[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4368":3,"related-4368":80},{"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":52,"slug":55,"view_count":56,"doi":57,"paper":58,"created_at":79},4368,"Dinâmica de supressão e recuperação florestal por meio do índice NDVI: um estudo de caso em Realeza – PR (2016 - 2026)","https:\u002F\u002Fdoi.org\u002F10.4025\u002Fbolgeogr.v44.a2026.e83759","O constante avanço agropecuário sobre o bioma da Mata Atlântica exige um monitoramento contínuo da integridade ecológica dos remanescentes florestais locais. Este estudo avaliou a dinâmica espaço-temporal da cobertura florestal no município de Realeza-PR, entre os anos de 2016 e 2026, quantificando as taxas de supressão e ganho florestal analisando a influência do relevo e da fragmentação na paisagem. A metodologia aplicada baseou-se em séries temporais orbitais do sensor Sentinel-2 (MSI), com a calibração de limiares locais do índice NDVI ( e ) para diferenciar a floresta nativa estável da agricultura sazonal. A declividade foi extraída a partir do Modelo Digital de Elevação ALOS PALSAR, submetido à compatibilização matricial via software GDAL e ao cálculo subsequente de métricas de paisagem, realizados em ambiente R. Os resultados obtidos apontaram um ganho líquido total de 775,81 hectares na cobertura florestal durante a década analisada. Apesar do saldo quantitativo positivo, as métricas estruturais registraram um aumento de 8,79% no Número de Manchas (NP) e uma queda de 1,14 pontos percentuais no Índice de Agregação (AI). Desse modo, conclui-se que o ganho absoluto de área florestal mascarou um processo contínuo de retalhamento e fragmentação dos corredores ecológicos, comprometendo a qualidade e a resiliência ecológica da paisagem a longo prazo.","农业活动对马塔大西洋森林生物群落的持续扩张，要求对当地森林残余的生态完整性进行持续监测。本研究评估了雷阿莱扎市（巴拉那州）2016年至2026年间森林覆盖的时空动态，量化了森林抑制与增益速率，并分析了地形和破碎化对景观的影响。所采用的方法基于Sentinel-2（MSI）传感器的轨道时间序列，通过校准NDVI指数的局部阈值（和）以区分稳定原生林与季节性农业。坡度数据提取自ALOS PALSAR数字高程模型，经GDAL软件进行矩阵兼容化处理，随后在R环境中计算景观指标。结果表明，在分析的十年间，森林覆盖净增总面积达775.81公顷。尽管数量上呈正平衡，但结构指标显示斑块数量（NP）增加了8.79%，聚集度指数（AI）下降了1.14个百分点。因此，结论是森林面积的绝对增益掩盖了生态廊道持续被切割和破碎化的过程，损害了景观的长期质量和生态韧性。",null,"Boletim de Geografia","2026-10-04T00:00:00Z","论文",10,false,66,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},8,20,17,12,9,1,"基于Sentinel-2与NDVI的十年森林动态监测案例，方法扎实、数据具体，但属地方性研究，公共影响有限。",[25],{"name":10,"url":6},[27,28,29,30,31],"农业遥感","NDVI","生态保护","遥感监测","森林覆盖",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":37},"unclassified","2026-10-22.2","rules",{"candidates":38,"agricultural_evidence":50,"reason":51},[39,45],{"code":40,"score":41,"supported":14,"title":42,"abstract":43,"tags":44},"I3",3,[],[],[28,27,30],{"code":46,"score":22,"supported":14,"title":47,"abstract":48,"tags":49},"H6",[],[],[29],true,"缺少足够的主题证据",[53,54],"Realeza PR NDVI 森林","Sentinel-2 森林覆盖 监测","RealezaPRNDVI森林-4368",2,"10.4025\u002Fbolgeogr.v44.a2026.e83759",{"doi":57,"openalex_id":59,"authors":60,"venue":10,"cited_by_count":70,"oa_url":71,"card":72,"direction":76,"ingested_from":78},"W7220453253",[61,64,67],{"name":62,"orcid":63},"Marcos Henrique Tomazini Mikoanski","https:\u002F\u002Forcid.org\u002F0000-0003-0715-0581",{"name":65,"orcid":66},"Francieli Alves Teixeira","https:\u002F\u002Forcid.org\u002F0009-0000-0055-9718",{"name":68,"orcid":69},"Fernando César Manosso","https:\u002F\u002Forcid.org\u002F0000-0003-2568-7403",0,"https:\u002F\u002Fperiodicos.uem.br\u002Fojs\u002Findex.php\u002FBolGeogr\u002Farticle\u002Fdownload\u002F83759\u002F751375163932",{"tldr":73,"method":74,"finding":75,"direction":76,"opportunity":77},"基于Sentinel-2 NDVI时序评估2016-2026年Realeza-PR森林消长与破碎化动","Sentinel-2 NDVI阈值校准、ALOS PALSAR坡度、R景观指标分","森林净增775.81公顷，但斑块数增8.79%、聚集度降1.14%，破碎化加剧。","农业遥感与作物表型","可结合多源遥感与景观连通性模型，探究农业扩张下森林恢复的生态质量与廊道功能。","openalex","2026-10-07T23:30:30.363166Z",{"total":81,"page":22,"page_size":81,"items":82},6,[83,158,221,267,332,400],{"id":84,"title":85,"url":86,"summary":87,"summary_zh":88,"content":9,"source_name":89,"source_url":86,"published_at":90,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":91,"score_detail":92,"sources":95,"tags":97,"primary_topic":9,"secondary_topics":100,"classification":101,"search_phrases":128,"slug":131,"view_count":22,"doi":132,"paper":133,"created_at":157},5012,"Evaluating Multi-Index Remote Sensing Frameworks for Drought Monitoring in Seasonally Dry Dipterocarp Deciduous Forests: A Case Study of Yok Don National Park, Vietnam","https:\u002F\u002Fdoi.org\u002F10.5755\u002Fj01.erem.82.3.44241","This study examined the effectiveness of the normalized difference vegetation index (NDVI), land surface temperature (LST), and their integrated indices in capturing dry-season drought dynamics in the dipterocarp deciduous forest ecosystems of Yok Don National Park from 2014 to 2024. Results indicate that individual remote-sensing indices captured drought processes but reflected different dimensions of drought conditions, with NDVI indicating canopy greenness and LST detecting early thermal stress. Integrated indices partially mitigated the limitations of single indicators during confirmed drought years by showing stronger correlations with reference data. The effectiveness of the integrated indices varied significantly depending on the integration framework. The spatial-normalization–based integration of NDVI and LST showed consistently strong correlations with surface evapotranspiration (ET), with correlation coefficients of approximately 0.8 across years, and was less sensitive to land-cover heterogeneity. In contrast, the triangular NDVI–LST space integration exhibited variable relationships with ET due to its sensitivity to edge definition and phenological shifts in dipterocarp deciduous forests. Phase-dependent behavior was observed, with integrated indices maintaining more stable relationships with ET during drying periods, while NDVI showed greater sensitivity under wetter conditions. The results also indicate that VHI demonstrated a more robust and stable performance than TVDI, suggesting its suitability as a primary indicator in heterogeneous forest environments. These findings provide practical guidance for drought monitoring in heterogeneous forest landscapes by identifying adaptive integration strategies tailored to different hydroclimatic conditions.","本研究考察了归一化差异植被指数（NDVI）、地表温度（LST）及其综合指数在捕捉2014至2024年 Yok Don 国家公园龙脑香落叶林生态系统旱季干旱动态方面的有效性。结果表明，单一遥感指数能够捕捉干旱过程，但反映的是干旱状况的不同维度，其中NDVI指示冠层绿度，LST探测早期热胁迫。综合指数在已确认的干旱年份中通过与参考数据表现出更强的相关性，部分缓解了单一指标的局限性。综合指数的有效性因集成框架的不同而差异显著。基于空间标准化的NDVI与LST集成与地表蒸散发（ET）表现出持续较强的相关性，各年份相关系数约为0.8，且对土地覆盖异质性较不敏感。相比之下，三角NDVI–LST空间集成与ET的关系多变，这是由于其边缘定义及龙脑香落叶林物候变化较为敏感。研究观察到相位依赖行为，综合指数在干旱期与ET保持更稳定的关系，而NDVI在较湿润条件下表现出更高的敏感性。结果还表明，VHI比TVDI表现出更稳健和稳定的性能，表明其适合作为异质森林环境中的主要指标。这些发现通过识别适应不同水文气候条件的自适应集成策略，为异质森林景观的干旱监测提供了实践指导。","Environmental Research Engineering and Management","2026-10-13T00:00:00Z",67,{"impact":17,"substance":18,"depth":19,"authority":93,"freshness":21,"relevant":22,"comment":94},13,"越南干旱区遥感监测方法学研究，方法新颖数据扎实，对农业遥感干旱监测有参考价值，但属区域性案例，公共影响有限。",[96],{"name":89,"url":86},[27,28,30,98,99],"干旱监测","森林生态",[],{"status":102,"locked":14,"version":35,"method":36,"evidence":103},"needs_review",{"candidates":104,"agricultural_evidence":14,"reason":127},[105,113,120],{"code":106,"score":107,"supported":50,"title":108,"abstract":110,"tags":112},"H7",15,[109],"drought",[111,109],"干旱",[98],{"code":40,"score":114,"supported":50,"title":115,"abstract":117,"tags":119},14,[116],"remote sensing",[118],"遥感",[28,27,30],{"code":121,"score":81,"supported":50,"title":122,"abstract":123,"tags":126},"H3",[],[124,125],"蒸散发","evapotranspiration",[],"农业相关性需要复核",[129,130],"Yok Don 国家公园 遥感 干旱","NDVI LST 干旱监测 森林","YokDon国家公园遥感干旱-5012","10.5755\u002Fj01.erem.82.3.44241",{"doi":132,"openalex_id":134,"authors":135,"venue":89,"cited_by_count":70,"oa_url":151,"card":152,"direction":76,"ingested_from":78},"W7221717828",[136,139,142,144,146,148],{"name":137,"orcid":138},"Mai Son Le","https:\u002F\u002Forcid.org\u002F0000-0002-5360-7119",{"name":140,"orcid":141},"Ha Thu Nguyen","https:\u002F\u002Forcid.org\u002F0000-0002-4490-308X",{"name":143,"orcid":9},"Hung Manh Nguyen",{"name":145,"orcid":9},"Huy Xuan Chu",{"name":147,"orcid":9},"Hoa Thanh Nguyen",{"name":149,"orcid":150},"Duy-Toan Dao","https:\u002F\u002Forcid.org\u002F0009-0003-6057-4436","https:\u002F\u002Ferem.ktu.lt\u002Findex.php\u002Ferem\u002Farticle\u002Fdownload\u002F44241\u002F23724",{"tldr":153,"method":154,"finding":155,"direction":76,"opportunity":156},"评估NDVI、LST及其集成指数在越南干旱季风落叶林干旱监测中的效果。","利用2014-2024年NDVI、LST遥感数据，构建空间归一化和三角空间集成指","空间归一化集成指数与ET相关性稳定（约0.8），VHI比TVDI更稳健，集成效果依赖框架。","可探索自适应集成框架在不同水热条件和异质景观下的干旱监测普适性。","2026-10-15T23:30:32.336137Z",{"id":159,"title":160,"url":161,"summary":162,"summary_zh":163,"content":9,"source_name":164,"source_url":161,"published_at":165,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":166,"score_detail":167,"sources":170,"tags":172,"primary_topic":9,"secondary_topics":175,"classification":176,"search_phrases":199,"slug":202,"view_count":70,"doi":203,"paper":204,"created_at":220},5481,"Comparison of Satellite-Based Drought Monitoring Indicators (NDVI, NDMI, and LST): The Case of the Northern Aegean Drainage Basin","https:\u002F\u002Fdoi.org\u002F10.30910\u002Fturkjans.2010067","Thanks to advancements in technology, satellite remote sensing provides a valuable means of monitoring the spatial and temporal responses of vegetation, surface moisture conditions, and land surface temperature to drought. Its capacity to monitor large areas with high temporal continuity significantly eliminates the spatial limitations of ground observations. This allows for the simultaneous and comparable analysis of key variables such as vegetation dynamics, soil moisture, and land surface temperature. Indicators such as the Normalized Difference Vegetation Index (NDVI), Normalized Difference Moisture Index (NDMI), and Land Surface Temperature (LST) provide complementary information on vegetation condition, surface moisture, and thermal stress, thereby supporting drought monitoring. This integrated monitoring framework supports the sustainable management of water resources while strengthening the scientific basis of decision-making processes, significantly contributing to disaster risk reduction. This study comparatively evaluates NDVI, NDMI, and LST values derived from Landsat 8 data obtained in March, April, and May between 2019 and 2022, and examines the relationships between these variables across the Northern Aegean Drainage Basin (NADB). The results show increasingly strong negative relationships between LST and the spectral indices from March to May, with the strongest correlations observed in May for NDMI–LST (r = −0.78) and NDVI–LST (r = −0.74). The NDMI-LST relationship can be said to be slightly better than the NDVI-LST relationship. The correlation coefficient between NDMI and NDVI was calculated to be lowest in March at r=0.54 and highest in May at r=0.90. It was determined that areas with high LST values had lower NDVI and NDMI values. Drought monitoring using remote sensing methods is important in terms of time and accuracy, but it is not sufficient on its own. In addition to problems arising from satellite imagery, land surfaces are affected by short-term climatic factors. Therefore, the indices obtained from satellite images must be evaluated together with meteorological and terrestrial data. However, it is thought that satellite-based drought monitoring will produce more reliable results under current technological and methodological conditions.","得益于技术进步，卫星遥感为监测植被、地表水分状况和地表温度对干旱的时空响应提供了重要手段。其大范围、高时间连续性的监测能力显著消除了地面观测的空间局限性，使得植被动态、土壤水分和地表温度等关键变量能够被同步、可比地分析。归一化差异植被指数（NDVI）、归一化差异水分指数（NDMI）和地表温度（LST）等指标提供了关于植被状况、地表水分和热胁迫的互补信息，从而支持干旱监测。这一综合监测框架既支持水资源的可持续管理，又增强了决策过程的科学基础，对减少灾害风险具有重要贡献。本研究对2019年至2022年3月、4月和5月获取的Landsat 8数据所衍生的NDVI、NDMI和LST值进行了比较评估，并考察了北爱琴海流域（NADB）内这些变量之间的关系。结果表明，从3月到5月，LST与光谱指数之间的负相关关系日益增强，5月观测到最强相关性，NDMI–LST（r = −0.78）和NDVI–LST（r = −0.74）。可以认为NDMI-LST关系略优于NDVI-LST关系。NDMI与NDVI之间的相关系数在3月最低（r=0.54），5月最高（r=0.90）。研究确定，LST值较高的区域其NDVI和NDMI值较低。利用遥感方法进行干旱监测在时间和精度方面具有重要意义，但仅靠其自身并不充分。除卫星影像带来的问题外，地表还受到短期气候因素的影响。因此，从卫星影像获得的指数必须与气象和地面数据结合评估。然而，可以认为在当前技术和方法条件下，基于卫星的干旱监测将产生更可靠的结果。","Türk Tarım ve Doğa Bilimleri Dergisi","2026-10-19T00:00:00Z",61,{"impact":17,"substance":168,"depth":107,"authority":20,"freshness":17,"relevant":22,"comment":169},18,"方法对比扎实、结论明确，但属区域案例研究，公共影响有限，适合作为遥感干旱监测主题的补充素材而非头条精选。",[171],{"name":164,"url":161},[28,30,98,173,174],"NDMI","LST",[],{"status":102,"locked":14,"version":35,"method":36,"evidence":177},{"candidates":178,"agricultural_evidence":14,"reason":127},[179,183,187,194],{"code":106,"score":107,"supported":50,"title":180,"abstract":181,"tags":182},[109],[111,109],[98],{"code":40,"score":17,"supported":50,"title":184,"abstract":185,"tags":186},[],[118,116],[28,30],{"code":188,"score":81,"supported":50,"title":189,"abstract":190,"tags":193},"H2",[],[191,192],"土壤","soil",[],{"code":121,"score":41,"supported":50,"title":195,"abstract":196,"tags":198},[],[197],"水资源",[],[200,201],"北爱琴海流域 干旱监测","干旱监测 遥感监测 NDMI NDVI","北爱琴海流域干旱监测-5481","10.30910\u002Fturkjans.2010067",{"doi":203,"openalex_id":205,"authors":206,"venue":164,"cited_by_count":70,"oa_url":213,"card":214,"direction":219,"ingested_from":78},"W7222954725",[207,210],{"name":208,"orcid":209},"Arman Monshizadeh Karimi","https:\u002F\u002Forcid.org\u002F0000-0002-2945-5346",{"name":211,"orcid":212},"İ̇smail Taş","https:\u002F\u002Forcid.org\u002F0000-0003-0872-2529","https:\u002F\u002Fdergipark.org.tr\u002Fen\u002Fdownload\u002Farticle-file\u002F6296754",{"tldr":215,"method":216,"finding":217,"direction":76,"opportunity":218},"比较Landsat 8的NDVI、NDMI与LST三种遥感干旱指标在北爱琴海流域的相关性。","用2019-2022年3-5月Landsat 8数据计算NDVI、NDMI、LS","LST与NDMI、NDVI负相关随春季增强，5月最强（r=-0.78、-0.74），NDMI略优。","可融合气象与地面数据构建多源干旱指数，并验证NDMI-LST关系在其他气候区的普适性。","智慧农业 \u002F 农业物联网","2026-10-21T23:30:10.758669Z",{"id":222,"title":223,"url":224,"summary":225,"summary_zh":226,"content":9,"source_name":227,"source_url":224,"published_at":228,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":229,"score_detail":230,"sources":232,"tags":234,"primary_topic":9,"secondary_topics":238,"classification":239,"search_phrases":250,"slug":253,"view_count":70,"doi":254,"paper":255,"created_at":266},5182,"Land Use Land Cover (LULC) Change Detection in Delhi NCT Using Multi-Sensor Satellite Data and Google Earth Engine","https:\u002F\u002Fdoi.org\u002F10.21275\u002Fsr26903183702","Land Use Land Cover (LULC) change detection is essential for understanding the dynamics of human modified landscapes and for supporting effective land management and urban planning. This study analyses LULC change across the Delhi National Capital Territory over a fifteen-year period, from 2010 to 2025, to assess urban sprawl and its environmental impact in one of India's fastest growing metropolitan regions. Multi temporal Landsat imagery was used as the primary data source, processed on Google Earth Engine, the cloud-based platform that enabled efficient handling and visualisation of large geospatial datasets. Five classes, built up area, agricultural land, vegetation, water, and barren land, were mapped and monitored, and classification accuracy was assessed for both dates. The Normalised Difference Vegetation Index was additionally employed to track vegetation vigour and ecological change. Results reveal a marked expansion of built-up areas alongside a substantial decline in agricultural and vegetated land, driven by peri urban growth along the northwestern and southwestern fringe. NDVI analysis shows rising greenness across the outer belt, reflecting more intensive cultivation on land that remains under crops, a pattern that complements rather than contradicts the measured loss of vegetated area. Combining classification based and an index-based approach provides a robust, scalable framework for LULC monitoring. By revealing where and how urban growth is reshaping the landscape, the study offers a transferable basis for environmental assessment, resource management, and sustainable urban planning in rapidly urbanising regions worldwide.","土地利用\u002F土地覆盖（LULC）变化检测对于理解人类改造景观的动态以及支撑有效的土地管理和城市规划至关重要。本研究分析了德里国家首都辖区在2010年至2025年十五年间的土地利用\u002F土地覆盖变化，以评估印度增长最快的大都市区之一的城市蔓延及其环境影响。研究以多时相Landsat影像作为主要数据源，在Google Earth Engine这一基于云的平台上进行处理，该平台能够高效处理和可视化大型地理空间数据集。研究对建成区、农业用地、植被、水体与裸地五个类别进行了制图和监测，并对两个时相的分类精度进行了评估。此外，研究采用归一化植被指数（NDVI）追踪植被长势与生态变化。结果表明，建成区显著扩张，农业用地和植被用地大幅减少，这一变化主要由西北部和西南部边缘的近郊增长所驱动。NDVI分析显示外围地带的绿度上升，反映出仍处于耕作状态的土地上集约化种植程度提高，这一格局与所测得的植被面积损失相辅相成而非相互矛盾。将基于分类的方法与基于指数的方法相结合，为土地利用\u002F土地覆盖监测提供了一个稳健且可扩展的框架。通过揭示城市增长在何处以及如何重塑景观，本研究为全球快速城市化地区的环境评估、资源管理和可持续城市规划提供了可迁移的基础。","International Journal of Science and Research (IJSR)","2026-10-15T00:00:00Z",62,{"impact":20,"substance":168,"depth":107,"authority":17,"freshness":21,"relevant":22,"comment":231},"方法可迁移的遥感LULC变化检测研究，对快速城市化地区土地管理有参考价值，但属区域案例、非三农核心议题。",[233],{"name":227,"url":224},[28,30,235,236,237],"土地利用","Google Earth Engine","城市化",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":240},{"candidates":241,"agricultural_evidence":50,"reason":51},[242,246],{"code":40,"score":41,"supported":14,"title":243,"abstract":244,"tags":245},[],[],[236,28,30],{"code":46,"score":22,"supported":14,"title":247,"abstract":248,"tags":249},[],[],[235],[251,252],"德里 土地利用 遥感","Google Earth Engine 城市扩张","德里土地利用遥感-5182","10.21275\u002Fsr26903183702",{"doi":254,"openalex_id":256,"authors":257,"venue":227,"cited_by_count":70,"oa_url":260,"card":261,"direction":76,"ingested_from":78},"W7222341300",[258],{"name":259,"orcid":9},"Neil Rohatgi","https:\u002F\u002Fwww.ijsr.net\u002Farchive\u002Fv15i10\u002FSR26903183702.pdf",{"tldr":262,"method":263,"finding":264,"direction":76,"opportunity":265},"基于多时相Landsat与GEE分析德里国家首都辖区2010-2025年土地利用变化。","多时相Landsat影像、Google Earth Engine平台、五类分类与","建设用地显著扩张，农业与植被用地大幅减少，城郊西北和西南边缘增长驱动。","可结合分类与指数方法构建可迁移框架，拓展至城乡交错带耕地保护与可持续规划研究。","2026-10-17T23:30:31.767409Z",{"id":268,"title":269,"url":270,"summary":271,"summary_zh":272,"content":9,"source_name":273,"source_url":270,"published_at":228,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":274,"score_detail":275,"sources":279,"tags":281,"primary_topic":9,"secondary_topics":284,"classification":285,"search_phrases":300,"slug":303,"view_count":22,"doi":304,"paper":305,"created_at":331},5177,"Long-term associations between land-surface indicators and dust-storm occurrence along the margins of the Taklimakan Desert","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-75251-3","Land-surface change and atmospheric variability jointly shape dust activity in arid regions. We examined land-cover transitions, station dust-storm records, a Sentinel-1 backscatter index, meteorological variables from the fifth-generation European Centre for Medium-Range Weather Forecasts reanalysis (ERA5) and dust diagnostics from the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) along the margins of the Taklimakan Desert. Within the mapped domain, grassland increased from 9.45% in 1990 to 10.59% in 2023, while barren land decreased from 86.45% to 82.30%. Annual dust-storm-day trends were negative at 21 of 30 stations over available 1990–2024 records, and Sentinel-1 index trends were positive at 22 stations during 2015–2024. Mixed-effects path analysis of 3,804 station-month observations showed that the Sentinel-1 index was negatively associated with modelled dust emission (standardized coefficient = − 0.191; 95% confidence interval: −0.289 to − 0.043). The normalized difference vegetation index (NDVI) and friction velocity had the highest training-set feature importance in the random forest analysis.","地表变化与大气变率共同塑造了干旱地区的沙尘活动。我们沿塔克拉玛干沙漠边缘，研究了土地覆盖转变、台站沙尘暴记录、Sentinel-1后向散射指数、来自欧洲中期天气预报中心第五代再分析资料（ERA5）的气象变量，以及来自现代研究与应用回顾分析第2版（MERRA-2）的沙尘诊断量。在制图范围内，草地面积从1990年的9.45%增加到2023年的10.59%，而裸地面积从86.45%减少到82.30%。在1990—2024年现有记录中，30个台站中有21个台站的年沙尘暴日数呈下降趋势；2015—2024年间，22个台站的Sentinel-1指数呈上升趋势。对3804个台站—月观测值的混合效应路径分析表明，Sentinel-1指数与模拟沙尘排放呈负相关（标准化系数=−0.191；95%置信区间：−0.289至−0.043）。在随机森林分析中，归一化植被指数（NDVI）和摩擦速度在训练集中的特征重要性最高。","Scientific Reports",78,{"impact":276,"substance":277,"depth":168,"authority":114,"freshness":17,"relevant":22,"comment":278},16,22,"基于多源遥感与30站长期记录，量化地表指标与沙尘暴关联，方法扎实、数据规模可观，对干旱区生态监测有参考价值。",[280],{"name":273,"url":270},[28,282,30,235,283],"荒漠化","沙尘暴",[],{"status":102,"locked":14,"version":35,"method":36,"evidence":286},{"candidates":287,"agricultural_evidence":14,"reason":127},[288,292,296],{"code":106,"score":41,"supported":50,"title":289,"abstract":290,"tags":291},[],[111],[],{"code":46,"score":56,"supported":14,"title":293,"abstract":294,"tags":295},[],[],[235,282],{"code":40,"score":56,"supported":14,"title":297,"abstract":298,"tags":299},[],[],[28,30],[301,302],"塔克拉玛干沙漠 沙尘暴 遥感","Sentinel-1 土地覆盖 沙尘","塔克拉玛干沙漠沙尘暴遥感-5177","10.1038\u002Fs41598-026-75251-3",{"doi":304,"openalex_id":306,"authors":307,"venue":273,"cited_by_count":70,"oa_url":325,"card":326,"direction":76,"ingested_from":78},"W7222086957",[308,310,313,316,318,320,322],{"name":309,"orcid":9},"Yifei Wang",{"name":311,"orcid":312},"Wen Huo","https:\u002F\u002Forcid.org\u002F0000-0002-7861-2424",{"name":314,"orcid":315},"Mayibaier Maihamuti","https:\u002F\u002Forcid.org\u002F0009-0007-4084-5891",{"name":317,"orcid":9},"Fan Yang",{"name":319,"orcid":9},"Chenglong Zhou",{"name":321,"orcid":9},"Xinghua Yang",{"name":323,"orcid":324},"Ali Mamtimin","https:\u002F\u002Forcid.org\u002F0000-0001-7335-801X","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-75251-3_reference.pdf",{"tldr":327,"method":328,"finding":329,"direction":76,"opportunity":330},"分析塔克拉玛干沙漠边缘地表指标与沙尘暴发生的长期关联。","结合土地覆盖、站点沙尘记录、Sentinel-1后向散射、ERA5与MERRA-","站点沙尘日数多呈下降趋势，Sentinel-1指数与模拟沙尘排放显著负相关。","可引入多源遥感与机器学习量化地表变化对沙尘的因果贡献，并拓展至绿洲农业防护。","2026-10-17T23:30:30.235613Z",{"id":333,"title":334,"url":335,"summary":336,"summary_zh":337,"content":9,"source_name":338,"source_url":335,"published_at":339,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":340,"score_detail":341,"sources":343,"tags":345,"primary_topic":9,"secondary_topics":349,"classification":350,"search_phrases":377,"slug":380,"view_count":22,"doi":381,"paper":382,"created_at":399},5001,"Mapping groundwater-dependent vegetation in temperate climates on the example of Central Germany","https:\u002F\u002Fdoi.org\u002F10.1098\u002Frsos.260538","Abstract Groundwater-dependent ecosystems are biodiversity hotspots that provide habitat for specialized species. The European Union (EU) Water Framework Directive (WFD) stresses the importance of identifying and protecting these ecosystems. So far, most studies focus on (semi-) arid regions, where groundwater use by ecosystems is both more prevalent and easier to detect. In this study, we adapt groundwater-dependent vegetation (GDV) mapping approaches developed for dry climates to temperate regions by replacing seasonal drought-based indicators with contrasts between dry and wet years. We implement this concept by integrating for the first time to our knowledge, ECOSTRESS evapotranspiration data with high-resolution remote sensing, regional geospatial data and botanical field observations. To test our framework, we trained and validated random forest models with eight predictor variables using 166 vegetation plots to identify GDV in Central Germany. Ground-truth GDV plots prefer cooler, wetter, nutrient-richer and more acidic to alkaline environments compared to non-GDV. The final model using a phreatophyte cover threshold of 25% representing GDV plots achieved a testing accuracy of 0.97, identifying 2067 km² of GDV. Currently, only 19% is protected under the EU WFD. The proposed mapping framework offers a solution for identifying GDV in temperate regions. The new GDV maps can contribute to managing groundwater resources and preserving biodiversity hotspots in regions facing increasing droughts, ultimately supporting the implementation of the EU WFD.","摘要 地下水依赖型生态系统是生物多样性热点区域，为特化物种提供栖息地。欧盟（EU）水框架指令（WFD）强调了识别和保护这些生态系统的重要性。迄今为止，大多数研究集中于（半）干旱地区，因为这些地区生态系统对地下水的利用更为普遍且更易检测。在本研究中，我们将针对干旱气候开发的地下水依赖型植被（GDV）制图方法推广至温带地区，以干湿年份之间的对比替代基于季节性干旱的指标。据我们所知，我们首次将ECOSTRESS蒸散发数据与高分辨率遥感、区域地理空间数据及植物学野外观测相结合来实现这一理念。为检验我们的框架，我们利用166个植被样地训练并验证了包含八个预测变量的随机森林模型，以识别德国中部的GDV。与未标记为GDV的样地相比，地面验证的GDV样地偏好更凉爽、更湿润、养分更丰富以及酸性至碱性的环境。最终模型采用25%的深根植物覆盖度阈值来判定GDV样地，达到了0.97的测试精度，识别出2067 km²的GDV。目前，其中仅有19%受到欧盟水框架指令的保护。所提出的制图框架为温带地区GDV的识别提供了解决方案。新的GDV地图可有助于管理地下水资源并保护面临日益加剧干旱区域的生物多样性热点，最终支持欧盟水框架指令的实施。","Royal Society Open Science","2026-10-14T00:00:00Z",81,{"impact":168,"substance":277,"depth":168,"authority":114,"freshness":21,"relevant":22,"comment":342},"首次将ECOSTRESS蒸散发数据用于温带地区地下水依赖植被制图，方法新颖、精度高，对干旱加剧背景下的生态保护与水资源管理有参考价值。",[344],{"name":338,"url":335},[346,29,30,347,348],"随机森林","地下水依赖植被","欧盟水框架指令",[],{"status":102,"locked":14,"version":35,"method":36,"evidence":351},{"candidates":352,"agricultural_evidence":14,"reason":127},[353,357,364,368,372],{"code":121,"score":21,"supported":50,"title":354,"abstract":355,"tags":356},[],[197,124,125],[],{"code":46,"score":358,"supported":50,"title":359,"abstract":360,"tags":363},7,[],[361,362],"生物多样性","biodiversity",[29],{"code":40,"score":358,"supported":50,"title":365,"abstract":366,"tags":367},[],[118,116],[30],{"code":106,"score":81,"supported":50,"title":369,"abstract":370,"tags":371},[],[111,109],[],{"code":373,"score":22,"supported":14,"title":374,"abstract":375,"tags":376},"I2",[],[],[346],[378,379],"ECOSTRESS 蒸散发 遥感","德国中部 地下水依赖植被","ECOSTRESS蒸散发遥感-5001","10.1098\u002Frsos.260538",{"doi":381,"openalex_id":383,"authors":384,"venue":338,"cited_by_count":70,"oa_url":393,"card":394,"direction":76,"ingested_from":78},"W7221647026",[385,388,390],{"name":386,"orcid":387},"Léonard El-Hokayem","https:\u002F\u002Forcid.org\u002F0000-0002-0552-3949",{"name":389,"orcid":9},"David Emanuel Schulz",{"name":391,"orcid":392},"Christopher Conrad","https:\u002F\u002Forcid.org\u002F0000-0003-1077-7086","https:\u002F\u002Froyalsocietypublishing.org\u002Frsos\u002Farticle-pdf\u002Fdoi\u002F10.1098\u002Frsos.260538\u002F6170405\u002Frsos.260538.pdf",{"tldr":395,"method":396,"finding":397,"direction":76,"opportunity":398},"将干旱区地下水依赖植被制图方法迁移到温带，以德国中部为例绘制GDV分布图。","融合ECOSTRESS蒸散、高分辨率遥感、地理数据与166个植被样地，训练随机森","模型精度0.97，识别出2067 km² GDV，但仅19%受欧盟水框架指令保护。","温带GDV制图框架可推广至其他区域，并探索GDV与农业用水、干旱风险的耦合机制。","2026-10-15T23:30:29.070183Z",{"id":401,"title":402,"url":403,"summary":404,"summary_zh":405,"content":9,"source_name":406,"source_url":403,"published_at":407,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":274,"score_detail":408,"sources":410,"tags":412,"primary_topic":415,"secondary_topics":421,"classification":422,"search_phrases":447,"slug":450,"view_count":70,"doi":451,"paper":452,"created_at":471},4835,"Spatiotemporal Patterns of Carbon Storage and Their Driving Mechanisms Under Multiple Land-Use Scenarios: A Case Study of the Henan Section of the South-to-North Water Diversion Project","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fland15101928","The water source area of the Henan Reach for the South-to-North Water Diversion Project is an important ecological protection zone, where variations in land-use patterns can adversely affect the region’s carbon sink capacity and long-term ecological stability. To examine the spatiotemporal distribution of carbon sinks and clarify the underlying factors driving their changes, an analytical framework combining PLUS, InVEST, and GeoDetector was applied. Xichuan County, Xixia County, Neixiang County, and Dengzhou City—four administrative units in Nanyang City that together form the Henan water source zone of the Middle Route—were selected as the research areas. Land-use records from 2002 to 2023 were analyzed to identify changes in the production–living–ecological (PLE) land-use structure and carbon storage dynamics through 2030 under four development scenarios: natural development, ecological protection, cultivated land protection, and engineering safety. The results indicated a gradual expansion of ecological land, while cultivated land and general ecological land exhibited distinct development trajectories. Spatially, carbon storage showed a pronounced west–high\u002Feast–low pattern, with high-density zones concentrated in the northwestern mountainous and hilly terrain under key ecological land and low-density zones prevailing in the eastern plains, urban built-up areas, and the Danjiangkou Reservoir surface. During the observation period, carbon gain patches proliferated across the northwestern mountains and reservoir-adjacent ecological restoration belts, whereas carbon loss areas gradually expanded from scattered hotspots toward the eastern plains and river valleys. Regional carbon sink initially decreased slightly but subsequently showed a gradual increase. Ecological land functioned as the primary carbon sink, whereas cultivated land contributed the largest share of carbon emissions. Across the simulated trajectories, the ecological conservation scenario resulted in the highest carbon sequestration, followed by the cultivated land protection scenario, while the natural development and engineering safety scenarios showed relatively limited carbon sequestration effects. Under the four scenarios, the projected 2030 spatial layout preserved the prevailing west–high\u002Feast–low configuration but diverged notably in the distribution of gains and losses: the ecological protection scenario generated connected carbon gain patches over the northwestern mountains and reservoir restoration belts, the cropland protection scenario triggered dispersed carbon loss zones around urban peripheries and river valley agricultural belts, and the engineering safety scenario maintained the most stable spatial pattern. GeoDetector analysis identified slope, elevation, and NDVI as the principal factors associated with spatial variations in carbon sink. Furthermore, the interaction between slope and NDVI produced the highest explanatory power, indicating that their combined influence on the phenomenon was significantly stronger than the effect of either factor alone. Based on these findings, adjustments to the land-use plan are proposed to enhance carbon sequestration and strengthen ecological security in the Henan section of the South-to-North Water Diversion Project.","南水北调工程河南段水源区是重要的生态保护区域，土地利用格局的变化会对区域碳汇能力和长期生态稳定性产生不利影响。为探究碳汇的时空分布特征并厘清其变化的驱动因素，本研究构建了PLUS、InVEST与GeoDetector相结合的分析框架。选取南阳市西川县、西峡县、内乡县和邓州市四个行政区作为研究区，四者共同构成中线工程河南水源区。基于2002—2023年土地利用记录，分析了生产—生活—生态（PLE）土地利用结构的变化，并在自然发展、生态保护、耕地保护和工程安全四种发展情景下模拟了至2030年的碳储量动态。结果表明，生态用地逐步扩张，而耕地和一般生态用地呈现出不同的发展轨迹。空间上，碳储量表现出显著的西高东低格局，高密度区集中于西北部山地丘陵地带的重点生态用地，低密度区则主要分布在东部平原、城市建成区和丹江口水库水面。观测期内，碳增益斑块在西北部山区和水库周边生态修复带不断扩展，而碳损失区域则从零星热点逐步向东部平原和河谷蔓延。区域碳汇初期略有下降，随后呈逐步增加趋势。生态用地是主要的碳汇来源，而耕地贡献了最大的碳排放份额。在各模拟情景中，生态保护情景的碳固持效果最佳，其次是耕地保护情景，而自然发展和工程安全情景的碳固持效果相对有限。四种情景下，2030年预测空间布局均保持了西高东低的基本格局，但在增益与损失的分布上存在显著差异：生态保护情景在西北部山区形成了连片的碳增益斑块，并","Land","2026-10-10T00:00:00Z",{"impact":276,"substance":277,"depth":168,"authority":93,"freshness":21,"relevant":22,"comment":409},"核心期刊论文，方法组合新颖、情景模拟数据扎实，对水源区生态保护与土地利用规划有参考价值，但属区域案例研究，公共影响层级有限。",[411],{"name":406,"url":403},[29,30,235,413,414],"碳储量","南水北调",{"code":416,"slug":417,"name":418,"path":419,"parent_code":420},"H4","carbon","农业碳与绿色低碳","\u002Ftopic\u002Fenvironment\u002Fcarbon","H",[],{"status":423,"locked":14,"version":35,"method":36,"evidence":424},"classified",{"candidates":425,"agricultural_evidence":50},[426,433,439,443],{"code":416,"score":13,"supported":50,"title":427,"abstract":428,"tags":432},[],[429,430,431],"碳排放","碳汇","carbon sequestration",[413],{"code":434,"score":41,"supported":50,"title":435,"abstract":436,"tags":438},"H1",[],[437],"耕地保护",[],{"code":46,"score":56,"supported":14,"title":440,"abstract":441,"tags":442},[],[],[235,29],{"code":40,"score":22,"supported":14,"title":444,"abstract":445,"tags":446},[],[],[30],[448,449],"南水北调 河南水源区 碳储量","PLUS InVEST GeoDetector 土地利用","南水北调河南水源区碳储量-4835","10.3390\u002Fland15101928",{"doi":451,"openalex_id":453,"authors":454,"venue":406,"cited_by_count":70,"oa_url":403,"card":465,"direction":469,"ingested_from":78},"W7221508293",[455,457,460,462],{"name":456,"orcid":9},"Xiaoyang Li",{"name":458,"orcid":459},"Wei Zhang","https:\u002F\u002Forcid.org\u002F0000-0002-6322-2597",{"name":461,"orcid":9},"Qi Jia",{"name":463,"orcid":464},"Lei Liu","https:\u002F\u002Forcid.org\u002F0000-0002-4966-5914",{"tldr":466,"method":467,"finding":468,"direction":469,"opportunity":470},"结合PLUS、InVEST与地理探测器，模拟南水北调河南水源区多情景土地利用与碳储时空演变及驱动机制","PLUS-InVEST-GeoDetector耦合框架，2002-2023年土地","碳储呈西高东低格局，生态保护情景固碳最优，生态用地为主要碳汇、耕地为主要排放源。","农业绿色发展与碳","可引入多目标优化与政策成本约束，量化水源区生态保护与耕地保护的碳汇权衡协同路径。","2026-10-13T23:30:53.020186Z"]