[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-5001":3,"related-5001":102},{"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":76,"slug":79,"view_count":21,"doi":80,"paper":81,"created_at":101},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。",null,"Royal Society Open Science","2026-10-14T00:00:00Z","论文",10,false,81,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,14,9,1,"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。",[24],{"name":10,"url":6},[26,27,28,29,30],"随机森林","生态保护","遥感监测","地下水依赖植被","欧盟水框架指令",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":36},"needs_review","2026-10-22.2","rules",{"candidates":37,"agricultural_evidence":14,"reason":75},[38,47,55,62,70],{"code":39,"score":20,"supported":40,"title":41,"abstract":42,"tags":46},"H3",true,[],[43,44,45],"水资源","蒸散发","evapotranspiration",[],{"code":48,"score":49,"supported":40,"title":50,"abstract":51,"tags":54},"H6",7,[],[52,53],"生物多样性","biodiversity",[27],{"code":56,"score":49,"supported":40,"title":57,"abstract":58,"tags":61},"I3",[],[59,60],"遥感","remote sensing",[28],{"code":63,"score":64,"supported":40,"title":65,"abstract":66,"tags":69},"H7",6,[],[67,68],"干旱","drought",[],{"code":71,"score":21,"supported":14,"title":72,"abstract":73,"tags":74},"I2",[],[],[26],"农业相关性需要复核",[77,78],"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":80,"openalex_id":82,"authors":83,"venue":10,"cited_by_count":92,"oa_url":93,"card":94,"direction":98,"ingested_from":100},"W7221647026",[84,87,89],{"name":85,"orcid":86},"Léonard El-Hokayem","https:\u002F\u002Forcid.org\u002F0000-0002-0552-3949",{"name":88,"orcid":9},"David Emanuel Schulz",{"name":90,"orcid":91},"Christopher Conrad","https:\u002F\u002Forcid.org\u002F0000-0003-1077-7086",0,"https:\u002F\u002Froyalsocietypublishing.org\u002Frsos\u002Farticle-pdf\u002Fdoi\u002F10.1098\u002Frsos.260538\u002F6170405\u002Frsos.260538.pdf",{"tldr":95,"method":96,"finding":97,"direction":98,"opportunity":99},"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。","农业遥感与作物表型","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。","openalex","2026-10-15T23:30:29.070183Z",{"total":64,"page":21,"page_size":64,"items":103},[104,182,244,317,372,429],{"id":105,"title":106,"url":107,"summary":108,"summary_zh":109,"content":9,"source_name":110,"source_url":107,"published_at":111,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":112,"score_detail":113,"sources":117,"tags":119,"primary_topic":123,"secondary_topics":129,"classification":130,"search_phrases":157,"slug":160,"view_count":92,"doi":161,"paper":162,"created_at":181},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",78,{"impact":114,"substance":18,"depth":17,"authority":115,"freshness":20,"relevant":21,"comment":116},16,13,"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。",[118],{"name":110,"url":107},[27,28,120,121,122],"土地利用","碳储量","南水北调",{"code":124,"slug":125,"name":126,"path":127,"parent_code":128},"H4","carbon","农业碳与绿色低碳","\u002Ftopic\u002Fenvironment\u002Fcarbon","H",[],{"status":131,"locked":14,"version":34,"method":35,"evidence":132},"classified",{"candidates":133,"agricultural_evidence":40},[134,141,148,153],{"code":124,"score":13,"supported":40,"title":135,"abstract":136,"tags":140},[],[137,138,139],"碳排放","碳汇","carbon sequestration",[121],{"code":142,"score":143,"supported":40,"title":144,"abstract":145,"tags":147},"H1",3,[],[146],"耕地保护",[],{"code":48,"score":149,"supported":14,"title":150,"abstract":151,"tags":152},2,[],[],[120,27],{"code":56,"score":21,"supported":14,"title":154,"abstract":155,"tags":156},[],[],[28],[158,159],"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":161,"openalex_id":163,"authors":164,"venue":110,"cited_by_count":92,"oa_url":107,"card":175,"direction":179,"ingested_from":100},"W7221508293",[165,167,170,172],{"name":166,"orcid":9},"Xiaoyang Li",{"name":168,"orcid":169},"Wei Zhang","https:\u002F\u002Forcid.org\u002F0000-0002-6322-2597",{"name":171,"orcid":9},"Qi Jia",{"name":173,"orcid":174},"Lei Liu","https:\u002F\u002Forcid.org\u002F0000-0002-4966-5914",{"tldr":176,"method":177,"finding":178,"direction":179,"opportunity":180},"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",{"id":183,"title":184,"url":185,"summary":186,"summary_zh":187,"content":9,"source_name":188,"source_url":185,"published_at":189,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":190,"score_detail":191,"sources":197,"tags":199,"primary_topic":9,"secondary_topics":203,"classification":204,"search_phrases":219,"slug":222,"view_count":92,"doi":223,"paper":224,"created_at":243},4689,"Landscape historic trough spatial changes of mangroves in Benin Coastal area (West-Africa) using random forest algorithm applied to Landsat images","https:\u002F\u002Fdoi.org\u002F10.52638\u002Frfpt.2026.736","Mangrove forests, recognized as the main ecosystem of coastal zones and providing various goods and services, are facing are facing increasing anthropogenic pressures and require continuous investigation. Ensuring the sustainable management of these ecosystems implies permanent monitoring of their dynamics. In line with this, the present study carried out on the mangrove forests of Benin (West-Africa) aimed to evaluate the effect of anthropic perturbation on their cover dynamics over the past four decades (including both Ramsar sites 1017 and 1018). The adopted method focused on remote sensing techniques using Landsat images acquired between 1986 and 2023. The Random Forest algorithm was used for image processing. Results revealed that whatever the period, the overall accuracy values obtained after post-classification analysis ranged from 84.56% to 96.52% indicating a correct image classification. From these results, a decline in mangrove cover was observed between 1986 and 2013 followed by an increase from 2013 to 2023. In Ramsar site 1017, mangroves lost about 61.90% (3261.06 ha) of their initial area from 1986 to 2000. Between 2000 and 2014, mangrove cover loss was negligible and estimated at 2.29% (59.04 ha). In contrast, from 2014 to 2023, mangroves experienced an increase estimated at about 16.66% (324.63 ha). The same trend was observed in Ramsar site 1018, with a loss of 31.12% (459.63 ha) and 27.44% (279.18 ha) respectively from 1986 to 2001 and from 2001 to 2013 followed by a gain estimated at 39.89% (279.18 ha) between 2013 and 2023. Landscape changes analysis revealed that regardless of the site or period, mangroves were mainly converted into meadow, other plant and water body (e.g. in Ramsar site 1017, from 1986 to 2001, about 568.26 ha of mangroves were converted into meadows, 33.66 ha into other plant, and 35.46 ha into water body). The principal changes observed in this study focused on Benin coastal zones confirm the vulnerability of mangrove forests facing anthropogenic pressure. Keywords: Mangrove, Dynamics, Remote sensing, Benin.","红树林被公认为海岸带的主要生态系统，提供多种商品和服务，正面临日益增加的人为压力，需要持续开展调查研究。确保这些生态系统的可持续管理意味着对其动态进行长期监测。据此，本研究针对贝宁（西非）的红树林开展，旨在评估人为干扰对其过去四十年（包括拉姆萨尔湿地1017和1018）覆盖动态的影响。所采用的方法侧重于利用1986年至2023年间获取的Landsat影像的遥感技术。采用随机森林（Random Forest）算法进行图像处理。结果表明，无论哪个时期，分类后分析获得的总体精度值介于84.56%至96.52%之间，表明图像分类正确。从这些结果来看，1986年至2013年间红树林覆盖面积下降，随后2013年至2023年间有所增加。在拉姆萨尔湿地1017中，1986年至2000年间红树林丧失了其初始面积的约61.90%（3261.06公顷）。2000年至2014年间，红树林覆盖损失可忽略不计，估计为2.29%（59.04公顷）。相比之下，2014年至2023年间，红树林增加了约16.66%（324.63公顷）。拉姆萨尔湿地1018也观察到相同趋势，1986年至2001年和2001年至2013年分别损失31.12%（459.63公顷）和27.44%（279.18公顷），随后2013年至2023年间增加了约39.89%（279.18公顷）。景观变化分析显示，无论哪个地点或时期，红树林主要转化为草地、其他植物和水体（例如在拉姆萨尔湿地1017中，1986年至2001年间，约568.26公顷红树林转化为草地，33.66公顷转化为其他植物，35.46公顷转化为水体）。本研究中观察到的贝宁海岸带主要变化证实了红树林面对人为压力的脆弱性。关键词：红树林，动态，遥感，贝宁。","Revue Française de Photogrammétrie et de Télédétection","2026-10-09T00:00:00Z",70,{"impact":192,"substance":193,"depth":194,"authority":115,"freshness":195,"relevant":21,"comment":196},12,20,17,8,"基于Landsat与随机森林的四十年红树林动态监测研究，方法成熟、数据翔实，对滨海湿地遥感监测有参考价值，但属区域案例研究，公共影响有限。",[198],{"name":188,"url":185},[26,28,200,201,202],"土地利用变化","红树林","滨海湿地",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":205},{"candidates":206,"agricultural_evidence":14,"reason":75},[207,211,215],{"code":56,"score":49,"supported":40,"title":208,"abstract":209,"tags":210},[],[59,60],[28],{"code":48,"score":149,"supported":14,"title":212,"abstract":213,"tags":214},[],[],[200,201],{"code":71,"score":21,"supported":14,"title":216,"abstract":217,"tags":218},[],[],[26],[220,221],"贝宁 红树林 遥感","Landsat 红树林 动态监测","贝宁红树林遥感-4689","10.52638\u002Frfpt.2026.736",{"doi":223,"openalex_id":225,"authors":226,"venue":188,"cited_by_count":92,"oa_url":237,"card":238,"direction":98,"ingested_from":100},"W7221179267",[227,229,231,233,235],{"name":228,"orcid":9},"Mahoutin Gildas Serge Zanvo",{"name":230,"orcid":9},"Nicolas DELBART",{"name":232,"orcid":9},"Paul PASSY",{"name":234,"orcid":9},"Kolawolé Valère Salako",{"name":236,"orcid":9},"Romain Glele-Kakai","https:\u002F\u002Frfpt.sfpt.fr\u002Findex.php\u002FRFPT\u002Farticle\u002Fdownload\u002F736\u002F528",{"tldr":239,"method":240,"finding":241,"direction":98,"opportunity":242},"利用随机森林与Landsat影像分析贝宁沿海红树林1986-2023年时空动态。","Landsat影像（1986-2023）结合随机森林分类与景观变化分析。","红树林先减后增，主要转为草甸、其他植被和水体，人为压力显著。","可结合多源遥感与驱动因子模型，探究红树林恢复的生态与社会机制。","2026-10-11T23:30:33.091927Z",{"id":245,"title":246,"url":247,"summary":248,"summary_zh":249,"content":9,"source_name":250,"source_url":247,"published_at":251,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":252,"score_detail":253,"sources":256,"tags":258,"primary_topic":261,"secondary_topics":266,"classification":267,"search_phrases":298,"slug":301,"view_count":21,"doi":302,"paper":303,"created_at":316},4506,"Spatial assessment of soil degradation under intensive agricultural pressure in the central loess plateau","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffenvs.2026.1940953","Soil degradation (SD) threatens the sustainability of semi-arid agricultural systems in the Central Loess Plateau, where intensive cultivation and repeated land-use cycles accelerate soil deterioration. This study developed a spatially explicit and uncertainty-aware framework for assessing and mapping SD by integrating soil quality indicators (SQIs), environmental data, remote sensing, and Random Forest (RF) modelling. A total of 350 surface-soil samples were collected and analyzed. Principal component analysis and the minimum data set (MDS) approach were used to identify representative soil-quality indicators. The final MDS included clay, soil bulk density, pH, soil organic carbon, calcium carbonate equivalent, cation exchange capacity, potassium, and sodium adsorption ratio. Among these indicators, pH, SAR, CEC, SOC, K, and clay received the greatest weights, highlighting the importance of soil chemical conditions, sodicity, nutrient retention, organic matter, and texture in soil quality assessment. Soil degradation was quantified by comparing SQI values of agricultural soils with reference values from natural ecosystems. Environmental predictors were then incorporated into an RF model to estimate the spatial distribution of SD and assess predictor importance. The model showed good predictive performance, with R 2 values of 0.83 for training and 0.76 for validation, and RMSE values ranging from 0.031 to 0.034. Severe degradation was mainly concentrated in the central and southwestern regions, reflecting the cumulative effects of intensive cultivation, nutrient depletion, and SOC loss. Prediction-interval analysis identified areas with greater uncertainty and priority for additional sampling. The proposed framework provides a practical basis for spatially targeted soil conservation, sustainable agricultural management, and long-term ecosystem resilience. Future research should incorporate biological indicators and temporal monitoring to improve SD prediction under changing climatic and land-use conditions.","土壤退化（SD）威胁着黄土高原中部半干旱农业系统的可持续性，该地区集约化耕作和反复的土地利用周期加速了土壤恶化。本研究通过整合土壤质量指标（SQIs）、环境数据、遥感技术和随机森林（RF）建模，开发了一个空间明确且具有不确定性感知的土壤退化评估与制图框架。共采集并分析了350个表层土壤样品。采用主成分分析和最小数据集（MDS）方法识别代表性土壤质量指标。最终的最小数据集包括黏粒、土壤容重、pH、土壤有机碳、碳酸钙当量、阳离子交换量、钾和钠吸附比。在这些指标中，pH、SAR、CEC、SOC、K和黏粒获得了最大的权重，凸显了土壤化学条件、钠质化、养分保持、有机质和质地在土壤质量评估中的重要性。通过比较农业土壤的SQI值与自然生态系统的参考值，对土壤退化进行了量化。随后将环境预测因子纳入随机森林模型，以估算土壤退化的空间分布并评估预测因子的重要性。该模型表现出良好的预测性能，训练集R²值为0.83，验证集R²值为0.76，RMSE值在0.031至0.034之间。严重退化主要集中在中部和西南部地区，反映了集约化耕作、养分耗竭和有机碳损失的累积效应。预测区间分析识别了不确定性较大且需要优先补充采样的区域。所提出的框架为空间针对性的土壤保护、可持续农业管理和长期生态系统恢复力提供了实践基础。未来研究应纳入生物指标和时间监测，以改进气候变化和土地利用变化条件下的土壤退化预测。","Frontiers in Environmental Science","2026-10-08T00:00:00Z",76,{"impact":254,"substance":18,"depth":17,"authority":115,"freshness":195,"relevant":21,"comment":255},15,"基于350个样点与随机森林模型的空间化土壤退化评估，方法扎实、结论可靠，对黄土高原耕地保护有参考价值，但属区域学术研究，公共影响有限。",[257],{"name":250,"url":247},[26,146,28,259,260],"土壤退化","黄土高原",{"code":262,"slug":263,"name":264,"path":265,"parent_code":128},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil",[],{"status":131,"locked":14,"version":34,"method":35,"evidence":268},{"candidates":269,"agricultural_evidence":40},[270,275,279,284,290,294],{"code":262,"score":19,"supported":40,"title":271,"abstract":272,"tags":274},[263],[273,263],"土壤",[],{"code":56,"score":49,"supported":40,"title":276,"abstract":277,"tags":278},[],[59,60],[28],{"code":63,"score":64,"supported":40,"title":280,"abstract":281,"tags":283},[],[282,67],"气候变化",[],{"code":285,"score":143,"supported":40,"title":286,"abstract":287,"tags":289},"H8",[],[288],"可持续农业",[],{"code":142,"score":21,"supported":14,"title":291,"abstract":292,"tags":293},[],[],[146],{"code":71,"score":21,"supported":14,"title":295,"abstract":296,"tags":297},[],[],[26],[299,300],"黄土高原 土壤退化 遥感","土壤质量 随机森林 评估","黄土高原土壤退化遥感-4506","10.3389\u002Ffenvs.2026.1940953",{"doi":302,"openalex_id":304,"authors":305,"venue":250,"cited_by_count":92,"oa_url":247,"card":311,"direction":98,"ingested_from":100},"W7221006056",[306,308],{"name":307,"orcid":9},"Xiandong Hou",{"name":309,"orcid":310},"Tao Bai","https:\u002F\u002Forcid.org\u002F0000-0002-2034-8026",{"tldr":312,"method":313,"finding":314,"direction":98,"opportunity":315},"构建融合土壤指标、遥感与随机森林的空间显式框架，评估黄土高原中部高强度农业下的土壤退化。","350个表层土样，PCA与最小数据集筛选指标，随机森林建模并做预测区间分析。","pH、SAR、CEC、SOC、钾和黏粒权重最高；严重退化集中于中部和西南部。","可引入生物指标与多时相遥感监测，提升气候变化和土地利用变化下土壤退化预测的时空精度。","2026-10-09T23:30:25.996370Z",{"id":318,"title":319,"url":320,"summary":321,"summary_zh":322,"content":9,"source_name":323,"source_url":320,"published_at":324,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":325,"score_detail":326,"sources":328,"tags":330,"primary_topic":9,"secondary_topics":334,"classification":335,"search_phrases":348,"slug":351,"view_count":149,"doi":352,"paper":353,"created_at":371},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。","Boletim de Geografia","2026-10-04T00:00:00Z",66,{"impact":195,"substance":193,"depth":194,"authority":192,"freshness":20,"relevant":21,"comment":327},"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。",[329],{"name":323,"url":320},[331,332,27,28,333],"农业遥感","NDVI","森林覆盖",[],{"status":336,"locked":14,"version":34,"method":35,"evidence":337},"unclassified",{"candidates":338,"agricultural_evidence":40,"reason":347},[339,343],{"code":56,"score":143,"supported":14,"title":340,"abstract":341,"tags":342},[],[],[332,331,28],{"code":48,"score":21,"supported":14,"title":344,"abstract":345,"tags":346},[],[],[27],"缺少足够的主题证据",[349,350],"Realeza PR NDVI 森林","Sentinel-2 森林覆盖 监测","RealezaPRNDVI森林-4368","10.4025\u002Fbolgeogr.v44.a2026.e83759",{"doi":352,"openalex_id":354,"authors":355,"venue":323,"cited_by_count":92,"oa_url":365,"card":366,"direction":98,"ingested_from":100},"W7220453253",[356,359,362],{"name":357,"orcid":358},"Marcos Henrique Tomazini Mikoanski","https:\u002F\u002Forcid.org\u002F0000-0003-0715-0581",{"name":360,"orcid":361},"Francieli Alves Teixeira","https:\u002F\u002Forcid.org\u002F0009-0000-0055-9718",{"name":363,"orcid":364},"Fernando César Manosso","https:\u002F\u002Forcid.org\u002F0000-0003-2568-7403","https:\u002F\u002Fperiodicos.uem.br\u002Fojs\u002Findex.php\u002FBolGeogr\u002Farticle\u002Fdownload\u002F83759\u002F751375163932",{"tldr":367,"method":368,"finding":369,"direction":98,"opportunity":370},"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。","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。","2026-10-07T23:30:30.363166Z",{"id":373,"title":374,"url":375,"summary":376,"summary_zh":377,"content":9,"source_name":378,"source_url":375,"published_at":379,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":92,"score_detail":380,"sources":382,"tags":384,"primary_topic":9,"secondary_topics":388,"classification":389,"search_phrases":409,"slug":412,"view_count":149,"doi":413,"paper":414,"created_at":428},3671,"CLOUD-BASED MULTI-TEMPORAL SAR-OPTICAL FUSION USING RANDOM FOREST FOR GEOSPATIAL INTELLIGENCE MONITORING OF KIPP IKN NUSANTARA","https:\u002F\u002Fdoi.org\u002F10.21163\u002Fgt_2027.221.08","The development of Indonesia's new capital (IKN) Nusantara drives dramatic land-cover transformation across a strategic 17,599-hectare area under the persistent cloud cover of Kalimantan, where optical remote sensing alone is insufficient for continuous monitoring.This study proposes a cloud-based multi-temporal SAR-optical fusion approach using Random Forest classification for geospatial-intelligence (GEOINT) monitoring of the Core Government Area (KIPP) of IKN.Using Google Earth Engine, we processed 70 Sentinel-1 GRD and 52 Sentinel-2 images for 2022 and 61 and 81 images for 2024, building a 12-band fusion stack (SAR backscatter VV, VH, VV\u002FVH; optical B2, B3, B4, B8, B11, B12; and NDVI, NDBI, NDWI).Six experiments compared SAR-only, optical-only, and fusion configurations.The Random Forest fusion achieved 88.89% overall accuracy and 0.8609 kappa for 2022 and 85.12% and 0.8127 for 2024 (assessed against ESA WorldCover 2021 class definitions; see Section 2.7), outperforming SAR-only (63.20%) and optical-only (86.44%).Change detection revealed 899.64 ha of direct vegetation-to-built-up conversion, a 60% expansion of built-up area driven by IKN construction.This is the first cloud-based multi-temporal fusion framework applied to KIPP IKN monitoring, with implications for spatial planning, security surveillance, and environmental compliance.","印度尼西亚新首都（IKN）努山塔拉的开发，在加里曼丹持续云覆盖下推动了一个战略性的17,599公顷区域发生剧烈的土地覆盖转变，仅依靠光学遥感不足以实现持续监测。本研究提出了一种基于云的多时相SAR-光学融合方法，采用随机森林分类对IKN核心政府区（KIPP）进行地理空间情报（GEOINT）监测。利用Google Earth Engine，我们处理了2022年的70景Sentinel-1 GRD和52景Sentinel-2影像，以及2024年的61景和81景影像，构建了一个12波段融合数据集（SAR后向散射VV、VH、VV\u002FVH；光学B2、B3、B4、B8、B11、B12；以及NDVI、NDBI、NDWI）。六组实验比较了仅SAR、仅光学和融合配置。随机森林融合在2022年达到88.89%的总体精度和0.8609的kappa系数，2024年为85.12%和0.8127（依据ESA WorldCover 2021类别定义进行评估；见第2.7节），优于仅SAR（63.20%）和仅光学（86.44%）。变化检测揭示了899.64公顷的植被直接转为建成区，IKN建设推动建成区面积扩张60%。这是首个应用于KIPP IKN监测的基于云的多时相融合框架，对空间规划、安全监控和环境合规具有重要意义。","Geographia Technica","2026-09-26T00:00:00Z",{"impact":92,"substance":92,"depth":92,"authority":92,"freshness":92,"relevant":92,"comment":381},"该研究聚焦印尼新首都建设的遥感监测，属地理空间情报领域，与三农、农业信息化、智慧农业等主题无直接关联，故不进入每日精选。",[383],{"name":378,"url":375},[26,28,385,386,387],"土地覆盖变化","SAR光学融合","空间规划",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":390},{"candidates":391,"agricultural_evidence":14,"reason":75},[392,396,400,404],{"code":56,"score":49,"supported":40,"title":393,"abstract":394,"tags":395},[],[59,60],[28],{"code":48,"score":21,"supported":14,"title":397,"abstract":398,"tags":399},[],[],[385],{"code":71,"score":21,"supported":14,"title":401,"abstract":402,"tags":403},[],[],[26],{"code":405,"score":21,"supported":14,"title":406,"abstract":407,"tags":408},"I4",[],[],[387],[410,411],"Sentinel-1 Sentinel-2 融合","土地覆盖变化 SAR光学融合 空间规划 遥感监测","Sentinel-1Sentinel-2融合-3671","10.21163\u002Fgt_2027.221.08",{"doi":413,"openalex_id":415,"authors":416,"venue":378,"cited_by_count":92,"oa_url":422,"card":423,"direction":98,"ingested_from":100},"W7214434955",[417,419],{"name":418,"orcid":9},"Fajar Sidik Suganda",{"name":420,"orcid":421},"Asep Adang Supriyadi","https:\u002F\u002Forcid.org\u002F0000-0003-1103-6669","https:\u002F\u002Ftechnicalgeography.org\u002Fpdf\u002F1_2027\u002F08_suganda.pdf",{"tldr":424,"method":425,"finding":426,"direction":98,"opportunity":427},"提出云端多时相SAR-光学融合与随机森林方法，监测印尼新首都核心区土地覆盖变化。","Google Earth Engine处理Sentinel-1\u002F2影像，构建12","融合分类精度达88.89%，检测到899.64公顷植被转建设用地，建成区扩张60%。","可迁移至多云农业区作物监测，探索融合特征优化与跨区域泛化能力。","2026-09-28T23:30:27.951741Z",{"id":430,"title":431,"url":432,"summary":433,"summary_zh":434,"content":9,"source_name":250,"source_url":432,"published_at":435,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":436,"score_detail":437,"sources":441,"tags":443,"primary_topic":9,"secondary_topics":446,"classification":447,"search_phrases":463,"slug":466,"view_count":92,"doi":467,"paper":468,"created_at":490},2871,"Spatial and Temporal characteristics and driving force analysis of vegetation cover change in Shanxi Province, China based on kNDVI and XGBoost-SHAP model","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffenvs.2026.1948848","Introduction Systematically clarifying the spatiotemporal evolution of vegetation coverage and elucidating its response mechanisms to climatic fluctuations and human activities carries substantial practical implications for advancing the Dual Carbon Strategy and optimizing the pattern of territorial spatial development and conservation across Shanxi Province, China. Methods Based on kernel Normalized Difference Vegetation Index (kNDVI) datasets, multi-source meteorological, topographic and socioeconomic datasets, this study integrated Theil-Sen slope estimation, Mann-Kendall significance test, Hurst exponent, standard deviational ellipse and gravity center migration model to systematically characterize the spatiotemporal patterns of vegetation coverage for the period 2000 to 2024 and predict the potential persistence of its future evolution. Meanwhile, an interpretable XGBoost-SHAP machine learning framework was constructed to quantify the independent contributions, nonlinear marginal responses, and temporal evolutionary characteristics of seven driving factors: elevation, slope, aspect, annual mean temperature, annual mean precipitation, population density and nighttime light intensity. Results (1) Temporally, the multi-year average kNDVI of the whole province reached approximately 0.1699, presenting an extremely significant fluctuating upward trend with an annual growth rate of 0.0041. Annual kNDVI ranges from 0.1132 recorded in 2001 to a peak value of 0.2227 in 2024. (2) Spatially, vegetation coverage exhibited a remarkable differentiation pattern of “low values in the north and high values in the south”. Higher kNDVI values were predominantly distributed within the forest-covered mountainous regions of the Taihang, Lüliang, Zhongtiao, and Taiyue Mountains, whereas relatively low values were distributed across the sandy-hilly regions of northern Shanxi and urban agglomerations along the Fen River Valley. Vegetation restoration was observed across 98.02% of the study area, among which roughly 92.18% exhibited an extremely significant improvement. (3) Over 2000–2024, the major axis of vegetation spatial distribution maintained a stable northeast-southwest orientation, accompanied by moderate outward expansion of the standard deviational ellipse, and a general northwestward shift of the vegetation gravity centre. (4) The Hurst exponent indicated that the vegetation improvement trend in 98.03% of the region would persist, while scattered patches of industrial, mining and urban land (accounting for 1.97%) faced the potential vegetation degradation risk. (5) The regional vegetation driving system experienced three evolutionary stages: a single precipitation-dominated natural driving stage (2000–2005), a climate-human coupled transitional stage (2005–2010), and a multi-factor synergistic balanced stage (2010–2020). Precipitation and temperature acted as core climatic drivers, and elevation largely determined the vertical differentiation baseline of vegetation distribution. Population density and nighttime light intensity exerted persistent suppressive effects on vegetation growth. Empirically derived tentative thresholds are identified for major predictors: elevation ∼1,200 m, slope ∼8°, annual mean temperature ∼7.5 °C, annual mean precipitation ∼500 mm, population density 500 persons\u002Fkm 2 , and nighttime light intensity ∼5. These values can serve as reference boundaries for differentiating ecological conservation zones from human-disturbed zones. Conclusion This research identified the intertemporal differentiation and nonlinear coupling laws governing vegetation dynamics in Shanxi Province, a temperate transition zone on the Loess Plateau. Differentiated ecological governance strategies targeting climate adaptation, topographic zoning, and anthropogenic-pressure regulation are proposed, which provide observational scientific support for the construction of ecological security barriers on China’s Loess Plateau.","引言 系统厘清植被覆盖的时空演变规律并阐明其对气候波动与人类活动的响应机制，对推进双碳战略、优化山西省国土空间开发保护格局具有重要现实意义。方法 基于核归一化植被指数（kNDVI）数据集及多源气象、地形和社会经济数据，本研究综合运用Theil-Sen斜率估计、Mann-Kendall显著性检验、Hurst指数、标准差椭圆和重心迁移模型，系统刻画了2000—2024年植被覆盖的时空格局，并预测其未来演变的潜在持续性。同时，构建了可解释的XGBoost-SHAP机器学习框架，量化了高程、坡度、坡向、年平均气温、年平均降水量、人口密度和夜间灯光强度7个驱动因子的独立贡献、非线性边际响应及时间演变特征。结果 （1）时间上，全省多年平均kNDVI约为0.1699，呈极显著波动上升趋势，年增长率为0.0041。年kNDVI从2001年的0.1132变化至2024年的峰值0.2227。（2）空间上，植被覆盖呈现“北低南高”的显著分异格局。较高kNDVI值主要分布于太行山、吕梁山、中条山和太岳山等森林覆盖山区，而较低值分布于晋北沙丘丘陵区和汾河谷地城市群。研究区98.02%的区域植被呈恢复态势，其中约92.18%表现为极显著改善。（3）2000—2024年间，植被空间分布的主轴保持稳定的东北—西南走向，标准差椭圆呈中度向外扩张，植被重心总体向西北方向迁移。（4）Hurst指数表明，98.03%区域的植被改善趋势将持续，而零星斑块区域的工业","2026-09-17T00:00:00Z",82,{"impact":17,"substance":438,"depth":439,"authority":115,"freshness":20,"relevant":21,"comment":440},23,19,"基于kNDVI与XGBoost-SHAP的山西植被时空演变与驱动力研究，方法新颖、数据扎实，对黄土高原生态治理有参考价值。",[442],{"name":250,"url":432},[444,27,28,445,260],"机器学习","植被覆盖",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":448},{"candidates":449,"agricultural_evidence":14,"reason":75},[450,455,459],{"code":71,"score":49,"supported":40,"title":451,"abstract":452,"tags":454},[],[444,453],"machine learning",[444],{"code":56,"score":149,"supported":14,"title":456,"abstract":457,"tags":458},[],[],[445,28],{"code":48,"score":21,"supported":14,"title":460,"abstract":461,"tags":462},[],[],[27],[464,465],"山西 kNDVI 植被覆盖","XGBoost-SHAP 植被驱动","山西kNDVI植被覆盖-2871","10.3389\u002Ffenvs.2026.1948848",{"doi":467,"openalex_id":469,"authors":470,"venue":250,"cited_by_count":92,"oa_url":484,"card":485,"direction":98,"ingested_from":100},"W7213469060",[471,473,475,477,479,482],{"name":472,"orcid":9},"Jie Chen",{"name":474,"orcid":9},"Yi Hou",{"name":476,"orcid":9},"Jianhua Xue",{"name":478,"orcid":9},"Jianhua Ni",{"name":480,"orcid":481},"Hao Liu","https:\u002F\u002Forcid.org\u002F0000-0001-8903-0983",{"name":483,"orcid":9},"Pengxiang Gao","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fenvironmental-science\u002Farticles\u002F10.3389\u002Ffenvs.2026.1948848\u002Fpdf",{"tldr":486,"method":487,"finding":488,"direction":98,"opportunity":489},"基于kNDVI与XGBoost-SHAP分析山西2000-2024年植被覆盖时空变化及驱动机制。","kNDVI数据结合Theil-Sen、Mann-Kendall、Hurst指数与","山西植被呈显著上升趋势，98.02%区域改善，驱动因子具非线性与时空差异。","可引入多源遥感与作物物候数据，将kNDVI驱动分析拓展至农田尺度精准管理与碳汇评估。","2026-09-18T23:30:29.212735Z"]