[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4439":3,"related-4439":96},{"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":9,"secondary_topics":32,"classification":33,"search_phrases":60,"slug":63,"view_count":64,"doi":65,"paper":66,"created_at":95},4439,"Evaluation of risk scenarios in Ecuador using Visible Infrared Imaging Radiometer Suite remote sensing, ERA5-Land climate data, and XGBoost","https:\u002F\u002Fdoi.org\u002F10.12912\u002F27197050\u002F240190","The present study aims to develop and optimize a spatial risk scenarios model for forest fires in Ecuador through the integration of machine learning and satellite remote sensing.To this end, 103,224 historical records of thermal anomalies captured by the VIIRS sensor were processed, along with five continuous meteorological variables from the ERA5-Land global reanalysis, spatial coordinates (latitude and longitude), and temporal indicators (month of the year), spanning the period between 2015 and 2025.The methodology was based on the construction of a 5-kilometer resolution spatial grid coupled with the XGBoost classification algorithm, designed to efficiently manage class imbalance in the face of extreme climate events.The computational system achieved an accuracy level of 81% on the test set, validating its robustness with a true positive rate of 0.86 and an area under the ROC curve of 0.8382.Furthermore, the feature importance analysis determined that precipitation deficits and seasonal timing act as the primary drivers of ignition risk, exerting a significantly greater influence than surface temperature, concluding that this scalable and open-access tool allows for high-precision mapping of vulnerable areas in the Andean and coastal regions through scenario-based experiments, aiming to optimize the distribution of state preventive resources against the constant threat of global warming.","本研究旨在通过整合机器学习与卫星遥感技术，开发并优化厄瓜多尔森林火灾的空间风险情景模型。为此，研究处理了VIIRS传感器捕获的103 224条历史热异常记录，以及来自ERA5-Land全球再分析数据的五个连续气象变量、空间坐标（纬度和经度）和时间指标（月份），时间跨度为2015年至2025年。方法基于构建5公里分辨率空间网格并结合XGBoost分类算法，旨在高效应对极端气候事件下的类别不平衡问题。该计算系统在测试集上达到了81%的准确率，以0.86的真阳性率和0.8382的ROC曲线下面积验证了其稳健性。此外，特征重要性分析确定，降水亏缺和季节时序是点火风险的主要驱动因素，其影响显著大于地表温度。研究结论认为，这一可扩展且开放获取的工具通过情景实验实现了对安第斯和沿海地区脆弱区域的高精度制图，旨在优化国家预防资源在全球变暖持续威胁下的配置。",null,"Ecological Engineering & Environmental Technology","2026-10-06T00:00:00Z","论文",10,false,66,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},8,20,17,12,9,1,"方法扎实、数据规模大的遥感+机器学习火灾风险建模论文，但属厄瓜多尔区域研究，对国内三农信息化仅有方法借鉴价值。",[25],{"name":10,"url":6},[27,28,29,30,31],"机器学习","遥感","森林防火","风险评估","厄瓜多尔",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":37},"needs_review","2026-10-22.2","rules",{"candidates":38,"agricultural_evidence":14,"reason":59},[39,47,54],{"code":40,"score":41,"supported":42,"title":43,"abstract":45,"tags":46},"I3",15,true,[44],"remote sensing",[28,44],[28],{"code":48,"score":49,"supported":42,"title":50,"abstract":51,"tags":53},"I2",7,[],[27,52],"machine learning",[27],{"code":55,"score":22,"supported":14,"title":56,"abstract":57,"tags":58},"H7",[],[],[29],"农业相关性需要复核",[61,62],"厄瓜多尔 森林火灾 风险","厄瓜多尔 机器学习 森林防火 风险评估","厄瓜多尔森林火灾风险-4439",0,"10.12912\u002F27197050\u002F240190",{"doi":65,"openalex_id":67,"authors":68,"venue":10,"cited_by_count":64,"oa_url":87,"card":88,"direction":92,"ingested_from":94},"W7220666178",[69,72,75,78,81,84],{"name":70,"orcid":71},"César José Avila Martínez","https:\u002F\u002Forcid.org\u002F0009-0009-3253-8300",{"name":73,"orcid":74},"Byrone Antonio Almeida Salazar","https:\u002F\u002Forcid.org\u002F0000-0002-4412-1125",{"name":76,"orcid":77},"Byron Gustavo Almeida Gavino","https:\u002F\u002Forcid.org\u002F0009-0005-7811-2226",{"name":79,"orcid":80},"Christian Manuel Quishpe Villa","https:\u002F\u002Forcid.org\u002F0009-0005-3710-860X",{"name":82,"orcid":83},"Fabián Alexander Molina Peralvo","https:\u002F\u002Forcid.org\u002F0009-0007-6296-0751",{"name":85,"orcid":86},"Alex Jesús López Campuzano","https:\u002F\u002Forcid.org\u002F0009-0001-0916-551X","https:\u002F\u002Fwww.ecoeet.com\u002Fpdf-240190-149057?filename=Evaluation-of-risk-scenar.pdf",{"tldr":89,"method":90,"finding":91,"direction":92,"opportunity":93},"结合VIIRS热异常、ERA5-Land气象数据与XGBoost，构建厄瓜多尔5公里分辨率森林火灾风","VIIRS热异常与ERA5-Land气象变量，5公里网格加XGBoost分类，处","测试集精度81%，AUC 0.8382；降水亏缺与季节时相比地表温度更主导火险。","农业遥感与作物表型","可将该遥感+机器学习火险框架迁移至中国农林交错带，融合植被指数与人类活动因子提升预警精度。","openalex","2026-10-08T23:30:30.817265Z",{"total":97,"page":22,"page_size":97,"items":98},6,[99,151,232,305,394,450],{"id":100,"title":101,"url":102,"summary":103,"summary_zh":9,"content":9,"source_name":104,"source_url":102,"published_at":105,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":106,"score_detail":107,"sources":111,"tags":113,"primary_topic":9,"secondary_topics":115,"classification":116,"search_phrases":136,"slug":139,"view_count":140,"doi":141,"paper":142,"created_at":150},4308,"GIS, Remote Sensing and Machine Learning Applications for Forest Fire Mapping, Susceptibility Assessment and Risk Analysis: A Systematic Review","https:\u002F\u002Fdoi.org\u002F10.21203\u002Frs.3.rs-11233692\u002Fv1","GIS, Remote Sensing and Machine Learning Applications for Forest Fire Mapping, Susceptibility Assessment and Risk Analysis: A Systematic Review。Research Square","Research Square","2026-10-05T00:00:00Z",63,{"impact":20,"substance":108,"depth":109,"authority":17,"freshness":21,"relevant":22,"comment":110},18,16,"系统综述梳理遥感与机器学习在森林火灾制图与风险评估中的应用，对林业信息化与防灾有参考价值，但属综述类论文、来源为预印本平台，权威性一般。",[112],{"name":104,"url":102},[27,28,114,29,30],"GIS",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":117},{"candidates":118,"agricultural_evidence":14,"reason":59},[119,123,127,132],{"code":48,"score":20,"supported":42,"title":120,"abstract":121,"tags":122},[52],[52],[27],{"code":40,"score":20,"supported":42,"title":124,"abstract":125,"tags":126},[44],[44],[28],{"code":128,"score":20,"supported":42,"title":129,"abstract":130,"tags":131},"I4",[114],[114],[114],{"code":55,"score":22,"supported":14,"title":133,"abstract":134,"tags":135},[],[],[29],[137,138],"森林火灾 遥感 机器学习","GIS 森林火险 评估","森林火灾遥感机器学习-4308",2,"10.21203\u002Frs.3.rs-11233692\u002Fv1",{"doi":141,"openalex_id":143,"authors":144,"venue":104,"cited_by_count":64,"oa_url":149,"card":9,"direction":92,"ingested_from":94},"W7220368159",[145,147],{"name":146,"orcid":9},"Lucius Malalu",{"name":148,"orcid":9},"Henry Chinthuli","https:\u002F\u002Fwww.researchsquare.com\u002Farticle\u002Frs-11233692\u002Flatest.pdf","2026-10-06T23:30:19.935677Z",{"id":152,"title":153,"url":154,"summary":155,"summary_zh":156,"content":9,"source_name":157,"source_url":158,"published_at":159,"category":12,"cover_url":9,"hotness":160,"is_selected":14,"score":161,"score_detail":162,"sources":166,"tags":170,"primary_topic":173,"secondary_topics":178,"classification":179,"search_phrases":216,"slug":219,"view_count":64,"doi":220,"paper":221,"created_at":231},5322,"MACHINE LEARNING BASED PREDICTION OF HYDROGEOLOGICAL PARAMETERS AND GROUNDWATER POTENTIAL INDEX USING GEOSPATIAL AND BOREHOLE DATA","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23434956","Abstract: Groundwater is a critical natural resource for domestic consumption, agricultural production, industrial development, and ecological sustainability. Accurate prediction of hydrogeological parameters and groundwater potential is essential for sustainable groundwater management, particularly in regions experiencing irregular rainfall, increasing water demand, and declining groundwater levels. This research paper presents a machine learning-based framework for predicting hydrogeological parameters and developing a Groundwater Potential Index (GWPI) by integrating geospatial datasets with borehole records. The proposed approach combines terrain characteristics, geological formations, soil properties, land use and land cover, drainage density, rainfall, slope, and remotely sensed indices with borehole-derived information, including water-table depth, aquifer thickness, lithology, and well yield. Machine learning algorithms such as Random Forest, Support Vector Regression, Gradient Boosting, and Artificial Neural Networks can be employed to estimate hydrogeological variables and identify areas with different groundwater potential levels. The predicted parameters are integrated within a geospatial framework to generate a spatially distributed groundwater potential index. Model performance can be assessed using the coefficient of determination (\\(R^2\\)), root mean square error (RMSE), and mean absolute error (MAE). The proposed framework offers a reproducible and scalable approach for groundwater exploration, borehole siting, and regional water-resource planning. Since no study-area dataset or model outputs have been provided, the paper presents a methodological framework rather than claiming experimentally validated results. Keywords: Machine Learning, Groundwater Potential Index, Hydrogeological Parameters, Geospatial Analysis, Borehole Data, Random Forest, Remote Sensing, Groundwater Management.","摘要：地下水是生活用水、农业生产、工业发展和生态可持续性的关键自然资源。准确预测水文地质参数和地下水潜力对于可持续地下水管理至关重要，尤其是在降雨不规律、用水需求不断增加和地下水位持续下降的地区。本文提出了一种基于机器学习的框架，通过将地理空间数据集与钻孔记录相结合，预测水文地质参数并构建地下水潜力指数（GWPI）。所提方法综合了地形特征、地质构造、土壤性质、土地利用与土地覆盖、河网密度、降雨、坡度以及遥感指数，并结合钻孔衍生信息，包括水位埋深、含水层厚度、岩性和井出水量。可采用随机森林、支持向量回归、梯度提升和人工神经网络等机器学习算法估算水文地质变量，并识别具有不同地下水潜力水平的区域。预测参数在地理空间框架内进行集成，以生成空间分布的地下水潜力指数。模型性能可通过决定系数（\\(R^2\\)）、均方根误差（RMSE）和平均绝对误差（MAE）进行评估。所提框架为地下水勘探、钻孔选址和区域水资源规划提供了一种可重复且可扩展的方法。由于未提供研究区数据集或模型输出，本文呈现的是方法学框架，而非声称经过实验验证的结果。关键词：机器学习，地下水潜力指数，水文地质参数，地理空间分析，钻孔数据，随机森林，遥感，地下水管理。","ACCENT JOURNAL OF  ECONOMICS ECOLOGY  & ENGINEERING","https:\u002F\u002Fajeee.co.in\u002Findex.php\u002Fajeee\u002Farticle\u002Fview\u002F6754","2026-10-17T00:00:00Z",25,47,{"impact":17,"substance":20,"depth":163,"authority":164,"freshness":21,"relevant":22,"comment":165},14,4,"方法框架类论文，无实测数据与验证结果，主题与农业水资源相关但公共价值有限，不宜进入每日精选。",[167,168],{"name":157,"url":158},{"name":157,"url":169},"https:\u002F\u002Fajeee.co.in\u002Findex.php\u002Fajeee\u002Farticle\u002Fdownload\u002F6754\u002F5489",[27,28,171,172],"水资源管理","地下水监测",{"code":48,"slug":174,"name":175,"path":176,"parent_code":177},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":180,"locked":14,"version":35,"method":36,"evidence":181},"classified",{"candidates":182,"agricultural_evidence":42},[183,187,191,198,204,210],{"code":48,"score":41,"supported":42,"title":184,"abstract":185,"tags":186},[52],[27,52],[27],{"code":40,"score":49,"supported":42,"title":188,"abstract":189,"tags":190},[],[28,44],[28],{"code":192,"score":97,"supported":42,"title":193,"abstract":194,"tags":197},"H2",[],[195,196],"土壤","soil",[],{"code":199,"score":164,"supported":42,"title":200,"abstract":201,"tags":203},"H3",[],[202],"水资源",[171],{"code":128,"score":205,"supported":42,"title":206,"abstract":207,"tags":209},3,[],[208],"空间分析",[],{"code":211,"score":205,"supported":42,"title":212,"abstract":213,"tags":215},"I7",[],[214],"methodological framework",[],[217,218],"机器学习 地下水潜力指数","钻孔数据 水文地质参数","机器学习地下水潜力指数-5322","10.5281\u002Fzenodo.23434956",{"doi":220,"openalex_id":222,"authors":223,"venue":157,"cited_by_count":64,"oa_url":158,"card":226,"direction":92,"ingested_from":94},"W7222805722",[224],{"name":225,"orcid":9},"Prof. I Pandu Ranga Reddy Dr. Hari Ramavath",{"tldr":227,"method":228,"finding":229,"direction":92,"opportunity":230},"提出融合地理空间与钻孔数据的机器学习框架，预测水文地质参数与地下水潜力指数。","随机森林、SVR、梯度提升、神经网络结合遥感与钻孔数据建模。","构建了可复现的地下水潜力评估方法框架，尚未经实测数据验证。","可将该框架落地到具体农业区，用实测钻孔数据验证并耦合灌溉需水预测。","2026-10-19T23:30:22.978385Z",{"id":233,"title":234,"url":235,"summary":236,"summary_zh":237,"content":9,"source_name":238,"source_url":235,"published_at":239,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":240,"score_detail":241,"sources":244,"tags":246,"primary_topic":250,"secondary_topics":255,"classification":256,"search_phrases":278,"slug":281,"view_count":64,"doi":282,"paper":283,"created_at":304},5319,"Synergistic Use of Multitemporal SAR and Optical Data with Random Forest Approach for Rice Mapping in Bangladesh","https:\u002F\u002Fdoi.org\u002F10.31223\u002Fx5f22w","Accurate in-season crop mapping is vital for national food security, yet traditional ground-based estimation methods in Bangladesh often lack precision. This study develops a robust machine-learning framework to map Boro rice in the Manikganj district by integrating Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 optical imagery. To overcome the challenge of seasonal cloud cover, Sentinel-1 VH polarization was utilized to identify unique backscatter reductions during the transplantation phase (late February to early March) caused by specular reflection from flooded fields. By combining these SAR signatures with high-resolution spectral indices (NDVI, EVI, and BSI) from Sentinel-2 during peak growth stages, the research captures the distinct phenological signatures of paddy rice. A pixel-based Random Forest algorithm was applied within the Google Earth Engine (GEE) platform, utilizing about 200 field-verified reference points per sub-district (1,385 in total) for training and validation. The results demonstrated high classification performance, with overall accuracies ranging from 88.1% to 94.9% across the seven sub-districts. Furthermore, a comparison with official government statistics revealed a minimal total area difference of only 2.34%, underscoring the reliability of this integrated satellite approach for operational crop monitoring.","准确的当季作物制图对国家粮食安全至关重要，然而孟加拉国传统的基于地面估计的方法往往缺乏精度。本研究开发了一个稳健的机器学习框架，通过整合Sentinel-1合成孔径雷达（SAR）和Sentinel-2光学影像，对马尼格甘杰县的Boro水稻进行制图。为克服季节性云覆盖的挑战，研究利用Sentinel-1 VH极化识别插秧期（2月下旬至3月上旬）由淹水田块镜面反射所引起的独特后向散射降低特征。通过将这些SAR特征与Sentinel-2在生长旺盛期的高分辨率光谱指数（NDVI、EVI和BSI）相结合，研究捕捉到了水稻独特的物候特征。在Google Earth Engine（GEE）平台上应用了基于像元的随机森林算法，利用每个乡约200个实地验证参考点（共计1，385个）进行训练和验证。结果表明分类性能较高，七个乡的总体精度在88.1%至94.9%之间。此外，与政府官方统计数据相比，总面积差异仅为2.34%，凸显了这种综合卫星方法在业务化作物监测中的可靠性。","OpenAlex","2026-10-18T00:00:00Z",79,{"impact":108,"substance":242,"depth":108,"authority":20,"freshness":21,"relevant":22,"comment":243},22,"方法扎实、精度可靠的水稻遥感制图研究，对农业遥感业务化监测有参考价值，但属区域性案例，影响层级有限。",[245],{"name":238,"url":235},[247,248,27,249,28],"水稻","粮食安全","作物制图",{"code":251,"slug":252,"name":247,"path":253,"parent_code":254},"A1","rice","\u002Ftopic\u002Ffield-crops\u002Frice","A",[],{"status":180,"locked":14,"version":35,"method":36,"evidence":257},{"candidates":258,"agricultural_evidence":42},[259,264,270,274],{"code":251,"score":108,"supported":42,"title":260,"abstract":261,"tags":263},[252],[247,252,262],"paddy",[247],{"code":265,"score":49,"supported":42,"title":266,"abstract":267,"tags":269},"A8",[],[248,268],"food security",[248],{"code":48,"score":164,"supported":42,"title":271,"abstract":272,"tags":273},[],[27],[27],{"code":40,"score":22,"supported":14,"title":275,"abstract":276,"tags":277},[],[],[28],[279,280],"Sentinel-1 Sentinel-2 水稻制图","孟加拉国 Boro 水稻 遥感","Sentinel-1Sentinel-2水稻制图-5319","10.31223\u002Fx5f22w",{"doi":282,"openalex_id":284,"authors":285,"venue":9,"cited_by_count":64,"oa_url":298,"card":299,"direction":92,"ingested_from":94},"W7222810209",[286,288,291,294,296],{"name":287,"orcid":9},"Mst. Farida Perveen",{"name":289,"orcid":290},"Sravan Shrestha","https:\u002F\u002Forcid.org\u002F0000-0001-6979-1726",{"name":292,"orcid":293},"Abdullah Al Mehadi","https:\u002F\u002Forcid.org\u002F0000-0002-9002-633X",{"name":295,"orcid":9},"Saima Kazmina",{"name":297,"orcid":9},"Amio Al Saem","https:\u002F\u002Feartharxiv.org\u002Frepository\u002Fobject\u002F15552\u002Fdownload\u002F26951\u002F",{"tldr":300,"method":301,"finding":302,"direction":92,"opportunity":303},"融合Sentinel-1 SAR与Sentinel-2光学影像，用随机森林在GEE上绘制孟加拉国Bo","Sentinel-1 VH后向散射与Sentinel-2 NDVI\u002FEVI\u002FBS","七个分县总体精度88.1%–94.9%，与官方统计面积仅差2.34%。","可将该SAR-光学协同框架迁移至其他多云雨稻区，并探索多时相特征优选与跨季泛化。","2026-10-19T23:30:22.772950Z",{"id":306,"title":307,"url":308,"summary":309,"summary_zh":310,"content":9,"source_name":311,"source_url":308,"published_at":312,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":313,"score_detail":314,"sources":317,"tags":319,"primary_topic":323,"secondary_topics":327,"classification":333,"search_phrases":348,"slug":351,"view_count":64,"doi":352,"paper":353,"created_at":393},5178,"Spatial Modeling of Topsoil Total Carbon and Texture from Spectral Characteristics of Bare Soil Surface Images of Landsat 5\u002F8 Using Machine Learning","https:\u002F\u002Fdoi.org\u002F10.1134\u002Fs1064229326601228","— This study examines the possibility for detecting bare soil surface (BSS), using remote sensing data and for applying this approach to model texture and total soil carbon in the topsoil. The study area comprises agricultural fields within the North Caucasus Federal Agricultural Research Center in the northeastern spurs of the Stavropol Upland. Landsat 5\u002F8 imagery and spectral indices (NDVI, NBR2, and BSI) were used to classify BSS. Optimal discrimination of BSS from green and non-photosynthetic vegetation was achieved, using a BSI threshold of >0.18 (F1-score = 0.98). Spatial modeling of topsoil properties was conducted by comparing five regression models (Linear Regression, K-Nearest Neighbors, Random Forest, Extreme Gradient Boosting, and Cubist). A linear regression model demonstrated the highest performance; key predictors included the mean and standard deviation of SWIR2. The model exhibits strong predictive capability for sand, silt, clay, and total carbon (adjusted R 2 = 0.82, 0.75, 0.78, and 0.74, respectively). The topsoil is characterized by the range of clay content from 5 to 30%, of silt content from 15 to 60%, of sand content from 20 to 80%, and of total carbon from 0.5 to 3%. Predictive maps of texture and total carbon derived from BSS reveal considerable local heterogeneity, with soils consisting of Chernozems and Phaeozems, influenced by parent rock variability. The modeling results highlight the high predictive power of the BSS spectral characteristics, even when used as a single predictor for digital soil mapping.","本研究探讨了利用遥感数据检测裸露土壤表面（BSS）的可能性，并将该方法应用于表层土壤质地和总碳的建模。研究区包括位于斯塔夫罗波尔高地东北支脉的北高加索联邦农业研究中心内的农田。利用Landsat 5\u002F8影像和光谱指数（NDVI、NBR2和BSI）对BSS进行分类。当BSI阈值>0.18时，实现了BSS与绿色植被及非光合植被的最优区分（F1分数=0.98）。通过比较五种回归模型（线性回归、K近邻、随机森林、极端梯度提升和Cubist）对表层土壤属性进行了空间建模。线性回归模型表现出最高的性能；关键预测变量包括SWIR2的均值和标准差。该模型对砂粒、粉粒、黏粒和总碳表现出较强的预测能力（调整R²分别为0.82、0.75、0.78和0.74）。表层土壤的黏粒含量范围为5%至30%，粉粒含量为15%至60%，砂粒含量为20%至80%，总碳含量为0.5%至3%。基于BSS生成的质地和总碳预测图揭示了显著的局部异质性，土壤由黑钙土和暗色始成土组成，受母岩变异性的影响。建模结果凸显了BSS光谱特征的高预测能力，即使仅作为数字土壤制图的单一预测变量使用亦然。","Eurasian Soil Science","2026-10-15T00:00:00Z",69,{"impact":20,"substance":18,"depth":109,"authority":315,"freshness":17,"relevant":22,"comment":316},13,"方法新颖、结论可靠，但属区域尺度研究，公共影响有限，适合作为技术类精选。",[318],{"name":311,"url":308},[27,28,320,321,322],"土壤碳","Landsat","数字土壤制图",{"code":192,"slug":196,"name":324,"path":325,"parent_code":326},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",[328,329],{"code":48,"slug":174,"name":175,"path":176,"parent_code":177},{"code":40,"slug":330,"name":331,"path":332,"parent_code":177},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":180,"locked":14,"version":35,"method":36,"evidence":334},{"candidates":335,"agricultural_evidence":42},[336,340,344],{"code":192,"score":163,"supported":42,"title":337,"abstract":338,"tags":339},[196],[195,196],[],{"code":48,"score":21,"supported":42,"title":341,"abstract":342,"tags":343},[52],[],[27],{"code":40,"score":17,"supported":42,"title":345,"abstract":346,"tags":347},[],[28,44],[321,28],[349,350],"Landsat 裸土 土壤碳","北高加索 土壤质地 遥感","Landsat裸土土壤碳-5178","10.1134\u002Fs1064229326601228",{"doi":352,"openalex_id":354,"authors":355,"venue":311,"cited_by_count":64,"oa_url":9,"card":388,"direction":92,"ingested_from":94},"W7222107492",[356,359,362,365,368,371,374,377,379,382,385],{"name":357,"orcid":358},"P. A. Braulov","https:\u002F\u002Forcid.org\u002F0000-0001-9919-7402",{"name":360,"orcid":361},"Pavel M. Shilov","https:\u002F\u002Forcid.org\u002F0000-0003-4113-6396",{"name":363,"orcid":364},"R. G. Gadzhiumarov","https:\u002F\u002Forcid.org\u002F0000-0002-4220-623X",{"name":366,"orcid":367},"V. K. Dridiger","https:\u002F\u002Forcid.org\u002F0000-0002-0510-2220",{"name":369,"orcid":370},"E. V. Bogdan","https:\u002F\u002Forcid.org\u002F0009-0006-4781-5786",{"name":372,"orcid":373},"V. O. Megmerova","https:\u002F\u002Forcid.org\u002F0000-0001-8655-6437",{"name":375,"orcid":376},"Petr Tsymbarovich","https:\u002F\u002Forcid.org\u002F0000-0001-8591-2612",{"name":378,"orcid":9},"A. E. Shatunov",{"name":380,"orcid":381},"Pavel P. Fil","https:\u002F\u002Forcid.org\u002F0000-0002-9851-5381",{"name":383,"orcid":384},"D. E. Mitichkin","https:\u002F\u002Forcid.org\u002F0009-0006-2397-060X",{"name":386,"orcid":387},"Dmitriy S. Fomin","https:\u002F\u002Forcid.org\u002F0000-0003-3733-0284",{"tldr":389,"method":390,"finding":391,"direction":92,"opportunity":392},"用Landsat 5\u002F8裸土光谱指数识别裸土并机器学习建模表层土壤质地与全碳。","Landsat 5\u002F8影像、NDVI\u002FNBR2\u002FBSI指数、五种回归模型对比。","BSI阈值0.18识别裸土F1达0.98，线性回归最优，SWIR2均值与标准差为关键预测因子。","可探索多时相裸土合成与深度学习模型，提升数字土壤制图精度与跨区域迁移能力。","2026-10-17T23:30:31.485949Z",{"id":395,"title":396,"url":397,"summary":398,"summary_zh":399,"content":9,"source_name":400,"source_url":397,"published_at":401,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":402,"sources":404,"tags":406,"primary_topic":410,"secondary_topics":411,"classification":412,"search_phrases":427,"slug":430,"view_count":64,"doi":431,"paper":432,"created_at":449},4903,"Integrating earth observation and machine learning for forest fire risk assessment in Rasuwa district, Nepal","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs44415-026-00138-0","The complex terrain and increasing forest fire incidents in recent years make high-mountain environments vulnerable to forest fires, posing a threat to ecological and economic well-being. However, research related to forest fires remains limited in the mountain region. The study aimed to assess the forest fire risk by integrating eleven predictive layers (biophysical, topographical, climatic, and anthropogenic factors) and a Random Forest (RF) model in Rasuwa, a mountainous district of Nepal. However, the precipitation factor was excluded due to multicollinearity concerns. The model was validated using Area Under the Curve (AUC) and True Skill Statistics (TSS). With an AUC of 0.9 and TSS of 0.67, the model shows strong predictive performance and high reliability of the generated fire risk map. The most significant factors in the model’s ability to predict the forest fire risk were elevation, temperature, Normalized Difference Vegetation Index (NDVI), and proximity to settlements. The study shows that 29.66% of Rasuwa district’s forest is at fire risk, with the highest risk in Kalika Rural Municipality (53.7%). In the study area, approximately 30% of fire incidents occurred at elevations above 3000 m. The results can assist the concerned stakeholders and authorities in developing effective forest management plans by prioritizing identified high-risk areas for early warning systems and fuel management.","近年来，复杂的地形和日益增多的森林火灾事件使高山环境易受森林火灾影响，对生态和经济福祉构成威胁。然而，山区相关的森林火灾研究仍然有限。本研究旨在通过整合十一个预测图层（生物物理、地形、气候和人为因素）和随机森林（RF）模型，评估尼泊尔山区拉苏瓦地区的森林火灾风险。由于多重共线性问题，降水因子被排除在外。模型采用曲线下面积（AUC）和真实技巧统计量（TSS）进行验证。模型的AUC为0.9，TSS为0.67，显示出较强的预测性能，生成的火灾风险图具有较高的可靠性。模型预测森林火灾风险的最重要因子为海拔、温度、归一化植被指数（NDVI）和距居民点的距离。研究表明，拉苏瓦地区29.66%的森林面临火灾风险，其中卡利卡农村自治市的风险最高（53.7%）。在研究区域内，约30%的火灾事件发生在海拔3000米以上。研究结果可帮助相关利益方和主管部门制定有效的森林管理计划，优先将已识别的高风险区域纳入早期预警系统和可燃物管理。","Discover Forests","2026-10-12T00:00:00Z",{"impact":17,"substance":18,"depth":19,"authority":315,"freshness":17,"relevant":22,"comment":403},"基于遥感与随机森林的高山森林火险评估，方法可靠、结论具体，对农业遥感与灾害预警有参考价值，但属境外区域研究，公共影响有限。",[405],{"name":400,"url":397},[407,28,29,408,409],"农业人工智能","灾害预警","尼泊尔",{"code":48,"slug":174,"name":175,"path":176,"parent_code":177},[],{"status":180,"locked":14,"version":35,"method":36,"evidence":413},{"candidates":414,"agricultural_evidence":42},[415,419,423],{"code":48,"score":21,"supported":42,"title":416,"abstract":417,"tags":418},[52],[],[407],{"code":55,"score":140,"supported":14,"title":420,"abstract":421,"tags":422},[],[],[29,408],{"code":40,"score":22,"supported":14,"title":424,"abstract":425,"tags":426},[],[],[28],[428,429],"Rasuwa 森林火灾 风险评估","随机森林 遥感 火险制图","Rasuwa森林火灾风险评估-4903","10.1007\u002Fs44415-026-00138-0",{"doi":431,"openalex_id":433,"authors":434,"venue":400,"cited_by_count":64,"oa_url":443,"card":444,"direction":92,"ingested_from":94},"W7221573341",[435,437,440],{"name":436,"orcid":9},"Sudeep Jogi Kanwar",{"name":438,"orcid":439},"Milan Dhakal","https:\u002F\u002Forcid.org\u002F0009-0008-9851-2173",{"name":441,"orcid":442},"A. V. Parajuli","https:\u002F\u002Forcid.org\u002F0000-0002-9401-888X","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs44415-026-00138-0.pdf",{"tldr":445,"method":446,"finding":447,"direction":92,"opportunity":448},"整合地球观测与随机森林模型评估尼泊尔Rasuwa地区森林火灾风险。","11个生物物理、地形、气候和人为因子，随机森林模型，AUC和TSS验证。","模型AUC达0.9，29.66%森林有火灾风险，海拔、温度、NDVI和居民点距离最关键。","可探索多源遥感数据与深度学习结合，提升高山地区火灾风险动态预测精度。","2026-10-14T23:30:30.551705Z",{"id":451,"title":452,"url":453,"summary":454,"summary_zh":455,"content":9,"source_name":456,"source_url":453,"published_at":401,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":457,"score_detail":458,"sources":460,"tags":462,"primary_topic":9,"secondary_topics":465,"classification":466,"search_phrases":488,"slug":491,"view_count":64,"doi":492,"paper":493,"created_at":516},4880,"Development of an Explainable GeoAI Wildfire Risk Analysis Model Using Multi-Source Geospatial Data","https:\u002F\u002Fdoi.org\u002F10.5194\u002Fisprs-archives-xlviii-m-13-2026-17-2026","Accounting for approximately 31% of the global land surface, forests play a critical and indispensable role in sustaining ecological balance. Among the various pressures affecting forests, forest fires constitute a major environmental problem, posing significant threats to natural ecosystems while disrupting forestry activities and leading to substantial economic losses. In Türkiye, forest fires are predominantly observed in the Aegean, Mediterranean, and Marmara regions. This study presents a proactive, explainable GeoAI framework for wildfire risk analysis by integrating multi-source geospatial data. The methodology calculates a physical hazard index by evaluating advanced ensemble machine learning algorithm, specifically extreme gradient boosting (XGBoost) with the latter demonstrating superior predictive performance. The model is driven by topographic, meteorological, biotic and anthropogenic parameters. In parallel, a comprehensive vulnerability matrix encompassing physical, social, economic and environmental dimensions is constructed utilizing the Best-Worst Method (BWM). These two main components are spatially synthesized in a Geographic Information Systems (GIS) environment. The model's interpretability is enhanced focusing on feature importance to identify the primary drivers of fire ignition and spread. Results demonstrate that anthropogenic pressures and Wildland-Urban Interface (WUI) dynamics are the most significant risk determinants. The resulting high-resolution maps provide a robust Spatial Decision Support System (SDSS) for local authorities, enabling targeted mitigation strategies and contributing to the development of sustainable, smart and resilient cities.","森林约占全球陆地表面的31%，在维持生态平衡方面发挥着不可或缺的关键作用。在影响森林的诸多压力中，森林火灾构成重大环境问题，对自然生态系统造成严重威胁，同时干扰林业活动并导致重大经济损失。在土耳其，森林火灾主要发生于爱琴海、地中海和马尔马拉地区。本研究通过整合多源地理空间数据，提出了一种具有前瞻性和可解释性的地理人工智能（GeoAI）框架，用于野火风险分析。该方法通过评估先进的集成机器学习算法计算物理危险性指数，其中极端梯度提升（XGBoost）表现出更优的预测性能。模型由地形、气象、生物和人为参数驱动。与此同时，利用最优最劣法（BWM）构建了涵盖物理、社会、经济和环境维度的综合脆弱性矩阵。这两个主要组成部分在地理信息系统（GIS）环境中进行空间综合。通过聚焦特征重要性以识别火灾点燃和蔓延的主要驱动因素，增强了模型的可解释性。结果表明，人为压力和野地-城市交界区（WUI）动态是最重要的风险决定因素。所生成的高分辨率地图为地方当局提供了稳健的空间决策支持系统（SDSS），有助于制定有针对性的减缓策略，并促进可持续、智慧和韧性城市的发展。","˜The œinternational archives of the photogrammetry, remote sensing and spatial information sciences\u002FInternational archives of the photogrammetry, remote sensing and spatial information sciences",71,{"impact":20,"substance":18,"depth":19,"authority":315,"freshness":21,"relevant":22,"comment":459},"将可解释GeoAI与多源地理空间数据结合用于野火风险分析，方法新颖、结论明确，对农业与乡村防灾减灾有参考价值。",[461],{"name":456,"url":453},[407,463,28,29,464],"防灾减灾","地理信息系统",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":467},{"candidates":468,"agricultural_evidence":14,"reason":59},[469,474,480,484],{"code":48,"score":13,"supported":42,"title":470,"abstract":471,"tags":473},[],[27,52,472],"decision support",[407],{"code":128,"score":13,"supported":42,"title":475,"abstract":476,"tags":479},[],[477,114,478],"地理信息","geographic information",[464],{"code":55,"score":140,"supported":14,"title":481,"abstract":482,"tags":483},[],[],[29,463],{"code":40,"score":22,"supported":14,"title":485,"abstract":486,"tags":487},[],[],[28],[489,490],"GeoAI 野火风险 XGBoost","土耳其 森林火灾 遥感","GeoAI野火风险XGBoost-4880","10.5194\u002Fisprs-archives-xlviii-m-13-2026-17-2026",{"doi":492,"openalex_id":494,"authors":495,"venue":456,"cited_by_count":64,"oa_url":509,"card":510,"direction":515,"ingested_from":94},"W7221503678",[496,499,502,504,506],{"name":497,"orcid":498},"Esra Günaydın","https:\u002F\u002Forcid.org\u002F0000-0003-1868-0887",{"name":500,"orcid":501},"Muhammed Oğuzhan Mete","https:\u002F\u002Forcid.org\u002F0000-0002-9312-1965",{"name":503,"orcid":9},"Furkan Ali Kucuk",{"name":505,"orcid":9},"Ayça Hafızoğlu",{"name":507,"orcid":508},"Mahmut Oğuz Selbesoğlu","https:\u002F\u002Forcid.org\u002F0000-0002-1132-3978","https:\u002F\u002Fisprs-archives.copernicus.org\u002Farticles\u002FXLVIII-M-13-2026\u002F17\u002F2026\u002Fisprs-archives-XLVIII-M-13-2026-17-2026.pdf",{"tldr":511,"method":512,"finding":513,"direction":92,"opportunity":514},"构建可解释GeoAI框架，融合多源地理数据评估土耳其野火风险并制图。","XGBoost算危险指数，BWM建脆弱性矩阵，GIS空间合成。","人为压力与野火城市交界带动态是最显著的风险决定因素。","可将该可解释GeoAI框架迁移至农业火险与秸秆焚烧风险，结合时序遥感做动态预警。","智慧农业 \u002F 农业物联网","2026-10-14T23:30:10.102337Z"]