[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3547":3,"related-3547":85},{"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":25,"secondary_topics":31,"classification":32,"search_phrases":64,"slug":67,"view_count":68,"doi":9,"paper":69,"created_at":84},3547,"Multi-source heterogeneous data integration for urban resilience: a systematic review and evidence map","https:\u002F\u002Fosf.io\u002F58p4z","Amendment recorded 23 September 2026 — change in the number of included studies from 166 to 162. During full-text data extraction, five of the 166 reports that had passed full-text eligibility assessment were flagged by the extractor as uncertain because the extracted data-source field contained only one category. Each was re-examined against the pre-specified nine-category data taxonomy and its counting rules, which are unchanged from the registered protocol. Four reports were found to draw their entire empirical material from a single data category and were therefore reclassified as ineligible: two studies whose material came from remote sensing alone and from a social media corpus alone (exclusion code E2, a study describing itself in multi-source terms while using a single category), one study whose material came from a textual corpus alone (E2), and one study whose only empirical source was a statistical yearbook despite announcing multi-source data in its title (E3, single statistical source only). The fifth report was retained: its data-category coding was corrected from one category to two, because it drew on site-investigation measurements and on statutory seismic zoning data, which are separate acquisition mechanisms under the registered taxonomy. The number of included studies is therefore 162. No eligibility criterion, taxonomy, counting rule, exclusion code or outcome was changed. The reclassification applied the registered criteria to information that was only available once the full texts were read at the extraction stage, and it is recorded here so that the discrepancy between the registered expectation and the reported figure is visible rather than silent. Record-level decisions for all four reclassified reports, and the corrected coding of the fifth, are provided in the review's supplementary materials. ### Review question Urban resilience research increasingly describes itself as drawing on multi-source or heterogeneous urban data, and attaches a methodological claim to that description: that combining sources whose errors and coverage gaps are independent yields assessments more robust than any single source supports. This review examines whether the published evidence bears that claim out. It asks three questions. First, which categories of urban data are combined in resilience research, in what numbers and in which combinations. Second, at which point in the analytical chain integration occurs, and which forms of heterogeneity between sources are explicitly handled. Third, which aspects of the resilience process the resulting evidence covers, and which remain unexamined. ### Searches Web of Science Core Collection and Scopus, searched on 15 September 2026. The search combines a resilience concept block with a data and smart-technology concept block, applied to titles, abstracts and keywords, limited to English-language journal articles and reviews published between 2010 and 2025. Two supplementary methods are used and reported separately: backward citation chasing of the thirty most closely related review articles, and an exploratory search, executed on 19 September 2026, for the social-ecological-technological systems literature, a conceptual stream whose terminology the main search string does not cover. Full search strings as executed, with hit counts and dates, are reported in the supplementary materials. ### Types of study to be included Peer-reviewed journal articles and review articles reporting empirical analysis of urban systems. Conference papers, conference reviews, book chapters, editorials, notes, errata, retracted publications and articles in press are excluded. ### Condition or domain being studied Urban resilience, understood as the capacity of an urban system to resist, absorb, adapt to, recover from and learn from disturbance. Disturbances include flooding, storms and typhoons, earthquakes, heat, fire, drought, pandemics, infrastructure outages and cascading failures, and explicitly multi-hazard settings. ### Participants \u002F population The unit of analysis is an urban system or a sub-unit of one: a city, a city cluster, a district, a neighbourhood, or an urban infrastructure network embedded in a city. Studies whose unit of analysis is a single building in isolation, a rural settlement, an agricultural landscape, a national economy or a non-spatial organisation are excluded. ### Intervention \u002F exposure Not an intervention review. The exposure of interest is the analytical practice of combining heterogeneous categories of urban data. Eligibility requires that a study empirically uses, or systematically discusses, at least two heterogeneous categories of urban data, counted against a nine-category taxonomy fixed before screening: remote sensing imagery; points of interest and map data; mobile trajectory and signalling data; social media and crowdsourced data; internet-of-things and sensor networks; statistical, census and survey data; infrastructure network topology; meteorological, hydrological and hazard data; and textual, policy and documentary sources. Three counting rules apply. Two sources belonging to the same category count as one category. Several variables derived from one acquisition mechanism count as one category. A category counts only where the study actually analyses it. ### Comparator None. This is an evidence map of study characteristics rather than a comparison of interventions. ### Primary outcomes The categories of urban data used and their number per study; the level at which integration occurs, classified as data level, feature level or decision level according to the point in the analytical chain at which the sources are brought together; and the forms of heterogeneity for which an explicit handling procedure is reported, including coordinate registration, spatio-temporal granularity alignment, scale transformation, semantic heterogeneity and missing values. ### Secondary outcomes The conception of resilience adopted or left unstated; the resilience dimensions and process phases covered; the assessment method used; the enabling technology and any named interoperability standard; the presence of a benchmark or external validation; the availability of data and code; and the spatial scale and disturbance type addressed. ### Data extraction and management Titles and abstracts are screened independently by two reviewers against a written decision manual specifying the eligibility criteria, the data taxonomy with its counting rules, and a hierarchy of ten mutually exclusive exclusion codes with a fixed priority order. Agreement is quantified with Cohen's kappa and disagreements are referred to a third reviewer who adjudicates each record against the same manual and records a basis and a rationale. Full texts are obtained for all records advancing from screening. Data are extracted by one reviewer for all included studies, and independently re-extracted by a second reviewer for a random twenty per cent sample. Agreement is computed and reported for every extracted field, using the Jaccard index for multi-valued fields, Cohen's kappa for single-valued categorical fields and a two-way intraclass correlation coefficient for ordinal and count fields. Fields that do not meet the reliability threshold are reported descriptively and are not used to support comparative claims. The complete extraction form, codebook and extracted matrix are provided as supplementary materials. ### Risk of bias \u002F quality assessment Because the included studies are methodologically heterogeneous and largely non-experimental, established risk-of-bias instruments do not apply. Each study is appraised on three items rated low, moderate or high: the adequacy of the methodological description, the transparency of the data provenance, and the extent to which the conclusions are supported by the evidence presented. The appraisal is applied by the extractor and independently verified on the twenty per cent sample. ### Strategy for data synthesis Narrative synthesis with an evidence map. Meta-analysis is not appropriate because the included studies address different disturbances, use different designs and report outcomes on non-commensurable constructs. Results are presented as counts and proportions of studies, a co-occurrence matrix of data categories, and a cross-tabulation of data category against resilience phase. Reporting follows PRISMA 2020, and a completed checklist is provided. ### Analysis of subgroups or subsets Distribution of the level of integration by publication year, by number of data categories and by assessment method. Coverage of resilience phases by data category. Any temporal comparison is accompanied by a test for trend, and differences that do not reach significance are reported as such rather than described as trends. ### Language and dissemination English-language literature only. Findings will be submitted to a peer-reviewed journal. All search strategies, screening records, calibration and reliability records, the extraction codebook and the full extracted data matrix will be made openly available alongside the publication.","修订记录于2026年9月23日——纳入研究数量由166项变更为162项。在全文数据提取阶段，166篇通过全文资格评估的报告中有5篇被提取者标记为不确定，原因是提取的数据来源字段仅包含一个类别。每篇报告均依据预先设定的九类别数据分类体系及其计数规则重新审查，该分类体系与计数规则与注册方案一致，未作变更。其中4篇报告被发现其全部实证材料均来自单一数据类别，因此被重新归类为不符合纳入标准：两项研究的材料分别仅来自遥感数据和仅来自社交媒体语料库（排除代码E2，即研究以多来源术语自我描述但实际使用单一类别），一项研究的材料仅来自文本语料库（E2），另一项研究的唯一实证来源为统计年鉴，尽管其标题宣称使用多来源数据（E3，仅单一统计来源）。第5篇报告予以保留：其数据类别编码从一个类别更正为两个类别，因为该研究依据了现场调查测量数据和法定地震区划数据，这两者在注册分类体系下属于不同的获取机制。因此，纳入研究数量为162项。任何纳入标准、分类体系、计数规则、排除代码或结局指标均未更改。重新归类是将注册标准应用于仅在提取阶段阅读全文后才能获得的信息，此处予以记录，以使注册预期与报告数字之间的差异可见而非被隐匿。所有4篇重新归类报告的记录层面决定，以及第5篇的更正编码，均提供于本综述的补充材料中。 ### 综述问题 城市韧性研究日益声称自身依据多来源或异质性城市数据，并将一项方法论主张附于该描述之上：即结合误差与覆盖缺口相互独立的来源，所得到的评估比任何单一来源所支持的评估更为稳健。本综述检验已发表证据是否支持该主张。它提出三个问题。第一，韧性研究中结合了哪些类别的城市数据，数量如何，以何种组合方式结合。第二，整合发生在分析链条的哪一环节，以及来源之间哪些形式的异质性",null,"OSF Preprints (OSF Preprints)","2026-09-23T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"该文为城市韧性领域多源异构数据整合的系统综述修订说明，属城市研究与数据科学范畴，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。",[19],{"name":10,"url":6},[21,22,23,24],"遥感监测","证据图谱","多源数据融合","城市韧性",{"code":26,"slug":27,"name":28,"path":29,"parent_code":30},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":36},"classified","2026-10-22.2","rules",{"candidates":37,"agricultural_evidence":40},[38,46,53,59],{"code":26,"score":39,"supported":40,"title":41,"abstract":42,"tags":45},7,true,[],[43,44],"遥感","remote sensing",[21],{"code":47,"score":48,"supported":40,"title":49,"abstract":50,"tags":52},"H7",3,[],[51],"drought",[],{"code":54,"score":55,"supported":14,"title":56,"abstract":57,"tags":58},"I5",1,[],[],[23],{"code":60,"score":55,"supported":14,"title":61,"abstract":62,"tags":63},"I7",[],[],[22],[65,66],"城市韧性 多源数据 系统综述","多源数据融合 城市韧性 证据图谱 遥感监测","城市韧性多源数据系统综述-3547",2,{"doi":9,"openalex_id":70,"authors":71,"venue":10,"cited_by_count":15,"oa_url":9,"card":76,"direction":82,"ingested_from":83},"W7213802657",[72,74],{"name":73,"orcid":9},"ZHUANGHAO SI",{"name":75,"orcid":9},"Jindan Zhang",{"tldr":77,"method":78,"finding":79,"direction":80,"opportunity":81},"系统综述城市韧性研究中多源异构数据整合的证据，最终纳入162项研究。","系统综述与证据图谱，基于九类数据分类法对全文进行数据源编码。","多数研究自称多源，实则仅用单一数据类别，真正整合多源证据有限。","数字乡村与农业信息化","可借鉴其数据分类与整合环节编码框架，研究农业多源数据整合的真实性与稳健性。","智慧农业 \u002F 农业物联网","openalex","2026-09-26T23:30:12.828664Z",{"total":86,"page":55,"page_size":86,"items":87},6,[88,164,249,312,382,427],{"id":89,"title":90,"url":91,"summary":92,"summary_zh":93,"content":9,"source_name":94,"source_url":91,"published_at":95,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":96,"sources":98,"tags":100,"primary_topic":104,"secondary_topics":105,"classification":106,"search_phrases":129,"slug":132,"view_count":15,"doi":133,"paper":134,"created_at":163},5390,"Land use and socio-economic changes caused by riverbank erosion - case study: Obrenovac municipality, Serbia","https:\u002F\u002Fdoi.org\u002F10.59954\u002Fstnv.797","Lateral bank erosion of meandering rivers represents a significant geomorphological process in the lower part of the Kolubara River Basin in Serbia. This process directly impacts land-use and agricultural productivity and lead to broader environmental, socioeconomic, and demographic changes. Historically, the Kolubara riverbanks have been intensively used for agriculture. Today, lateral channel migration and riverbank erosion, driven by the interaction between natural process and human activities, directly threaten traditionally active land plots along the river. This study investigates the long-term dynamics of riverbank erosion along the four active meanders of the Kolubara River in Obrenovac Municipality over a 97-year period (1930–2026). Main tasks include quantification of lateral channel migration and land loss rates, analysis of land-use changes within the endangered plots, and an investigation of socioeconomic consequences and demographic dynamics in affected settlements. The methodological framework integrates cadastral maps analysis, remote sensing techniques (UAVs), and GIS software, with analysis of demographic and land-use data. The results indicate that maximum lateral channel migration reached 259.3 m during the observed period, while the total land loss amounted to 17.6 ha, consisting mostly of arable land. This reduction has directly affected the local agricultural sector, resulting in an estimated land loss value of approximately 24,122 USD. Demographic analysis reveals population decline exceeding 40% in the analysed settlements (Draževac, Konatice and Poljane) between 1961 and 2022, from over 90% of the economically active population in 1961 to less than 2% in 2022. Ultimately, by highlighting the socioeconomic impacts of riverbank erosion such as irreversible agricultural land loss and rural livelihood disruption, these findings offer valuable insights for spatial planning and sustainable land management.","蜿蜒河流的侧向河岸侵蚀是塞尔维亚科卢巴拉河（Kolubara River）流域下游一个重要的地貌过程。该过程直接影响土地利用和农业生产力，并引发更广泛的环境、社会经济和人口变化。历史上，科卢巴拉河岸曾被密集用于农业生产。如今，在自然过程与人类活动相互作用驱动下的侧向河道迁移和河岸侵蚀，直接威胁着沿河传统上活跃的地块。本研究考察了奥布雷诺瓦茨市（Obrenovac Municipality）科卢巴拉河四个活跃曲流段在97年期间（1930—2026年）河岸侵蚀的长期动态。主要任务包括：量化侧向河道迁移和土地流失速率，分析受威胁地块内的土地利用变化，以及调查受影响居民点的社会经济后果和人口动态。方法框架整合了地籍图分析、遥感技术（无人机，UAVs）和GIS软件，并结合人口与土地利用数据分析。结果表明，在观测期内侧向河道迁移最大距离达259.3 m，土地流失总量为17.6 ha，其中大部分为耕地。这一减少直接影响了当地农业部门，估计土地流失价值约为24,122美元。人口分析显示，1961年至2022年间，所分析居民点（Draževac、Konatice和Poljane）的人口下降幅度超过40%，经济活动人口比例从1961年的超过90%降至2022年的不足2%。最终，通过突出河岸侵蚀的社会经济影响，如不可逆的农业土地流失和农村生计破坏，这些发现为空间规划和可持续土地管理提供了有价值的见解。","Stanovnistvo","2026-10-19T00:00:00Z",{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":97},"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。",[99],{"name":94,"url":91},[21,101,102,103],"土地利用","土壤侵蚀","乡村人口",{"code":26,"slug":27,"name":28,"path":29,"parent_code":30},[],{"status":33,"locked":14,"version":34,"method":35,"evidence":107},{"candidates":108,"agricultural_evidence":40},[109,113,119,124],{"code":26,"score":39,"supported":40,"title":110,"abstract":111,"tags":112},[],[43,44],[21],{"code":114,"score":48,"supported":40,"title":115,"abstract":116,"tags":118},"I4",[],[117],"GIS",[],{"code":60,"score":48,"supported":40,"title":120,"abstract":121,"tags":123},[],[122],"methodological framework",[],{"code":125,"score":55,"supported":14,"title":126,"abstract":127,"tags":128},"H6",[],[],[101],[130,131],"Kolubara 河岸侵蚀","Obrenovac 土地利用","Kolubara河岸侵蚀-5390","10.59954\u002Fstnv.797",{"doi":133,"openalex_id":135,"authors":136,"venue":94,"cited_by_count":15,"oa_url":91,"card":156,"direction":162,"ingested_from":83},"W7222945959",[137,140,143,145,148,151,154],{"name":138,"orcid":139},"Slavoljub Dragićević","https:\u002F\u002Forcid.org\u002F0000-0002-1417-5405",{"name":141,"orcid":142},"Marko Langović","https:\u002F\u002Forcid.org\u002F0000-0002-4614-7265",{"name":144,"orcid":9},"Милан Радовић",{"name":146,"orcid":147},"Radislav Tošić","https:\u002F\u002Forcid.org\u002F0000-0002-3994-125X",{"name":149,"orcid":150},"Milica Langović","https:\u002F\u002Forcid.org\u002F0000-0002-6979-377X",{"name":152,"orcid":153},"Novica Lovrić","https:\u002F\u002Forcid.org\u002F0009-0006-0838-4370",{"name":155,"orcid":9},"Manojlović Stefan",{"tldr":157,"method":158,"finding":159,"direction":160,"opportunity":161},"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。","农业遥感与作物表型","2026-10-20T23:30:18.982943Z",{"id":165,"title":166,"url":167,"summary":168,"summary_zh":169,"content":9,"source_name":170,"source_url":167,"published_at":171,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":172,"score_detail":173,"sources":180,"tags":182,"primary_topic":187,"secondary_topics":193,"classification":195,"search_phrases":220,"slug":223,"view_count":15,"doi":224,"paper":225,"created_at":248},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":174,"substance":175,"depth":176,"authority":177,"freshness":178,"relevant":55,"comment":179},16,22,18,13,8,"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。",[181],{"name":170,"url":167},[183,184,21,185,186],"智慧农业","无人机","冬小麦","生物量估算",{"code":188,"slug":189,"name":190,"path":191,"parent_code":192},"A2","wheat","小麦","\u002Ftopic\u002Ffield-crops\u002Fwheat","A",[194],{"code":26,"slug":27,"name":28,"path":29,"parent_code":30},{"status":33,"locked":14,"version":34,"method":35,"evidence":196},{"candidates":197,"agricultural_evidence":40},[198,204,208,215],{"code":188,"score":199,"supported":40,"title":200,"abstract":202,"tags":203},26,[201,189],"winter wheat",[190,201,189],[185],{"code":26,"score":174,"supported":40,"title":205,"abstract":206,"tags":207},[44],[43,44],[186,21],{"code":209,"score":86,"supported":40,"title":210,"abstract":211,"tags":214},"I2",[],[212,213],"机器学习","machine learning",[],{"code":216,"score":55,"supported":14,"title":217,"abstract":218,"tags":219},"I1",[],[],[183],[221,222],"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":224,"openalex_id":226,"authors":227,"venue":170,"cited_by_count":15,"oa_url":167,"card":243,"direction":162,"ingested_from":83},"W7222806066",[228,230,232,234,237,239,241],{"name":229,"orcid":9},"Guangtao Wang",{"name":231,"orcid":9},"Sumei Yao",{"name":233,"orcid":9},"Yanzheng Xie",{"name":235,"orcid":236},"Anzhen Qin","https:\u002F\u002Forcid.org\u002F0000-0002-2550-0015",{"name":238,"orcid":9},"Xiangdong Chen",{"name":240,"orcid":9},"Yuhao Zhang",{"name":242,"orcid":9},"Lina Jiang",{"tldr":244,"method":245,"finding":246,"direction":162,"opportunity":247},"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":250,"title":251,"url":252,"summary":253,"summary_zh":254,"content":9,"source_name":255,"source_url":252,"published_at":171,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":256,"sources":258,"tags":260,"primary_topic":264,"secondary_topics":265,"classification":275,"search_phrases":294,"slug":297,"view_count":15,"doi":298,"paper":299,"created_at":311},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":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":257},"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。",[259],{"name":255,"url":252},[261,21,262,263],"防灾减灾","洪水风险评估","GIS应用",{"code":26,"slug":27,"name":28,"path":29,"parent_code":30},[266,270],{"code":114,"slug":267,"name":268,"path":269,"parent_code":30},"gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",{"code":47,"slug":271,"name":272,"path":273,"parent_code":274},"climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",{"status":33,"locked":14,"version":34,"method":35,"evidence":276},{"candidates":277,"agricultural_evidence":40},[278,283,289],{"code":26,"score":279,"supported":40,"title":280,"abstract":281,"tags":282},15,[44],[43,44],[21],{"code":114,"score":284,"supported":40,"title":285,"abstract":286,"tags":288},14,[117],[287,117],"地理信息",[],{"code":47,"score":178,"supported":40,"title":290,"abstract":291,"tags":293},[],[292,51],"干旱",[262,261],[295,296],"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":298,"openalex_id":300,"authors":301,"venue":255,"cited_by_count":15,"oa_url":305,"card":306,"direction":162,"ingested_from":83},"W7222885988",[302],{"name":303,"orcid":304},"Birendra Yadav","https:\u002F\u002Forcid.org\u002F0009-0007-0864-474X","https:\u002F\u002Fwww.ijfmr.com\u002Fpapers\u002F2026\u002F5\u002F89708.pdf",{"tldr":307,"method":308,"finding":309,"direction":162,"opportunity":310},"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":313,"title":314,"url":315,"summary":316,"summary_zh":317,"content":9,"source_name":318,"source_url":315,"published_at":95,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":319,"score_detail":320,"sources":323,"tags":325,"primary_topic":330,"secondary_topics":331,"classification":332,"search_phrases":344,"slug":347,"view_count":15,"doi":348,"paper":349,"created_at":381},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":175,"substance":321,"depth":176,"authority":284,"freshness":13,"relevant":55,"comment":322},20,"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。",[324],{"name":318,"url":315},[326,21,327,328,329],"农业政策","卫星遥感","秸秆焚烧","环保督察",{"code":26,"slug":27,"name":28,"path":29,"parent_code":30},[],{"status":33,"locked":14,"version":34,"method":35,"evidence":333},{"candidates":334,"agricultural_evidence":40},[335,339],{"code":26,"score":174,"supported":40,"title":336,"abstract":337,"tags":338},[44],[43,44],[327,21],{"code":340,"score":55,"supported":14,"title":341,"abstract":342,"tags":343},"H5",[],[],[328],[345,346],"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":348,"openalex_id":350,"authors":351,"venue":318,"cited_by_count":15,"oa_url":9,"card":375,"direction":162,"ingested_from":83},"W7222877438",[352,354,356,358,360,362,364,367,369,371,373],{"name":353,"orcid":9},"Lu Dong",{"name":355,"orcid":9},"Linru Zhang",{"name":357,"orcid":9},"Na Liu",{"name":359,"orcid":9},"Xin Long",{"name":361,"orcid":9},"Ziyi Wang",{"name":363,"orcid":9},"Siyue Yang",{"name":365,"orcid":366},"Jie Tian","https:\u002F\u002Forcid.org\u002F0000-0001-6585-3643",{"name":368,"orcid":9},"Qiyuan Wang",{"name":370,"orcid":9},"Zhanfeng Dong",{"name":372,"orcid":9},"Long Wu",{"name":374,"orcid":9},"Yichen Wang",{"tldr":376,"method":377,"finding":378,"direction":379,"opportunity":380},"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":383,"title":384,"url":385,"summary":386,"summary_zh":387,"content":9,"source_name":388,"source_url":385,"published_at":171,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":389,"sources":391,"tags":393,"primary_topic":397,"secondary_topics":398,"classification":399,"search_phrases":406,"slug":409,"view_count":15,"doi":410,"paper":411,"created_at":426},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":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":390},"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。",[392],{"name":388,"url":385},[21,394,395,396],"地表温度","城市热岛","MODIS",{"code":26,"slug":27,"name":28,"path":29,"parent_code":30},[],{"status":33,"locked":14,"version":34,"method":35,"evidence":400},{"candidates":401,"agricultural_evidence":40},[402],{"code":26,"score":178,"supported":40,"title":403,"abstract":404,"tags":405},[],[43,44],[394,21],[407,408],"Faridabad 城市热岛","MODIS 地表温度 遥感","Faridabad城市热岛-5244","10.9734\u002Fajgr\u002F2026\u002Fv9i4489",{"doi":410,"openalex_id":412,"authors":413,"venue":388,"cited_by_count":15,"oa_url":385,"card":421,"direction":162,"ingested_from":83},"W7222794736",[414,417,419],{"name":415,"orcid":416},"Abhishek Malik","https:\u002F\u002Forcid.org\u002F0009-0009-4878-0871",{"name":418,"orcid":9},"Shubham Mor",{"name":420,"orcid":9},"Gaurav Kalotra",{"tldr":422,"method":423,"finding":424,"direction":162,"opportunity":425},"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",{"id":428,"title":429,"url":430,"summary":431,"summary_zh":432,"content":9,"source_name":433,"source_url":434,"published_at":435,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":436,"score_detail":437,"sources":440,"tags":442,"primary_topic":446,"secondary_topics":447,"classification":448,"search_phrases":474,"slug":477,"view_count":15,"doi":478,"paper":479,"created_at":515},5183,"Zionist Entity Settlement Expansion and Agricultural Land Conversion in the West Bank, Palestine (2011–2026): A Multi-Temporal Remote Sensing Analysis","https:\u002F\u002Fdoi.org\u002F10.63318\u002Fwaujpasv4i2_60","أُجريت هذه الدراسة لتقييم التغيرات في أنماط الاستيطان التابعة للكيان الصهيوني في الضفة الغربية بفلسطين، وذلك خلال الفترة الممتدة من عام 2011 إلى عام 2026، مع التركيز بشكل خاص على التغيرات التي طرأت على الأراضي الزراعية. وقد وظّف البحث تقنيات الاستشعار عن بعد لتحليل البيانات وتقييم اتجاهات التوسع الاستيطاني في المنطقة المختارة؛ حيث استخدم الباحثون صور الأقمار الصناعية ونظم المعلومات الجغرافية لرصد نمو المستوطنات، وتحديد المناطق المطوّرة حديثاً، ودراسة التغيرات في الغطاء الأرضي. وهدفت الدراسة إلى تحليل المناطق المبنية، والأراضي الزراعية، والغطاء النباتي، والأراضي القاحلة، والموارد المائية، فضلاً عن تحديد بؤر التوسع الاستيطاني، والمقارنة بين التجمعات الاستيطانية المختارة، ورصد التغيرات في شبكة الطرق، ودراسة التحولات في استخدامات الأراضي داخل المنطقة. ولتحقيق أهداف الدراسة، اعتمد الباحثون على مزيج من المنهجيات التي تشمل تصنيف الصور، وكشف التغيرات، وتحليل التراكب، وتحليل البؤر الساخنة. وتمثّل الهدف الرئيسي للدراسة في إرساء أساس لفهم انتشار المستوطنات ورصد التغيرات المرتبطة بالزراعة والتجمعات السكانية المجاورة بشكل أفضل، وذلك بالاستعانة بتقنيات الاستشعار عن بعد ونظم المعلومات الجغرافية.","本研究旨在评估2011年至2026年期间巴勒斯坦约旦河西岸犹太复国主义实体定居模式的变化，特别关注农业用地的变化。研究采用遥感技术分析数据并评估所选地区定居点扩张的趋势；研究人员利用卫星图像和地理信息系统监测定居点的增长、确定新开发区域并研究地表覆盖变化。研究旨在分析建成区、农业用地、植被覆盖、干旱土地和水资源，并确定定居点扩张的热点区域，比较选定的定居点集群，监测道路网络的变化，以及研究区域内土地利用的转变。为实现研究目标，研究人员采用了多种方法相结合的方式，包括图像分类、变化检测、叠加分析和热点分析。研究的主要目标是为理解定居点的扩散以及更好地监测与农业和邻近居民点相关的变化奠定基础，借助遥感技术和地理信息系统实现这一目标。","Wadi Alshatti University Journal of Pure and Applied Sciences","https:\u002F\u002Fwaujpas.com\u002Findex.php\u002Fjournal\u002Farticle\u002Fview\u002F775","2026-10-15T00:00:00Z",78,{"impact":176,"substance":175,"depth":176,"authority":438,"freshness":178,"relevant":55,"comment":439},12,"该研究利用多时相遥感与GIS定量分析2011–2026年西岸定居点扩张导致的农地转换，方法规范、数据跨度长，对农业土地变化监测有参考价值，但地域性强、政策关联度有限，可作国际农业遥感案例收录。",[441],{"name":433,"url":434},[21,443,117,444,445],"土地利用变化","农地非农化","巴勒斯坦农业",{"code":26,"slug":27,"name":28,"path":29,"parent_code":30},[],{"status":33,"locked":14,"version":34,"method":35,"evidence":449},{"candidates":450,"agricultural_evidence":40},[451,455,460,466,470],{"code":26,"score":438,"supported":40,"title":452,"abstract":453,"tags":454},[44],[43],[21],{"code":114,"score":456,"supported":40,"title":457,"abstract":458,"tags":459},4,[],[287],[117],{"code":461,"score":48,"supported":40,"title":462,"abstract":463,"tags":465},"H3",[],[464],"水资源",[],{"code":47,"score":48,"supported":40,"title":467,"abstract":468,"tags":469},[],[292],[],{"code":125,"score":55,"supported":14,"title":471,"abstract":472,"tags":473},[],[],[443],[475,476],"West Bank 遥感 农地变化","巴勒斯坦 定居点 农地转换","WestBank遥感农地变化-5183","10.63318\u002Fwaujpasv4i2_60",{"doi":478,"openalex_id":480,"authors":481,"venue":433,"cited_by_count":15,"oa_url":434,"card":510,"direction":162,"ingested_from":83},"W7222438368",[482,484,486,488,491,493,495,497,499,501,504,505,506,508],{"name":483,"orcid":9},"أسامة الشلطامي",{"name":485,"orcid":9},"مصطفى بن حكومة",{"name":487,"orcid":9},"حمزة الماطوني",{"name":489,"orcid":490},"Ramzi SALEM","https:\u002F\u002Forcid.org\u002F0000-0003-1539-1596",{"name":492,"orcid":9},"Mansour Salem",{"name":494,"orcid":9},"هالة الخزندار",{"name":496,"orcid":9},"ياسر نصار",{"name":498,"orcid":9},"Osama Shaltami",{"name":500,"orcid":9},"Mustafa Ben Hkoma",{"name":502,"orcid":503},"HAMZA AL MATONI","https:\u002F\u002Forcid.org\u002F0000-0002-4748-065X",{"name":489,"orcid":490},{"name":492,"orcid":9},{"name":507,"orcid":9},"Hala El-Khozondar",{"name":509,"orcid":9},"Yasser Nassar",{"tldr":511,"method":512,"finding":513,"direction":162,"opportunity":514},"用多时相遥感分析2011–2026年约旦河西岸定居点扩张及农地转化。","多时相卫星影像与GIS，结合影像分类、变化检测、叠加和热点分析。","定居点扩张显著侵占农地与植被，道路网络扩展驱动土地利用转变。","可延伸至多时相遥感监测土地权属冲突区农地流失，结合高分影像与开放GIS做定量归因。","2026-10-17T23:30:32.831824Z"]