[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4434":3,"related-4434":72},{"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":26,"classification":27,"search_phrases":48,"slug":51,"view_count":52,"doi":53,"paper":54,"created_at":71},4434,"Explainable GeoAI for urban wildfire structure loss: vegetation and built environment in the 2025 palisades fire","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffrsus.2026.1975724","Urban wildfire resilience depends on how built form, vegetation condition, and extreme fire weather interact. Thus far, city-scale decision tools often combine spatial data without testing whether their apparent predictive skill persists when evaluated in spatially separated settings. We developed a spatially validated geospatial artificial intelligence (GeoAI) and urban-informatics workflow for the January 2025 Palisades Fire, linking 12,081 California Department of Forestry and Fire Protection (CAL FIRE) damage inspections to pre-fire Sentinel-2 vegetation amount and moisture, Landsat surface temperature, Landscape Fire and Resource Management Planning Tool (LANDFIRE) fuels, 10 m terrain, and a dated OpenStreetMap (OSM) representation of buildings and roads. Residential destruction (5,566 of 9,883 inspected residential structures) was modeled using logistic and gradient-boosting models with nested tuning under random and 1 km spatial-block cross-validation. Random cross-validation suggested excellent discrimination for the integrated gradient-boosting model (ROC-AUC 0.92), but spatial validation reduced performance to 0.75; an interpretable logistic model achieved a similar mean Receiver Operating Characteristic and Area Under the Curve (ROC-AUC) under spatial blocking and had a calibration slope closer to one. Within the inspected residential set, neighborhood building count was the strongest predictor: conditional destruction odds ratio (OR) increased 3.74-fold per standard deviation in building count within a 100 m-radius disk. Higher neighborhood Normalized Difference Moisture Index (NDMI) was associated with lower destruction odds ratio, whereas the positive Normalized Difference Vegetation Index (NDVI) coefficient in the joint model reversed when NDVI was entered separately; the opposing vegetation signals were therefore specification-dependent. Neighborhood-scale predictor sets tended to outperform immediate-surrounding sets, although their fold-level variability overlapped. A separate impact track described spectral change, relative greenness, and community context without introducing post-fire predictors. The results position open geospatial modeling as a neighborhood-scale screening tool rather than a parcel-level prediction system. The findings support neighborhood-focused assessment of built form and vegetation condition, with spatial validation and probability diagnostics reported alongside prediction maps. The reproducible workflow provides a basis for cross-event comparison; applying its coefficients or density ranges operationally requires independent-event validation.","城市野火韧性取决于建筑形态、植被状况与极端火险天气之间的相互作用。迄今为止，城市尺度的决策工具往往只是将空间数据加以组合，而未检验其表面上的预测能力在空间分离的设置中评估时是否依然成立。我们针对2025年1月帕利塞兹大火（Palisades Fire）开发了一套经过空间验证的地理空间人工智能（GeoAI）与城市信息学工作流程，将12,081条加州林业和消防局（CAL FIRE）损失检查记录与火灾前的Sentinel-2植被数量与水分、Landsat地表温度、景观火灾与资源管理规划工具（LANDFIRE）可燃物、10 m地形数据以及带日期的OpenStreetMap（OSM）建筑与道路表示相连接。住宅损毁情况（9,883栋受检住宅建筑中有5,566栋损毁）采用逻辑回归和梯度提升模型进行建模，并在随机交叉验证和1 km空间块交叉验证下进行嵌套调参。随机交叉验证显示，集成梯度提升模型具有极佳的区分能力（ROC-AUC 0.92），但空间验证使性能降至0.75；一个可解释的逻辑回归模型在空间分块下取得了相近的平均受试者工作特征曲线下面积（ROC-AUC），且校准斜率更接近1。在受检住宅集合内，邻域建筑数量是最强的预测因子：在100 m半径圆盘内，建筑数量每增加一个标准差，条件损毁优势比（OR）增加3.74倍。较高的邻域归一化差异水分指数（NDMI）与较低的损毁优势比相关，而联合模型中归一化差异植被指数（NDVI）的正系数在NDVI单独纳入时发生反转；因此，相互对立的植被信号依赖于模型设定。邻域尺度的预测变量集往往优于紧邻周边变量集，尽管其折间变异性存在重叠。一个单独的影響分析轨道描述了光谱变化、相对绿度与社区背景，而未引入火灾后预测因子。研究结果将开放地理空间建模定位为邻域尺度的筛查工具，而非地块尺度的预测系统。研究结果支持以邻域为重点评估建筑形态与植被状况，并将空间验证和概率诊断与预测图一并报告。该可复现工作流程提供了",null,"Frontiers in Sustainability","2026-10-07T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"研究城市野火建筑损毁的可解释GeoAI建模，属城市灾害与遥感交叉领域，与三农、农业信息化、数字乡村无直接关联，不建议进入每日精选。",[19],{"name":10,"url":6},[21,22,23,24,25],"遥感监测","植被指数","城市野火","地理空间AI","建筑环境",[],{"status":28,"locked":14,"version":29,"method":30,"evidence":31},"needs_review","2026-10-22.2","rules",{"candidates":32,"agricultural_evidence":14,"reason":47},[33,41],{"code":34,"score":35,"supported":36,"title":37,"abstract":38,"tags":40},"I2",3,true,[],[39],"artificial intelligence",[],{"code":42,"score":43,"supported":14,"title":44,"abstract":45,"tags":46},"I3",2,[],[],[22,21],"农业相关性需要复核",[49,50],"CAL FIRE 建筑损毁 遥感","地理空间AI 城市野火 建筑环境 植被指数","CALFIRE建筑损毁遥感-4434",1,"10.3389\u002Ffrsus.2026.1975724",{"doi":53,"openalex_id":55,"authors":56,"venue":10,"cited_by_count":15,"oa_url":63,"card":64,"direction":68,"ingested_from":70},"W7220848474",[57,60],{"name":58,"orcid":59},"Parastoo Farajpoor","https:\u002F\u002Forcid.org\u002F0009-0003-1457-2683",{"name":61,"orcid":62},"Mohammadreza Narimani","https:\u002F\u002Forcid.org\u002F0009-0001-7302-405X","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainability\u002Farticles\u002F10.3389\u002Ffrsus.2026.1975724\u002Fpdf",{"tldr":65,"method":66,"finding":67,"direction":68,"opportunity":69},"用可解释GeoAI建模2025年帕利塞兹火灾住宅损毁，强调空间验证。","CAL FIRE损毁数据+Sentinel-2、Landsat、LANDFIRE","随机交叉验证AUC 0.92，空间验证降至0.75；邻里建筑数量是最强预测因子，植被信号依赖模型设定","农业遥感与作物表型","可将空间块验证与可解释模型推广到其他城市野火，并探究植被指数设定对结论稳健性的影响。","openalex","2026-10-08T23:30:29.448647Z",{"total":73,"page":52,"page_size":73,"items":74},6,[75,155,216,283,344,413],{"id":76,"title":77,"url":78,"summary":79,"summary_zh":80,"content":9,"source_name":81,"source_url":78,"published_at":82,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":83,"score_detail":84,"sources":91,"tags":93,"primary_topic":97,"secondary_topics":102,"classification":109,"search_phrases":126,"slug":129,"view_count":15,"doi":130,"paper":131,"created_at":154},4826,"Multi-Index Remote Sensing Assessment of Agro-Climatic Change and Agricultural Responses in Temperate and Semi-Arid Regions","https:\u002F\u002Fdoi.org\u002F10.3390\u002Frs18203496","Diverse climatic regions exhibit unique agro-climatic vulnerabilities that are significantly shaped by climate stress. Understanding these distinct vulnerabilities is essential for developing effective policies to mitigate climate change impacts and ensuring sustainable environmental management. This study examines agro-climatic vulnerabilities in Nottinghamshire County (United Kingdom) and Udaipur District (India) from 2000 to 2024, comparing climate change impacts across temperate and semi-arid agricultural systems that represent fundamentally different system behaviours under contrasting climatic conditions. Using multi-temporal Landsat imagery and WorldClim climate data, nine environmental indices were analysed: the Normalized Difference Vegetation Index (NDVI), Vegetation Condition Index (VCI), Normalized Difference Water Index (NDWI), Moisture Stress Index (MSI), Land Surface Temperature (LST), Temperature Condition Index (TCI), Vegetation Health Index (VHI), Drought Risk Index (DRI), and Crop Health Index (CHI). The results reveal contrasting regional trajectories that challenge conventional assumptions about climate vulnerability. Udaipur exhibited improved vegetation and moisture conditions across the observation period, whereas Nottinghamshire showed intensifying moisture stress and declining precipitation. Warming was more pronounced in Nottinghamshire, while strong inter-index correlations validated the monitoring approach. These findings indicate that Udaipur benefits from intensified monsoon patterns, requiring flood mitigation and water-storage infrastructure, whereas Nottinghamshire’s rising moisture stress underscores the need for sustained monitoring and adaptive management. This comparative framework provides evidence-based insights for region-specific adaptation strategies essential to sustainable agricultural development across diverse agro-climatic zones.","不同气候区域表现出独特的农业气候脆弱性，而这些脆弱性在很大程度上受到气候胁迫的影响。理解这些不同的脆弱性对于制定有效政策以减缓气候变化影响、确保可持续环境管理至关重要。本研究考察了2000年至2024年间英国诺丁汉郡与印度乌代布尔县的农业气候脆弱性，比较了温带与半干旱农业系统在气候变化影响下的差异，这两类系统在截然不同的气候条件下代表了根本不同的系统行为。研究利用多时相Landsat影像和WorldClim气候数据，分析了九个环境指数：归一化植被指数（NDVI）、植被状态指数（VCI）、归一化水体指数（NDWI）、水分胁迫指数（MSI）、地表温度（LST）、温度状态指数（TCI）、植被健康指数（VHI）、干旱风险指数（DRI）和作物健康指数（CHI）。结果揭示了截然不同的区域变化轨迹，挑战了关于气候脆弱性的传统假设。乌代布尔在观测期内表现出植被和水分条件的改善，而诺丁汉郡则显示出加剧的水分胁迫和降水减少。诺丁汉郡的增温更为显著，而指数间较强的相关性验证了该监测方法。这些发现表明，乌代布尔受益于季风模式的增强，需要防洪和水储存基础设施，而诺丁汉郡不断上升的水分胁迫则凸显了持续监测和适应性管理的必要性。这一比较框架为因地制宜的适应策略提供了循证依据，对跨不同农业气候区的可持续发展至关重要。","Remote Sensing","2026-10-12T00:00:00Z",75,{"impact":85,"substance":86,"depth":87,"authority":88,"freshness":89,"relevant":52,"comment":90},12,22,18,14,9,"基于多时相遥感与九项环境指数的跨区域对比研究，方法扎实、结论具政策参考价值，但属学术论文，产业影响有限。",[92],{"name":81,"url":78},[21,22,94,95,96],"干旱风险","适应策略","农业气候",{"code":42,"slug":98,"name":99,"path":100,"parent_code":101},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",[103],{"code":104,"slug":105,"name":106,"path":107,"parent_code":108},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",{"status":110,"locked":14,"version":29,"method":30,"evidence":111},"classified",{"candidates":112,"agricultural_evidence":36},[113,118],{"code":42,"score":13,"supported":36,"title":114,"abstract":116,"tags":117},[115],"remote sensing",[],[22,21],{"code":104,"score":89,"supported":36,"title":119,"abstract":120,"tags":125},[],[121,122,123,124],"气候变化","干旱","climate change","drought",[],[127,128],"Landsat 遥感 农业气候","诺丁汉郡 乌代布尔","Landsat遥感农业气候-4826","10.3390\u002Frs18203496",{"doi":130,"openalex_id":132,"authors":133,"venue":81,"cited_by_count":15,"oa_url":78,"card":149,"direction":68,"ingested_from":70},"W7221570035",[134,137,140,143,146],{"name":135,"orcid":136},"Bhanwar Vishvendra Raj Singh","https:\u002F\u002Forcid.org\u002F0000-0001-7750-5240",{"name":138,"orcid":139},"Vivek Agarwal","https:\u002F\u002Forcid.org\u002F0000-0003-1185-3802",{"name":141,"orcid":142},"Stuart Marsh","https:\u002F\u002Forcid.org\u002F0000-0002-1168-6760",{"name":144,"orcid":145},"Gajendra Singh Sisodiya","https:\u002F\u002Forcid.org\u002F0009-0006-4118-7491",{"name":147,"orcid":148},"Ajay Sharma","https:\u002F\u002Forcid.org\u002F0009-0001-6077-9875",{"tldr":150,"method":151,"finding":152,"direction":68,"opportunity":153},"用多时相遥感和九项指数对比英国诺丁汉郡与印度乌代浦2000—2024年农业气候脆弱性。","Landsat多时相影像与WorldClim气候数据，计算NDVI、VCI、ND","乌代浦植被与水分条件改善、季风增强，诺丁汉郡水分胁迫加剧、降水下降且升温更明显。","可拓展多区域长时序遥感指数耦合机器学习，构建跨气候带农业气候脆弱性预警与适应决策模型。","2026-10-13T23:30:24.062929Z",{"id":156,"title":157,"url":158,"summary":159,"summary_zh":160,"content":9,"source_name":161,"source_url":158,"published_at":82,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":162,"score_detail":163,"sources":166,"tags":168,"primary_topic":9,"secondary_topics":172,"classification":173,"search_phrases":192,"slug":195,"view_count":15,"doi":196,"paper":197,"created_at":215},4823,"Comparing Sentinel-2 Spectral Indices for Mapping Forest Fire Damage in Pollino National Park, Italy","https:\u002F\u002Fdoi.org\u002F10.5194\u002Fisprs-archives-xlviii-m-13-2026-1-2026","In recent years, climate change has significantly increased the severity and frequency of wildfires across Europe, with the 2025 season marking an unprecedented peak of one million hectares burned. Rapid and cost-effective mapping of these damaged areas is crucial for environmental recovery, making satellite remote sensing an indispensable tool. This study evaluates and compares the performance of four Sentinel-2 spectral burn indices for mapping forest fire damage within the Pollino National Park (Italy) occurred in September 2025, an area severely affected by the devastating summer fires of 2025. The analyzed indices include the Normalized Burn Ratio (NBR), Normalized Burn Ratio Plus (NBR+), Normalized Burn Ratio Short Wave Infrared (NBR-SWIR), and Normalized Difference Short Wave Infrared (ND-SWIR). An image differencing technique was applied to pre-fire (June 7, 2025) and post-fire (November 14, 2025) datasets, followed by a supervised Maximum Likelihood classification to map burned and unburned classes. Accuracy assessment via confusion matrices showed that NBR and NBR+ delivered the highest performance, achieving an Overall Accuracy (OA) of 0.976 and 0.974, respectively. ND-SWIR performed closely at 0.966, whereas NBR-SWIR yielded a lower OA of 0.873 due to the misclassification of bare soil transitions as burned areas. These findings highlight the critical role of specific SWIR-band combinations in optimizing automated wildfire monitoring systems.","近年来，气候变化显著加剧了欧洲野火的严重程度和发生频率，2025年火灾季烧毁面积达到一百万公顷，创下历史新高。快速且经济高效地绘制这些受损区域对于环境恢复至关重要，使卫星遥感成为不可或缺的工具。本研究评估并比较了四种哨兵2号（Sentinel-2）光谱燃烧指数在绘制2025年9月发生于意大利波利诺国家公园（Pollino National Park）的森林火灾损害中的表现，该地区受2025年夏季毁灭性火灾的严重影响。所分析的指数包括归一化燃烧比率（NBR）、归一化燃烧比率增强版（NBR+）、归一化燃烧比率短波红外（NBR-SWIR）以及归一化差值短波红外（ND-SWIR）。研究对火灾前（2025年6月7日）和火灾后（2025年11月14日）数据集应用了图像差值技术，随后采用监督最大似然分类法绘制燃烧与未燃烧类别。通过混淆矩阵进行的精度评估表明，NBR和NBR+表现最佳，总体精度（OA）分别达到0.976和0.974。ND-SWIR紧随其后，为0.966，而NBR-SWIR由于将裸土转变误分类为燃烧区域，总体精度较低，为0.873。这些发现凸显了特定短波红外波段组合在优化自动化野火监测系统中的关键作用。","˜The œinternational archives of the photogrammetry, remote sensing and spatial information sciences\u002FInternational archives of the photogrammetry, remote sensing and spatial information sciences",74,{"impact":85,"substance":86,"depth":87,"authority":164,"freshness":89,"relevant":52,"comment":165},13,"方法对比扎实、结论明确，但属区域案例研究，对农业信息化有间接参考价值，可入精选但非必选。",[167],{"name":161,"url":158},[169,21,170,171,22],"Sentinel-2","灾害评估","森林火灾",[],{"status":28,"locked":14,"version":29,"method":30,"evidence":174},{"candidates":175,"agricultural_evidence":14,"reason":47},[176,181,186],{"code":42,"score":89,"supported":36,"title":177,"abstract":178,"tags":180},[],[179,115],"遥感",[169,22,21],{"code":104,"score":182,"supported":36,"title":183,"abstract":184,"tags":185},8,[],[121,123],[171,170],{"code":187,"score":35,"supported":36,"title":188,"abstract":189,"tags":191},"H2",[],[190],"soil",[],[193,194],"Sentinel-2 森林火灾 遥感","Pollino 国家公园 火烧迹地","Sentinel-2森林火灾遥感-4823","10.5194\u002Fisprs-archives-xlviii-m-13-2026-1-2026",{"doi":196,"openalex_id":198,"authors":199,"venue":161,"cited_by_count":15,"oa_url":209,"card":210,"direction":68,"ingested_from":70},"W7221528348",[200,202,204,207],{"name":201,"orcid":9},"Giuseppina Costanzo",{"name":203,"orcid":9},"Gianluigi Ferrara",{"name":205,"orcid":206},"Claudio Parente","https:\u002F\u002Forcid.org\u002F0000-0002-8328-9764",{"name":208,"orcid":9},"Vincenzo Verde","https:\u002F\u002Fisprs-archives.copernicus.org\u002Farticles\u002FXLVIII-M-13-2026\u002F1\u002F2026\u002Fisprs-archives-XLVIII-M-13-2026-1-2026.pdf",{"tldr":211,"method":212,"finding":213,"direction":68,"opportunity":214},"比较四种 Sentinel-2 光谱指数在意大利 Pollino 国家公园 2025 年林火损伤制图","Sentinel-2 火前火后影像差值 + 最大似然监督分类，用混淆矩阵评估四种","NBR 与 NBR+ 精度最高（OA 0.976、0.974），NBR-SWIR 因裸土误判仅 0.","可探索多指数融合与机器学习分类器，提升复杂地表下火烧迹地自动制图鲁棒性。","2026-10-13T23:30:23.838067Z",{"id":217,"title":218,"url":219,"summary":220,"summary_zh":221,"content":9,"source_name":222,"source_url":219,"published_at":223,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":224,"score_detail":225,"sources":229,"tags":231,"primary_topic":235,"secondary_topics":236,"classification":238,"search_phrases":264,"slug":267,"view_count":15,"doi":268,"paper":269,"created_at":282},4747,"Spatiotemporal mapping of agricultural drought using geospatial approach in the East Bale Zone, Ethiopia","https:\u002F\u002Fdoi.org\u002F10.1002\u002Fagg2.70475","Abstract Agricultural drought significantly threatens food security, livelihoods, and ecosystem stability, especially in regions prone to drought, such as the East Bale zone. This study investigates the spatial and temporal patterns of agricultural drought over 30 years (1993–2024) by combining meteorological station data with satellite‐derived vegetation and thermal indices. To assess meteorological drought, the standardized precipitation index (SPI) was calculated at 3‐, 6‐, and 12‐month intervals. For agricultural and thermal stress analysis, remote sensing‐based indices including the vegetation condition index (VCI), temperature condition index, and vegetation health index (VHI) were utilized. The results indicate that the years 2003, 2008, and 2018 experienced the most severe droughts, as shown consistently across all indices. Spatial analysis revealed that the central, southern, and eastern lowland areas faced the highest frequency and severity of drought, while the northwestern highlands remained comparatively resilient. Cross‐index correlation revealed strong agreement between SPI and VCI ( R 2 = 0.77), and between VHI and VCI ( R 2 = 0.83), supporting the effectiveness of integrated drought monitoring approaches. This research advances the field of drought assessment by enhancing spatial and temporal resolution and validating cross‐index relationships at the subregional level. The results provide critical insights for localized drought early warning systems, climate adaptation strategies, and future research to improve agricultural resilience under changing climatic conditions. Hence, agricultural drought risk mapping serves as a valuable tool for guiding decision‐making in drought monitoring and for mitigating its impacts on agricultural productivity.","摘要 农业干旱严重威胁粮食安全、生计和生态系统稳定性，尤其是在东巴莱区等易旱地区。本研究结合气象站数据与卫星反演的植被和热力指数，探讨了30年（1993—2024年）间农业干旱的时空格局。为评估气象干旱，计算了3个月、6个月和12个月尺度的标准化降水指数（SPI）。在农业与热胁迫分析方面，采用了基于遥感的植被状态指数（VCI）、温度状态指数和植被健康指数（VHI）。结果表明，2003年、2008年和2018年经历了最严重的干旱，各指数均一致反映了这一情况。空间分析显示，中部、南部和东部低地地区干旱发生频率最高、程度最严重，而西北部高地则相对具有较强恢复力。跨指数相关性分析表明，SPI与VCI之间（R²=0.77）以及VHI与VCI之间（R²=0.83）具有高度一致性，支持了综合干旱监测方法的有效性。本研究通过提高时空分辨率并在次区域尺度上验证跨指数关系，推进了干旱评估领域的发展。研究结果为地方性干旱预警系统、气候适应策略以及未来研究提供了重要见解，有助于在气候变化条件下提升农业韧性。因此，农业干旱风险制图为干旱监测决策及其对农业生产影响的减缓提供了有价值的工具。","Agrosystems Geosciences & Environment","2026-10-11T00:00:00Z",68,{"impact":182,"substance":226,"depth":227,"authority":164,"freshness":89,"relevant":52,"comment":228},21,17,"基于30年气象与遥感指数交叉验证的农业干旱时空制图研究，方法扎实、结论可靠，但属区域性案例，对国内三农信息化仅有方法借鉴价值。",[230],{"name":222,"url":219},[232,233,21,22,234],"粮食安全","防灾减灾","农业干旱",{"code":104,"slug":105,"name":106,"path":107,"parent_code":108},[237],{"code":42,"slug":98,"name":99,"path":100,"parent_code":101},{"status":110,"locked":14,"version":29,"method":30,"evidence":239},{"candidates":240,"agricultural_evidence":36},[241,246,250,257],{"code":104,"score":87,"supported":36,"title":242,"abstract":243,"tags":245},[124],[121,244,122,124],"气候适应",[233],{"code":42,"score":182,"supported":36,"title":247,"abstract":248,"tags":249},[],[179,115],[22,21],{"code":251,"score":252,"supported":36,"title":253,"abstract":254,"tags":256},"A8",7,[],[232,255],"food security",[232],{"code":258,"score":73,"supported":36,"title":259,"abstract":260,"tags":263},"I4",[],[261,262],"空间分析","spatial analysis",[],[265,266],"埃塞俄比亚 East Bale 农业干旱","SPI VCI 干旱监测","埃塞俄比亚EastBale农业干旱-4747","10.1002\u002Fagg2.70475",{"doi":268,"openalex_id":270,"authors":271,"venue":222,"cited_by_count":15,"oa_url":219,"card":277,"direction":68,"ingested_from":70},"W7221455809",[272,274],{"name":273,"orcid":9},"Murad Hedo Haji",{"name":275,"orcid":276},"Karuturi Venkata Suryabhagavan","https:\u002F\u002Forcid.org\u002F0000-0003-2528-9106",{"tldr":278,"method":279,"finding":280,"direction":68,"opportunity":281},"结合气象站与卫星植被热指数，绘制埃塞俄比亚East Bale区30年农业干旱时空图。","用SPI（3\u002F6\u002F12月）与遥感VCI、TCI、VHI指数做交叉相关分析。","2003、2008、2018年干旱最重，中南部和东部低地最脆弱，SPI与VCI相关性R²=0.77。","可在小区域尺度融合多源指数构建干旱预警模型，并验证指数在不同作物区的适用性。","2026-10-12T23:30:24.342375Z",{"id":284,"title":285,"url":286,"summary":287,"summary_zh":288,"content":9,"source_name":289,"source_url":286,"published_at":290,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":291,"score_detail":292,"sources":296,"tags":298,"primary_topic":9,"secondary_topics":301,"classification":302,"search_phrases":318,"slug":321,"view_count":52,"doi":322,"paper":323,"created_at":343},4582,"Assessing mangrove sensitivity to thermal variations through a satellite-based analysis on the southern São Paulo coast, Brazil","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs10661-026-16005-6","Abstract Mangroves near subtropical range limits may benefit from fewer cold constraints, yet warmer conditions can also increase atmospheric water demand. These competing possibilities were examined in the Cananéia-Iguape Coastal System, southeastern Brazil, by combining ERA5 2 m air temperature with MODIS normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and leaf area index (LAI). Air-temperature trends were assessed for 1979–2019 with the Mann-Kendall test and Sen’s slope. Temperature-vegetation associations were calculated for 2003–2019 from mangrove-wide means and from individual grid cells at annual, monthly, and fixed calendar-quarter scales. Mean annual air temperature increased by about 0.02 °C year⁻ 1 ; the highest quarter-scale rate occurred in July–September (0.03 °C year⁻ 1 ). Annual temperature correlated positively with NDVI ( r = 0.61, R 2 = 0.38, p = 0.009) and EVI ( r = 0.55, R 2 = 0.30, p = 0.022), whereas the LAI relationship was not significant ( r = 0.27, R 2 = 0.07, p = 0.304). The sign of the association changed within the year: NDVI and EVI were negative in July–September, while positive EVI and LAI relationships occurred in October-December. Significant grid-cell relationships occupied limited and discontinuous areas. The temperature record shows regional warming, whereas canopy associations vary among metrics, periods, and locations. Given the bivariate design and coarse ERA5 grid, these associations should not be interpreted as causal or as responses to temperature alone.","摘要 靠近亚热带分布界限的红树林可能受益于较少的低温限制，但更温暖的条件也可能增加大气水分需求。为检验这两种相互竞争的的可能性，本研究在巴西东南部的卡纳内亚-伊瓜佩海岸系统，将ERA5 2米气温数据与MODIS归一化差异植被指数（NDVI）、增强植被指数（EVI）和叶面积指数（LAI）相结合进行了分析。采用Mann-Kendall检验和Sen斜率法评估了1979—2019年的气温趋势。基于2003—2019年红树林全域均值及单个网格单元，在年、月和固定日历季度尺度上计算了气温-植被关联。年平均气温上升约0.02 °C yr⁻¹；季度尺度最高升温速率出现在7—9月（0.03 °C yr⁻¹）。年气温与NDVI呈正相关（r = 0.61，R² = 0.38，p = 0.009），与EVI也呈正相关（r = 0.55，R² = 0.30，p = 0.022），而与LAI的关系不显著（r = 0.27，R² = 0.07，p = 0.304）。关联方向在年内发生变化：NDVI和EVI在7—9月为负，而EVI和LAI的正相关关系出现在10—12月。显著的网格单元关系占据有限且不连续的区域。气温记录显示区域变暖，而冠层关联因指标、时段和地点而异。鉴于双变量设计和ERA5网格较粗，这些关联不应被解释为因果关系或仅对温度的响应。","Environmental Monitoring and Assessment","2026-10-09T00:00:00Z",66,{"impact":182,"substance":293,"depth":294,"authority":164,"freshness":89,"relevant":52,"comment":295},20,16,"方法规范、结论审慎的区域性遥感研究，对农业信息化中的生态遥感监测有参考价值，但影响范围限于学术层面。",[297],{"name":289,"url":286},[121,21,22,299,300],"红树林","海岸带生态",[],{"status":28,"locked":14,"version":29,"method":30,"evidence":303},{"candidates":304,"agricultural_evidence":14,"reason":47},[305,309,314],{"code":42,"score":43,"supported":14,"title":306,"abstract":307,"tags":308},[],[],[22,21],{"code":310,"score":52,"supported":14,"title":311,"abstract":312,"tags":313},"H6",[],[],[299],{"code":104,"score":52,"supported":14,"title":315,"abstract":316,"tags":317},[],[],[121],[319,320],"巴西 圣保罗 红树林 遥感","MODIS NDVI 红树林 温度","巴西圣保罗红树林遥感-4582","10.1007\u002Fs10661-026-16005-6",{"doi":322,"openalex_id":324,"authors":325,"venue":289,"cited_by_count":15,"oa_url":337,"card":338,"direction":68,"ingested_from":70},"W7220983448",[326,329,332,334],{"name":327,"orcid":328},"Jakeline Baratto","https:\u002F\u002Forcid.org\u002F0000-0003-0311-2369",{"name":330,"orcid":331},"Paulo Miguel De Bodas Terassi","https:\u002F\u002Forcid.org\u002F0000-0002-8184-0348",{"name":333,"orcid":9},"Nádia Gilma Beserra de Lima",{"name":335,"orcid":336},"Emerson Galvani","https:\u002F\u002Forcid.org\u002F0000-0002-8082-5963","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs10661-026-16005-6.pdf",{"tldr":339,"method":340,"finding":341,"direction":68,"opportunity":342},"结合气温与植被指数分析巴西东南部红树林对温度变化的敏感性。","ERA5气温与MODIS NDVI\u002FEVI\u002FLAI，用Mann-Kendall趋","区域增温，但植被与温度关联随指标、时段和地点变化，非因果。","可引入多源遥感与因果推断，探究亚热带红树林对增温的滞后与非线性响应。","2026-10-10T23:30:27.692383Z",{"id":345,"title":346,"url":347,"summary":348,"summary_zh":349,"content":9,"source_name":350,"source_url":347,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":224,"score_detail":351,"sources":353,"tags":355,"primary_topic":9,"secondary_topics":359,"classification":360,"search_phrases":380,"slug":383,"view_count":252,"doi":384,"paper":385,"created_at":412},4436,"Tracing the invisible footprint of mining in Iran: spectral mapping of heavy metal contamination in arid soils with a remote sensing approach","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs12518-026-00809-9","This research applies an interdisciplinary framework to investigate the interactions between mining activities, heavy metal contamination, and vegetation response in the Sangan mining areas of Khaf County, Iran. Geochemical analyses of 24 surface soil samples were integrated with remote sensing data from Landsat 8-OLI to develop a robust methodology for monitoring stressed ecosystems. Chemical analysis revealed substantial accumulation of cadmium (Cd, barium (Ba) and arsenic (As). The mean Cd concentration (26.5 mg kg⁻¹) was about 88 times higher than the global shale average (0.3 mg kg⁻¹), which is likely associated with to intensive iron ore mining. This finding underscores the risk of these metals entering the food chain and threatening human health, especially in children, who exhibit higher absorption rates. From a remote-sensing perspective, soil-adjusted vegetation indices such as RVI ( R = 0.82) and MSAVI2 ( R = 0.79) outperformed NDVI ( R = 0.40) in arid environments with sparse vegetation, providing a methodological advance for ecological monitoring. Correlation analyses between heavy metals and vegetation indices revealed a strong negative association between copper (Cu) and most indices, particularly IPVI ( R = − 0.69), indicating copper’s phytotoxic effects on chlorophyll and biomass. In contrast, chromium (Cr) showed a weak positive correlation with NDVI ( R = 0.36). These results highlight the need to select vegetation indices according to local environmental conditions and offer a practical framework for enhancing remote sensing–based monitoring of contaminated ecosystems. The study improves understanding of mining impacts and provides a replicable model for sustainable management of mining-affected landscapes worldwide.","本研究采用跨学科框架，探讨伊朗哈夫县桑甘矿区采矿活动、重金属污染与植被响应之间的相互作用。研究将24个表层土壤样品的地球化学分析与Landsat 8-OLI遥感数据相结合，构建了一套用于监测受胁迫生态系统的稳健方法。化学分析显示，镉（Cd）、钡（Ba）和砷（As）显著累积。镉的平均浓度（26.5 mg kg⁻¹）约为全球页岩平均值（0.3 mg kg⁻¹）的88倍，这很可能与密集的铁矿石开采有关。该发现凸显了这些金属进入食物链并威胁人体健康的风险，尤其是对吸收率更高的儿童而言。从遥感角度看，在植被稀疏的干旱环境中，土壤调节植被指数如RVI（R = 0.82）和MSAVI2（R = 0.79）优于NDVI（R = 0.40），为生态监测提供了方法学进展。重金属与植被指数之间的相关分析显示，铜（Cu）与大多数指数呈强负相关，尤其是与IPVI（R = −0.69），表明铜对叶绿素和生物量具有植物毒性效应。相比之下，铬（Cr）与NDVI呈弱正相关（R = 0.36）。这些结果凸显了根据当地环境条件选择植被指数的必要性，并为加强基于遥感的污染生态系统监测提供了实用框架。该研究增进了对采矿影响的理解，并为全球受采矿影响景观的可持续管理提供了一个可复制的模型。","Applied Geomatics",{"impact":182,"substance":226,"depth":227,"authority":164,"freshness":89,"relevant":52,"comment":352},"方法新颖、数据扎实的矿区土壤重金属遥感监测研究，对农业生态与耕地污染监测有借鉴价值，但属境外案例、影响范围有限。",[354],{"name":350,"url":347},[21,356,22,357,358],"矿区生态","土壤修复","重金属污染",[],{"status":28,"locked":14,"version":29,"method":30,"evidence":361},{"candidates":362,"agricultural_evidence":14,"reason":47},[363,367,372,376],{"code":42,"score":294,"supported":36,"title":364,"abstract":365,"tags":366},[115],[179,115],[22,21],{"code":187,"score":182,"supported":36,"title":368,"abstract":369,"tags":371},[],[370,190],"土壤",[357,358],{"code":104,"score":35,"supported":36,"title":373,"abstract":374,"tags":375},[],[122],[],{"code":310,"score":52,"supported":14,"title":377,"abstract":378,"tags":379},[],[],[356],[381,382],"伊朗 Sangan 矿区 重金属","Landsat 8 OLI 植被指数","伊朗Sangan矿区重金属-4436","10.1007\u002Fs12518-026-00809-9",{"doi":384,"openalex_id":386,"authors":387,"venue":350,"cited_by_count":15,"oa_url":406,"card":407,"direction":68,"ingested_from":70},"W7220854014",[388,391,394,397,400,403],{"name":389,"orcid":390},"Javad Momeni Damaneh","https:\u002F\u002Forcid.org\u002F0009-0006-9131-6167",{"name":392,"orcid":393},"E Tamassoki","https:\u002F\u002Forcid.org\u002F0000-0002-9873-1957",{"name":395,"orcid":396},"Mohammed Achite","https:\u002F\u002Forcid.org\u002F0000-0001-6084-5759",{"name":398,"orcid":399},"Seyed Mohammad Tajbakhsh","https:\u002F\u002Forcid.org\u002F0000-0002-2097-9944",{"name":401,"orcid":402},"Kusum Pandey","https:\u002F\u002Forcid.org\u002F0009-0005-4888-6650",{"name":404,"orcid":405},"Celso Augusto Guimarães Santos","https:\u002F\u002Forcid.org\u002F0000-0001-7927-9718","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs12518-026-00809-9.pdf",{"tldr":408,"method":409,"finding":410,"direction":68,"opportunity":411},"结合土壤地球化学与Landsat 8遥感，绘制伊朗干旱矿区重金属污染与植被胁迫图谱。","24个表层土壤样地球化学分析，结合Landsat 8-OLI植被指数与相关性分析","Cd超标88倍；RVI、MSAVI2优于NDVI，Cu与植被指数强负相关。","干旱稀疏植被区需筛选适宜植被指数，可发展重金属胁迫遥感反演与食物链风险预警模型。","2026-10-08T23:30:29.578924Z",{"id":414,"title":415,"url":416,"summary":417,"summary_zh":418,"content":9,"source_name":419,"source_url":416,"published_at":420,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":421,"score_detail":422,"sources":424,"tags":426,"primary_topic":9,"secondary_topics":430,"classification":431,"search_phrases":447,"slug":450,"view_count":52,"doi":451,"paper":452,"created_at":464},4222,"Fixed-ground multisensor monitoring resolves longitudinal vegetation condition and fuel-state structure in Southern California chaparral and oak scenes","https:\u002F\u002Fdoi.org\u002F10.31223\u002Fx58r40","Wildfire-relevant vegetation and fuel conditions evolve through interacting changes in vegetation condition, three-dimensional fuel structure, thermal response, and atmospheric drying, yet these dimensions are commonly monitored separately. This study evaluated repeated fixed-ground multisensor monitoring at two fixed vegetation scenes in Placerita Canyon, California. Multispectral imaging, radiometric thermal imaging, light detection and ranging (LiDAR), and local atmospheric measurements were integrated across persistent dense, mixed, and dry vegetation regions during 10 quality-controlled visits at a native-chaparral hillside and 24 visits at an oak\u002Fcanopy-centered scene. Persistent vegetation regions remained strongly differentiated across sensing domains while also exhibiting longitudinal change. Dense vegetation exceeded dry vegetation in median normalized difference vegetation index (NDVI) by 0.259 and 0.113 at the two sites, showed 4.11 m and 0.56 m greater LiDAR vertical spread, and remained 3.37 °C and 3.88 °C cooler relative to ambient air. Dense chaparral NDVI declined markedly, while dense, mixed, and dry regions at the oak-centered site showed sustained NDVI declines. Corresponding decreases across three additional spectral indices and spatially differentiated oak-canopy changes showed that the temporal signal extended beyond a single spectral index or whole-canopy average. Multisensor analysis of 96 complete visit × site × region observations showed vegetation region as the dominant source of multivariate separation (partial R² = 0.491, p = 0.0005). By preserving spatial identity while integrating spectral, structural, thermal, and atmospheric measurements, this fixed-ground framework provides a local longitudinal approach for characterizing evolving vegetation and fuel conditions before ignition.","与野火相关的植被和燃料条件通过植被状况、三维燃料结构、热响应和大气干燥度的交互变化而演变，但这些维度通常被分别监测。本研究评估了在加利福尼亚州普莱塞里塔峡谷两个固定植被样地进行的重复固定地面多传感器监测。在10次质量控制的访问中，对本地查帕拉尔灌丛山坡的多光谱成像、辐射热成像、激光雷达（LiDAR）和局地大气测量进行了整合，覆盖了持续存在的密集、混合和干燥植被区域；在24次访问中，对以橡树\u002F冠层为中心的样地进行了同样的整合。持续存在的植被区域在不同传感域之间保持强烈分化，同时也表现出纵向变化。在两个样地中，密集植被的中位归一化差异植被指数（NDVI）分别比干燥植被高0.259和0.113，LiDAR垂直展布分别大4.11 m和0.56 m，并且相对于环境空气分别低3.37 °C和3.88 °C。密集查帕拉尔灌丛的NDVI显著下降，而以橡树为中心的样地中的密集、混合和干燥区域则表现出持续的NDVI下降。另外三个光谱指数的相应下降以及空间分化的橡树冠层变化表明，时间信号超出了单一光谱指数或整个冠层平均值。对96次完整访问×样地×区域观测的多传感器分析表明，植被区域是多变量分离的主导来源（偏R² = 0.491，p = 0.0005）。通过在整合光谱、结构、热和大气测量的同时保持空间身份，这一固定地面框架为表征点火前演变中的植被和燃料条件提供了一种局地纵向方法。","OpenAlex","2026-10-03T00:00:00Z",69,{"impact":182,"substance":86,"depth":87,"authority":85,"freshness":89,"relevant":52,"comment":423},"多传感器固定地面监测方法新颖、数据扎实，对林火燃料与植被状态遥感研究有参考价值，但属基础研究、应用面偏窄。",[425],{"name":419,"url":416},[21,427,428,22,429],"生态监测","森林防火","多源遥感",[],{"status":28,"locked":14,"version":29,"method":30,"evidence":432},{"candidates":433,"agricultural_evidence":14,"reason":47},[434,439,443],{"code":42,"score":73,"supported":36,"title":435,"abstract":436,"tags":438},[],[437],"multispectral",[429,22,21],{"code":310,"score":52,"supported":14,"title":440,"abstract":441,"tags":442},[],[],[427],{"code":104,"score":52,"supported":14,"title":444,"abstract":445,"tags":446},[],[],[428],[448,449],"Placerita Canyon 灌丛 橡树 多传感器监测","NDVI LiDAR 热成像 燃料状态","PlaceritaCanyon灌丛橡树多传感器监测-4222","10.31223\u002Fx58r40",{"doi":451,"openalex_id":453,"authors":454,"venue":9,"cited_by_count":15,"oa_url":457,"card":458,"direction":463,"ingested_from":70},"W7218368544",[455],{"name":456,"orcid":9},"Andrew Lee","https:\u002F\u002Feartharxiv.org\u002Frepository\u002Fobject\u002F15343\u002Fdownload\u002F26599\u002F",{"tldr":459,"method":460,"finding":461,"direction":68,"opportunity":462},"固定地面多传感器长期监测南加州灌丛与橡树林，揭示植被与可燃物状态变化。","多光谱、热红外、LiDAR与大气测量，10-24次重复观测两个固定样地。","植被区域是多元分离主因，密集植被NDVI下降且比干燥区更凉、垂直结构更复杂。","可扩展至更多生态系统，融合多源时序数据构建可燃物动态预警模型。","智慧农业 \u002F 农业物联网","2026-10-05T23:30:14.188546Z"]