[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4098":3,"related-4098":100},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":26,"primary_topic":9,"secondary_topics":32,"classification":33,"search_phrases":67,"slug":70,"view_count":71,"doi":72,"paper":73,"created_at":99},4098,"Monitoring Sustainable Rural Tourism Development in Post-Conflict Areas Using Remote Sensing and Geospatial Analysis","https:\u002F\u002Fdoi.org\u002F10.26493\u002F1854-6935.24.433-464","This study examines how rural tourism supports sustainable development, economic recovery, social stability, and cultural preservation in post-conflict regions, using Transcarpathia, Ukraine, as a case study for 2022–2024. It applies a mixed-methods approach combining satellite remote sensing, GIS-based spatial analysis, and socio-economic indicators to assess land-use change, infrastructure recovery, ecological stability, and socio-economic resilience. Unlike existing geospatial monitoring studies that focus on single dimensions (e.g. land cover or tourist flows) in stable environments. This research introduces a theoretically grounded, multi-dimensional framework that integrates environmental, infrastructural, socio-economic, and digital indicators into a unified composite sustainability index, enabling continuous monitoring where conventional field data collection is constrained. The findings indicate notable recovery and resilience: tourism-related infrastructure rebounded strongly, ecological indicators improved over time, and digital accessibility expanded substantially, supporting tourism development despite ongoing post-conflict challenges. The study offers a novel integrated geospatial framework for monitoring sustainable rural tourism transitions in data-constrained and crisis-affected territories.","本研究以乌克兰外喀尔巴阡（Transcarpathia）为案例，考察2022—2024年期间乡村旅游如何促进冲突后地区的可持续发展、经济复苏、社会稳定与文化保护。研究采用混合方法，结合卫星遥感、基于GIS的空间分析和社会经济指标，评估土地利用变化、基础设施恢复、生态稳定性及社会经济韧性。与现有聚焦于稳定环境中单一维度（如土地覆盖或游客流量）的地理空间监测研究不同，本研究提出了一个具有理论根基的多维框架，将环境、基础设施、社会经济和数字指标整合为统一的复合可持续性指数，从而在传统实地数据采集受限的情况下实现持续监测。研究结果表明了显著的恢复与韧性：旅游相关基础设施强劲反弹，生态指标随时间改善，数字可及性大幅扩展，在持续面临冲突后挑战的情况下支撑了旅游发展。本研究为在数据受限和受危机影响的地区监测可持续乡村旅游转型提供了一个新颖的综合地理空间框架。",null,"Managing Global Transitions","2026-09-30T00:00:00Z","论文",10,false,72,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,21,18,13,8,1,"该研究提出融合遥感、GIS与社会经济指标的多维可持续性指数框架，方法新颖且对数据受限地区有参考价值，但案例局限于乌克兰局部区域，对国内三农信息化实践的直接影响力有限。",[25],{"name":10,"url":6},[27,28,29,30,31],"数字乡村","可持续发展","乡村旅游","遥感监测","GIS",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":37},"needs_review","2026-10-22.2","rules",{"candidates":38,"agricultural_evidence":42,"reason":66},[39,47,54,61],{"code":40,"score":41,"supported":42,"title":43,"abstract":45,"tags":46},"G4",15,true,[44],"rural tourism",[29,44],[29],{"code":48,"score":41,"supported":42,"title":49,"abstract":51,"tags":53},"I3",[50],"remote sensing",[52,50],"遥感",[30],{"code":55,"score":13,"supported":42,"title":56,"abstract":57,"tags":60},"I4",[],[58,31,59],"空间分析","spatial analysis",[31],{"code":62,"score":22,"supported":14,"title":63,"abstract":64,"tags":65},"I6",[],[],[27],"多个产业证据相当，需要确认主归属",[68,69],"遥感 GIS 可持续乡村旅游","可持续发展 乡村旅游 数字乡村 遥感监测","遥感GIS可持续乡村旅游-4098",2,"10.26493\u002F1854-6935.24.433-464",{"doi":72,"openalex_id":74,"authors":75,"venue":10,"cited_by_count":90,"oa_url":91,"card":92,"direction":96,"ingested_from":98},"W7214899546",[76,78,80,82,85,87],{"name":77,"orcid":9},"Valeriia Shcherbak",{"name":79,"orcid":9},"Oleksandr Dorokhov",{"name":81,"orcid":9},"Viktoriia Riashchenko",{"name":83,"orcid":84},"Valentina Djakona","https:\u002F\u002Forcid.org\u002F0000-0002-9199-3559",{"name":86,"orcid":9},"Oleksii Yermolenko",{"name":88,"orcid":89},"Maja Meško","https:\u002F\u002Forcid.org\u002F0000-0001-5615-9801",0,"https:\u002F\u002Fojs.upr.si\u002Ffm\u002Farticle\u002Fview\u002F270\u002F115",{"tldr":93,"method":94,"finding":95,"direction":96,"opportunity":97},"用遥感与GIS构建多维可持续指数，监测乌克兰战后乡村旅游业恢复。","卫星遥感、GIS空间分析与社经指标，构建复合可持续指数。","旅游基础设施强劲反弹，生态改善，数字可及性扩大，支撑战后恢复。","农业遥感与作物表型","可将该多维遥感框架迁移至数据受限的乡村农业旅游与生计恢复监测研究。","openalex","2026-10-03T23:30:37.512385Z",{"total":101,"page":22,"page_size":101,"items":102},6,[103,190,289,334,416,461],{"id":104,"title":105,"url":106,"summary":107,"summary_zh":108,"content":9,"source_name":109,"source_url":106,"published_at":110,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":111,"score_detail":112,"sources":117,"tags":119,"primary_topic":122,"secondary_topics":127,"classification":128,"search_phrases":158,"slug":161,"view_count":90,"doi":162,"paper":163,"created_at":189},2533,"Quantifying the Environmental Impact of Artisanal and Small‐Scale Mining: An Integrated Approach for Sustainable Development Insights","https:\u002F\u002Fdoi.org\u002F10.1002\u002Fsd.71693","ABSTRACT Artisanal and small‐scale mining (ASM) provides livelihoods for over 44.5 million people globally, yet its largely unregulated nature causes severe environmental degradation in host communities. In Nigeria, where ASM accounts for over 90% of solid mineral production, the sector increasingly encroaches on agricultural land. Despite growing recognition of ASM's environmental impacts across West Africa, no study has yet applied the novel SDGSAT‐1 satellite purpose‐built for SDG monitoring at 10 m resolution to quantify ASM land degradation in Nigeria, and few studies simultaneously integrate multi‐sensor remote sensing, in‐situ soil contamination assessment, and hydrological transport modelling within a single integrated framework. This study addresses that gap. This study integrates SDGSAT‐1 Multispectral Imager and Landsat 9 imagery (both acquired 12 January 2024) with field‐based soil sampling (November 2023, dry season) and GIS hydrological modelling for Igbojaiye community, Oyo State, Nigeria. Eight vegetation, water and soil indices (NDVI, EVI, SAVI, TSAVI, TVI, NDWI, BSI) and Land Surface Temperature were derived from satellite imagery. Soil samples from eight mining‐affected points and four undisturbed control points were analyzed for physicochemical properties and heavy metals (Fe, Pb, Zn) using Atomic Absorption Spectrometry. Contamination Factor, Geoaccumulation Index, Pollution Load Index, and a Potential Ecological Risk Index were computed. A Digital Elevation Model was used to delineate contaminant transport pathways. ASM‐affected areas exhibited severe vegetation loss (68% of land within 500 m of mining pits classified as Degraded; mean NDVI −0.08 vs. +0.18 in control areas) and elevated Land Surface Temperature (mean 35.2°C ± 1.3°C vs. 28.9°C ± 0.8°C in controls). Mining soils showed markedly degraded physicochemical properties (organic carbon 4× lower, moisture 5× lower, pH more acidic). Lead exhibited very high contamination (mean Contamination Factor 10.0; Geoaccumulation Index Class 3: moderately to heavily contaminated) with a statistically significant distance‐decay gradient ( r = −0.60, p = 0.04). No metal exceeded WHO\u002FFAO soil limits in absolute terms. Hydrological modelling identified direct flow pathways from mining pits to downstream farmland and waterbodies. ASM in Igbojaiye initiates a cascade of compounding degradation, soil toxicity, microclimatic disruption, and hydrological pollutant dispersal that collectively undermine agricultural productivity and water security, directly impeding SDGs 2, 6, 13, and 15. This study represents one of the first applications of SDGSAT‐1 for ASM monitoring in Nigeria, establishing a replicable multi‐sensor framework for evidence‐based environmental governance of ASM in data‐scarce sub‐Saharan African contexts. Remediation priorities include sediment traps at mining pit outflows, phytoremediation with native metal‐tolerant species, and satellite‐based biannual monitoring protocols.","手工和小规模采矿（ASM）为全球超过4450万人提供了生计，但其基本上不受监管的性质对所在社区造成了严重的环境退化。在尼日利亚，手工和小规模采矿占固体矿产产量的90%以上，该行业日益侵占农业用地。尽管西非地区对ASM环境影响的认知不断增加，但尚无研究应用专为可持续发展目标监测而设计的10米分辨率SDGSAT-1卫星来量化尼日利亚的ASM土地退化，且很少有研究将多传感器遥感、原位土壤污染评估和水文输移建模同时整合于单一综合框架中。本研究填补了这一空白。本研究将SDGSAT-1多光谱成像仪和Landsat 9影像（均于2024年1月12日获取）与实地土壤采样（2023年11月，旱季）及GIS水文建模相结合，以尼日利亚奥约州Igbojaiye社区为研究区。从卫星影像中提取了八个植被、水体和土壤指数（NDVI、EVI、SAVI、TSAVI、TVI、NDWI、BSI）及地表温度。采用原子吸收光谱法对来自八个受采矿影响点和四个未受干扰对照点的土壤样品进行了理化性质和重金属（Fe、Pb、Zn）分析。计算了污染因子、地累积指数、污染负荷指数和潜在生态风险指数。利用数字高程模型划定了污染物输移路径。受ASM影响的区域表现出严重的植被丧失（采矿坑500米范围内68%的土地被归类为退化；NDVI均值为−0.08，对照区为+0.18）和升高的地表温度（均值35.2°C ± 1.3°C，对照区为28.9°C ± 0.8°C）。矿区土壤理化性质显著退化（有机碳低4倍，含水量低5倍，pH更偏酸性）。铅表现出极高的污染水平（污染因子均值10.0；地累积指数3级：中度至重度污染），并具有统计学显著的距离衰减梯度（r = −0.60，p = 0.04）。就绝对值而言，无金属超过WHO\u002FFAO土壤限值。水文建模识别出从采矿坑到下游农田和水体的直接径流路径。Igbojaiye的ASM引发了一系列叠加退化、土壤毒性、微气候扰动和水文污染物扩散的级联效应，共同导致","Sustainable Development","2026-09-14T00:00:00Z",82,{"impact":19,"substance":113,"depth":114,"authority":115,"freshness":21,"relevant":22,"comment":116},23,19,14,"首次将SDGSAT-1遥感与土壤、水文模型整合量化尼日利亚小规模采矿对农地的复合退化，方法可复用于数据稀缺地区农业环境治理，对农业遥感与耕地保护主题有聚合价值。",[118],{"name":109,"url":106},[27,28,120,30,121],"耕地保护","农业环境",{"code":48,"slug":123,"name":124,"path":125,"parent_code":126},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",[],{"status":129,"locked":14,"version":35,"method":36,"evidence":130},"classified",{"candidates":131,"agricultural_evidence":42},[132,137,144,149,154],{"code":48,"score":13,"supported":42,"title":133,"abstract":134,"tags":136},[],[52,50,135],"multispectral",[30],{"code":138,"score":101,"supported":42,"title":139,"abstract":140,"tags":143},"H2",[],[141,142],"土壤","soil",[],{"code":55,"score":145,"supported":42,"title":146,"abstract":147,"tags":148},3,[],[31],[],{"code":150,"score":22,"supported":14,"title":151,"abstract":152,"tags":153},"H1",[],[],[120],{"code":62,"score":22,"supported":14,"title":155,"abstract":156,"tags":157},[],[],[27],[159,160],"可持续发展 农业环境 数字乡村 耕地保护","可持续发展 农业环境","可持续发展农业环境数字乡村耕地保护-2533","10.1002\u002Fsd.71693",{"doi":162,"openalex_id":164,"authors":165,"venue":109,"cited_by_count":90,"oa_url":9,"card":184,"direction":96,"ingested_from":98},"W7213179964",[166,169,171,174,176,178,181],{"name":167,"orcid":168},"Sehinde Akinbiola","https:\u002F\u002Forcid.org\u002F0000-0001-9602-2420",{"name":170,"orcid":9},"Julia Blumensaat",{"name":172,"orcid":173},"Ayobami Salami","https:\u002F\u002Forcid.org\u002F0000-0002-7567-9762",{"name":175,"orcid":9},"Jürgen Runge",{"name":177,"orcid":9},"Tella Iyiola",{"name":179,"orcid":180},"Olamide Omolafe Ogunremi","https:\u002F\u002Forcid.org\u002F0000-0002-6776-4983",{"name":182,"orcid":183},"Ayomide Emmanuel Olubaju","https:\u002F\u002Forcid.org\u002F0000-0003-2118-6759",{"tldr":185,"method":186,"finding":187,"direction":96,"opportunity":188},"用SDGSAT-1等多源遥感与实地采样量化尼日利亚手工采矿对土地与农田的环境退化。","SDGSAT-1与Landsat 9影像、8种植被水体土壤指数、土壤重金属AAS","矿区植被严重退化、地表温度升高、铅污染极重，并存在向农田与水体扩散的水文通道。","可将SDGSAT-1 10米影像与土壤、水文模型耦合，构建矿区农田退化与重金属风险的早期预警框架。","2026-09-15T23:30:20.829150Z",{"id":191,"title":192,"url":193,"summary":194,"summary_zh":195,"content":9,"source_name":196,"source_url":193,"published_at":197,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":198,"score_detail":199,"sources":203,"tags":205,"primary_topic":209,"secondary_topics":214,"classification":221,"search_phrases":271,"slug":274,"view_count":22,"doi":275,"paper":276,"created_at":288},5086,"Artificial Intelligence for Environmental Sustainability and Climate Change Mitigation: Recent Advances, Challenges, and Future Perspectives","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23399627","Abstract Climate change and environmental degradation have emerged as two of the most pressing global challenges of the twenty-first century, threatening ecosystems, biodiversity, human health, and sustainable development. Artificial Intelligence (AI) has transformed environmental research and management by enabling advanced data analytics, real-time monitoring, predictive modeling, and intelligent decision-support systems. This review critically examines the applications of AI in addressing environmental and climate-related challenges, including climate forecasting, air and water quality monitoring, biodiversity conservation, precision agriculture, renewable energy optimization, carbon emission assessment, waste management, and disaster risk reduction. Furthermore, The review also identifies current research gaps and highlights future opportunities for developing transparent, energy-efficient, and explainable AI systems that support evidence-based environmental decisionmaking. It concludes that AI has become a transformative tool for accelerating progress toward climate change mitigation, environmental sustainability, and the achievement of the United Nations Sustainable Development Goals (SDGs). Strengthening interdisciplinary collaboration, responsible AI governance, and policy support will be essential to maximize the environmental benefits of AI while minimizing its ecological footprint. Keywords: Artificial Intelligence; Climate Change; Environmental Sustainability; Machine Learning; Deep Learning; Remote Sensing; Internet of Things; Renewable Energy; Biodiversity Conservation; Sustainable Development Goals.","摘要 气候变化和环境退化已成为二十一世纪最紧迫的全球性挑战之一，威胁着生态系统、生物多样性、人类健康和可持续发展。人工智能（AI）通过实现先进的数据分析、实时监测、预测建模和智能决策支持系统，改变了环境研究与管理的方式。本文综述批判性地考察了AI在应对环境和气候相关挑战中的应用，包括气候预测、空气和水质监测、生物多样性保护、精准农业、可再生能源优化、碳排放评估、废物管理和灾害风险降低。此外，本综述还指出了当前的研究空白，并强调了未来开发透明、节能且可解释的AI系统的机遇，以支持基于证据的环境决策。文章得出结论，AI已成为加速气候变化减缓、环境可持续发展以及实现联合国可持续发展目标（SDGs）的变革性工具。加强跨学科合作、负责任的AI治理和政策支持，对于最大化AI的环境效益并同时最小化其生态足迹至关重要。关键词：人工智能；气候变化；环境可持续性；机器学习；深度学习；遥感；物联网；可再生能源；生物多样性保护；可持续发展目标。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-15T00:00:00Z",70,{"impact":19,"substance":200,"depth":41,"authority":17,"freshness":201,"relevant":22,"comment":202},16,9,"综述性论文，覆盖AI在气候预测、精准农业、遥感监测等方向的应用，与农业信息化相关但偏宏观综述，增量有限。",[204],{"name":196,"url":193},[206,207,28,208,30],"智慧农业","农业人工智能","气候变化",{"code":210,"slug":211,"name":212,"path":213,"parent_code":126},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",[215],{"code":216,"slug":217,"name":218,"path":219,"parent_code":220},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",{"status":129,"locked":14,"version":35,"method":36,"evidence":222},{"candidates":223,"agricultural_evidence":42},[224,233,238,246,250,257,264],{"code":210,"score":19,"supported":42,"title":225,"abstract":227,"tags":232},[226],"artificial intelligence",[228,229,230,231,226],"深度学习","机器学习","machine learning","deep learning",[207],{"code":216,"score":41,"supported":42,"title":234,"abstract":236,"tags":237},[235],"climate change",[208,235],[208],{"code":239,"score":240,"supported":42,"title":241,"abstract":242,"tags":245},"I1",7,[],[243,244],"精准农业","precision agriculture",[206],{"code":48,"score":240,"supported":42,"title":247,"abstract":248,"tags":249},[],[52,50],[30],{"code":251,"score":101,"supported":42,"title":252,"abstract":253,"tags":256},"H4",[],[254,255],"碳排放","carbon emission",[],{"code":258,"score":101,"supported":42,"title":259,"abstract":260,"tags":263},"H6",[],[261,262],"生物多样性","biodiversity",[],{"code":265,"score":101,"supported":42,"title":266,"abstract":267,"tags":270},"I5",[],[268,269],"物联网","internet of things",[],[272,273],"AI 气候变化","人工智能 环境可持续","AI气候变化-5086","10.5281\u002Fzenodo.23399627",{"doi":275,"openalex_id":277,"authors":278,"venue":196,"cited_by_count":90,"oa_url":193,"card":281,"direction":287,"ingested_from":98},"W7222092855",[279],{"name":280,"orcid":9},"Deepmala Suryavanshi",{"tldr":282,"method":283,"finding":284,"direction":285,"opportunity":286},"综述AI在气候变化缓解与环境可持续领域的应用进展、挑战与未来方向。","文献综述，涵盖机器学习、深度学习、遥感、物联网等技术应用。","AI已成为推动气候减缓与可持续发展目标实现的变革性工具。","农业绿色发展与碳","可聚焦可解释、低能耗AI在农业碳减排与精准环境决策中的落地研究。","智慧农业 \u002F 农业物联网","2026-10-16T23:30:15.059867Z",{"id":290,"title":291,"url":292,"summary":293,"summary_zh":9,"content":9,"source_name":294,"source_url":9,"published_at":9,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":295,"score_detail":296,"sources":299,"tags":301,"primary_topic":9,"secondary_topics":305,"classification":306,"search_phrases":320,"slug":323,"view_count":90,"doi":9,"paper":324,"created_at":333},4964,"数字技术重构乡村旅游产业链的逻辑与路径","http:\u002F\u002Fwww.qikanzj.com\u002Fhek\u002Fshkxj\u002F503.html","李鹏(平顶山学院旅游与规划学院;中南财经政法大学工商管理学院)发表的研究基于'技术赋能—资源整合—价值共享'逻辑主线,系统阐释数字技术通过数据要素驱动、生态化网络协同、可行能力提升与四链协同机制,推动乡村旅游产业链从效率优先向公平与效率并重转型。大数据平台的动态供需匹配、区块链技术的可信追溯体系、AIoT设备的自动化运营,有效破解信息孤岛与资源错配难题。可行能力理论的引入,为技术赋能与共同富裕的传导机制提供理论支撑。","《社会科学家》投稿参考范文 2026 年 5 月",66,{"impact":200,"substance":19,"depth":297,"authority":20,"freshness":71,"relevant":22,"comment":298},17,"理论性论文，从技术赋能视角阐释乡村旅游产业链数字化转型，对数字乡村研究有参考价值，但时效性偏弱。",[300],{"name":294,"url":292},[27,302,303,29,304],"共同富裕","区块链","AIoT",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":307},{"candidates":308,"agricultural_evidence":14,"reason":319},[309,313],{"code":40,"score":20,"supported":42,"title":310,"abstract":311,"tags":312},[29],[29],[29,302],{"code":62,"score":314,"supported":42,"title":315,"abstract":316,"tags":318},4,[],[317],"数据要素",[27],"农业相关性需要复核",[321,322],"平顶山学院 数字技术 乡村旅游","乡村旅游 共同富裕 数字乡村 区块链","平顶山学院数字技术乡村旅游-4964",{"doi":9,"openalex_id":9,"authors":325,"venue":9,"cited_by_count":90,"oa_url":9,"card":326,"direction":330,"ingested_from":332},[],{"tldr":327,"method":328,"finding":329,"direction":330,"opportunity":331},"阐释数字技术通过四链协同机制重构乡村旅游产业链、推动公平与效率并重的逻辑与路径。","基于'技术赋能—资源整合—价值共享'框架，结合大数据、区块链、AIoT与可行能力","数字技术破解信息孤岛与资源错配，推动乡村旅游产业链从效率优先转向公平与效率并重。","数字乡村与农业信息化","可实证检验大数据供需匹配、区块链追溯对农户增收与共同富裕的传导效应及区域异质性。","agent","2026-10-15T00:06:08.407354Z",{"id":335,"title":336,"url":337,"summary":338,"summary_zh":339,"content":9,"source_name":340,"source_url":337,"published_at":341,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":342,"score_detail":343,"sources":345,"tags":347,"primary_topic":9,"secondary_topics":351,"classification":352,"search_phrases":382,"slug":385,"view_count":71,"doi":386,"paper":387,"created_at":415},4902,"Delineation of groundwater potential zones in Chalan Beel (inland wetland) for sustainable ecosystem in Lower Atrai River Basin NW Bangladesh","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs44288-026-00768-z","Groundwater plays a critical role in sustaining the ecological integrity and water security of the Chalan Beel, the largest inland wetland (5097 km2) in the Lower Atrai River Basin of northwestern Bangladesh. This study delineates groundwater potential zones (GWPZs) using an integrated Geographic Information System (GIS), Remote Sensing (RS), and Analytical Hierarchy Process (AHP) approach. Seventeen hydrogeological, topographic, climatic, and environmental thematic layers, including rainfall, soil type, soil permeability, groundwater level, aquifer depth, land use\u002Fland cover, geomorphology, drainage density, NDVI, and NDWI, were analyzed to evaluate groundwater recharge and storage potential. The AHP results indicate that rainfall (20%), soil type (16%), and soil permeability (13%) are the most influential factors controlling groundwater occurrence and recharge. The generated GWPZ map classified the study area into three categories: very high (65 km2, 1%), medium (3933 km2, 77%), and low (1099 km2, 22%) potential zones. Temporal NDVI analysis (1990–2024) revealed substantial changes in vegetation and moisture conditions, with the moderate NDVI class increasing from 45 to 67%, indicating improved vegetation cover and its strong association with groundwater recharge potential. The identified high- and moderate-potential zones provide important information for groundwater development, irrigation planning, and ecosystem conservation. The study offers a scientific basis for sustainable groundwater-dependent ecosystem (SGDE) management and supports informed decision-making for water-resource planning under increasing climatic variability and anthropogenic pressures in northwestern Bangladesh.","地下水在维持孟加拉国西北部阿特拉伊河下游流域最大内陆湿地——查兰比尔湿地（5097平方公里）的生态完整性和水安全方面发挥着关键作用。本研究采用地理信息系统（GIS）、遥感（RS）和层次分析法（AHP）相结合的综合方法，圈定地下水潜力区（GWPZs）。通过分析十七个水文地质、地形、气候和环境专题图层，包括降雨量、土壤类型、土壤渗透性、地下水位、含水层深度、土地利用\u002F土地覆盖、地貌、河网密度、归一化植被指数（NDVI）和归一化水体指数（NDWI），评估地下水补给和储存潜力。层次分析法结果表明，降雨量（20%）、土壤类型（16%）和土壤渗透性（13%）是控制地下水赋存和补给的最主要因素。生成的地下水潜力区图将研究区划分为三类：极高潜力区（65平方公里，1%）、中等潜力区（3933平方公里，77%）和低潜力区（1099平方公里，22%）。时序NDVI分析（1990—2024年）揭示了植被和湿度条件的显著变化，中等NDVI等级从45%增至67%，表明植被覆盖改善及其与地下水补给潜力的密切关联。所识别的高潜力和中等潜力区为地下水开发、灌溉规划和生态系统保护提供了重要信息。本研究为可持续地下水依赖型生态系统（SGDE）管理提供了科学依据，并为孟加拉国西北部在气候变率加剧和人为压力增大背景下的水资源规划提供了知情决策支持。","Discover Geoscience","2026-10-12T00:00:00Z",67,{"impact":21,"substance":18,"depth":297,"authority":20,"freshness":21,"relevant":22,"comment":344},"该研究运用GIS、遥感和AHP方法评估孟加拉国湿地地下水潜力，方法新颖、数据详实，对农业水资源管理和生态保护有参考价值，但地域性较强，影响范围有限。",[346],{"name":340,"url":337},[348,30,31,349,350],"水资源管理","地下水潜力","湿地生态",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":353},{"candidates":354,"agricultural_evidence":14,"reason":319},[355,364,370,374,378],{"code":356,"score":357,"supported":42,"title":358,"abstract":359,"tags":363},"H3",11,[],[360,361,362],"灌溉","水资源","irrigation",[349,348],{"code":55,"score":13,"supported":42,"title":365,"abstract":366,"tags":369},[],[367,31,368],"地理信息","geographic information",[31],{"code":48,"score":240,"supported":42,"title":371,"abstract":372,"tags":373},[],[52,50],[30],{"code":138,"score":101,"supported":42,"title":375,"abstract":376,"tags":377},[],[141,142],[],{"code":258,"score":22,"supported":14,"title":379,"abstract":380,"tags":381},[],[],[350],[383,384],"Chalan Beel 湿地 地下水","孟加拉国 地下水潜力 遥感","ChalanBeel湿地地下水-4902","10.1007\u002Fs44288-026-00768-z",{"doi":386,"openalex_id":388,"authors":389,"venue":340,"cited_by_count":90,"oa_url":409,"card":410,"direction":96,"ingested_from":98},"W7221572372",[390,392,395,398,401,403,405,407],{"name":391,"orcid":9},"Wahedul Alam",{"name":393,"orcid":394},"Iftekharul Islam","https:\u002F\u002Forcid.org\u002F0000-0002-8025-5729",{"name":396,"orcid":397},"Chowdhury Sarwar Jahan","https:\u002F\u002Forcid.org\u002F0009-0008-1316-4684",{"name":399,"orcid":400},"Md. Nahid Hassan","https:\u002F\u002Forcid.org\u002F0009-0000-9210-383X",{"name":402,"orcid":9},"Mostofa Kamal",{"name":404,"orcid":9},"Fayed Islam Tishad",{"name":406,"orcid":9},"Quamrul Hasan Mazumder",{"name":408,"orcid":9},"Md. Golam Mostafa","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs44288-026-00768-z.pdf",{"tldr":411,"method":412,"finding":413,"direction":96,"opportunity":414},"用GIS、遥感和AHP划分孟加拉Chalan Beel湿地地下水潜力区，支持可持续生态系统管理。","集成GIS、RS与AHP，分析17个水文地质、地形、气候和环境图层。","降雨、土壤类型和渗透性最关键；高潜力区仅1%，中潜力区占77%。","可结合时序遥感与机器学习提升GWPZ动态制图精度，并耦合灌溉与生态需水决策。","2026-10-14T23:30:30.477606Z",{"id":417,"title":418,"url":419,"summary":420,"summary_zh":9,"content":421,"source_name":422,"source_url":9,"published_at":423,"category":424,"cover_url":9,"hotness":13,"is_selected":14,"score":425,"score_detail":426,"sources":429,"tags":431,"primary_topic":435,"secondary_topics":439,"classification":440,"search_phrases":456,"slug":459,"view_count":90,"doi":9,"paper":9,"created_at":460},4613,"重庆445万块耕地实现赋码管理:45项信息一图统揽、一码查地","https:\u002F\u002Fnyncw.cq.gov.cn\u002Fzwxx_161\u002Fzwdt\u002F202610\u002Ft20261009_16204300.html","重庆市依托巴渝良田应用系统,整合农业农村、自然资源、水利、林业等部门123个图层数据,将全市445万块耕地纳入农地一张图,每块地赋唯一编码、关联地类、质量、坡度、权属、高标准农田建设情况等45项信息。AI识别+无人机巡查可半小时巡完上千亩耕地、秒级预警隐蔽问题;建设资金专户管理与预算一体化平台互联互通,实现资金流、项目流、业务流三流贯通全程可追溯。","本报讯  10月8日，记者从市农业农村委获悉，我市依托“巴渝良田”应用系统，全市445万块耕地实现“赋码管理”，通过整合123个图层数据，构建起“农地一张图”，实现地类、质量、坡度等45项信息“一图统揽、一码查地”。\n\n10月8日，在垫江县桂东街道龙田村，种粮大户李伦明站在田埂上，掏出手机对着高标准农田建设项目标识牌上的二维码轻轻一扫，屏幕上立刻弹出地块信息：面积326亩，坡度小于15度，土壤类型为紫色土，属于2025年高标准农田改造提升项目区，施工单位、监理单位、建设工期清清楚楚。“有了这个‘电子身份证’，哪块地什么情况，一目了然。”李伦明说。\n\n李伦明口中的“电子身份证”，正是我市全面启动的“巴渝良田”应用系统。该系统通过整合农业农村、自然资源、水利、林业等部门的123个图层数据，将全市445万块耕地纳入“农地一张图”，每块地都被赋予唯一编码，关联地类、质量、坡度、权属、高标准农田建设情况等45项信息。管理人员和群众扫码即可查地，彻底告别了过去翻图纸、跑现场、层层问询的传统模式。\n\n垫江县农业农村委工作人员王强打开“巴渝良田”系统，轻点鼠标，一架无人机沿着预设航线对在建的高标准农田项目进行空中巡查。高清画面实时回传，AI很快识别出某处沟渠铺设厚度不达标。系统立即生成整改工单，同步推送给施工方、监理方和项目负责人。“以前发现这种隐蔽问题，得靠人下到田里一处处量，现在‘空中慧眼’半小时就能巡完上千亩耕地，问题秒级预警、闭环整改。”王强说。\n\n除了“管地”，数字监管还“管钱”。我市将所有高标准农田项目建设资金纳入专户管理，依托“巴渝良田”系统与预算管理一体化平台互联互通，让资金流、项目流、业务流“三流贯通”，每一笔资金拥有唯一“身份证号”，实现全程可追溯、可核验。","重庆市农业农村委员会 2026-10-09","2026-10-09T00:00:00Z","报道",81,{"impact":427,"substance":427,"depth":297,"authority":17,"freshness":21,"relevant":22,"comment":428},22,"省级耕地数字化管理落地案例，445万块耕地赋码、123图层整合与AI无人机巡查等细节扎实，具备主题聚合与检索价值。",[430],{"name":422,"url":419},[27,432,120,30,433,434],"高标准农田","农地一张图","赋码管理",{"code":150,"slug":436,"name":437,"path":438,"parent_code":220},"farmland","耕地保护与高标准农田","\u002Ftopic\u002Fenvironment\u002Ffarmland",[],{"status":129,"locked":14,"version":35,"method":36,"evidence":441},{"candidates":442,"agricultural_evidence":42},[443,448,452],{"code":150,"score":21,"supported":42,"title":444,"abstract":445,"tags":447},[],[432,446],"农田建设",[120,432],{"code":48,"score":22,"supported":14,"title":449,"abstract":450,"tags":451},[],[],[30],{"code":62,"score":22,"supported":14,"title":453,"abstract":454,"tags":455},[],[],[27],[457,458],"重庆 巴渝良田 赋码管理","重庆 高标准农田 一码查地","重庆巴渝良田赋码管理-4613","2026-10-11T00:09:35.623381Z",{"id":462,"title":463,"url":464,"summary":465,"summary_zh":466,"content":9,"source_name":467,"source_url":464,"published_at":468,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":198,"score_detail":469,"sources":472,"tags":474,"primary_topic":478,"secondary_topics":482,"classification":483,"search_phrases":498,"slug":501,"view_count":71,"doi":502,"paper":503,"created_at":525},4596,"Spatio-temporal analysis of land use\u002Fland cover (LULC) changes using GIS techniques in Upper Bhavani River Basin, Nilgiri Hills, India","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs44274-026-00915-w","The study investigates the land use and land cover (LULC) changes in Upper Bhavani River basin of the Western Ghats over the past four decades (1989–2019) using remote sensing (RS) and geographic information system (GIS) techniques. It also explores the correlation between LULC changes and population growth during this period. The study highlights the importance of mapping and monitoring LULC changes in developing better management strategies for the sustainable development of natural resources. The iterative self-organising data analysis technique was used for unsupervised classification to create LULC maps of the riverbasin.The study identified and categorised seven LULC categories, namely water bodies, deciduous forest, evergreen forest, agriculture, plantation, barren land, and settlement. The results of the study showed a complex series of modifications in the LULC in the study basin over the past forty years due to policy changes and conventional agroforestry systems. The study revealed a decrease in agriculture, evergreen forest, and deciduous forest by − 6.88%, − 31.42%, and − 9.88%, respectively, while water bodies, plantations, barren land, and settlement area increased by 7.06%, 17.64%, 13.29%, and 10.23%, respectively. The findings of the study provide insights into specific changes in LULC and help predict which land uses will be impacted in the future. The overall accuracy of the classification was0.73.Overall, this study emphasised the importance of monitoring and mapping LULC changes using RS and GIS techniques to develop better management strategies for the sustainable development of natural resources. The findings of the study were useful for policymakers, land-use planners, and conservationists in developing effective strategies to maintain the ecological balance of the Western Ghats.","本研究利用遥感（RS）和地理信息系统（GIS）技术，探讨了过去四十年（1989—2019年）西高止山脉上巴瓦尼河流域的土地利用与土地覆盖（LULC）变化，并分析了该时期LULC变化与人口增长之间的相关性。研究强调了制图和监测LULC变化对于制定自然资源可持续发展管理策略的重要性。采用迭代自组织数据分析技术进行非监督分类，以生成该流域的LULC图。研究识别并划分了七类LULC，分别为水体、落叶林、常绿林、农业、种植园、裸地和居民点。研究结果表明，受政策变化和传统农林业系统的影响，过去四十年研究流域的LULC经历了一系列复杂的改变。研究揭示，农业、常绿林和落叶林分别减少了−6.88%、−31.42%和−9.88%，而水体、种植园、裸地和居民点面积分别增加了7.06%、17.64%、13.29%和10.23%。研究结果揭示了LULC的具体变化，有助于预测未来哪些土地利用类型将受到影响。分类总体精度为0.73。总体而言，本研究强调了利用RS和GIS技术监测和制图LULC变化对于制定自然资源可持续发展管理策略的重要性。研究结果有助于政策制定者、土地利用规划者和保护主义者制定有效策略，以维持西高止山脉的生态平衡。","Discover Environment","2026-10-08T00:00:00Z",{"impact":17,"substance":470,"depth":297,"authority":20,"freshness":21,"relevant":22,"comment":471},20,"基于遥感与GIS的四十年土地利用变化实证研究，数据翔实、结论可靠，对流域生态管理与土地规划有参考价值，但属区域案例研究，公共影响层级有限。",[473],{"name":467,"url":464},[30,475,31,476,477],"土地利用","流域治理","西高止山",{"code":55,"slug":479,"name":480,"path":481,"parent_code":126},"gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",[],{"status":129,"locked":14,"version":35,"method":36,"evidence":484},{"candidates":485,"agricultural_evidence":42},[486,490,494],{"code":55,"score":19,"supported":42,"title":487,"abstract":488,"tags":489},[31],[367,31,368],[31],{"code":48,"score":240,"supported":42,"title":491,"abstract":492,"tags":493},[],[52,50],[30],{"code":258,"score":22,"supported":14,"title":495,"abstract":496,"tags":497},[],[],[475],[499,500],"土地利用 流域治理 西高止山 遥感监测","土地利用 流域治理","土地利用流域治理西高止山遥感监测-4596","10.1007\u002Fs44274-026-00915-w",{"doi":502,"openalex_id":504,"authors":505,"venue":467,"cited_by_count":90,"oa_url":519,"card":520,"direction":96,"ingested_from":98},"W7221028855",[506,508,511,513,515,517],{"name":507,"orcid":9},"Sapna Kinattinkara",{"name":509,"orcid":510},"Thangavelu Arumugam","https:\u002F\u002Forcid.org\u002F0000-0002-2984-283X",{"name":512,"orcid":9},"Karpagamani Vellingiri",{"name":514,"orcid":9},"Manoj Kumar Patley",{"name":516,"orcid":9},"Manimaran Arumugam",{"name":518,"orcid":9},"Nigatu Wondrade","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs44274-026-00915-w.pdf",{"tldr":521,"method":522,"finding":523,"direction":96,"opportunity":524},"用遥感与GIS分析印度上巴瓦尼河流域1989-2019年土地利用变化及其与人口增长的关系。","遥感与GIS、ISODATA非监督分类，划分七类LULC，总体精度0.73。","农业、常绿林和落叶林分别减少6.88%、31.42%、9.88%，水体、种植园、裸地和居民地增加。","可结合高精度分类与人口、政策数据，构建LULC变化驱动力模型并预测未来情景。","2026-10-10T23:30:30.955766Z"]