[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3547":3,"related-3547":45},{"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,"search_phrases":25,"slug":28,"view_count":15,"doi":9,"paper":29,"created_at":44},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",[26,27],"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",{"doi":9,"openalex_id":30,"authors":31,"venue":10,"cited_by_count":15,"oa_url":9,"card":36,"direction":42,"ingested_from":43},"W7213802657",[32,34],{"name":33,"orcid":9},"ZHUANGHAO SI",{"name":35,"orcid":9},"Jindan Zhang",{"tldr":37,"method":38,"finding":39,"direction":40,"opportunity":41},"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":46,"page":47,"page_size":46,"items":48},6,1,[49,107,135,176,214,250],{"id":50,"title":51,"url":52,"summary":53,"summary_zh":54,"content":9,"source_name":55,"source_url":52,"published_at":56,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":57,"score_detail":58,"sources":65,"tags":67,"search_phrases":71,"slug":74,"view_count":15,"doi":75,"paper":76,"created_at":106},2639,"Post-disturbance soil monitoring in forests using remote sensing: an evidence map","https:\u002F\u002Fdoi.org\u002F10.5194\u002Fsoil-12-885-2026","Forest soils underpin ecosystem resilience and productivity but are increasingly threatened by natural and anthropogenic disturbances. Monitoring post-disturbance soil degradation at operational scales remains challenging in forests, where ground-signal obstruction and reliance on proxy indicators constrain remote sensing (RS) applications. To identify where RS can benefit soil monitoring and inform emerging reporting needs, we developed a structured evidence map of studies assessing post-disturbance forest soil degradation using RS methods. From 4338 records, 72 primary studies were synthesized across disturbance types, biomes, platforms, scales, and indicators. The evidence base is dominated by wildfire and harvesting, reflecting disturbance pathways that produce observable surface impacts. Multispectral satellite data remain the primary tool for mapping post-fire severity and erosion-related indicators, while LiDAR and stereo-photogrammetry are most often used to quantify surface deformation after harvest operations. Indicators tied to subsurface physical, chemical, or biological change remain sparsely represented due to observability limits. Overall, RS is most effective for mapping disturbance footprints, detecting surface-expressed indicators, and stratifying landscapes for targeted field assessment, rather than directly measuring soil properties. This evidence map clarifies the benefits and limits of RS, identifies persistent gaps, and highlights priorities for developing disturbance-aware soil-monitoring frameworks. It also clarifies which soil indicators are most consistently observable with RS and which require complementary approaches. By linking disturbance processes to observable indicators, this synthesis helps identify realistic RS-supported objectives that may inform future reporting frameworks within national forest monitoring and assessment programs.","森林土壤支撑着生态系统的韧性与生产力，却日益受到自然和人为干扰的威胁。在森林中，由于地面信号遮挡以及对代理指标的依赖限制了遥感（RS）应用，在可操作尺度上监测干扰后的土壤退化仍具挑战性。为明确遥感可在何处助力土壤监测并满足新兴的报告需求，我们编制了一份结构化证据图，涵盖利用遥感方法评估干扰后森林土壤退化的研究。从4338条记录中，综合了72项原始研究，涉及干扰类型、生物群系、平台、尺度和指标。证据基础以野火和采伐为主，反映了可产生可观测地表影响的干扰路径。多光谱卫星数据仍是绘制火灾后严重程度和侵蚀相关指标的主要工具，而激光雷达（LiDAR）和立体摄影测量最常用于量化采伐作业后的地表形变。由于可观测性限制，与地下物理、化学或生物变化相关的指标仍鲜有涉及。总体而言，遥感最适用于绘制干扰足迹、探测地表表现的指标，以及为针对性实地评估进行景观分层，而非直接测量土壤属性。该证据图阐明了遥感的优势与局限，识别了持续存在的空白，并指出了开发干扰感知土壤监测框架的优先事项。它还明确了哪些土壤指标最易通过遥感持续观测，哪些需要互补方法。通过将干扰过程与可观测指标相联系，本综合有助于确定现实的遥感支持目标，可为国家级森林监测与评估项目未来的报告框架提供参考。","SOIL","2026-09-15T00:00:00Z",79,{"impact":59,"substance":60,"depth":61,"authority":62,"freshness":63,"relevant":47,"comment":64},16,22,18,14,9,"系统梳理遥感监测灾后森林土壤退化的证据图谱，方法规范、数据规模可观，对林业遥感与土壤监测应用有实质参考价值。",[66],{"name":55,"url":52},[68,21,69,70,22],"生态修复","森林土壤","林业信息化",[72,73],"林业信息化 森林土壤 生态修复 证据图谱","林业信息化 森林土壤","林业信息化森林土壤生态修复证据图谱-2639","10.5194\u002Fsoil-12-885-2026",{"doi":75,"openalex_id":77,"authors":78,"venue":55,"cited_by_count":47,"oa_url":99,"card":100,"direction":42,"ingested_from":43},"W7140718837",[79,81,83,85,87,90,93,95,97],{"name":80,"orcid":9},"Maisy Roach-Krajewski",{"name":82,"orcid":9},"Xavier Giroux-Bougard",{"name":84,"orcid":9},"David Paré",{"name":86,"orcid":9},"Catlan Dallaire",{"name":88,"orcid":89},"Luc Guindon","https:\u002F\u002Forcid.org\u002F0000-0002-4346-7351",{"name":91,"orcid":92},"Florian Jordan","https:\u002F\u002Forcid.org\u002F0000-0003-2242-7411",{"name":94,"orcid":9},"Charlotte Norris",{"name":96,"orcid":9},"Kara Webster",{"name":98,"orcid":9},"Jérôme Laganière","https:\u002F\u002Fsoil.copernicus.org\u002Farticles\u002F12\u002F885\u002F2026\u002Fsoil-12-885-2026.pdf",{"tldr":101,"method":102,"finding":103,"direction":104,"opportunity":105},"用证据图方法系统梳理遥感监测干扰后森林土壤退化的72项研究，明确其能力与局限。","证据图法，从4338条记录筛选72项研究，按干扰、平台、指标等分类。","遥感擅长制图干扰范围与地表指标，难以直接测量土壤理化生物属性。","农业遥感与作物表型","可构建干扰感知的土壤监测框架，并研发地表-地下指标耦合的遥感反演方法。","2026-09-16T23:30:09.927061Z",{"id":108,"title":109,"url":110,"summary":111,"summary_zh":9,"content":112,"source_name":113,"source_url":9,"published_at":114,"category":115,"cover_url":9,"hotness":13,"is_selected":14,"score":116,"score_detail":117,"sources":122,"tags":124,"search_phrases":130,"slug":133,"view_count":15,"doi":9,"paper":9,"created_at":134},3586,"黑龙江省农科院科技包联现场观摩会举办——'松粳22'优质食味水稻新品种示范田实测亩产557.5公斤","https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002F2026-09\u002F25\u002Fcontent_588143.html","9月22日，黑龙江省农科院科技包联哈尔滨市现场观摩会暨乔府大院第十一届开镰节开幕式上，试验田新品种展示区块18个水稻品种分区规整排布，其中9个是黑龙江省农科院自主选育品种。黑龙江省农科院生物技术研究所（五常水稻研究所）副所长、研究员闫平介绍，新品种'松粳22'150亩示范田按照14.5%标准含水率折算，平均亩产达到557.5公斤，在优质食味水稻中属于上乘水平，抗倒伏能力显著优于传统优质稻。秸秆基质育苗示范点位展示秸秆基质育苗技术，今年在哈尔滨18个点位示范。","![Image 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[专题](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fztxw\u002Fnode_335.html)\n*   [English](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002FEnglish\u002Findex.html)\n*   [滚动](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002Fnode_324.html)\n\n[![Image 3](https:\u002F\u002Fstatics.kjrb.com.cn\u002Fresource\u002Fkjw\u002Fimages\u002Ffdj.png)](https:\u002F\u002Fsearch.stdaily.com:8888\u002Ffounder\u002FNewSearchServlet.do?siteID=1)[登录](https:\u002F\u002Fwww.stdaily.com\u002Fapi\u002Fwebsite\u002FpersonalCenter.html?url=https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002F2026-09\u002F25\u002Fcontent_588143.html)[-](https:\u002F\u002Fwww.stdaily.com\u002Fapi\u002Fwebsite\u002FpersonalCenter.html)\n\n 所在位置： [中国科技网首页](https:\u002F\u002Fwww.stdaily.com\u002Findex.html)>[滚动](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002Fnode_324.html)> 正文 \n\n# 乡村行 看振兴丨科技赋能丰产 定制深耕良田——黑龙江省农科院科技包联现场观摩会举办\n\n 2026-09-25 12:39:56 来源: 科技日报 点击数：\n\n![Image 4](https:\u002F\u002Fstatics.kjrb.com.cn\u002Fresource\u002Fkjw\u002Fimg\u002F20240301dzv.png)0\n\n[](javascript:; \"新浪微博\")[](javascript:; \"微信\")\n\n**科技日报记者 朱虹**\n\n金色稻海漫过黑龙江五常的黑土地，稻浪深处，开镰号令响起。随着乔府大院董事长乔文志一声“开镰”，嘉宾们手持镰刀踏入金黄稻田，割下了今秋第一束稻穗。\n\n9月22日，黑龙江省农科院科技包联哈尔滨市现场观摩会暨乔府大院第十一届开镰节开幕式上，传统农耕与现代科技撞个满怀，一场丰收开镰，也是一次看得见摸得着的农业科技大考。\n\n在试验田新品种展示区块，18个水稻品种分区规整排布，其中9个是黑龙江省农科院自主选育品种。该院生物技术研究所（五常水稻研究所）副所长、研究员闫平站在田垄之间，伸手托起沉甸甸的“松粳22”稻穗，招呼围观嘉宾观察茎秆与籽粒状态。现场发布前，实收测产数据显示，150亩示范田按照14.5%标准含水率折算，松粳22平均亩产达到557.5公斤，在优质食味水稻中属于上乘水平。\n\n“传统稻花香大米口感出众，可茎秆偏软，遇上风雨天气极易大面积倒伏。”闫平指着成片挺立的稻株讲解道，“今天展示的新品种，抗倒伏能力显著优于传统优质稻。”\n\n一句“抗倒伏”，点破了好米难产的旧账。传统优质稻往往“娇贵”——品质好却秆软易倒、产量不稳，种高端米像押宝。新品种把“好吃”和“抗倒”拧在一起：抗倒伏，意味着风雨天不趴窝、机械收割进得去、规模种植稳得住，高端米供给才算有了“压舱石”。从“靠天吃饭”到“凭种稳产”，良种攻关为高端稻米产业蹚出一条新路。\n\n米食品鉴区，新米出锅，香气四溢，米粒莹润油亮。定制客户捧着一碗米饭感慨：“一碗好米的根基就在种子，科研选育的新品种，让老百姓既吃得饱，更吃得好。”\n\n观摩队伍在试验田里走走停停。秸秆基质育苗示范点位旁，黑龙江省农科院耕作栽培研究所作物栽培与装备研究室主任董文军被团团围住，他弯腰捧起一把棕褐色基质，讲起秸秆基质育苗的门道。\n\n“原料就是经多年沤熟的秸秆有机肥，配上三成秸秆打包回收时附带的表层土，育苗完全不用再挖黑土。”董文军介绍，杀菌剂、控旺剂都已预配好，农户拿到手直接铺苗床，取土、晒土、混拌这些累活全省了。“今年在哈尔滨18个点位示范，秧苗长势不比传统育苗土差，产量持平就可以得到推广，前期试验发现不同的配方基质比当地基质和黑土能增产5%到15%。”董文军说。秸秆从田间收走、发酵成基质，秧苗育成再回归水田。这条闭环，把曾经的“田间负担”变成“黑土养料”，围观的种植大户连连点头，当场追问推广条件。\n\n智慧农业贯穿作物生长每一环。黑龙江省农科院农业遥感与信息研究所副所长张有智介绍：“无人机图像结合人工智能模型算法，实现作物的长势情况和杂草分布反演识别，自动生成施肥处方图和施药处方图，直接推送到无人机飞手账号进行作业。作业精度能到厘米级。”今年试验，这套技术实现减肥6%、减药17%左右。\n\n黑龙江省农科院耕作栽培研究所所长李柱刚告诉科技日报记者，2023年黑龙江省农科院建立科技包联服务机制以来，哈尔滨已建成粮食单产提升示范田、科技服务田28个，示范推广面积25800亩。\n\n“为龙头企业提供高端定制服务，是科技包联的重头戏。”黑龙江省农科院副院长焦少杰站在田埂上说，“从品种定向选育、栽培技术集成，到植保方案、加工品鉴，全程‘一对一’定制，让高端米从‘卖原粮’升级为‘卖标准、卖品质’。”他介绍，黑龙江省农科院已深化科技包联13个市（地）服务机制，推动“百项技术、千个基地、万名人才”增粮示范落地。\n\n责任编辑：王倩\n\n相关稿件：\n\n![Image 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21](https:\u002F\u002Fwww.stdaily.com\u002Fresource\u002FtemplateRes\u002F\u002F202408\u002F19\u002F345\u002Fimages\u002Ffirefox.jpg)](http:\u002F\u002Fwww.firefox.com.cn\u002Fdownload\u002F)[![Image 22](https:\u002F\u002Fwww.stdaily.com\u002Fresource\u002FtemplateRes\u002F\u002F202408\u002F19\u002F345\u002Fimages\u002Fchrome.jpg)](http:\u002F\u002Fwww.chromeliulanqi.com\u002F)\n\n## 3.暂不升级，继续浏览\n\n[继续浏览](javascript:;)","科技日报 2026-09-25","2026-09-25T00:00:00Z","报道",75,{"impact":61,"substance":118,"depth":59,"authority":119,"freshness":120,"relevant":47,"comment":121},20,13,8,"省级科研机构科技包联现场会，含新品种实测产量、秸秆基质育苗与遥感处方图等实质数据，对种业与智慧农业主题有聚合价值。",[123],{"name":113,"url":110},[125,126,21,127,128,129],"智慧农业","种业振兴","水稻新品种","科技包联","秸秆基质育苗",[131,132],"黑龙江省农科院 松粳22 水稻","五常 科技包联 观摩会","黑龙江省农科院松粳22水稻-3586","2026-09-27T00:05:16.125922Z",{"id":136,"title":137,"url":138,"summary":139,"summary_zh":140,"content":9,"source_name":141,"source_url":138,"published_at":114,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":142,"score_detail":143,"sources":148,"tags":150,"search_phrases":155,"slug":158,"view_count":15,"doi":159,"paper":160,"created_at":175},3568,"Interpretable multi-index framework for extracting dry-season built-up areas: benchmarking machine learning with Sentinel-2","https:\u002F\u002Fdoi.org\u002F10.4995\u002Fraet.2027.25885","Spectral similarity between built-up surfaces and exposed dry soil significantly constrains built-up area extraction during dry seasons. Although machine-learning techniques can address this challenge by modeling complex spectral relationships, they generally require sufficiently large and representative labeled training datasets. This study therefore investigates a less data-demanding, rule-based multi-index approach for dry-season built-up mapping, aiming to reduce reliance on large labeled training datasets while maintaining effective classification performance. Sentinel-2A imagery acquired in April 2024 was analyzed, and temporal validation was conducted for 2020, 2022, and 2024. Among the tested combinations, the Built-up Area Extraction Index (BAEI), Dry BareSoil Index (DBSI), and Normalized Difference Vegetation Index (NDVI) achieved the highest performance, yielding 95 % overall accuracy and a Kappa coefficient of 0.89. This represents a substantial improvement over singleindex BAEI (80 % overall accuracy; Kappa 0.55), increasing the built-up user’s accuracy from 61 % to 92 %. Multitemporal validation confirmed that the optimized BAEI–DBSI–NDVI decision rules remained stable across the 2020–2024 dry-season images without recalibration, achieving 93–96 % overall accuracy and a built-up F1-score of 84–93 %. Spatial analysis demonstrated robust performance across peripheral and agricultural zones, with moderate variability in dense urban cores due to spectral heterogeneity. To evaluate the operational robustness of the proposed framework, it was benchmarked against machine-learning classifiers, specifically Support Vector Machine and Random Forest (RF). Under the specific conditions of this study, the proposed rule-based method (95 % accuracy) marginally outperformed both SVM (93 %) and RF (92 %), while offering greater transparency and reducing dependency on large, labeled training datasets. Furthermore, feature importance analysis confirmed the critical role of these selected indices in resolving spectral confusion. These findings suggest that the proposed framework offers a transparent and computationally efficient approach for dry-season urban monitoring in tropical coastal environments similar to Visakhapatnam.","建成表面与裸露干土之间的光谱相似性显著制约了旱季建成区提取。尽管机器学习技术可通过建模复杂光谱关系来应对这一挑战，但其通常需要足够大且具有代表性的标记训练数据集。因此，本研究探讨了一种对数据需求较低、基于规则的多指数方法用于旱季建成区制图，旨在减少对大规模标记训练数据集的依赖，同时保持有效的分类性能。研究分析了2024年4月获取的Sentinel-2A影像，并对2020年、2022年和2024年进行了时间验证。在测试的组合中，建成区提取指数（BAEI）、干裸土指数（DBSI）和归一化差异植被指数（NDVI）表现最佳，总体精度达95%，Kappa系数为0.89。相较于单一指数BAEI（总体精度80%；Kappa 0.55），这一结果有显著提升，建成区用户精度从61%提高至92%。多时相验证证实，优化后的BAEI–DBSI–NDVI决策规则在2020—2024年旱季影像上无需重新校准即可保持稳定，总体精度达93%—96%，建成区F1分数为84%—93%。空间分析表明，该方法在外围和农业区域表现稳健，而在密集城市核心区由于光谱异质性存在中等程度变异。为评估所提框架的业务化稳健性，将其与机器学习分类器进行了基准比较，具体为支持向量机（SVM）和随机森林（RF）。在本研究的特定条件下，所提出的基于规则的方法（95%精度）略优于SVM（93%）和RF（92%），同时具有更高的透明性并减少了对大规模标记训练数据集的依赖。此外，特征重要性分析证实了所选指数在解决光谱混淆方面的关键作用。这些发现表明，所提框架为类似维沙卡帕特南的热带沿海环境旱季城市监测提供了一种透明且计算高效的方法。","Revista de teledetección: Revista de la Asociación Española de Teledetección",72,{"impact":144,"substance":145,"depth":146,"authority":119,"freshness":63,"relevant":47,"comment":147},12,21,17,"该研究提出可解释的多指数规则框架，在旱季建成区提取上以更少标注数据达到95%精度并优于SVM\u002FRF，方法新颖、验证充分，对农业遥感与乡村土地利用监测有参考价值。",[149],{"name":141,"url":138},[151,152,153,21,154],"Sentinel-2","机器学习","NDVI","建成区提取",[156,157],"Sentinel-2 建成区提取","BAEI DBSI NDVI 旱季","Sentinel-2建成区提取-3568","10.4995\u002Fraet.2027.25885",{"doi":159,"openalex_id":161,"authors":162,"venue":141,"cited_by_count":15,"oa_url":138,"card":169,"direction":174,"ingested_from":43},"W7214385607",[163,166],{"name":164,"orcid":165},"Sarah Sejari","https:\u002F\u002Forcid.org\u002F0009-0002-7300-9084",{"name":167,"orcid":168},"Vazeer Mahammood","https:\u002F\u002Forcid.org\u002F0000-0003-1667-5232",{"tldr":170,"method":171,"finding":172,"direction":104,"opportunity":173},"提出可解释多指数规则框架，用Sentinel-2提取旱季建成区，性能优于机器学习。","Sentinel-2A影像，BAEI、DBSI、NDVI组合规则，对比SVM与随","BAEI-DBSI-NDVI规则达95%精度、Kappa 0.89，优于单指数及SVM\u002FRF且无需重","可探索该规则框架向其他气候带与传感器迁移，并结合少量样本的半监督学习提升城市核心区精度。","农业人工智能与决策模型","2026-09-26T23:30:53.200163Z",{"id":177,"title":178,"url":179,"summary":180,"summary_zh":181,"content":9,"source_name":182,"source_url":179,"published_at":183,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":184,"score_detail":185,"sources":187,"tags":189,"search_phrases":193,"slug":196,"view_count":15,"doi":197,"paper":198,"created_at":213},3558,"Meteorolojik Kuraklığın Vejetasyon Sağlığı Üzerindeki Mekânsal ve Zamansal Etkilerinin SPI ve NDVI ile Analizi: Gediz Havzası Örneği","https:\u002F\u002Fdoi.org\u002F10.48123\u002Frsgis.1858019","Kuraklık, özellikle yarı kurak iklim koşullarına sahip havzalarda su kaynakları, tarımsal üretim ve ekosistem sürdürülebilirliği üzerinde belirleyici etkiler oluşturan başlıca doğal afetlerden biridir. Bu bağlamda, kuraklığın bitki örtüsü üzerindeki mekânsal ve zamansal etkilerinin bütüncül yaklaşımlarla değerlendirilmesi büyük önem taşımaktadır. Bu çalışmada, meteorolojik kuraklığın vejetasyon sağlığı üzerindeki mekânsal ve zamansal etkileri, Standartlaştırılmış Yağış İndeksi (SPI) ve Normalize Fark Bitki Örtüsü İndeksi (NDVI) kullanılarak Gediz Havzası örneğinde analiz edilmiştir. SPI analizleri 1970–2023 yılları arasındaki uzun dönem yağış verilerine dayanırken, NDVI verileri 2000–2023 dönemine ait MODIS uydu görüntülerinden elde edilmiştir. İki veri seti arasındaki ilişkiler, ortak dönem olan 2000–2023 yılları için incelenmiştir. Elde edilen bulgular, meteorolojik kuraklık ile vejetasyon sağlığı arasındaki ilişkinin mevsimsel olarak değişkenlik gösterdiğini ortaya koymaktadır. En güçlü ilişki yaz mevsiminde gözlenmiş (r ≈ 0.70) ve bu durum vejetasyonun yağış eksikliklerine en duyarlı olduğu dönemin yaz ayları olduğunu göstermiştir. Buna karşılık, kış (r ≈ 0.05) ve ilkbahar (r ≈ 0.02) mevsimlerinde ilişki oldukça zayıf bulunmuştur. Belirlenen kurak yıllarda (2004, 2008 ve 2022) NDVI değerlerinde belirgin düşüşler gözlenmiştir. Sonuç olarak, vejetasyonun kuraklığa verdiği tepkinin yıl boyunca homojen olmadığı, mevsimsel iklim koşulları ve diğer çevresel faktörlere bağlı olarak değiştiği belirlenmiştir.","干旱是主要自然灾害之一，尤其在具有半干旱气候条件的流域中，对水资源、农业生产和生态系统可持续性产生决定性影响。在此背景下，以整体性方法评估干旱对植被的时空影响具有重要意义。本研究以盖迪兹流域为例，利用标准化降水指数（SPI）和归一化植被指数（NDVI）分析了气象干旱对植被健康的时空影响。SPI分析基于1970—2023年的长期降水数据，而NDVI数据则来自2000—2023年期间的MODIS卫星影像。两个数据集之间的关系在共同时段2000—2023年进行了分析。研究结果表明，气象干旱与植被健康之间的关系呈现季节性变化。最显著的关系出现在夏季（r ≈ 0.70），这表明夏季是植被对降水亏缺最为敏感的时期。相比之下，冬季（r ≈ 0.05）和春季（r ≈ 0.02）的关系则非常微弱。在确定的干旱年份（2004年、2008年和2022年），NDVI值出现了明显下降。综上，植被对干旱的响应在全年并非均匀一致，而是随季节性气候条件及其他环境因素而变化。","Turkish Journal of Remote Sensing and GIS","2026-09-24T00:00:00Z",71,{"impact":144,"substance":145,"depth":146,"authority":119,"freshness":120,"relevant":47,"comment":186},"基于SPI与NDVI长时序数据揭示气象干旱对植被健康影响的季节性差异，方法规范、结论可靠，对农业干旱遥感监测有参考价值，但属区域案例研究，公共影响有限。",[188],{"name":182,"url":179},[153,21,190,191,192],"干旱监测","植被指数","SPI",[194,195],"Gediz Havzası SPI NDVI","气象干旱 植被健康 遥感","GedizHavzasıSPINDVI-3558","10.48123\u002Frsgis.1858019",{"doi":197,"openalex_id":199,"authors":200,"venue":182,"cited_by_count":15,"oa_url":207,"card":208,"direction":104,"ingested_from":43},"W7214166880",[201,204],{"name":202,"orcid":203},"Kemal Yurddaş","https:\u002F\u002Forcid.org\u002F0000-0003-4691-4038",{"name":205,"orcid":206},"Murat Karabulut","https:\u002F\u002Forcid.org\u002F0000-0002-1456-6908","https:\u002F\u002Fdergipark.org.tr\u002Ftr\u002Fdownload\u002Farticle-file\u002F5578303",{"tldr":209,"method":210,"finding":211,"direction":104,"opportunity":212},"用SPI和NDVI分析土耳其盖迪兹流域气象干旱对植被健康的时空影响。","基于1970-2023年降水SPI与2000-2023年MODIS NDVI，做","干旱与植被关系随季节变化，夏季最强(r≈0.70)，冬春极弱；干旱年NDVI明显下降。","可引入滞后效应与多尺度SPI，结合土壤水分和灌溉数据，提升干旱对植被影响的预测能力。","2026-09-26T23:30:31.357310Z",{"id":215,"title":216,"url":217,"summary":218,"summary_zh":219,"content":9,"source_name":220,"source_url":217,"published_at":183,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":57,"score_detail":221,"sources":223,"tags":225,"search_phrases":230,"slug":233,"view_count":15,"doi":234,"paper":235,"created_at":249},3557,"Recent Changes in Lake Chad’s Surface Water Extent: A Remote Sensing Assessment Using Sentinel-2 Land Cover Data (2017-2025)","https:\u002F\u002Fdoi.org\u002F10.31223\u002Fx5b22z","Lake Chad is an endorheic freshwater lake located at the junction of four countries; Nigeria, Niger, Chad, and Cameroon in West-Central Africa. Lake Chad, once one of Africa’s largest freshwater bodies, has experienced significant hydrological fluctuations over the past decades. While long-term historical analyses highlight a severe 90% surface area contraction since the 1960s, short-term operational variations over the last decade require precise quantification to inform active transboundary water management strategies. To address this critical gap, this study aims to develop a replicable remote sensing framework using high-resolution satellite data to quantify Lake Chad’s open water and flooded vegetation dynamics from 2017 to 2025, providing evidence-based insights for sustainable water resource management. The methodology encompasses a comprehensive remote sensing analysis of the contemporary surface water extent of Lake Chad using the high-resolution (10-meter) Esri Sentinel-2 Land Use\u002FLand Cover (LULC) time-series dataset. Through a systematic methodology involving image clipping, reclassification, and area calculation, the study tracked changes in open water, flooded vegetation (wetlands), and total lake system extent. Findings reveal a substantial recovery of the lake system, with total area expanding from 5,808.12 km² in 2017 to 11,588.14 km² in 2025, representing an absolute net growth of 5,780.02 km² (99.5% increase). While the lake has not fully recovered to its historic 1960s extent of approximately 25,000 km², it currently exhibits its most water-abundant state since the turn of the 21st century. These findings have significant implications for water resource management, regional food security, and the livelihoods of approximately 50 million people dependent on the Lake Chad basin.","乍得湖是位于非洲中西部尼日利亚、尼日尔、乍得和喀麦隆四国交界处的内陆淡水湖。乍得湖曾是非洲最大的淡水水体之一，过去数十年来经历了显著的水文波动。尽管长期历史分析显示，自20世纪60年代以来其表面积严重萎缩了90%，但近十年来的短期运行变化仍需精确定量，以指导当前活跃的跨境水资源管理策略。为填补这一关键空白，本研究旨在开发一个可复制的遥感框架，利用高分辨率卫星数据量化2017年至2025年乍得湖开阔水域和淹没植被的动态变化，为可持续水资源管理提供循证依据。方法上，本研究采用高分辨率（10米）Esri Sentinel-2土地利用\u002F土地覆盖（LULC）时间序列数据集，对乍得湖当代地表水范围进行了全面的遥感分析。通过图像裁剪、重分类和面积计算等系统方法，研究追踪了开阔水域、淹没植被（湿地）及湖泊系统总范围的变化。研究结果显示，湖泊系统大幅恢复，总面积从2017年的5,808.12平方公里扩展至2025年的11,588.14平方公里，绝对净增长5,780.02平方公里（增幅99.5%）。尽管该湖尚未完全恢复到20世纪60年代约25,000平方公里的历史范围，但目前呈现出21世纪以来水量最丰沛的状态。这些发现对水资源管理、区域粮食安全以及依赖乍得湖流域的约5,000万人的生计具有重大意义。","OpenAlex",{"impact":61,"substance":60,"depth":61,"authority":144,"freshness":63,"relevant":47,"comment":222},"基于Sentinel-2十年时序数据量化乍得湖水面恢复99.5%，方法可复制、数据翔实，对跨境水资源与区域粮食安全有参考价值，但属非洲区域研究，国内三农关联度有限。",[224],{"name":220,"url":217},[226,227,21,228,229],"粮食安全","水资源管理","湖泊湿地","跨境流域",[231,232],"乍得湖 Sentinel-2 遥感","乍得湖 水域面积 变化","乍得湖Sentinel-2遥感-3557","10.31223\u002Fx5b22z",{"doi":234,"openalex_id":236,"authors":237,"venue":9,"cited_by_count":15,"oa_url":243,"card":244,"direction":104,"ingested_from":43},"W7214164320",[238,241],{"name":239,"orcid":240},"Umar Yusuf","https:\u002F\u002Forcid.org\u002F0009-0008-3703-1069",{"name":242,"orcid":9},"Umar Usman","https:\u002F\u002Feartharxiv.org\u002Frepository\u002Fobject\u002F15133\u002Fdownload\u002F26255\u002F",{"tldr":245,"method":246,"finding":247,"direction":104,"opportunity":248},"基于Sentinel-2土地覆盖数据量化2017-2025年乍得湖地表水面积变化。","使用10米分辨率Esri Sentinel-2 LULC时序数据，经裁剪、重分类","乍得湖总面积从5808增至11588平方公里，净增99.5%，为21世纪以来最丰水状态。","可延伸研究乍得湖扩张对流域农业灌溉、湿地生态及跨境水资源管理的影响。","2026-09-26T23:30:31.292812Z",{"id":251,"title":252,"url":253,"summary":254,"summary_zh":255,"content":9,"source_name":256,"source_url":253,"published_at":183,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":257,"score_detail":258,"sources":260,"tags":262,"search_phrases":267,"slug":270,"view_count":15,"doi":271,"paper":272,"created_at":286},3555,"Ecosystem Resilience and Land-Use Governance: Remote Sensing Insights for Environmental Management in a Tropical Dry Forest","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs00267-026-02629-4","Seasonally Dry Tropical Forests (SDTFs), such as the Brazilian Caatinga, are highly vulnerable to climate change and chronic anthropogenic disturbances. Despite extensive land-use conversion, understanding how vegetation productivity and resilience vary across different land governance regimes remains a critical gap. Here, we integrated two decades (2004-2024) of remote sensing data to evaluate Net Primary Productivity (NPP) and Water Use Efficiency (WUE). We applied interannual climate anomaly analyses (Z-scores) and adapted a trend-based classification framework to assess ecosystem trajectories. These biophysical metrics were cross-referenced with the spatial boundaries of Conservation Units, Indigenous Lands, Quilombola Territories, and private properties, including a distance-decay edge effect analysis. While long-term temporal trends for NPP and WUE remained relatively stable over the two decades, standardized anomaly analyses revealed a strong, statistically significant inverse coupling between productivity and water-use efficiency (r = - 0.741, p \u003C 0.001), primarily mediated by surface moisture retention (LSWI) rather than immediate precipitation alone. Spatial analyses demonstrated that Conservation Units act as the main ecological refugia, maintaining peak restoration within a 0-2 km internal buffer. Notably, Indigenous Lands and Quilombola Territories exhibited remarkable edge-to-core resilience, maintaining homogeneous conservation levels that effectively buffer external degradation. To safeguard the Caatinga's structural integrity against desertification, public policies must move beyond reactive drought subsidies towards proactive landscape management that legally empowers traditional communities and incentivizes conservation on private lands.","季节性干旱热带森林（Seasonally Dry Tropical Forests, SDTFs），如巴西卡廷加（Caatinga），极易受到气候变化和长期人为干扰的影响。尽管土地利用转换广泛发生，但关于植被生产力与恢复力在不同土地治理制度下如何变化的认识仍存在关键空白。本研究整合了二十年（2004—2024年）的遥感数据，评估净初级生产力（Net Primary Productivity, NPP）和水分利用效率（Water Use Efficiency, WUE）。我们采用年际气候异常分析（Z分数），并改编了基于趋势的分类框架以评估生态系统轨迹。这些生物物理指标与保护单元、原住民土地、基隆博拉领地及私有财产的空间边界进行了交叉比对，包括距离衰减边缘效应分析。尽管NPP和WUE的长期时间趋势在二十年间保持相对稳定，但标准化异常分析揭示出生产力与水分利用效率之间存在强烈的、统计显著的反向耦合关系（r = -0.741，p \u003C 0.001），这一关系主要由地表水分保持（LSWI）而非仅由即时降水所介导。空间分析表明，保护单元是主要的生态避难所，在0—2 km的内部缓冲区内维持着最高的恢复水平。值得注意的是，原住民土地和基隆博拉领地表现出显著边缘到核心的恢复力，维持着均质的保护水平，有效缓冲了外部退化。为保障卡廷加的结构完整性以抵御荒漠化，公共政策必须超越被动的干旱补贴，转向积极的景观管理，在法律上赋权传统社区并激励私有土地上的保护行为。","Environmental Management",80,{"impact":61,"substance":60,"depth":61,"authority":62,"freshness":120,"relevant":47,"comment":259},"基于20年遥感数据揭示保护地与原住民领地生态缓冲作用，方法扎实、结论对土地治理有参考价值，但属区域案例研究，公共影响有限。",[261],{"name":256,"url":253},[21,263,264,265,266],"生态系统韧性","土地利用治理","热带干旱林","退化土地修复",[268,269],"巴西 Caatinga 遥感 植被生产力","保护地 原住民领地 生态韧性","巴西Caatinga遥感植被生产力-3555","10.1007\u002Fs00267-026-02629-4",{"doi":271,"openalex_id":273,"authors":274,"venue":256,"cited_by_count":15,"oa_url":280,"card":281,"direction":104,"ingested_from":43},"W7214117687",[275,278],{"name":276,"orcid":277},"Lucas  Nascimento da Silva","https:\u002F\u002Forcid.org\u002F0009-0004-5435-0986",{"name":279,"orcid":9},"Bartolomeu Israel de Souza","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs00267-026-02629-4.pdf",{"tldr":282,"method":283,"finding":284,"direction":104,"opportunity":285},"整合20年遥感数据评估巴西卡廷加NPP与WUE，分析不同土地治理制度下的生态韧性。","2004-2024年遥感NPP\u002FWUE、气候异常Z评分、趋势分类与边缘衰减分析。","保护单元是主要生态避难所，原住民与Quilombola领地边缘到核心韧性突出。","可探究传统社区土地权属如何通过制度设计提升干旱森林韧性，并量化边缘效应的政策阈值。","2026-09-26T23:30:30.120235Z"]