIRS和Landsat 9 OLI-2\u002FTIRS-2影像，以提取NDVI、WET、NDBSI和LST，分别代表绿度、湿度、干度和热度。在构建RSEI之前，使用MNDWI掩膜开阔水域。四个指标经归一化后通过PCA进行整合，并将年度RSEI图分为五个生态质量等级。结果表明，流域生态质量表现出强烈的年际变化，而非一致趋势。RSEI均值在2022年降至最低水平，在2025年达到最高水平。基于等级的分析显示，到研究期末，低生态质量区域明显减少，中等生态质量区域扩大。PCA结果证实，PC1在所有年份均捕捉到了主导生态梯度，支持其用于年度RSEI构建。本研究表明，RSEI为监测湿地-农业流域生态质量提供了一个稳健且可重复的框架，并为加拉湖流域的长期环境管理提供了基线信息。",null,"Journal of Anatolian Environmental and Animal Sciences","2026-09-14T00:00:00Z","论文",10,false,63,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},8,18,16,12,9,1,"基于Landsat与Google Earth Engine的湿地-农业流域生态质量遥感评估，方法规范、数据扎实，但属区域案例研究，对国内三农实践的直接参考价值有限。",[25],{"name":10,"url":6},[27,28,29,30,31],"农业信息化","遥感监测","湿地农业","生态质量","RSEI",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",[],{"status":40,"locked":14,"version":41,"method":42,"evidence":43},"classified","2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":47},[45,53,60,65],{"code":33,"score":46,"supported":47,"title":48,"abstract":50,"tags":52},15,true,[49],"remote sensing",[51,49],"遥感",[28],{"code":54,"score":55,"supported":47,"title":56,"abstract":57,"tags":59},"H2",3,[],[58],"soil",[],{"code":61,"score":22,"supported":14,"title":62,"abstract":63,"tags":64},"H6",[],[],[30],{"code":66,"score":22,"supported":14,"title":67,"abstract":68,"tags":69},"I1",[],[],[27],[71,72],"农业信息化 湿地农业 生态质量 遥感监测","农业信息化 湿地农业","农业信息化湿地农业生态质量遥感监测-2532","10.35229\u002Fjaes.1965400",{"doi":74,"openalex_id":76,"authors":77,"venue":10,"cited_by_count":81,"oa_url":6,"card":82,"direction":86,"ingested_from":88},"W7213086036",[78],{"name":79,"orcid":80},"Enes Özgenç","https:\u002F\u002Forcid.org\u002F0000-0003-0878-6418",0,{"tldr":83,"method":84,"finding":85,"direction":86,"opportunity":87},"基于RSEI评估土耳其Gala湖流域2016-2025年生态质量时空变化。","Landsat影像在GEE中提取NDVI、WET、NDBSI、LST，PCA构建","生态质量年际波动明显，2022年最低、2025年最高，低质量区减少、中质量区扩张。","农业遥感与作物表型","可结合作物物候与灌溉数据，解析湿地-农业复合区生态质量波动的驱动机制。","openalex","2026-09-15T23:30:20.761810Z",{"total":91,"page":22,"page_size":91,"items":92},6,[93,165,226,298,370,449],{"id":94,"title":95,"url":96,"summary":97,"summary_zh":98,"content":9,"source_name":99,"source_url":96,"published_at":100,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":101,"score_detail":102,"sources":106,"tags":108,"primary_topic":112,"secondary_topics":113,"classification":114,"search_phrases":141,"slug":144,"view_count":145,"doi":146,"paper":147,"created_at":164},4355,"Agricultural Flood Damage Assessment: A Systematic Review of Methods, Coverage, and Integration Gaps","https:\u002F\u002Fdoi.org\u002F10.5194\u002Fegusphere-2026-3701","Abstract. Agricultural flooding causes tens of billions of dollars in annual losses globally, yet the methods used to assess these losses systematically capture only a fraction of their true economic scope. This systematic review synthesizes 65 peer-reviewed studies published between 1984 and 2025 to evaluate how agricultural flood damage is measured, what damage categories are covered, and where critical methodological gaps persist. We find a structural asymmetry across the reviewed literature – all 65 reviewed studies address direct immediate damages – primarily crop yield loss estimated through depth-damage functions or remote sensing – while direct induced effects appear in only 8 studies, indirect damages in 9, and intangible damages in fewer than 5. This asymmetry has persisted for four decades, through a major remote sensing revolution, because the dominant methods are structurally incapable of measuring damage categories beyond direct immediate losses. We introduce a Methods-by-Damage-Category Matrix that maps eight assessment methods against five damage categories, making the coverage gap visible and providing a diagnostic framework for method selection. Three post-2013 damage frontiers – long-term land productivity degradation, supply chain finance disruption, and farmer mental health – are identified as emerging categories warranting dedicated methodological development. Existing agricultural flood damage estimates used in benefit–cost analyses and disaster compensation are systematically downward-biased; closing this bias requires integrating remote sensing-based physical detection with economic valuation methods capable of capturing the full damage taxonomy.","摘要：农业洪水每年在全球造成数百亿美元损失，然而用于系统评估这些损失的方法仅能捕捉其真实经济影响的一小部分。本系统综述综合了1984年至2025年间发表的65项同行评审研究，评估农业洪水损失如何被测量、涵盖了哪些损失类别，以及关键方法论空白存在于何处。我们发现，所综述文献中存在结构性不对称——全部65项研究均涉及直接即时损失，主要是通过深度-损失函数或遥感估算的作物产量损失——而直接诱发效应仅出现在8项研究中，间接损失出现在9项研究中，无形损失则不足5项。这种不对称已持续四十年，历经遥感技术的重大革命，因为主流方法在结构上无法测量直接即时损失之外的损失类别。我们引入“方法-损失类别矩阵”，将八种评估方法与五类损失类别进行对应，使覆盖缺口清晰可见，并为方法选择提供诊断框架。2013年后的三个损失前沿——长期土地生产力退化、供应链金融中断和农民心理健康——被识别为值得专门方法论开发的新兴类别。现有用于成本效益分析和灾害补偿的农业洪水损失估算存在系统性低估；弥合这一偏差需要将基于遥感的物理检测与能够捕捉完整损失分类的经济估值方法相结合。","OpenAlex","2026-10-06T00:00:00Z",79,{"impact":18,"substance":103,"depth":18,"authority":104,"freshness":17,"relevant":22,"comment":105},22,13,"系统综述揭示农业洪灾损失评估长期偏低估的方法学缺口，提出方法-损失类别矩阵，对灾害补偿与成本收益分析有实质参考价值。",[107],{"name":99,"url":96},[27,109,110,28,111],"粮食安全","农业保险","灾害评估",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":115},{"candidates":116,"agricultural_evidence":47},[117,122,127,132,137],{"code":33,"score":118,"supported":47,"title":119,"abstract":120,"tags":121},7,[],[51,49],[28],{"code":123,"score":22,"supported":14,"title":124,"abstract":125,"tags":126},"A8",[],[],[109],{"code":128,"score":22,"supported":14,"title":129,"abstract":130,"tags":131},"G3",[],[],[110],{"code":133,"score":22,"supported":14,"title":134,"abstract":135,"tags":136},"H7",[],[],[111],{"code":66,"score":22,"supported":14,"title":138,"abstract":139,"tags":140},[],[],[27],[142,143],"农业洪涝灾害 损失评估","遥感 农作物 灾害损失","农业洪涝灾害损失评估-4355",2,"10.5194\u002Fegusphere-2026-3701",{"doi":146,"openalex_id":148,"authors":149,"venue":9,"cited_by_count":81,"oa_url":96,"card":159,"direction":86,"ingested_from":88},"W7220668753",[150,153,156],{"name":151,"orcid":152},"Shokhrukh–Mirzo Jalilov","https:\u002F\u002Forcid.org\u002F0000-0002-2428-9094",{"name":154,"orcid":155},"Haluk Gedikoğlu","https:\u002F\u002Forcid.org\u002F0000-0002-0158-2597",{"name":157,"orcid":158},"Robert Maltsbarger","https:\u002F\u002Forcid.org\u002F0009-0003-7801-8390",{"tldr":160,"method":161,"finding":162,"direction":86,"opportunity":163},"系统综述65项农业洪水损失评估研究，揭示方法仅覆盖直接即时损失的结构性偏差。","系统综述1984-2025年65篇文献，构建方法-损失类别矩阵。","所有研究仅测直接即时损失，间接、诱导及无形损失严重缺失，致损失估计系统性偏低。","可开发融合遥感与经济学估值的方法，量化长期土地退化、供应链金融与农户心理健康等新兴损失类别。","2026-10-07T23:30:25.887527Z",{"id":166,"title":167,"url":168,"summary":169,"summary_zh":170,"content":9,"source_name":171,"source_url":168,"published_at":172,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":173,"score_detail":174,"sources":177,"tags":179,"primary_topic":183,"secondary_topics":184,"classification":185,"search_phrases":202,"slug":205,"view_count":206,"doi":207,"paper":208,"created_at":225},3361,"Spatiotemporal Patterns and Associations of Ecological Quality and Carbon Storage Across Three Major Agricultural Provinces in Central China","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fland15101781","Ecological quality, vegetation productivity, and carbon storage represent different ecosystem properties and may respond differently to land-use change. This study assessed their spatiotemporal patterns and spatial relationships across Hubei, Henan, and Anhui, three major agricultural provinces in central China. Ecological quality was assessed using the Remote Sensing Ecological Index (RSEI), vegetation productivity was estimated as net primary productivity (NPP) using the Carnegie–Ames–Stanford Approach (CASA) model, and carbon storage was estimated using the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model. Spearman correlation, GeoDetector, and a descriptive coordination index were used to compare their spatial relationships. Ecological quality declined from 2000 to 2010 and recovered only modestly thereafter, with persistently lower values in the eastern and northern plains than in the western and southern mountains. From 1990 to 2020, built-up land expanded by 112.03%, while model-estimated carbon storage declined by 34.59 × 106 t. RSEI and NPP were predominantly positively correlated, but correlations varied substantially among provinces, ecological-quality classes, and land-use types. RSEI had the greatest explanatory power for spatial variation in carbon storage among the composite factors (q = 0.420–0.467), and NDVI was the strongest RSEI component. GeoDetector identified bivariate and nonlinear enhancement, indicating that paired factors explained more spatial variation than individual factors. Moderate and primary coordination together accounted for more than 99% of valid pixels, while severe imbalance declined from 0.88% to 0.44%. These findings show that the three indicators are spatially associated but not interchangeable. Together, the results support protecting forested mountain areas, limiting built-up expansion and cropland loss in plains and urban fringes, and using multiple indicators to identify areas requiring local assessment.","生态质量、植被生产力与碳储量表征不同的生态系统属性，可能对土地利用变化作出不同响应。本研究评估了华中三个农业大省——湖北、河南和安徽——上述指标的时空格局及其空间关系。生态质量采用遥感生态指数（RSEI）评估，植被生产力以Carnegie–Ames–Stanford Approach（CASA）模型估算的净初级生产力（NPP）表征，碳储量则采用生态系统服务与权衡综合评估（InVEST）模型估算。运用Spearman相关分析、地理探测器及描述性协调指数比较其空间关系。2000—2010年生态质量下降，此后仅略有恢复，东部和北部平原的值持续低于西部和南部山区。1990—2020年，建设用地扩张112.03%，而模型估算的碳储量下降34.59 × 10⁶ t。RSEI与NPP总体呈正相关，但相关性在各省份、生态质量等级和土地利用类型间差异显著。在复合因子中，RSEI对碳储量空间变异的解释力最强（q = 0.420–0.467），NDVI是RSEI中最强的组分。地理探测器识别出双因子和非线性增强，表明成对因子解释的空间变异多于单因子。中度协调和初级协调合计占有效像元的99%以上，而严重失调从0.88%降至0.44%。研究结果表明，三个指标在空间上相互关联但不可互换。综合而言，研究结果支持保护山地森林区域、限制平原和城市边缘的建设用地扩张与耕地流失，并利用多指标识别需开展局部评估的区域。","Land","2026-09-23T00:00:00Z",78,{"impact":19,"substance":103,"depth":18,"authority":175,"freshness":17,"relevant":22,"comment":176},14,"基于RSEI、CASA与InVEST模型对中部三大农业省生态质量与碳储量的长时序空间关联分析，方法规范、数据扎实，对农业生态保护与国土空间管控有参考价值。",[178],{"name":171,"url":168},[180,28,181,30,182],"农业生态","土地利用变化","碳储量",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":186},{"candidates":187,"agricultural_evidence":47},[188,192,197],{"code":33,"score":118,"supported":47,"title":189,"abstract":190,"tags":191},[],[51,49],[28],{"code":61,"score":91,"supported":47,"title":193,"abstract":194,"tags":196},[],[195],"生态系统服务",[180,181,30],{"code":198,"score":22,"supported":14,"title":199,"abstract":200,"tags":201},"H4",[],[],[182],[203,204],"湖北 河南 安徽 农业生态","RSEI 碳储量 遥感","湖北河南安徽农业生态-3361",4,"10.3390\u002Fland15101781",{"doi":207,"openalex_id":209,"authors":210,"venue":171,"cited_by_count":81,"oa_url":168,"card":219,"direction":86,"ingested_from":88},"W7214114544",[211,213,216],{"name":212,"orcid":9},"Xiaohan Liu",{"name":214,"orcid":215},"Lei Wang","https:\u002F\u002Forcid.org\u002F0000-0002-7163-3644",{"name":217,"orcid":218},"Hui Min Zhao","https:\u002F\u002Forcid.org\u002F0000-0001-7587-1116",{"tldr":220,"method":221,"finding":222,"direction":223,"opportunity":224},"评估中部三大农业省生态质量、植被生产力与碳储量的时空格局及空间关联。","用RSEI、CASA模型、InVEST模型结合Spearman相关、GeoDet","生态质量先降后微升，建设用地增112%，碳储减3459万吨；三指标空间关联但不可互换。","农业绿色发展与碳","可探究不同农业省土地利用变化下生态质量与碳储量的非线性驱动机制及分区管控策略。","2026-09-24T23:30:20.927597Z",{"id":227,"title":228,"url":229,"summary":230,"summary_zh":231,"content":9,"source_name":232,"source_url":229,"published_at":233,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":234,"score_detail":235,"sources":237,"tags":239,"primary_topic":243,"secondary_topics":248,"classification":250,"search_phrases":272,"slug":275,"view_count":22,"doi":276,"paper":277,"created_at":297},2323,"Land cover transformations in northern Algeria from 2001 to 2021: A MODIS-based spatiotemporal analysis","https:\u002F\u002Fdoi.org\u002F10.30493\u002Fdas.2026.011109","Land use\u002Fland cover (LULC) changes in northern Algeria from 2001 to 2021 were investigated in this research. The assessment utilized the MODIS MCD12Q1 dataset combined with Geographic Information System (GIS) techniques. Substantial landscape transformations were highlighted by the findings. Forests (+47.52%), shrublands (+17.65%), wetlands (+58.64%), and natural vegetation (+465.72%) experienced notable growth, whereas a drop of −14.13% was documented for barren lands by the end of study period. Moderate overall growth was displayed by agricultural lands, despite various fluctuations throughout the timeline. Concurrently, a continuous expansion (+3.90%) driven by escalating anthropogenic pressure was recorded in urban and built-up areas. Regional landscape evolution is clearly shaped by a combination of factors. Climatic variability, ecological succession, land management practices, and human activities collectively drive these observed dynamics. Partial alignment with previously documented vegetation recovery and urban expansion trends in Algeria was demonstrated by comparative literature reviews. However, the moderate spatial resolution (500 m) of the MODIS MCD12Q1 product likely caused several identified data discrepancies. Ultimately, the immense value of remote sensing and GIS approaches for regional environmental monitoring is proven by this project. Achieving better classification accuracy in heterogeneous Mediterranean environments, however, strictly necessitates the use of higher-resolution datasets.","本研究探讨了2001年至2021年阿尔及利亚北部土地利用\u002F土地覆盖（LULC）变化。评估采用MODIS MCD12Q1数据集结合地理信息系统（GIS）技术。研究结果凸显了显著的土地景观转变。森林（+47.52%）、灌木地（+17.65%）、湿地（+58.64%）和自然植被（+465.72%）显著增长，而裸地在研究期末则记录了−14.13%的下降。尽管农业用地在时间线上经历了多次波动，但总体上呈现出适度增长。与此同时，城市和建成区因人为压力不断加剧而记录了持续扩张（+3.90%）。区域景观演变显然由多种因素共同塑造。气候变率、生态演替、土地管理实践和人类活动共同驱动了这些观测到的动态。比较文献综述表明，这与阿尔及利亚先前记录的植被恢复和城市扩张趋势部分吻合。然而，MODIS MCD12Q1产品的中等空间分辨率（500米）可能导致了若干已识别的数据差异。最终，本项目证明了遥感和GIS方法在区域环境监测中的巨大价值。然而，要在异质性较强的地中海环境中实现更好的分类精度，严格需要使用更高分辨率的数据集。","DYSONA – Applied Science","2026-09-11T00:00:00Z",62,{"impact":17,"substance":18,"depth":46,"authority":20,"freshness":21,"relevant":22,"comment":236},"基于MODIS数据的阿尔及利亚北部20年土地覆被变化分析，方法规范、数据翔实，对遥感监测与区域生态评估有参考价值，但属境外区域研究，国内产业影响有限。",[238],{"name":232,"url":229},[27,240,28,241,242],"荒漠化","土地利用","植被恢复",{"code":244,"slug":245,"name":246,"path":247,"parent_code":37},"I4","gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",[249],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":251},{"candidates":252,"agricultural_evidence":47},[253,260,264,268],{"code":244,"score":21,"supported":47,"title":254,"abstract":255,"tags":259},[],[256,257,258],"地理信息","GIS","geographic information",[],{"code":33,"score":17,"supported":47,"title":261,"abstract":262,"tags":263},[],[51,49],[242,28],{"code":61,"score":145,"supported":14,"title":265,"abstract":266,"tags":267},[],[],[241,240],{"code":66,"score":22,"supported":14,"title":269,"abstract":270,"tags":271},[],[],[27],[273,274],"农业信息化 土地利用 植被恢复 遥感监测","农业信息化 土地利用","农业信息化土地利用植被恢复遥感监测-2323","10.30493\u002Fdas.2026.011109",{"doi":276,"openalex_id":278,"authors":279,"venue":232,"cited_by_count":81,"oa_url":229,"card":292,"direction":86,"ingested_from":88},"W7212238590",[280,282,285,288,290],{"name":281,"orcid":9},"Bilel Zerouali",{"name":283,"orcid":284},"Richarde Marques da Silva","https:\u002F\u002Forcid.org\u002F0000-0001-6601-5174",{"name":286,"orcid":287},"Nadjem Bailek","https:\u002F\u002Forcid.org\u002F0000-0001-9051-8548",{"name":289,"orcid":9},"Valeriano Carneiro de Lima Silva",{"name":291,"orcid":9},"Celso Augusto Guimarães Santos",{"tldr":293,"method":294,"finding":295,"direction":86,"opportunity":296},"基于MODIS数据分析了2001—2021年阿尔及利亚北部土地覆盖的时空变化。","使用MODIS MCD12Q1数据集结合GIS技术进行LULC分类与变化检测。","森林、灌丛、湿地和自然植被显著增长，裸地减少，建设用地持续扩张。","可引入高分辨率遥感数据提升地中海异质景观分类精度，并耦合气候与人为驱动因子做归因分析。","2026-09-13T23:30:23.070977Z",{"id":299,"title":300,"url":301,"summary":302,"summary_zh":303,"content":9,"source_name":304,"source_url":301,"published_at":305,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":81,"score_detail":306,"sources":308,"tags":310,"primary_topic":313,"secondary_topics":314,"classification":315,"search_phrases":336,"slug":339,"view_count":81,"doi":340,"paper":341,"created_at":369},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":81,"substance":81,"depth":81,"authority":81,"freshness":81,"relevant":81,"comment":307},"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。",[309],{"name":304,"url":301},[28,241,311,312],"土壤侵蚀","乡村人口",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":316},{"candidates":317,"agricultural_evidence":47},[318,322,326,332],{"code":33,"score":118,"supported":47,"title":319,"abstract":320,"tags":321},[],[51,49],[28],{"code":244,"score":55,"supported":47,"title":323,"abstract":324,"tags":325},[],[257],[],{"code":327,"score":55,"supported":47,"title":328,"abstract":329,"tags":331},"I7",[],[330],"methodological framework",[],{"code":61,"score":22,"supported":14,"title":333,"abstract":334,"tags":335},[],[],[241],[337,338],"Kolubara 河岸侵蚀","Obrenovac 土地利用","Kolubara河岸侵蚀-5390","10.59954\u002Fstnv.797",{"doi":340,"openalex_id":342,"authors":343,"venue":304,"cited_by_count":81,"oa_url":301,"card":363,"direction":86,"ingested_from":88},"W7222945959",[344,347,350,352,355,358,361],{"name":345,"orcid":346},"Slavoljub Dragićević","https:\u002F\u002Forcid.org\u002F0000-0002-1417-5405",{"name":348,"orcid":349},"Marko Langović","https:\u002F\u002Forcid.org\u002F0000-0002-4614-7265",{"name":351,"orcid":9},"Милан Радовић",{"name":353,"orcid":354},"Radislav Tošić","https:\u002F\u002Forcid.org\u002F0000-0002-3994-125X",{"name":356,"orcid":357},"Milica Langović","https:\u002F\u002Forcid.org\u002F0000-0002-6979-377X",{"name":359,"orcid":360},"Novica Lovrić","https:\u002F\u002Forcid.org\u002F0009-0006-0838-4370",{"name":362,"orcid":9},"Manojlović Stefan",{"tldr":364,"method":365,"finding":366,"direction":367,"opportunity":368},"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":371,"title":372,"url":373,"summary":374,"summary_zh":375,"content":9,"source_name":376,"source_url":373,"published_at":377,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":378,"score_detail":379,"sources":381,"tags":383,"primary_topic":388,"secondary_topics":394,"classification":396,"search_phrases":420,"slug":423,"view_count":81,"doi":424,"paper":425,"created_at":448},5367,"Integration of multi-source UAV remote sensing features and derived features for winter wheat aboveground biomass estimation","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.atech.2026.102656","Accurate estimation of winter wheat ( Triticum aestivum L.) aboveground biomass (AGB) is important for crop growth monitoring and field management. However, unmanned aerial vehicle (UAV)-based AGB estimation remains challenging due to variations in canopy conditions across growth stages, and different remote sensing features can capture complementary characteristics related to AGB estimation. This study integrated multi-source UAV-derived features, including vegetation indices (VIs), texture features (TFs), light detection and ranging (LiDAR)-derived features, and derived features (DFs) generated from the original feature sets, with four machine learning algorithms for winter wheat AGB estimation. Results showed that TFs provided better prediction performance than other single-source feature sets. Compared with single-source feature sets, the integration of VIs, TFs, LiDAR-derived features, and DFs further improved AGB estimation, and the random forest (RF) model achieved the best performance among the evaluated models (R² = 0.923, RMSE = 1.731 Mg ha⁻¹). Incremental R² analysis based on a stage-only baseline model showed that the RF model explained additional variation in AGB beyond growth-stage information, with an incremental R² of 0.479. Stage-specific evaluation indicated that model performance varied among growth stages, with the highest accuracy observed at the booting stage (R² = 0.632, RMSE = 1.425 Mg ha⁻¹). Feature contribution analysis based on the full-feature fusion model using all growth-stage data identified canopy coverage and several DFs as important predictors, while stage-specific analyses showed differences in dominant predictors across growth stages. The integration of multi-source UAV-derived features and DFs with machine learning provides an effective approach for winter wheat AGB estimation, while model performance remains strongly dependent on growth stage.","准确估算冬小麦（Triticum aestivum L.）地上生物量（aboveground biomass, AGB）对作物生长监测和田间管理具有重要意义。然而，由于不同生育期冠层条件存在差异，且不同遥感特征能够捕捉与AGB估算相关的互补信息，基于无人机（unmanned aerial vehicle, UAV）的AGB估算仍面临挑战。本研究将多源无人机衍生特征，包括植被指数（vegetation indices, VIs）、纹理特征（texture features, TFs）、激光雷达（light detection and ranging, LiDAR）衍生特征以及由原始特征集生成的衍生特征（derived features, DFs），与四种机器学习算法相结合，用于冬小麦AGB估算。结果表明，TFs的预测性能优于其他单一来源特征集。与单一来源特征集相比，融合VIs、TFs、LiDAR衍生特征和DFs进一步提高了AGB估算精度，其中随机森林（random forest, RF）模型在所评估的模型中表现最佳（R² = 0.923，RMSE = 1.731 Mg ha⁻¹）。基于仅含生育期信息的基线模型进行的增量R²分析表明，RF模型在生育期信息之外解释了AGB的额外变异，增量R²为0.479。分生育期评估表明，模型性能在不同生育期之间存在差异，其中在孕穗期观察到最高精度（R² = 0.632，RMSE = 1.425 Mg ha⁻¹）。基于使用全部生育期数据的全特征融合模型进行的特征贡献分析识别出冠层覆盖度和若干DFs为重要预测因子，而分生育期分析则表明不同生育期的主导预测因子存在差异。将多源无人机衍生特征和DFs与机器学习相结合，为冬小麦AGB估算提供了一种有效方法，但模型性能仍强烈依赖于生育期。","Smart Agricultural Technology","2026-10-17T00:00:00Z",77,{"impact":19,"substance":103,"depth":18,"authority":104,"freshness":17,"relevant":22,"comment":380},"该研究融合无人机多源遥感特征与衍生特征，用随机森林估算冬小麦地上生物量，方法新颖、结论可靠，对作物长势监测有参考价值。",[382],{"name":376,"url":373},[384,385,28,386,387],"智慧农业","无人机","冬小麦","生物量估算",{"code":389,"slug":390,"name":391,"path":392,"parent_code":393},"A2","wheat","小麦","\u002Ftopic\u002Ffield-crops\u002Fwheat","A",[395],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":397},{"candidates":398,"agricultural_evidence":47},[399,405,409,416],{"code":389,"score":400,"supported":47,"title":401,"abstract":403,"tags":404},26,[402,390],"winter wheat",[391,402,390],[386],{"code":33,"score":19,"supported":47,"title":406,"abstract":407,"tags":408},[49],[51,49],[387,28],{"code":410,"score":91,"supported":47,"title":411,"abstract":412,"tags":415},"I2",[],[413,414],"机器学习","machine learning",[],{"code":66,"score":22,"supported":14,"title":417,"abstract":418,"tags":419},[],[],[384],[421,422],"无人机 多源遥感 冬小麦 生物量","冬小麦 地上生物量 随机森林","无人机多源遥感冬小麦生物量-5367","10.1016\u002Fj.atech.2026.102656",{"doi":424,"openalex_id":426,"authors":427,"venue":376,"cited_by_count":81,"oa_url":373,"card":443,"direction":86,"ingested_from":88},"W7222806066",[428,430,432,434,437,439,441],{"name":429,"orcid":9},"Guangtao Wang",{"name":431,"orcid":9},"Sumei Yao",{"name":433,"orcid":9},"Yanzheng Xie",{"name":435,"orcid":436},"Anzhen Qin","https:\u002F\u002Forcid.org\u002F0000-0002-2550-0015",{"name":438,"orcid":9},"Xiangdong Chen",{"name":440,"orcid":9},"Yuhao Zhang",{"name":442,"orcid":9},"Lina Jiang",{"tldr":444,"method":445,"finding":446,"direction":86,"opportunity":447},"融合无人机多源遥感特征与衍生特征，用机器学习估算冬小麦地上生物量。","无人机植被指数、纹理、LiDAR特征及衍生特征结合四种机器学习算法建模。","多源特征融合优于单一特征，随机森林最优（R²=0.923），精度随生育期变化。","可探索生育期自适应建模与特征选择，提升跨期生物量估算的稳健性与可迁移性。","2026-10-20T23:30:03.106267Z",{"id":450,"title":451,"url":452,"summary":453,"summary_zh":454,"content":9,"source_name":455,"source_url":452,"published_at":377,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":81,"score_detail":456,"sources":458,"tags":460,"primary_topic":464,"secondary_topics":465,"classification":472,"search_phrases":489,"slug":492,"view_count":81,"doi":493,"paper":494,"created_at":506},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",{"impact":81,"substance":81,"depth":81,"authority":81,"freshness":81,"relevant":81,"comment":457},"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。",[459],{"name":455,"url":452},[461,28,462,463],"防灾减灾","洪水风险评估","GIS应用",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[466,467],{"code":244,"slug":245,"name":246,"path":247,"parent_code":37},{"code":133,"slug":468,"name":469,"path":470,"parent_code":471},"climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",{"status":40,"locked":14,"version":41,"method":42,"evidence":473},{"candidates":474,"agricultural_evidence":47},[475,479,483],{"code":33,"score":46,"supported":47,"title":476,"abstract":477,"tags":478},[49],[51,49],[28],{"code":244,"score":175,"supported":47,"title":480,"abstract":481,"tags":482},[257],[256,257],[],{"code":133,"score":17,"supported":47,"title":484,"abstract":485,"tags":488},[],[486,487],"干旱","drought",[462,461],[490,491],"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":493,"openalex_id":495,"authors":496,"venue":455,"cited_by_count":81,"oa_url":500,"card":501,"direction":86,"ingested_from":88},"W7222885988",[497],{"name":498,"orcid":499},"Birendra Yadav","https:\u002F\u002Forcid.org\u002F0009-0007-0864-474X","https:\u002F\u002Fwww.ijfmr.com\u002Fpapers\u002F2026\u002F5\u002F89708.pdf",{"tldr":502,"method":503,"finding":504,"direction":86,"opportunity":505},"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"]