[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4097":3,"related-4097":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":9,"secondary_topics":31,"classification":32,"search_phrases":74,"slug":77,"view_count":21,"doi":78,"paper":79,"created_at":95},4097,"Urban Forestry: A Bibliometric Analysis","https:\u002F\u002Fdoi.org\u002F10.58812\u002Fwsnt.v4i03.3145","This study presents a bibliometric analysis of urban forestry research to map its intellectual structure, thematic evolution, and collaboration patterns. A total of 468 Scopus-indexed documents published between 2000 and 2026 were analyzed using VOSviewer through keyword co-occurrence, overlay, and density visualizations, co-authorship analysis at the author, institution, and country levels, and citation analysis. The results show that the field is organized around five interrelated themes: ecological sustainability, management and governance, human–tree interactions, tree planting and canopy management, and greenspace-oriented urbanization. The overlay map indicates a shift from management, policy, and participation topics toward climate change, ecosystem services, biodiversity, carbon sequestration, and remote sensing in recent years. Citation analysis highlights foundational works on the definition, costs, ecosystem benefits, and governance of urban forestry. Co-authorship networks reveal several cohesive research groups connected by a few influential scholars, while institutional collaboration remains limited. At the country level, the United States is the dominant hub, followed by Canada, China, Italy, Germany, the United Kingdom, and Brazil. These findings provide an overview of the development of urban forestry research and indicate directions for future studies.","本研究对城市林业研究进行了文献计量分析，以描绘其知识结构、主题演变与合作模式。研究采用VOSviewer对2000年至2026年间Scopus收录的468篇文献进行分析，通过关键词共现、叠加和密度可视化，作者、机构和国家层面的合著分析，以及引文分析。结果表明，该领域围绕五个相互关联的主题组织：生态可持续性、管理与治理、人与树木的互动、植树与树冠管理，以及面向绿地的城市化。叠加图谱显示，近年来研究重心已从管理、政策和参与议题转向气候变化、生态系统服务、生物多样性、碳固存和遥感。引文分析凸显了关于城市林业的定义、成本、生态系统效益和治理的基础性著作。合著网络揭示了若干由少数有影响力的学者连接的内聚性研究群体，而机构间合作仍然有限。在国家层面，美国是主导中心，其次是加拿大、中国、意大利、德国、英国和巴西。这些发现概述了城市林业研究的发展，并指出了未来研究的方向。",null,"West Science Nature and Technology","2026-09-30T00:00:00Z","论文",10,false,56,{"impact":17,"substance":18,"depth":19,"authority":13,"freshness":20,"relevant":21,"comment":22},8,18,16,4,1,"基于468篇Scopus文献的计量分析，方法规范、结论清晰，但属综述性研究且时效偏旧，公共价值有限。",[24],{"name":10,"url":6},[26,27,28,29,30],"气候变化","遥感","文献计量","生态系统服务","城市林业",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":36},"needs_review","2026-10-22.2","rules",{"candidates":37,"agricultural_evidence":14,"reason":73},[38,46,53,60,66],{"code":39,"score":40,"supported":41,"title":42,"abstract":44,"tags":45},"I7",15,true,[43],"bibliometric",[28,43],[28],{"code":47,"score":13,"supported":41,"title":48,"abstract":49,"tags":52},"H6",[],[50,29,51],"生物多样性","biodiversity",[29],{"code":54,"score":55,"supported":41,"title":56,"abstract":57,"tags":59},"H7",7,[],[26,58],"climate change",[26],{"code":61,"score":55,"supported":41,"title":62,"abstract":63,"tags":65},"I3",[],[27,64],"remote sensing",[27],{"code":67,"score":68,"supported":41,"title":69,"abstract":70,"tags":72},"H4",3,[],[71],"carbon sequestration",[],"农业相关性需要复核",[75,76],"城市林业 研究热点","生态系统服务 城市林业 文献计量 气候变化","城市林业研究热点-4097","10.58812\u002Fwsnt.v4i03.3145",{"doi":78,"openalex_id":80,"authors":81,"venue":10,"cited_by_count":86,"oa_url":87,"card":88,"direction":92,"ingested_from":94},"W7214898413",[82,84],{"name":83,"orcid":9},"Loso Judijanto",{"name":85,"orcid":9},"Sintia Permata Sari",0,"https:\u002F\u002Fwsj.westsciences.com\u002Findex.php\u002Fwsnt\u002Farticle\u002Fdownload\u002F3145\u002F3163",{"tldr":89,"method":90,"finding":91,"direction":92,"opportunity":93},"用文献计量分析2000-2026年468篇城市林业文献，梳理主题演变与合作网络。","VOSviewer关键词共现、叠加、密度图及合著与引文分析，Scopus数据。","领域分五大主题，近年转向气候变化、生态系统服务、生物多样性与遥感。","农业遥感与作物表型","城市林业遥感与碳汇、生态系统服务结合的研究仍稀缺，可切入城市绿地碳计量。","openalex","2026-10-03T23:30:37.434360Z",{"total":97,"page":21,"page_size":97,"items":98},6,[99,172,233,306,383,429],{"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":112,"tags":114,"primary_topic":9,"secondary_topics":117,"classification":118,"search_phrases":145,"slug":148,"view_count":21,"doi":149,"paper":150,"created_at":171},4228,"Mapping satellite based research on climate change impacts and environmental responses through bibliometric analysis","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs44274-026-01090-8","Climate change affects ecosystems, water resources, and human systems worldwide, while satellite remote sensing provides large-scale and repeated observations of environmental change. However, existing bibliometric assessments often examine individual environmental applications, methodological domains, or broader Earth observation research, providing limited understanding of the integrated development of satellite-based climate–environment research. This study addresses this gap by systematically examining the growth, scientific structure, collaboration patterns, and thematic evolution of satellite-based research on climate change impacts and environmental responses from 2001 to 2025. A dataset of 1,112 documents was compiled from the Web of Science Core Collection and Scopus and analyzed using bibliometric indicators, network analysis, Multiple Correspondence Analysis, thematic mapping, and thematic evolution. The results demonstrate sustained growth in the research field, concentration within a relatively small group of core journals, and a semi-centralized collaboration structure involving prominent authors, countries, and institutions. The thematic analysis reveals continuity of established MODIS- and NDVI-based environmental research alongside the emergence of climate-change-oriented, machine-learning, and cloud-based approaches, particularly random forest and Google Earth Engine. The study provides an integrated view of how satellite-based climate–environment research has developed across interconnected ecosystems, hydrological processes, vegetation dynamics, and climate-related environmental responses. The findings can support researchers in identifying major knowledge structures, emerging research directions, collaboration opportunities, and methodological trends, while also highlighting the need to interpret bibliometric patterns within the coverage limitations of WoS- and Scopus-indexed English-language literature.","气候变化影响全球生态系统、水资源和人类系统，而卫星遥感为环境变化提供了大尺度、重复观测。然而，现有文献计量评估多考察单一环境应用、方法学领域或更广泛的地球观测研究，对基于卫星的气候—环境研究综合发展的认识有限。本研究通过系统考察2001—2025年基于卫星的气候变化影响与环境响应研究的增长、科学结构、合作模式及主题演化，弥补这一空白。研究从Web of Science核心合集和Scopus编制了1,112篇文献的数据集，并采用文献计量指标、网络分析、多重对应分析、主题制图和主题演化进行分析。结果表明，该研究领域持续增长，成果集中于少数核心期刊，并呈现涉及知名作者、国家和机构的半集中式合作结构。主题分析揭示了基于MODIS和NDVI的既有环境研究的延续性，同时出现了面向气候变化、机器学习和云端方法的研究方向，尤其是随机森林和Google Earth Engine。本研究提供了关于基于卫星的气候—环境研究如何在相互关联的生态系统、水文过程、植被动态和气候相关环境响应中发展的综合视角。研究结果可帮助研究人员识别主要知识结构、新兴研究方向、合作机会和方法趋势，同时也强调需在WoS和Scopus索引英文文献的覆盖局限内解读文献计量模式。","Discover Environment","2026-10-04T00:00:00Z",64,{"impact":17,"substance":18,"depth":19,"authority":109,"freshness":110,"relevant":21,"comment":111},13,9,"基于1112篇文献的卫星遥感气候变化研究计量分析，方法规范但偏学术综述，对农业信息化有间接参考价值。",[113],{"name":105,"url":102},[26,27,28,115,116],"Google Earth Engine","植被动态",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":119},{"candidates":120,"agricultural_evidence":14,"reason":73},[121,125,129,133,139],{"code":54,"score":40,"supported":41,"title":122,"abstract":123,"tags":124},[58],[26,58],[26],{"code":39,"score":40,"supported":41,"title":126,"abstract":127,"tags":128},[43],[28,43],[28],{"code":61,"score":110,"supported":41,"title":130,"abstract":131,"tags":132},[],[27,64],[115,116,27],{"code":134,"score":68,"supported":41,"title":135,"abstract":136,"tags":138},"H3",[],[137],"水资源",[],{"code":140,"score":68,"supported":41,"title":141,"abstract":142,"tags":144},"I2",[],[143],"机器学习",[],[146,147],"卫星遥感 气候变化 文献计量","文献计量 植被动态 气候变化 遥感","卫星遥感气候变化文献计量-4228","10.1007\u002Fs44274-026-01090-8",{"doi":149,"openalex_id":151,"authors":152,"venue":105,"cited_by_count":86,"oa_url":165,"card":166,"direction":92,"ingested_from":94},"W7219744000",[153,156,159,162],{"name":154,"orcid":155},"Md. Nazmul Islam","https:\u002F\u002Forcid.org\u002F0000-0003-0528-8296",{"name":157,"orcid":158},"Md. Ashiqur Rahman","https:\u002F\u002Forcid.org\u002F0009-0005-1511-497X",{"name":160,"orcid":161},"Md. Shahajada Masud Anowarul Haque","https:\u002F\u002Forcid.org\u002F0000-0001-6415-8665",{"name":163,"orcid":164},"Josiline Phiri Chigwada","https:\u002F\u002Forcid.org\u002F0000-0003-0964-3582","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs44274-026-01090-8.pdf",{"tldr":167,"method":168,"finding":169,"direction":92,"opportunity":170},"用文献计量分析2001-2025年卫星遥感气候变化与环境响应研究的增长、结构与主题演化。","1112篇WoS\u002FScopus文献，用文献计量指标、网络分析、多重对应分析与主题","领域持续增长且期刊集中，MODIS\u002FNDVI研究延续，机器学习与GEE等云方法兴起。","可结合机器学习与云平台，将卫星气候环境响应研究延伸至农业生态系统与作物表型监测。","2026-10-05T23:30:24.867196Z",{"id":173,"title":174,"url":175,"summary":176,"summary_zh":177,"content":9,"source_name":178,"source_url":175,"published_at":179,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":180,"score_detail":181,"sources":184,"tags":186,"primary_topic":9,"secondary_topics":190,"classification":191,"search_phrases":202,"slug":205,"view_count":21,"doi":206,"paper":207,"created_at":232},5246,"Spatial divergence of climate–vegetation relationships in Florida: A 43-year multi-method analysis (1982–2024)","https:\u002F\u002Fdoi.org\u002F10.1088\u002F2515-7620\u002Faeb5af","Abstract Attributing long-term vegetation change to climate is difficult in subtropical ecosystems, where temperature and precipitation act simultaneously, are confounded by abrupt disturbances, and where statewide averages can conceal offsetting associations. Here we apply a multi-method convergence-of-evidence framework to a sensor-harmonized 43-year record (1982–2024) of NOAA CDR NDVI, temperature, and precipitation across Florida, which spans cool temperate forests to warm subtropical wetlands. Modified Mann–Kendall tests with false-discovery-rate correction detected monotonic trends; pixel-level correlation, spatial-error regression, and lagged cross-correlation evaluated association strength, direction, and timing; Empirical Orthogonal Function decomposition of deseasonalized anomalies separated persistent climatic signals from episodic disturbance, with the leading mode (58.4% of variance) tracking latitudinal warming and a lower-order mode (PC3) tracking hurricane impacts. Florida warmed by +0.033 °C yr⁻¹, significant in 99.6% of land pixels, while precipitation showed no field-significant trend. The temperature–greenness association was positive across essentially the entire state, marginally stronger in the south; localized negative responses were confined to specific southern wetland and urban-fringe pixels rather than forming a systematic north–south reversal. The state-mean precipitation–NDVI correlation was negative at zero lag and reversed from r = −0.263 to r = +0.102 when incoming shortwave radiation was partialed out, consistent with cloud-associated light suppression rather than water limitation—a phenomenon we term the Water Paradox. SPI\u002FSPEI–NDVI correlations were near zero, and Everglades water levels were largely decoupled from wetland greenness (r = 0.08, n.s.), indicating that inundation acts as a localized threshold modifier rather than a dominant suppressor. Climate–vegetation models for comparable high-rainfall subtropical systems may therefore need to account for spatially divergent temperature sensitivities and treat precipitation as an energy constraint rather than a uniform benefit.","摘要 在亚热带生态系统中，将长期植被变化归因于气候十分困难，因为温度和降水同时起作用，又受到突发干扰的混杂影响，而且全州尺度的平均值可能掩盖相互抵消的关联。本文采用多方法证据汇聚框架，对佛罗里达州1982—2024年共43年经传感器协调的NOAA CDR NDVI、温度和降水记录进行分析，该区域涵盖从冷温带森林到暖亚热带湿地。采用经错误发现率校正的改进Mann–Kendall检验检测单调趋势；通过像元级相关、空间误差回归和滞后互相关评估关联强度、方向和时间；对去季节化异常进行经验正交函数分解，以区分持续性气候信号与偶发干扰，其中主导模态（占总方差58.4%）追踪纬度变暖，低阶模态（PC3）追踪飓风影响。佛罗里达州以+0.033 °C yr⁻¹的速率变暖，在99.6%的陆地像元上显著，而降水未显示出区域显著趋势。温度—绿度关联在全州几乎均为正，南部略强；局地负响应仅限于特定的南部湿地和城市边缘像元，而非形成系统的南北反转。州平均降水—NDVI相关在零滞后时为负，当偏除入射短波辐射后由r = −0.263反转为r = +0.102，这与云相关的光抑制而非水分限制相一致——我们将这一现象称为“水悖论”。SPI\u002FSPEI—NDVI相关接近零，大沼泽地水位与湿地绿度基本解耦（r = 0.08，不显著），表明淹水作为局地阈值调节因子而非主导抑制因子起作用。因此，针对可比的高降雨亚热带系统的气候—植被模型可能需要考虑空间分异的温度敏感性，并将降水视为能量约束而非均一益处。","Environmental Research Communications","2026-10-16T00:00:00Z",70,{"impact":17,"substance":182,"depth":18,"authority":109,"freshness":110,"relevant":21,"comment":183},22,"基于43年NDVI遥感记录的多方法气候-植被关系研究，方法扎实、结论新颖，但属区域基础研究，对国内三农信息化仅有方法借鉴价值。",[185],{"name":178,"url":175},[26,27,187,188,189],"NDVI","植被监测","佛罗里达",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":192},{"candidates":193,"agricultural_evidence":14,"reason":73},[194,198],{"code":61,"score":68,"supported":14,"title":195,"abstract":196,"tags":197},[],[],[187,188,27],{"code":54,"score":21,"supported":14,"title":199,"abstract":200,"tags":201},[],[],[26],[203,204],"Florida NDVI 气候变化","佛罗里达 植被 遥感","FloridaNDVI气候变化-5246","10.1088\u002F2515-7620\u002Faeb5af",{"doi":206,"openalex_id":208,"authors":209,"venue":178,"cited_by_count":86,"oa_url":226,"card":227,"direction":92,"ingested_from":94},"W7222444517",[210,213,216,219,222,224],{"name":211,"orcid":212},"A. Mandal","https:\u002F\u002Forcid.org\u002F0009-0004-0192-7497",{"name":214,"orcid":215},"Aman Kumar Thapa","https:\u002F\u002Forcid.org\u002F0009-0003-8956-6632",{"name":217,"orcid":218},"Madan Thapa Chhetri","https:\u002F\u002Forcid.org\u002F0009-0002-3567-7591",{"name":220,"orcid":221},"Hongbo Su","https:\u002F\u002Forcid.org\u002F0000-0002-2147-3646",{"name":223,"orcid":9},"Yan Yong",{"name":225,"orcid":9},"Fred Bloetscher","https:\u002F\u002Fiopscience.iop.org\u002Farticle\u002F10.1088\u002F2515-7620\u002Faeb5af\u002Fpdf",{"tldr":228,"method":229,"finding":230,"direction":92,"opportunity":231},"用多方法分析佛罗里达43年气候与植被关系，揭示空间分异。","NOAA CDR NDVI与气候数据，结合趋势检验、回归、EOF分解。","全州变暖显著，温度与绿度正相关；降水因云量抑制光照呈负相关。","高降雨亚热带区需将降水视为能量约束，并关注温度敏感性的空间分异。","2026-10-18T23:30:31.356484Z",{"id":234,"title":235,"url":236,"summary":237,"summary_zh":238,"content":9,"source_name":239,"source_url":236,"published_at":240,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":241,"score_detail":242,"sources":245,"tags":247,"primary_topic":9,"secondary_topics":251,"classification":252,"search_phrases":274,"slug":277,"view_count":278,"doi":279,"paper":280,"created_at":305},4881,"Mapping the knowledge structure and evolution of artificial intelligence for water reuse and sustainable water management","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs44163-026-02389-4","Artificial intelligence (AI) has become a powerful instrument to solve complex problems related to sustainable water resource management because it allows creating advanced, data-driven modelling, prediction, and optimization of water systems. Although the studies in this sphere have increased quickly, the global scientific tendencies have not been thoroughly examined. This research paper presents a bibliometric review of the literature about AI applications in green water management that was published from 2015 to 2025. The Scopus database was searched with a structured search strategy on bibliographic data, and 3,280 peer-reviewed articles were analyzed. VOSviewer was used to perform bibliometric mapping and network visualization to investigate the trends in publications, most active journals, most impactful authors, contributions of institutions, country-level partnerships, and research clusters. The findings indicate that the publishing activity has grown tremendously in the last ten years, which suggests that there is a growing interest in AI-based solutions to water quality monitoring, hydrological prediction, optimization of wastewater treatment, and smart water management system. China, the United States and India became the most productive nations with the University of Chinese Academy of Sciences and Beijing normal university as some of the top contributing institutions. The analysis of the key words co-occurrence (dominant research themes) was based on machine learning, water-quality prediction, hydrological modelling, remote sensing, and decision-support systems. The co-citation analysis has shown the core studies in machine learning algorithms, including random forests, neural networks, and boosting algorithms, which underpin the present studies in this field. Overall, the results depict the growing role of AI technologies to enhance the effectiveness of water resources management and wastewater treatment. This research offers a clear insight into the intellectual framework and the future research perspective of AI-based environmentally sustainable water management which can be of great value to researchers, policymakers, and practitioners who would wish to come up with innovative and data-based water management solutions.","人工智能（AI）已成为解决可持续水资源管理相关复杂问题的有力工具，因为它能够构建先进的数据驱动模型，实现水系统的预测与优化。尽管该领域的研究迅速增加，但全球科学趋势尚未得到充分审视。本文对2015年至2025年间发表的关于人工智能在绿色水资源管理中应用的文献进行了文献计量学综述。采用结构化检索策略对Scopus数据库中的文献数据进行检索，共分析了3，280篇同行评审论文。使用VOSviewer进行文献计量制图和网络可视化，以探究发文趋势、最活跃期刊、最具影响力作者、机构贡献、国家层面合作及研究聚类。研究结果表明，过去十年间发文活动急剧增长，表明基于人工智能的水质监测、水文预测、废水处理优化和智能水管理系统解决方案日益受到关注。中国、美国和印度成为最高产国家，中国科学院和北京师范大学为贡献最多的机构之一。关键词共现分析（主导研究主题）基于机器学习、水质预测、水文建模、遥感和决策支持系统。共被引分析揭示了机器学习算法的核心研究，包括随机森林、神经网络和提升算法，这些构成了该领域当前研究的基础。总体而言，研究结果描绘了人工智能技术在提升水资源管理和废水处理效能方面日益增长的作用。本研究为基于人工智能的环境可持续水资源管理的知识框架和未来研究视角提供了清晰见解，对希望提出创新性和数据驱动型水资源管理方案的研究人员、政策制定者和实践者具有重要价值。","Discover Artificial Intelligence","2026-10-12T00:00:00Z",67,{"impact":243,"substance":18,"depth":19,"authority":109,"freshness":17,"relevant":21,"comment":244},12,"基于3280篇文献的AI水资源管理文献计量综述，方法规范、结论有参考价值，但属学术综述类，产业与政策落地性有限。",[246],{"name":239,"url":236},[248,27,28,249,250],"农业人工智能","水资源管理","智慧水务",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":253},{"candidates":254,"agricultural_evidence":14,"reason":73},[255,261,266,270],{"code":140,"score":18,"supported":41,"title":256,"abstract":258,"tags":260},[257],"artificial intelligence",[143,259,257],"machine learning",[248],{"code":134,"score":55,"supported":41,"title":262,"abstract":263,"tags":265},[],[137,264],"water resource",[249],{"code":61,"score":55,"supported":41,"title":267,"abstract":268,"tags":269},[],[27,64],[27],{"code":39,"score":55,"supported":41,"title":271,"abstract":272,"tags":273},[],[28,43],[28],[275,276],"AI 水资源管理 文献计量","人工智能 再生水 可持续水管理","AI水资源管理文献计量-4881",2,"10.1007\u002Fs44163-026-02389-4",{"doi":279,"openalex_id":281,"authors":282,"venue":239,"cited_by_count":86,"oa_url":297,"card":298,"direction":304,"ingested_from":94},"W7221506022",[283,286,289,291,293,295],{"name":284,"orcid":285},"Narendra Kumar","https:\u002F\u002Forcid.org\u002F0000-0002-5578-3255",{"name":287,"orcid":288},"Mangey Ram Nagar","https:\u002F\u002Forcid.org\u002F0000-0003-3635-3548",{"name":290,"orcid":9},"Isha Dharsandia",{"name":292,"orcid":9},"Ananya Tiwari",{"name":294,"orcid":9},"Santosh Kumar Mishra",{"name":296,"orcid":9},"Mohit Kumar","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs44163-026-02389-4.pdf",{"tldr":299,"method":300,"finding":301,"direction":302,"opportunity":303},"对2015-2025年AI用于水回用与可持续水管理的3280篇文献做文献计量分析，梳理知识结构与演化","Scopus检索3280篇同行评议文献，用VOSviewer做共现与共被引网络可","发文量十年激增，中国、美国、印度领先；主题聚焦机器学习、水质预测、水文建模、遥感与决策支持。","农业人工智能与决策模型","可将AI水管理方法迁移至农业灌溉与面源污染场景，构建农业水-肥-碳协同的智能决策模型。","智慧农业 \u002F 农业物联网","2026-10-14T23:30:10.246538Z",{"id":307,"title":308,"url":309,"summary":310,"summary_zh":311,"content":9,"source_name":312,"source_url":309,"published_at":313,"category":12,"cover_url":9,"hotness":314,"is_selected":14,"score":315,"score_detail":316,"sources":319,"tags":323,"primary_topic":9,"secondary_topics":327,"classification":328,"search_phrases":367,"slug":370,"view_count":278,"doi":371,"paper":372,"created_at":382},4169,"Spatial Patterns and Dynamics of Agricultural Land Use: A Geographical Analysis","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23117515","This study examines the spatial patterns and dynamics of agricultural land use through a geographical perspective, emphasizing the relationship between land use, land cover, and socio-economic as well as environmental factors. The research integrates spatial analysis methods, remote sensing, and GIS techniques to understand the distribution, transformation, and management of agricultural landscapes across different regions. It highlights how factors such as market forces, technological advancements, policy interventions, and environmental constraints influence land-use decisions and transitions. The study also explores agricultural typologies, landscape ecosystems, and regional variations in land-use practices, supported by global case studies including Europe, North America, East Asia, Sub-Saharan Africa, and South America. Furthermore, it discusses the impacts of agricultural land use on biodiversity, soil, water resources, and climate systems, emphasizing sustainability challenges and ecosystem services. The findings underline the importance of integrated land-use planning, governance reforms, and adaptive strategies to promote sustainable agricultural development in the face of climate change and increasing global demand for food.","本研究从地理学视角考察农业土地利用的空间格局与动态变化，重点关注土地利用、土地覆盖与社会经济及环境因素之间的关系。研究综合运用空间分析方法、遥感与GIS技术，理解不同区域农业景观的分布、转型与管理。研究强调市场力量、技术进步、政策干预和环境约束等因素如何影响土地利用决策与转变。研究还探讨了农业类型学、景观生态系统以及土地利用实践的区域差异，并辅以欧洲、北美、东亚、撒哈拉以南非洲和南美洲的全球案例研究。此外，研究讨论了农业土地利用对生物多样性、土壤、水资源和气候系统的影响，强调可持续性挑战与生态系统服务。研究结论强调，面对气候变化和全球粮食需求不断增长，综合土地利用规划、治理改革和适应性策略对于促进农业可持续发展具有重要意义。","Zenodo (CERN European Organization for Nuclear Research)","2026-11-30T00:00:00Z",25,49,{"impact":17,"substance":317,"depth":40,"authority":243,"freshness":86,"relevant":21,"comment":318},14,"综述性论文，方法整合遥感与GIS，但缺乏新数据与具体案例，且发布日期在未来，时效性不足，不建议进入每日精选。",[320,321],{"name":312,"url":309},{"name":312,"url":322},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23117514",[324,27,325,29,326],"可持续农业","GIS","农业土地利用",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":329},{"candidates":330,"agricultural_evidence":41,"reason":366},[331,335,342,346,353,357,361],{"code":47,"score":13,"supported":41,"title":332,"abstract":333,"tags":334},[],[50,29,51],[29],{"code":336,"score":13,"supported":41,"title":337,"abstract":338,"tags":341},"I4",[],[339,325,340],"空间分析","spatial analysis",[325],{"code":61,"score":55,"supported":41,"title":343,"abstract":344,"tags":345},[],[27,64],[27],{"code":347,"score":97,"supported":41,"title":348,"abstract":349,"tags":352},"H2",[],[350,351],"土壤","soil",[],{"code":54,"score":97,"supported":41,"title":354,"abstract":355,"tags":356},[],[26,58],[],{"code":134,"score":68,"supported":41,"title":358,"abstract":359,"tags":360},[],[137],[],{"code":362,"score":21,"supported":14,"title":363,"abstract":364,"tags":365},"H8",[],[],[324],"多个产业证据相当，需要确认主归属",[368,369],"农业土地利用 空间格局 遥感","农业土地利用 生态系统服务 可持续农业 遥感","农业土地利用空间格局遥感-4169","10.5281\u002Fzenodo.23117515",{"doi":371,"openalex_id":373,"authors":374,"venue":312,"cited_by_count":86,"oa_url":309,"card":377,"direction":92,"ingested_from":94},"W7218853570",[375],{"name":376,"orcid":9},"Anjali Harshavardhan Thorat",{"tldr":378,"method":379,"finding":380,"direction":92,"opportunity":381},"从地理视角分析全球农业土地利用的空间格局与动态变化。","整合空间分析、遥感和GIS技术，结合多区域案例研究。","市场、技术、政策和环境共同驱动土地利用转变，需综合规划与适应性治理。","可结合多源遥感与机器学习，量化政策与市场对土地利用变化的非线性驱动机制。","2026-10-04T23:30:24.369006Z",{"id":384,"title":385,"url":386,"summary":387,"summary_zh":9,"content":9,"source_name":388,"source_url":386,"published_at":389,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":390,"score_detail":391,"sources":393,"tags":395,"primary_topic":9,"secondary_topics":397,"classification":398,"search_phrases":413,"slug":416,"view_count":20,"doi":417,"paper":418,"created_at":428},3794,"Assessing Climate Change Impacts on Forest Productivity Using Integrated GIS Remote Sensing Climate and Socioeconomic Methods in Itang Special District Gambella Region Ethiopia","https:\u002F\u002Fdoi.org\u002F10.21203\u002Frs.3.rs-10789368\u002Fv1","Assessing Climate Change Impacts on Forest Productivity Using Integrated GIS Remote Sensing Climate and Socioeconomic Methods in Itang Special District Gambella Region Ethiopia。Research Square","Research Square","2026-09-28T00:00:00Z",60,{"impact":17,"substance":18,"depth":40,"authority":13,"freshness":110,"relevant":21,"comment":392},"埃塞俄比亚区域尺度的GIS遥感与气候社会经济综合方法研究，方法有参考价值但地域性强、公共影响有限，时效新。",[394],{"name":388,"url":386},[26,27,325,396],"森林生产力",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":399},{"candidates":400,"agricultural_evidence":14,"reason":73},[401,405,409],{"code":54,"score":243,"supported":41,"title":402,"abstract":403,"tags":404},[58],[58],[26],{"code":61,"score":243,"supported":41,"title":406,"abstract":407,"tags":408},[64],[64],[27],{"code":336,"score":243,"supported":41,"title":410,"abstract":411,"tags":412},[325],[325],[325],[414,415],"Gambella Itang 森林生产力","GIS 遥感 气候变化","GambellaItang森林生产力-3794","10.21203\u002Frs.3.rs-10789368\u002Fv1",{"doi":417,"openalex_id":419,"authors":420,"venue":388,"cited_by_count":86,"oa_url":427,"card":9,"direction":92,"ingested_from":94},"W7214650564",[421,423,425],{"name":422,"orcid":9},"KOAT MACHAR PUR",{"name":424,"orcid":9},"DAK BANG RUON",{"name":426,"orcid":9},"DUOL NHIAL LUAK","https:\u002F\u002Fwww.researchsquare.com\u002Farticle\u002Frs-10789368\u002Flatest.pdf","2026-09-29T23:30:30.100133Z",{"id":430,"title":431,"url":432,"summary":433,"summary_zh":434,"content":9,"source_name":435,"source_url":432,"published_at":436,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":437,"score_detail":438,"sources":441,"tags":443,"primary_topic":447,"secondary_topics":451,"classification":452,"search_phrases":475,"slug":478,"view_count":278,"doi":479,"paper":480,"created_at":515},3628,"Identifying ecosystem service drivers to inform land use scenario simulation: An InVEST-PLSR-PLUS framework for the Danjiangkou Reservoir Basin, China","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.ecolind.2026.115581","The Danjiangkou Reservoir Basin is the core water source of the Middle Route of China's South-to-North Water Diversion Project, yet research in this basin has largely focused on static ecosystem service (ES) assessments, leaving the drivers of ES dynamics and their future trajectories poorly understood. This study develops an integrated InVEST–PLSR–PLUS framework that quantifies the spatiotemporal patterns of water yield, soil conservation, and carbon storage, identifies their dominant drivers objectively via Partial Least Squares Regression (PLSR), and projects land-use dynamics to 2030 using the PLUS model parameterized with PLSR-derived drivers rather than empirical selection. The main findings are as follows. (1) From 2010 to 2020, water yield increased markedly (from 79.22 to 895.38 to 166.69–1000.24 mm year −1 ), soil conservation declined (maximum from 941.60 to 641.91 t ha −1 year −1 ), and carbon storage remained stable (maximum around 20.4 t ha −1 year −1 ); high soil conservation and carbon storage values were concentrated in ecological and economic forests, whereas elevated water yield occurred mainly in farmland, orchards, and built-up land. (2) Vegetation and land-use variables dominated ES dynamics: EVI (VIP = 1.609), NDVI (VIP = 1.583), ecological forest area (VIP = 1.553), economic forest area (VIP = 1.476), and farmland area (VIP = 1.445) exerted strong positive effects; among landscape-pattern metrics, the landscape shape index (LSI; VIP = 1.313) was positively associated with all three services, whereas patch-subdivision metrics (PD, ED, IJI) showed negative associations; climatic and socioeconomic variables contributed only marginally (VIP \u003C 0.8, except GDP at 0.864). (3) Under the natural evolution scenario, ecological forest and orchard areas are projected to decrease by 40.89 km 2 and 20.54 km 2 by 2030, while economic forest (+29.63 km 2 ), water area (+24.10 km 2 ), farmland (+1.68 km 2 ), and built-up land (+5.98 km 2 ) expand. These findings demonstrate that vegetation cover and landscape connectivity fundamentally regulate ES provision, and indicate that ecological forest protection, control landscape fragmentation, and regulate built-up land and orchard expansion to safeguard the ecological security of this strategic water-source region.","丹江口库区是中国南水北调中线工程的核心水源区，但该区域的研究多集中于静态生态系统服务（ES）评估，对其动态变化的驱动机制及未来演变趋势的认识仍十分有限。本研究构建了InVEST–PLSR–PLUS集成框架，量化了产水量、土壤保持和碳储量的时空格局，通过偏最小二乘回归（PLSR）客观识别其主导驱动因子，并利用以PLSR-derived驱动因子而非经验选择参数化的PLUS模型，将土地利用动态模拟至2030年。主要结论如下：（1）2010—2020年，产水量显著增加（从79.22增至895.38，再到166.69~1000.24 mm·a⁻¹），土壤保持量下降（最大值从941.60降至641.91 t·ha⁻¹·a⁻¹），碳储量保持稳定（最大值约为20.4 t·ha⁻¹·a⁻¹）；土壤保持和碳储量的高值集中于生态林和经济林，而产水量的高值主要出现在耕地、果园和建设用地。（2）植被和土地利用变量主导了ES动态变化：EVI（VIP=1.609）、NDVI（VIP=1.583）、生态林面积（VIP=1.553）、经济林面积（VIP=1.476）和耕地面积（VIP=1.445）具有较强正向影响；在景观格局指标中，景观形状指数（LSI；VIP=1.313）与三项服务均呈正相关，而斑块细分指标（PD、ED、IJI）呈负相关；气候和社会经济变量的贡献较小（VIP\u003C0.8，GDP为0.864除外）。（3）在自然演变情景下，预计到2030年生态林和果园面积将分别减少40.89 km²和20.54 km²，而经济林（+29.63 km²）、水域（+24.10 km²）、耕地（+1.68 km²）和建设用地（+5.98 km²）将扩张。研究结果表明，植被覆盖和景观连通性从根本上调控着生态系统服务的供给，并指出应保护生态林、控制景观破碎化、调控建设用地和果园扩张，以保障这一战略性水源区的生态安全。","Ecological Indicators","2026-09-25T00:00:00Z",80,{"impact":19,"substance":439,"depth":18,"authority":317,"freshness":110,"relevant":21,"comment":440},23,"以PLSR驱动PLUS模型预测丹江口库区生态系统服务与土地利用变化，方法有创新、数据扎实，对水源区生态安全与农业空间管控有参考价值。",[442],{"name":435,"url":432},[27,29,444,445,446],"土地利用模拟","水源地保护","丹江口库区",{"code":47,"slug":51,"name":448,"path":449,"parent_code":450},"生态保护与生物多样性","\u002Ftopic\u002Fenvironment\u002Fbiodiversity","H",[],{"status":453,"locked":14,"version":34,"method":35,"evidence":454},"classified",{"candidates":455,"agricultural_evidence":41},[456,461,465,471],{"code":47,"score":40,"supported":41,"title":457,"abstract":459,"tags":460},[458],"ecosystem service",[29,458],[29],{"code":347,"score":97,"supported":41,"title":462,"abstract":463,"tags":464},[],[350,351],[],{"code":466,"score":68,"supported":41,"title":467,"abstract":468,"tags":470},"B2",[],[469],"orchard",[],{"code":61,"score":21,"supported":14,"title":472,"abstract":473,"tags":474},[],[],[27],[476,477],"丹江口水库 生态系统服务","InVEST PLUS 土地利用模拟","丹江口水库生态系统服务-3628","10.1016\u002Fj.ecolind.2026.115581",{"doi":479,"openalex_id":481,"authors":482,"venue":435,"cited_by_count":86,"oa_url":508,"card":509,"direction":302,"ingested_from":94},"W7214192017",[483,486,489,491,494,496,498,501,504,506],{"name":484,"orcid":485},"Bojun Ma","https:\u002F\u002Forcid.org\u002F0000-0002-7867-3817",{"name":487,"orcid":488},"Kun Sun","https:\u002F\u002Forcid.org\u002F0000-0002-9460-6681",{"name":490,"orcid":9},"Jian Zhang",{"name":492,"orcid":493},"Chucai Peng","https:\u002F\u002Forcid.org\u002F0000-0003-3366-9547",{"name":495,"orcid":9},"孙宝洋",{"name":497,"orcid":9},"Xiaoshuang Wang",{"name":499,"orcid":500},"Jigen Liu","https:\u002F\u002Forcid.org\u002F0009-0006-6798-2864",{"name":502,"orcid":503},"Jinquan Huang","https:\u002F\u002Forcid.org\u002F0000-0001-7372-1915",{"name":505,"orcid":9},"Lin Li",{"name":507,"orcid":9},"Feipeng Ren","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS1470160X26009830\u002Fpdf",{"tldr":510,"method":511,"finding":512,"direction":513,"opportunity":514},"构建InVEST-PLSR-PLUS框架，识别丹江口库区生态系统服务驱动因子并模拟2030年土地利用","InVEST模型、偏最小二乘回归（PLSR）与PLUS模型耦合，基于2010-2","植被与土地利用主导生态系统服务，自然演变下生态林和果园将减少，经济林与建设用地扩张。","农业绿色发展与碳","可将PLSR驱动因子筛选推广至其他水源区，并结合多情景政策模拟优化生态补偿与土地管控。","2026-09-27T23:31:29.684586Z"]