[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4096":3,"related-4096":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":23,"tags":25,"primary_topic":31,"secondary_topics":37,"classification":38,"search_phrases":71,"slug":74,"view_count":75,"doi":76,"paper":77,"created_at":95},4096,"Construction of Methodology for Mapping Fish Farming in the Legal Amazon","https:\u002F\u002Fdoi.org\u002F10.34257\u002Fljrs226981uk","Pisciculture has been established as a strategic activity in the Amazon, with emphasis on the cultivation of tambaqui (Colossoma macropomum) and its hybrids. This study analyzed the spatial dynamics of aquaculture in three municipalities of the Legal Amazon: Ariquemes (RO), Sorriso (MT) and Paragominas (PA), using geotechnologies and remote sensing. Using Sentinel-2 images, Google Earth Engine and the DBSCAN algorithm, excavated tanks were mapped, analyzed in terms of area, perimeter, compactness and spatial organization. The average area of the nurseries was 0.57 ha, higher than the national average. There was high structural and productive variability among the municipalities. Ariquemes had larger and more compact nurseries, while Paragominas stood out for the irregularity in the shapes of the tanks. The multivariate analysis indicated that factors such as urban proximity, logistical infrastructure and level of technification directly influence the distribution and performance of fish farming. It was also observed the coexistence between small producers and large enterprises, revealing a heterogeneous production structure. Among the challenges identified, we highlight the low spatial resolution of images, the overlap of structures and inappropriate environmental practices, such as stream barrages, which make it difficult to distinguish between natural water bodies and excavated tanks. It is concluded that the integration between geotechnologies and socioeconomic data is essential for the sustainable planning of aquaculture in the region. Regional public policies, infrastructure improvement and incentives for environmental regularization are fundamental to the strengthening and sustainability of the sector.","水产养殖业已成为亚马孙地区的战略性活动，其中以巨骨舌鱼（Colossoma macropomum）及其杂交种的养殖为重点。本研究利用地理技术和遥感手段，分析了亚马孙法定地区三个市镇——阿里克梅斯（罗赖马州）、索里索（马托格罗索州）和帕拉戈米纳斯（帕拉州）——水产养殖的空间动态。研究使用Sentinel-2影像、Google Earth Engine和DBSCAN算法，对挖掘池塘进行了制图，并从面积、周长、紧凑度和空间组织等方面进行了分析。育苗池的平均面积为0.57公顷，高于全国平均水平。各市镇之间存在较高的结构性和生产性差异。阿里克梅斯的育苗池较大且较为紧凑，而帕拉戈米纳斯则以池塘形状不规则而突出。多变量分析表明，城市邻近度、物流基础设施和技术化水平等因素直接影响水产养殖的分布和表现。研究还观察到小生产者与大型企业并存，揭示了异质性的生产结构。在已识别的挑战中，我们重点关注影像空间分辨率低、结构物重叠以及不当的环境实践（如溪流筑坝），这些因素使得自然水体与挖掘池塘难以区分。研究结论认为，地理技术与社会经济数据的整合对于该地区水产养殖的可持续规划至关重要。区域公共政策、基础设施改善以及环境规范化激励措施，是该行业强化与可持续发展的根本保障。",null,"London Journal of Research in Science Natural & Formal","2026-09-30T00:00:00Z","论文",10,false,69,{"impact":17,"substance":18,"depth":19,"authority":17,"freshness":20,"relevant":21,"comment":22},12,20,17,8,1,"该研究利用遥感与地理空间技术绘制亚马逊合法区域水产养殖分布，方法新颖、数据扎实，对农业信息化与智慧渔业有参考价值，但地域性强、影响范围有限。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧渔业","水产养殖","空间分析","遥感监测","亚马逊农业",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",[],{"status":39,"locked":14,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":47},[44,54,61,66],{"code":45,"score":46,"supported":47,"title":48,"abstract":50,"tags":53},"D",18,true,[49],"fish farming",[51,52,49],"水产","aquaculture",[27],{"code":32,"score":55,"supported":47,"title":56,"abstract":57,"tags":60},7,[],[58,59],"遥感","remote sensing",[29],{"code":62,"score":21,"supported":14,"title":63,"abstract":64,"tags":65},"D5",[],[],[26],{"code":67,"score":21,"supported":14,"title":68,"abstract":69,"tags":70},"I4",[],[],[28],[72,73],"亚马逊 水产养殖 遥感","Sentinel-2 鱼塘 制图","亚马逊水产养殖遥感-4096",4,"10.34257\u002Fljrs226981uk",{"doi":76,"openalex_id":78,"authors":79,"venue":10,"cited_by_count":86,"oa_url":87,"card":88,"direction":92,"ingested_from":94},"W7214887718",[80,83],{"name":81,"orcid":82},"Victor Tiago Da Silva Catuxo","https:\u002F\u002Forcid.org\u002F0009-0000-3360-0350",{"name":84,"orcid":85},"Carlos Alberto Martins Cordeiro","https:\u002F\u002Forcid.org\u002F0000-0002-2698-6918",0,"https:\u002F\u002Fdoc.journalspress.com\u002F3hosoh_226981\u002Farticle\u002Fconstruction-of-methodology-for-mapping-fish-farming-in-the-legal-amazon.pdf?v=fefa94e5f597",{"tldr":89,"method":90,"finding":91,"direction":92,"opportunity":93},"利用遥感与地理技术绘制巴西亚马逊法定区三个市镇的鱼塘空间分布图。","Sentinel-2影像、Google Earth Engine与DBSCAN算","鱼塘平均面积0.57公顷，各市镇结构差异大，受城市邻近、物流与技术化影响。","农业遥感与作物表型","可结合高分辨率影像与 socioeconomic 数据，提升小规模鱼塘识别精度并支持可持续水产规划。","openalex","2026-10-03T23:30:36.241658Z",{"total":97,"page":21,"page_size":97,"items":98},6,[99,161,232,315,379,448],{"id":100,"title":101,"url":102,"summary":103,"summary_zh":104,"content":9,"source_name":105,"source_url":102,"published_at":106,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":107,"score_detail":108,"sources":111,"tags":113,"primary_topic":117,"secondary_topics":118,"classification":119,"search_phrases":141,"slug":144,"view_count":86,"doi":145,"paper":146,"created_at":160},5391,"Spatio-temporal analysis and forecasting of land use\u002Fland cover change in East Medinipur district, India using CA-Markov model","https:\u002F\u002Fdoi.org\u002F10.3846\u002Fgac.2026.23669","Land Use\u002FLand Cover (LU\u002FLC) serve as essential indicators of global environmental changes. This study examined the LU\u002FLC change in the East Medinipur district of West Bengal, India, using remote sensing data and the CA-Markov modeling approach. Through GIS tools and satellite imagery, the study maps, monitors, and analyzes LU\u002FLC changes over a 20-year period (2001–2021), utilizing data from Landsat 4–5 and Landsat 8. Analytical tools including ArcGIS 9, IDRISI Terset, and ERDAS 2014 are employed to project LU\u002FLC trends up to 2041. The results highlight significant transformations, notable reductions in agriculture, vegetation, barren land, and water bodies, alongside substantial increases in aquaculture and built-up areas from 2001 to 2021. Projections indicates that these trends will continue through 2041, with further declines in agriculture, vegetation, barren land, and water bodies, accompanied by continued growth in aquaculture and built-up areas.","土地利用\u002F土地覆盖（Land Use\u002FLand Cover，LU\u002FLC）是全球环境变化的重要指标。本研究利用遥感数据和CA-Markov建模方法，考察了印度西孟加拉邦东梅迪尼普尔县的土地利用\u002F土地覆盖变化。通过GIS工具和卫星影像，研究利用Landsat 4–5和Landsat 8数据，对2001—2021年20年间的土地利用\u002F土地覆盖变化进行了制图、监测与分析。研究采用ArcGIS 9、IDRISI Terset和ERDAS 2014等分析工具，对至2041年的土地利用\u002F土地覆盖趋势进行预测。结果表明，2001年至2021年间发生了显著转变，农业、植被、裸地和水体明显减少，水产养殖和建成区则大幅增加。预测显示，这些趋势将持续至2041年，农业、植被、裸地和水体进一步减少，水产养殖和建成区持续增长。","Geodesy and Cartography","2026-10-19T00:00:00Z",61,{"impact":20,"substance":46,"depth":109,"authority":17,"freshness":20,"relevant":21,"comment":110},15,"该研究基于遥感与CA-Markov模型分析印度区域土地利用变化，方法规范、结论明确，但属区域性案例，对国内三农与农业信息化的直接参考价值有限，可作为遥感应用类论文收录，不宜进入每日精选。",[112],{"name":105,"url":102},[27,58,114,115,116],"土地利用","耕地变化","CA-Markov",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":120},{"candidates":121,"agricultural_evidence":47},[122,126,130,136],{"code":45,"score":55,"supported":47,"title":123,"abstract":124,"tags":125},[],[51,52],[27],{"code":32,"score":55,"supported":47,"title":127,"abstract":128,"tags":129},[],[58,59],[58],{"code":67,"score":131,"supported":47,"title":132,"abstract":133,"tags":135},3,[],[134],"GIS",[],{"code":137,"score":21,"supported":14,"title":138,"abstract":139,"tags":140},"H6",[],[],[114],[142,143],"Landsat 土地利用 遥感监测","土地利用 水产养殖 耕地变化 遥感","Landsat土地利用遥感监测-5391","10.3846\u002Fgac.2026.23669",{"doi":145,"openalex_id":147,"authors":148,"venue":105,"cited_by_count":86,"oa_url":154,"card":155,"direction":92,"ingested_from":94},"W7222963948",[149,152],{"name":150,"orcid":151},"Parag Jyoti Kashyap","https:\u002F\u002Forcid.org\u002F0000-0003-4273-841X",{"name":153,"orcid":9},"Sahana Bose","https:\u002F\u002Fjournals.vilniustech.lt\u002Findex.php\u002FGAC\u002Farticle\u002Fdownload\u002F23669\u002F13567",{"tldr":156,"method":157,"finding":158,"direction":92,"opportunity":159},"用遥感与CA-Markov模型分析印度东梅迪尼普尔2001-2021年土地利用变化并预测至2041年","Landsat影像、GIS与IDRISI\u002FERDAS，CA-Markov模型做L","农业、植被、裸地和水体持续减少，水产养殖与建设用地显著扩张并将延续至2041年。","可将CA-Markov与作物适宜性\u002F粮食安全模型耦合，评估养殖与建设用地扩张对农业生产的长期威胁。","2026-10-20T23:30:19.057951Z",{"id":162,"title":163,"url":164,"summary":165,"summary_zh":166,"content":9,"source_name":167,"source_url":164,"published_at":106,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":86,"score_detail":168,"sources":170,"tags":172,"primary_topic":175,"secondary_topics":176,"classification":177,"search_phrases":198,"slug":201,"view_count":86,"doi":202,"paper":203,"created_at":231},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",{"impact":86,"substance":86,"depth":86,"authority":86,"freshness":86,"relevant":86,"comment":169},"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。",[171],{"name":167,"url":164},[29,114,173,174],"土壤侵蚀","乡村人口",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":178},{"candidates":179,"agricultural_evidence":47},[180,184,188,194],{"code":32,"score":55,"supported":47,"title":181,"abstract":182,"tags":183},[],[58,59],[29],{"code":67,"score":131,"supported":47,"title":185,"abstract":186,"tags":187},[],[134],[],{"code":189,"score":131,"supported":47,"title":190,"abstract":191,"tags":193},"I7",[],[192],"methodological framework",[],{"code":137,"score":21,"supported":14,"title":195,"abstract":196,"tags":197},[],[],[114],[199,200],"Kolubara 河岸侵蚀","Obrenovac 土地利用","Kolubara河岸侵蚀-5390","10.59954\u002Fstnv.797",{"doi":202,"openalex_id":204,"authors":205,"venue":167,"cited_by_count":86,"oa_url":164,"card":225,"direction":92,"ingested_from":94},"W7222945959",[206,209,212,214,217,220,223],{"name":207,"orcid":208},"Slavoljub Dragićević","https:\u002F\u002Forcid.org\u002F0000-0002-1417-5405",{"name":210,"orcid":211},"Marko Langović","https:\u002F\u002Forcid.org\u002F0000-0002-4614-7265",{"name":213,"orcid":9},"Милан Радовић",{"name":215,"orcid":216},"Radislav Tošić","https:\u002F\u002Forcid.org\u002F0000-0002-3994-125X",{"name":218,"orcid":219},"Milica Langović","https:\u002F\u002Forcid.org\u002F0000-0002-6979-377X",{"name":221,"orcid":222},"Novica Lovrić","https:\u002F\u002Forcid.org\u002F0009-0006-0838-4370",{"name":224,"orcid":9},"Manojlović Stefan",{"tldr":226,"method":227,"finding":228,"direction":229,"opportunity":230},"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":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":246,"tags":248,"primary_topic":253,"secondary_topics":259,"classification":261,"search_phrases":286,"slug":289,"view_count":86,"doi":290,"paper":291,"created_at":314},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":242,"substance":243,"depth":46,"authority":244,"freshness":20,"relevant":21,"comment":245},16,22,13,"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。",[247],{"name":238,"url":235},[249,250,29,251,252],"智慧农业","无人机","冬小麦","生物量估算",{"code":254,"slug":255,"name":256,"path":257,"parent_code":258},"A2","wheat","小麦","\u002Ftopic\u002Ffield-crops\u002Fwheat","A",[260],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":262},{"candidates":263,"agricultural_evidence":47},[264,270,274,281],{"code":254,"score":265,"supported":47,"title":266,"abstract":268,"tags":269},26,[267,255],"winter wheat",[256,267,255],[251],{"code":32,"score":242,"supported":47,"title":271,"abstract":272,"tags":273},[59],[58,59],[252,29],{"code":275,"score":97,"supported":47,"title":276,"abstract":277,"tags":280},"I2",[],[278,279],"机器学习","machine learning",[],{"code":282,"score":21,"supported":14,"title":283,"abstract":284,"tags":285},"I1",[],[],[249],[287,288],"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":290,"openalex_id":292,"authors":293,"venue":238,"cited_by_count":86,"oa_url":235,"card":309,"direction":92,"ingested_from":94},"W7222806066",[294,296,298,300,303,305,307],{"name":295,"orcid":9},"Guangtao Wang",{"name":297,"orcid":9},"Sumei Yao",{"name":299,"orcid":9},"Yanzheng Xie",{"name":301,"orcid":302},"Anzhen Qin","https:\u002F\u002Forcid.org\u002F0000-0002-2550-0015",{"name":304,"orcid":9},"Xiangdong Chen",{"name":306,"orcid":9},"Yuhao Zhang",{"name":308,"orcid":9},"Lina Jiang",{"tldr":310,"method":311,"finding":312,"direction":92,"opportunity":313},"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":316,"title":317,"url":318,"summary":319,"summary_zh":320,"content":9,"source_name":321,"source_url":318,"published_at":239,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":86,"score_detail":322,"sources":324,"tags":326,"primary_topic":330,"secondary_topics":331,"classification":342,"search_phrases":361,"slug":364,"view_count":86,"doi":365,"paper":366,"created_at":378},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":86,"substance":86,"depth":86,"authority":86,"freshness":86,"relevant":86,"comment":323},"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。",[325],{"name":321,"url":318},[327,29,328,329],"防灾减灾","洪水风险评估","GIS应用",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[332,336],{"code":67,"slug":333,"name":334,"path":335,"parent_code":36},"gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",{"code":337,"slug":338,"name":339,"path":340,"parent_code":341},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",{"status":39,"locked":14,"version":40,"method":41,"evidence":343},{"candidates":344,"agricultural_evidence":47},[345,349,355],{"code":32,"score":109,"supported":47,"title":346,"abstract":347,"tags":348},[59],[58,59],[29],{"code":67,"score":350,"supported":47,"title":351,"abstract":352,"tags":354},14,[134],[353,134],"地理信息",[],{"code":337,"score":20,"supported":47,"title":356,"abstract":357,"tags":360},[],[358,359],"干旱","drought",[328,327],[362,363],"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":365,"openalex_id":367,"authors":368,"venue":321,"cited_by_count":86,"oa_url":372,"card":373,"direction":92,"ingested_from":94},"W7222885988",[369],{"name":370,"orcid":371},"Birendra Yadav","https:\u002F\u002Forcid.org\u002F0009-0007-0864-474X","https:\u002F\u002Fwww.ijfmr.com\u002Fpapers\u002F2026\u002F5\u002F89708.pdf",{"tldr":374,"method":375,"finding":376,"direction":92,"opportunity":377},"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",{"id":380,"title":381,"url":382,"summary":383,"summary_zh":384,"content":9,"source_name":385,"source_url":382,"published_at":106,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":386,"score_detail":387,"sources":389,"tags":391,"primary_topic":396,"secondary_topics":397,"classification":398,"search_phrases":410,"slug":413,"view_count":86,"doi":414,"paper":415,"created_at":447},5318,"Lagged Effects and Mechanisms of Central Environmental Inspection Policies on Agricultural Straw Burning: Evidence from Remote Sensing Data in China","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs10668-026-08214-3","Lagged Effects and Mechanisms of Central Environmental Inspection Policies on Agricultural Straw Burning: Evidence from Remote Sensing Data in China。Environment Development and Sustainability","中央环保督察政策对农业秸秆焚烧的滞后效应与作用机制：来自中国遥感数据的证据。《环境、发展与可持续性》","Environment Development and Sustainability",84,{"impact":243,"substance":18,"depth":46,"authority":350,"freshness":13,"relevant":21,"comment":388},"基于全国遥感数据评估中央环保督察对秸秆焚烧的滞后效应，数据规模大、方法新颖，对农业环境政策有参考价值，值得进入每日精选。",[390],{"name":385,"url":382},[392,29,393,394,395],"农业政策","卫星遥感","秸秆焚烧","环保督察",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":399},{"candidates":400,"agricultural_evidence":47},[401,405],{"code":32,"score":242,"supported":47,"title":402,"abstract":403,"tags":404},[59],[58,59],[393,29],{"code":406,"score":21,"supported":14,"title":407,"abstract":408,"tags":409},"H5",[],[],[394],[411,412],"中央环保督察 秸秆焚烧","遥感数据 秸秆焚烧","中央环保督察秸秆焚烧-5318","10.1007\u002Fs10668-026-08214-3",{"doi":414,"openalex_id":416,"authors":417,"venue":385,"cited_by_count":86,"oa_url":9,"card":441,"direction":92,"ingested_from":94},"W7222877438",[418,420,422,424,426,428,430,433,435,437,439],{"name":419,"orcid":9},"Lu Dong",{"name":421,"orcid":9},"Linru Zhang",{"name":423,"orcid":9},"Na Liu",{"name":425,"orcid":9},"Xin Long",{"name":427,"orcid":9},"Ziyi Wang",{"name":429,"orcid":9},"Siyue Yang",{"name":431,"orcid":432},"Jie Tian","https:\u002F\u002Forcid.org\u002F0000-0001-6585-3643",{"name":434,"orcid":9},"Qiyuan Wang",{"name":436,"orcid":9},"Zhanfeng Dong",{"name":438,"orcid":9},"Long Wu",{"name":440,"orcid":9},"Yichen Wang",{"tldr":442,"method":443,"finding":444,"direction":445,"opportunity":446},"利用遥感数据评估中央环保督察政策对秸秆焚烧的滞后效应与作用机制。","基于中国遥感火点数据构建面板，采用滞后效应模型识别政策影响机制。","中央环保督察对秸秆焚烧具有显著但滞后的抑制效应，并存在区域异质性。","农业绿色发展与碳","可结合地块级遥感与农户调查，量化督察滞后期内秸秆离田利用与替代处置的路径。","2026-10-19T23:30:22.693590Z",{"id":449,"title":450,"url":451,"summary":452,"summary_zh":453,"content":9,"source_name":454,"source_url":451,"published_at":239,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":86,"score_detail":455,"sources":457,"tags":459,"primary_topic":463,"secondary_topics":464,"classification":465,"search_phrases":472,"slug":475,"view_count":86,"doi":476,"paper":477,"created_at":492},5244,"Diurnal and Seasonal Surface Urban Heat Island Dynamics and Thermal Field Variance in Faridabad District, India: A MODIS-Based Assessment (2001–2025)","https:\u002F\u002Fdoi.org\u002F10.9734\u002Fajgr\u002F2026\u002Fv9i4489","Aims: To examine the spatial and temporal dynamics of land surface temperature (LST), urban–rural biophysical indices (BUI and NDVI), daily Surface Urban Heat Island Intensity (SUHII), and the Urban Thermal Field Variance Index (UTFVI) on a diurnal and seasonal basis in Faridabad District of northern India. Study Design: Multi-temporal geospatial remote sensing and statistical modelling study. Place and Duration of Study: Faridabad District (Urban Core and Rural Hinterland), Haryana, India; Summer and Winter Seasons Analysed through Six Benchmark Periods (2001, 2005, 2010, 2015, 2020, and 2025). Methodology: MODIS optical (MOD09A1) and thermal infrared (MOD11A2) composite data were analysed using Google Earth Engine at the daytime (10:30 am) and night-time (10:30 pm) overpasses. LST values were grouped into UTFVI (Urban Thermal Field Variance Index) classes. Multivariable OLS regressions and empirical biophysical sensitivities such as Vegetation Cooling Efficiency (VCE) and Impervious Heating Effect (IHE) were evaluated in Python using a stratified random grid (N = 2,500 sampling locations; 235 n 872 pixels). Results: Over the 25-year study period, the built-up gradient for winter (BUI) increased from +0.096 to +0.233 in association with agricultural changes and urban development in the region, while the vegetative deficit for winter (NDVI) increased from -0.061 to -0.136. In terms of temperature, Faridabad showed a SUHI regime during summer (ranging from to ), where the urban core nocturnal LST increased from in 2001 to in 2025, together with regional warming and compression of the urban DTR. The pre-monsoon diurnal regimes showed the SUCI phenomenon (as low as in 2005) owing to the effects of sensible heating in the desiccated fallow rural lands after harvest season. Urban Core thermal field variance showed that moderate thermal stress (UTFVI Class 3) expanded from to , while severe stress tiers (Classes 4–6) concentrated within industrial estates. Daytime vegetation provided consistent cooling (VCE up to per ), whereas nocturnal VCE exhibited temporal variability and diminished transpirational cooling following stomatal closure. Conclusion: Urban greening offers a strong mechanism for daytime heat mitigation but is insufficient to address independently nocturnal heat trapping resulting from the high thermal inertia of construction materials. The sustainable heat-mitigation strategy in industrial nodes entails a combination of urban greening and reflective cool coatings on industrial sheds and impervious surfaces.","目的：考察印度北部法里达巴德县地表温度（LST）、城乡生物物理指数（BUI和NDVI）、日间地表城市热岛强度（SUHII）以及城市热场方差指数（UTFVI）在昼夜和季节尺度上的时空动态。研究设计：多时相地理空间遥感与统计建模研究。研究地点与时长：印度哈里亚纳邦法里达巴德县（城市核心区与乡村腹地）；通过六个基准时段（2001、2005、2010、2015、2020和2025年）分析夏季与冬季。方法：利用Google Earth Engine分析MODIS光学（MOD09A1）和热红外（MOD11A2）合成数据，分别对应白天（上午10：30）和夜间（晚上10：30）过境时刻。LST值按UTFVI（城市热场方差指数）类别分组。在Python中采用分层随机网格（N=2，500个采样点；235 n 872像素）评估多变量OLS回归及经验生物物理敏感性指标，如植被冷却效率（VCE）和不透水面增温效应（IHE）。结果：在25年研究期内，冬季建成区梯度（BUI）从+0.096增至+0.233，与该地区农业变化和城市发展相关；冬季植被亏缺（NDVI）从-0.061增至-0.136。温度方面，法里达巴德在夏季呈现SUHI态势（范围为至），城市核心区夜间LST从2001年的增至2025年的，同时伴随区域增暖和城市日较差（DTR）压缩。季风前昼夜态势因收获季后干燥休耕乡村土地的感热加热效应而呈现SUCI现象（2005年低至）。城市核心区热场方差显示，中度热胁迫（UTFVI第3类）从扩大至，而严重胁迫层级（第4—6类）集中于工业园区。白天植被提供持续冷却（VCE最高达每），而夜间VCE呈现时间变异性，且气孔关闭后蒸腾冷却减弱。结论：城市绿化是白天热缓解的有力机制，但不足以独立应对建筑材料高热惯性导致的夜间热量滞留。工业节点的可持续热缓解策略需要将城市绿化与反射性","Asian Journal of Geographical Research",{"impact":86,"substance":86,"depth":86,"authority":86,"freshness":86,"relevant":86,"comment":456},"印度城市热岛遥感研究，属城市气候与环境领域，与三农、农业信息化、智慧农业无直接关联，不建议进入每日精选。",[458],{"name":454,"url":451},[29,460,461,462],"地表温度","城市热岛","MODIS",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":466},{"candidates":467,"agricultural_evidence":47},[468],{"code":32,"score":20,"supported":47,"title":469,"abstract":470,"tags":471},[],[58,59],[460,29],[473,474],"Faridabad 城市热岛","MODIS 地表温度 遥感","Faridabad城市热岛-5244","10.9734\u002Fajgr\u002F2026\u002Fv9i4489",{"doi":476,"openalex_id":478,"authors":479,"venue":454,"cited_by_count":86,"oa_url":451,"card":487,"direction":92,"ingested_from":94},"W7222794736",[480,483,485],{"name":481,"orcid":482},"Abhishek Malik","https:\u002F\u002Forcid.org\u002F0009-0009-4878-0871",{"name":484,"orcid":9},"Shubham Mor",{"name":486,"orcid":9},"Gaurav Kalotra",{"tldr":488,"method":489,"finding":490,"direction":92,"opportunity":491},"基于MODIS分析印度Faridabad 2001–2025年昼夜与季节地表城市热岛及热场差异演变。","MODIS光学与热红外数据、Google Earth Engine、OLS回归及","白天绿化降温显著，但夜间建筑热惯性导致的热滞留难以单独缓解。","可结合城乡梯度与作物休耕期感热效应，研究农业用地变化对昼夜热岛的非对称贡献。","2026-10-18T23:30:31.211684Z"]