[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-5179":3,"related-5179":104},{"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":66,"slug":69,"view_count":22,"doi":70,"paper":71,"created_at":103},5179,"Monitoring Carbon Hotspots in Patchy Landscapes from Sparse Lidar and Dense Satellite Time Series","https:\u002F\u002Fdoi.org\u002F10.31223\u002Fx56j7f","Monitoring aboveground biomass (AGB) stocks as an indicator of carbon storage is essential for climate mitigation, adaptation, and land management. Across much of Earth’s land surface, carbon stocks are concentrated in fine-scale hotspots, including isolated trees in pastures, shrub patches in tundra, and riparian vegetation in drylands. These carbon hotspots are vulnerable to change but are difficult to monitor with remote sensing at broad scales due to three interconnected challenges: (1) spatial heterogeneity, (2) temporal change, and (3) modeling uncertainty. To address these challenges, we developed a workflow that integrates moderate-resolution satellite sensors, aerial lidar collections, and forestry-derived allometric models to produce annual maps of AGB in riparian corridors in the semi-arid western United States. To resolve spatial heterogeneity, we modeled canopy height and vegetation class at 10-m resolution, achieving canopy height mean absolute error of 1.56 m and overall vegetation classification accuracy of 84%. To measure uncertainty, we quantified prediction intervals using conformal prediction for canopy height and classification, ensemble disagreement for epistemic uncertainty, and Bayesian posterior distributions for allometric biomass models. The resulting annual maps provide pixel-level AGB estimates with statistically verified uncertainty bounds. These products reveal gradual changes in riparian biomass and can guide management decisions, from prioritizing restoration efforts to identifying early warning signs of ecosystem degradation. This framework could be adapted for monitoring AGB in other patchy landscapes, incorporated into different or changing land cover categories for future work, and updated as more and better data sources become available.","监测地上生物量（AGB）储量作为碳储量的指标，对于气候减缓、适应和土地管理至关重要。在地球陆地表面的大部分区域，碳储量集中在细尺度热点区域，包括牧场中的孤立树木、苔原中的灌木斑块以及旱地中的河岸植被。这些碳热点易受变化影响，但由于三个相互关联的挑战，难以在广阔尺度上通过遥感进行监测：（1）空间异质性，（2）时间变化，（3）建模不确定性。为应对这些挑战，我们开发了一套工作流程，整合中等分辨率卫星传感器、航空激光雷达采集和林业衍生的异速生长模型，以生成美国西部半干旱地区河岸走廊AGB的年度地图。为解决空间异质性问题，我们在10米分辨率下对冠层高度和植被类别进行建模，实现了冠层高度平均绝对误差1.56米和植被分类总体精度84%。为度量不确定性，我们使用保形预测量化冠层高度和分类的预测区间，使用集成不一致性度量认知不确定性，并使用贝叶斯后验分布构建异速生长生物量模型。由此生成的年度地图提供了具有统计验证不确定性边界的像素级AGB估算。这些产品揭示了河岸生物量的渐进变化，并可指导管理决策，从优先安排恢复工作到识别生态系统退化的早期预警信号。该框架可适用于监测其他斑块状景观中的AGB，可纳入未来工作中不同或变化的土地覆盖类别，并可随着更多更好的数据源的出现而更新。",null,"OpenAlex","2026-10-15T00:00:00Z","论文",10,false,73,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,21,18,13,9,1,"方法新颖、不确定性量化扎实，但属区域尺度遥感论文，对农业信息化有间接参考价值，未达每日精选门槛。",[25],{"name":10,"url":6},[27,28,29,30,31],"遥感监测","生态监测","生物量估算","碳储量","激光雷达",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":37},"needs_review","2026-10-22.2","rules",{"candidates":38,"agricultural_evidence":14,"reason":65},[39,48,55,60],{"code":40,"score":41,"supported":42,"title":43,"abstract":44,"tags":47},"I3",8,true,[],[45,46],"遥感","remote sensing",[29,27],{"code":49,"score":50,"supported":42,"title":51,"abstract":52,"tags":54},"H7",3,[],[53],"干旱",[],{"code":56,"score":22,"supported":14,"title":57,"abstract":58,"tags":59},"H4",[],[],[30],{"code":61,"score":22,"supported":14,"title":62,"abstract":63,"tags":64},"H6",[],[],[28],"农业相关性需要复核",[67,68],"稀疏激光雷达 卫星时序 生物量","河岸带 碳热点 遥感监测","稀疏激光雷达卫星时序生物量-5179","10.31223\u002Fx56j7f",{"doi":70,"openalex_id":72,"authors":73,"venue":9,"cited_by_count":94,"oa_url":95,"card":96,"direction":100,"ingested_from":102},"W7222218373",[74,77,79,81,83,85,87,90,92],{"name":75,"orcid":76},"Erik Nati‐Johnson","https:\u002F\u002Forcid.org\u002F0009-0009-3923-4115",{"name":78,"orcid":9},"Jodi Brandt",{"name":80,"orcid":9},"Nicholas Kolarik",{"name":82,"orcid":9},"Rose Graves",{"name":84,"orcid":9},"Anand Roopsind",{"name":86,"orcid":9},"Marie-Anne de Graff",{"name":88,"orcid":89},"Juan Camilo Rojas Lucero","https:\u002F\u002Forcid.org\u002F0009-0004-4934-3108",{"name":91,"orcid":9},"Colin Martin",{"name":93,"orcid":9},"Trevor Caughlin",0,"https:\u002F\u002Feartharxiv.org\u002Frepository\u002Fobject\u002F15555\u002Fdownload\u002F26955\u002F",{"tldr":97,"method":98,"finding":99,"direction":100,"opportunity":101},"融合稀疏激光雷达与密集卫星时序，绘制半干旱河岸带地上生物量年度图。","中分辨率卫星、航空激光雷达与异速生长模型，结合共形预测与贝叶斯不确定性量化。","10米分辨率冠层高度误差1.56米、分类精度84%，可检测河岸生物量渐变。","农业遥感与作物表型","可将该框架迁移至农田防护林、零星树木等破碎景观碳热点监测，并融合更高分辨率数据。","openalex","2026-10-17T23:30:31.577923Z",{"total":105,"page":22,"page_size":105,"items":106},6,[107,193,264,335,386,481],{"id":108,"title":109,"url":110,"summary":111,"summary_zh":112,"content":9,"source_name":113,"source_url":110,"published_at":114,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":115,"score_detail":116,"sources":120,"tags":122,"primary_topic":126,"secondary_topics":132,"classification":138,"search_phrases":164,"slug":167,"view_count":94,"doi":168,"paper":169,"created_at":192},5367,"Integration of multi-source UAV remote sensing features and derived features for winter wheat aboveground biomass estimation","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.atech.2026.102656","Accurate estimation of winter wheat ( Triticum aestivum L.) aboveground biomass (AGB) is important for crop growth monitoring and field management. However, unmanned aerial vehicle (UAV)-based AGB estimation remains challenging due to variations in canopy conditions across growth stages, and different remote sensing features can capture complementary characteristics related to AGB estimation. This study integrated multi-source UAV-derived features, including vegetation indices (VIs), texture features (TFs), light detection and ranging (LiDAR)-derived features, and derived features (DFs) generated from the original feature sets, with four machine learning algorithms for winter wheat AGB estimation. Results showed that TFs provided better prediction performance than other single-source feature sets. Compared with single-source feature sets, the integration of VIs, TFs, LiDAR-derived features, and DFs further improved AGB estimation, and the random forest (RF) model achieved the best performance among the evaluated models (R² = 0.923, RMSE = 1.731 Mg ha⁻¹). Incremental R² analysis based on a stage-only baseline model showed that the RF model explained additional variation in AGB beyond growth-stage information, with an incremental R² of 0.479. Stage-specific evaluation indicated that model performance varied among growth stages, with the highest accuracy observed at the booting stage (R² = 0.632, RMSE = 1.425 Mg ha⁻¹). Feature contribution analysis based on the full-feature fusion model using all growth-stage data identified canopy coverage and several DFs as important predictors, while stage-specific analyses showed differences in dominant predictors across growth stages. The integration of multi-source UAV-derived features and DFs with machine learning provides an effective approach for winter wheat AGB estimation, while model performance remains strongly dependent on growth stage.","准确估算冬小麦（Triticum aestivum L.）地上生物量（aboveground biomass, AGB）对作物生长监测和田间管理具有重要意义。然而，由于不同生育期冠层条件存在差异，且不同遥感特征能够捕捉与AGB估算相关的互补信息，基于无人机（unmanned aerial vehicle, UAV）的AGB估算仍面临挑战。本研究将多源无人机衍生特征，包括植被指数（vegetation indices, VIs）、纹理特征（texture features, TFs）、激光雷达（light detection and ranging, LiDAR）衍生特征以及由原始特征集生成的衍生特征（derived features, DFs），与四种机器学习算法相结合，用于冬小麦AGB估算。结果表明，TFs的预测性能优于其他单一来源特征集。与单一来源特征集相比，融合VIs、TFs、LiDAR衍生特征和DFs进一步提高了AGB估算精度，其中随机森林（random forest, RF）模型在所评估的模型中表现最佳（R² = 0.923，RMSE = 1.731 Mg ha⁻¹）。基于仅含生育期信息的基线模型进行的增量R²分析表明，RF模型在生育期信息之外解释了AGB的额外变异，增量R²为0.479。分生育期评估表明，模型性能在不同生育期之间存在差异，其中在孕穗期观察到最高精度（R² = 0.632，RMSE = 1.425 Mg ha⁻¹）。基于使用全部生育期数据的全特征融合模型进行的特征贡献分析识别出冠层覆盖度和若干DFs为重要预测因子，而分生育期分析则表明不同生育期的主导预测因子存在差异。将多源无人机衍生特征和DFs与机器学习相结合，为冬小麦AGB估算提供了一种有效方法，但模型性能仍强烈依赖于生育期。","Smart Agricultural Technology","2026-10-17T00:00:00Z",77,{"impact":117,"substance":118,"depth":19,"authority":20,"freshness":41,"relevant":22,"comment":119},16,22,"该研究融合无人机多源遥感特征与衍生特征，用随机森林估算冬小麦地上生物量，方法新颖、结论可靠，对作物长势监测有参考价值。",[121],{"name":113,"url":110},[123,124,27,125,29],"智慧农业","无人机","冬小麦",{"code":127,"slug":128,"name":129,"path":130,"parent_code":131},"A2","wheat","小麦","\u002Ftopic\u002Ffield-crops\u002Fwheat","A",[133],{"code":40,"slug":134,"name":135,"path":136,"parent_code":137},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"status":139,"locked":14,"version":35,"method":36,"evidence":140},"classified",{"candidates":141,"agricultural_evidence":42},[142,148,152,159],{"code":127,"score":143,"supported":42,"title":144,"abstract":146,"tags":147},26,[145,128],"winter wheat",[129,145,128],[125],{"code":40,"score":117,"supported":42,"title":149,"abstract":150,"tags":151},[46],[45,46],[29,27],{"code":153,"score":105,"supported":42,"title":154,"abstract":155,"tags":158},"I2",[],[156,157],"机器学习","machine learning",[],{"code":160,"score":22,"supported":14,"title":161,"abstract":162,"tags":163},"I1",[],[],[123],[165,166],"无人机 多源遥感 冬小麦 生物量","冬小麦 地上生物量 随机森林","无人机多源遥感冬小麦生物量-5367","10.1016\u002Fj.atech.2026.102656",{"doi":168,"openalex_id":170,"authors":171,"venue":113,"cited_by_count":94,"oa_url":110,"card":187,"direction":100,"ingested_from":102},"W7222806066",[172,174,176,178,181,183,185],{"name":173,"orcid":9},"Guangtao Wang",{"name":175,"orcid":9},"Sumei Yao",{"name":177,"orcid":9},"Yanzheng Xie",{"name":179,"orcid":180},"Anzhen Qin","https:\u002F\u002Forcid.org\u002F0000-0002-2550-0015",{"name":182,"orcid":9},"Xiangdong Chen",{"name":184,"orcid":9},"Yuhao Zhang",{"name":186,"orcid":9},"Lina Jiang",{"tldr":188,"method":189,"finding":190,"direction":100,"opportunity":191},"融合无人机多源遥感特征与衍生特征，用机器学习估算冬小麦地上生物量。","无人机植被指数、纹理、LiDAR特征及衍生特征结合四种机器学习算法建模。","多源特征融合优于单一特征，随机森林最优（R²=0.923），精度随生育期变化。","可探索生育期自适应建模与特征选择，提升跨期生物量估算的稳健性与可迁移性。","2026-10-20T23:30:03.106267Z",{"id":194,"title":195,"url":196,"summary":197,"summary_zh":198,"content":9,"source_name":199,"source_url":196,"published_at":200,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":201,"score_detail":202,"sources":205,"tags":207,"primary_topic":211,"secondary_topics":212,"classification":213,"search_phrases":238,"slug":241,"view_count":94,"doi":242,"paper":243,"created_at":263},4900,"Landscape fragmentation and carbon storage dynamics under co-evolving governance in the Shaanxi Yellow River wetland","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-67308-0","Wetland sustainability emerges from coupled interactions among governance, hydrology, land-use change, landscape structure and ecosystem services. Guided by Evolutionary Governance Theory, this study examines their co-evolution in the Shaanxi Yellow River Wetland from 1992 to 2023. Governance trajectories, hydrological dynamics and human land-use processes are treated as interacting antecedent conditions expressed through land use\u002Fland cover transitions, while multidimensional landscape fragmentation and land use\u002Fland cover-based estimated carbon storage are assessed as complementary structural and functional outcomes. Descriptive policy-document frequency and milestone analyses, multi-source remote sensing, the Integrated Landscape Fragmentation Index, InVEST carbon modelling, spatial autocorrelation, and overlay analysis were integrated to examine the relationships between governance evolution, landscape fragmentation and carbon storage. Estimated total carbon storage increased by 34.64%. Fragmentation, however, followed a fluctuating trajectory, intensifying through 2010, temporarily easing in 2020 and increasing again by 2023, with persistent spatial hotspots. High estimated carbon storage repeatedly co-occurred with high fragmentation, indicating heterogeneous pattern–function alignment and mismatch rather than uniform ecological recovery. Interpreted contextually, these trajectories show that more frequent policy issuance and increasing legal integration coexisted with hydrological variability, cropland transitions, aquaculture expansion and path-dependent land-use pressures. Carbon-storage gains alone therefore cannot indicate landscape recovery. Adaptive wetland governance should align restoration with hydrological processes, landscape configuration and multiple ecosystem functions.","湿地可持续性源于治理、水文、土地利用变化、景观结构与生态系统服务之间的耦合互动。本研究以演化治理理论为指导，考察1992年至2023年陕西黄河湿地上述要素的共演化过程。治理轨迹、水文动态与人类土地利用过程被视为相互作用的前置条件，并通过土地利用\u002F土地覆盖转移加以表征；而多维景观破碎化与基于土地利用\u002F土地覆盖估算的碳储量则作为互补的结构性与功能性结果予以评估。研究综合运用描述性政策文件频次与里程碑分析、多源遥感、综合景观破碎化指数、InVEST碳模型、空间自相关及叠加分析，以考察治理演化、景观破碎化与碳储量之间的关系。估算碳储量总量增加了34.64%。然而，破碎化呈波动轨迹，至2010年持续加剧，2020年暂时缓解，到2023年再度上升，并存在持续的空间热点。高估算碳储量与高破碎化反复共现，表明格局—功能关系呈现异质性的一致与错配，而非均质的生态恢复。结合语境加以解读，这些轨迹表明，政策发布频率增加与法律整合程度提高，与水文变异性、耕地转移、水产养殖扩张及路径依赖型土地利用压力并存。因此，仅凭碳储量增益无法表明景观恢复。适应性湿地治理应将恢复与水文过程、景观配置及多重生态系统功能相协调。","Scientific Reports","2026-10-12T00:00:00Z",78,{"impact":117,"substance":118,"depth":19,"authority":203,"freshness":41,"relevant":22,"comment":204},14,"基于多源遥感与InVEST模型揭示陕西黄河湿地治理—景观—碳储协同演化，方法扎实、结论有政策参考价值。",[206],{"name":199,"url":196},[27,208,30,209,210],"湿地保护","生态治理","景观破碎化",{"code":40,"slug":134,"name":135,"path":136,"parent_code":137},[],{"status":139,"locked":14,"version":35,"method":36,"evidence":214},{"candidates":215,"agricultural_evidence":42},[216,221,228,234],{"code":40,"score":217,"supported":42,"title":218,"abstract":219,"tags":220},7,[],[45,46],[27],{"code":222,"score":105,"supported":42,"title":223,"abstract":224,"tags":227},"D",[],[225,226],"水产","aquaculture",[],{"code":61,"score":229,"supported":42,"title":230,"abstract":231,"tags":233},4,[],[232],"生态系统服务",[208],{"code":56,"score":22,"supported":14,"title":235,"abstract":236,"tags":237},[],[],[30],[239,240],"陕西黄河湿地 碳储量","黄河湿地 景观破碎化","陕西黄河湿地碳储量-4900","10.1038\u002Fs41598-026-67308-0",{"doi":242,"openalex_id":244,"authors":245,"venue":199,"cited_by_count":94,"oa_url":256,"card":257,"direction":100,"ingested_from":102},"W7221528662",[246,248,251,254],{"name":247,"orcid":9},"Menghan Gao",{"name":249,"orcid":250},"Khai Ern Lee","https:\u002F\u002Forcid.org\u002F0000-0002-1463-3787",{"name":252,"orcid":253},"Aida Soraya Shamsuddin","https:\u002F\u002Forcid.org\u002F0000-0001-5212-7714",{"name":255,"orcid":9},"Bing He","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-67308-0_reference.pdf",{"tldr":258,"method":259,"finding":260,"direction":261,"opportunity":262},"研究陕西黄河湿地1992-2023年治理、景观破碎化与碳储量的协同演变关系。","政策文本分析、多源遥感、景观破碎化指数、InVEST碳模型与空间自相关。","碳储量增34.64%，但破碎化波动加剧，高碳储量与高破碎化反复共存。","农业绿色发展与碳","可探究治理政策频率与景观格局错配机制，构建水文-格局-碳功能协同的适应性治理评估框架。","2026-10-14T23:30:28.853576Z",{"id":265,"title":266,"url":267,"summary":268,"summary_zh":269,"content":9,"source_name":270,"source_url":267,"published_at":271,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":201,"score_detail":272,"sources":274,"tags":276,"primary_topic":280,"secondary_topics":285,"classification":286,"search_phrases":311,"slug":314,"view_count":94,"doi":315,"paper":316,"created_at":334},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":117,"substance":118,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":273},"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。",[275],{"name":270,"url":267},[277,27,278,30,279],"生态保护","土地利用","南水北调",{"code":56,"slug":281,"name":282,"path":283,"parent_code":284},"carbon","农业碳与绿色低碳","\u002Ftopic\u002Fenvironment\u002Fcarbon","H",[],{"status":139,"locked":14,"version":35,"method":36,"evidence":287},{"candidates":288,"agricultural_evidence":42},[289,296,302,307],{"code":56,"score":13,"supported":42,"title":290,"abstract":291,"tags":295},[],[292,293,294],"碳排放","碳汇","carbon sequestration",[30],{"code":297,"score":50,"supported":42,"title":298,"abstract":299,"tags":301},"H1",[],[300],"耕地保护",[],{"code":61,"score":303,"supported":14,"title":304,"abstract":305,"tags":306},2,[],[],[278,277],{"code":40,"score":22,"supported":14,"title":308,"abstract":309,"tags":310},[],[],[27],[312,313],"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":315,"openalex_id":317,"authors":318,"venue":270,"cited_by_count":94,"oa_url":267,"card":329,"direction":261,"ingested_from":102},"W7221508293",[319,321,324,326],{"name":320,"orcid":9},"Xiaoyang Li",{"name":322,"orcid":323},"Wei Zhang","https:\u002F\u002Forcid.org\u002F0000-0002-6322-2597",{"name":325,"orcid":9},"Qi Jia",{"name":327,"orcid":328},"Lei Liu","https:\u002F\u002Forcid.org\u002F0000-0002-4966-5914",{"tldr":330,"method":331,"finding":332,"direction":261,"opportunity":333},"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。","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":336,"title":337,"url":338,"summary":339,"summary_zh":340,"content":9,"source_name":10,"source_url":338,"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":368,"slug":371,"view_count":22,"doi":372,"paper":373,"created_at":385},4222,"Fixed-ground multisensor monitoring resolves longitudinal vegetation condition and fuel-state structure in Southern California chaparral and oak scenes","https:\u002F\u002Fdoi.org\u002F10.31223\u002Fx58r40","Wildfire-relevant vegetation and fuel conditions evolve through interacting changes in vegetation condition, three-dimensional fuel structure, thermal response, and atmospheric drying, yet these dimensions are commonly monitored separately. This study evaluated repeated fixed-ground multisensor monitoring at two fixed vegetation scenes in Placerita Canyon, California. Multispectral imaging, radiometric thermal imaging, light detection and ranging (LiDAR), and local atmospheric measurements were integrated across persistent dense, mixed, and dry vegetation regions during 10 quality-controlled visits at a native-chaparral hillside and 24 visits at an oak\u002Fcanopy-centered scene. Persistent vegetation regions remained strongly differentiated across sensing domains while also exhibiting longitudinal change. Dense vegetation exceeded dry vegetation in median normalized difference vegetation index (NDVI) by 0.259 and 0.113 at the two sites, showed 4.11 m and 0.56 m greater LiDAR vertical spread, and remained 3.37 °C and 3.88 °C cooler relative to ambient air. Dense chaparral NDVI declined markedly, while dense, mixed, and dry regions at the oak-centered site showed sustained NDVI declines. Corresponding decreases across three additional spectral indices and spatially differentiated oak-canopy changes showed that the temporal signal extended beyond a single spectral index or whole-canopy average. Multisensor analysis of 96 complete visit × site × region observations showed vegetation region as the dominant source of multivariate separation (partial R² = 0.491, p = 0.0005). By preserving spatial identity while integrating spectral, structural, thermal, and atmospheric measurements, this fixed-ground framework provides a local longitudinal approach for characterizing evolving vegetation and fuel conditions before ignition.","与野火相关的植被和燃料条件通过植被状况、三维燃料结构、热响应和大气干燥度的交互变化而演变，但这些维度通常被分别监测。本研究评估了在加利福尼亚州普莱塞里塔峡谷两个固定植被样地进行的重复固定地面多传感器监测。在10次质量控制的访问中，对本地查帕拉尔灌丛山坡的多光谱成像、辐射热成像、激光雷达（LiDAR）和局地大气测量进行了整合，覆盖了持续存在的密集、混合和干燥植被区域；在24次访问中，对以橡树\u002F冠层为中心的样地进行了同样的整合。持续存在的植被区域在不同传感域之间保持强烈分化，同时也表现出纵向变化。在两个样地中，密集植被的中位归一化差异植被指数（NDVI）分别比干燥植被高0.259和0.113，LiDAR垂直展布分别大4.11 m和0.56 m，并且相对于环境空气分别低3.37 °C和3.88 °C。密集查帕拉尔灌丛的NDVI显著下降，而以橡树为中心的样地中的密集、混合和干燥区域则表现出持续的NDVI下降。另外三个光谱指数的相应下降以及空间分化的橡树冠层变化表明，时间信号超出了单一光谱指数或整个冠层平均值。对96次完整访问×样地×区域观测的多传感器分析表明，植被区域是多变量分离的主导来源（偏R² = 0.491，p = 0.0005）。通过在整合光谱、结构、热和大气测量的同时保持空间身份，这一固定地面框架为表征点火前演变中的植被和燃料条件提供了一种局地纵向方法。","2026-10-03T00:00:00Z",69,{"impact":41,"substance":118,"depth":19,"authority":17,"freshness":21,"relevant":22,"comment":344},"多传感器固定地面监测方法新颖、数据扎实，对林火燃料与植被状态遥感研究有参考价值，但属基础研究、应用面偏窄。",[346],{"name":10,"url":338},[27,28,348,349,350],"森林防火","植被指数","多源遥感",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":353},{"candidates":354,"agricultural_evidence":14,"reason":65},[355,360,364],{"code":40,"score":105,"supported":42,"title":356,"abstract":357,"tags":359},[],[358],"multispectral",[350,349,27],{"code":61,"score":22,"supported":14,"title":361,"abstract":362,"tags":363},[],[],[28],{"code":49,"score":22,"supported":14,"title":365,"abstract":366,"tags":367},[],[],[348],[369,370],"Placerita Canyon 灌丛 橡树 多传感器监测","NDVI LiDAR 热成像 燃料状态","PlaceritaCanyon灌丛橡树多传感器监测-4222","10.31223\u002Fx58r40",{"doi":372,"openalex_id":374,"authors":375,"venue":9,"cited_by_count":94,"oa_url":378,"card":379,"direction":384,"ingested_from":102},"W7218368544",[376],{"name":377,"orcid":9},"Andrew Lee","https:\u002F\u002Feartharxiv.org\u002Frepository\u002Fobject\u002F15343\u002Fdownload\u002F26599\u002F",{"tldr":380,"method":381,"finding":382,"direction":100,"opportunity":383},"固定地面多传感器长期监测南加州灌丛与橡树林，揭示植被与可燃物状态变化。","多光谱、热红外、LiDAR与大气测量，10-24次重复观测两个固定样地。","植被区域是多元分离主因，密集植被NDVI下降且比干燥区更凉、垂直结构更复杂。","可扩展至更多生态系统，融合多源时序数据构建可燃物动态预警模型。","智慧农业 \u002F 农业物联网","2026-10-05T23:30:14.188546Z",{"id":387,"title":388,"url":389,"summary":390,"summary_zh":391,"content":9,"source_name":392,"source_url":389,"published_at":393,"category":12,"cover_url":9,"hotness":13,"is_selected":42,"score":394,"score_detail":395,"sources":400,"tags":402,"primary_topic":404,"secondary_topics":405,"classification":406,"search_phrases":421,"slug":424,"view_count":217,"doi":425,"paper":426,"created_at":480},4078,"Assessing plant area index and 3D plant area distribution in wheat canopies from a terrestrial LiDAR system for high-throughput plant phenotyping","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.rse.2026.115711","This study investigates a new methodology to estimate plant area index ( PAI ) and its spatial distribution from 3D point clouds acquired by a system of 3 LiDARs (2 nadir-viewing and one at 45°) mounted on the Phenomobile Unmanned Ground Vehicle designed for high-throughput phenotyping of herbaceous crops. It relies on the computation of the gap fraction at the voxel level using the trajectories of the laser beams to numerically invert the Beer-Lambert law (BL), enabling the simultaneous estimation of PAI and AIA (Average Inclination Angle, in°). The method is evaluated on wheat crops both in silico (i.e. simulations based on the AdelWheat functional structural crop model coupled with a point cloud simulator) and in actual phenotyping trials of a panel of 10 bread wheat genotypes grown in two study sites under different treatments of plant density, water stress and sowing dates. The in silico validation indicates that the numeric inversion of BL with the LiDAR system of the Phenomobile leads to a relative error of 9.8% (RMSE = 0.32, R 2 = 0.99) and 8.4% (RMSE =5.4°, R 2 = 0.95) in the estimation of PAI and AIA , respectively. The validation in actual phenotyping experiments against destructive measurements produced satisfactory results for canopy PAI (RMSE = 1.44, R 2 = 0.82), while requiring prior knowledge on AIA , e.g. either by (i) introducing a regularization term in the cost function used to invert BL, to avoid unexpected high AIA estimations, (ii) using a fixed, reference value of 60°. The analysis of the results indicates that the inclusion of a regularization term mitigates the impact of actual differences in leaf inclination among cultivars on PAI accuracy, while providing a relative description of the actual variability in the vertical profile of leaf inclination across the different cultivars.","本研究探讨了一种新方法，用于从三维点云中估算植物面积指数（PAI）及其空间分布。该点云由安装在Phenomobile无人地面车辆上的3台激光雷达（LiDAR）系统（2台天底视角和1台45°视角）获取，该车辆专为草本作物的高通量表型设计。该方法依赖于利用激光束轨迹在体素水平上计算间隙率，以数值反演Beer-Lambert定律（BL），从而能够同时估算PAI和AIA（平均倾角，单位为°）。该方法在小麦作物上进行了评估，既包括计算机模拟（即基于AdelWheat功能结构作物模型结合点云模拟器的模拟），也包括在实际表型试验中对10个面包小麦基因型组成的 panel 在两个研究地点、不同植株密度、水分胁迫和播种日期处理下的种植。计算机模拟验证表明，利用Phenomobile的LiDAR系统对BL进行数值反演，在PAI和AIA估算中分别产生9.8%（RMSE = 0.32，R² = 0.99）和8.4%（RMSE = 5.4°，R² = 0.95）的相对误差。在实际表型试验中与破坏性测量进行验证，冠层PAI获得了令人满意的结果（RMSE = 1.44，R² = 0.82），但需要关于AIA的先验知识，例如通过以下方式之一：（i）在用于反演BL的代价函数中引入正则化项，以避免出现意外的高AIA估算值；（ii）使用固定的参考值60°。结果分析表明，引入正则化项减轻了品种间叶片倾角实际差异对PAI精度的影响，同时提供了不同品种间叶片倾角垂直剖面实际变异性的相对描述。","Remote Sensing of Environment","2026-10-02T00:00:00Z",84,{"impact":19,"substance":396,"depth":397,"authority":398,"freshness":21,"relevant":22,"comment":399},23,19,15,"基于车载三激光雷达点云反演小麦植物面积指数与倾角分布，仿真与田间验证扎实，属高通量表型领域方法学新进展。",[401],{"name":392,"url":389},[123,129,27,403,31],"高通量表型",{"code":127,"slug":128,"name":129,"path":130,"parent_code":131},[],{"status":139,"locked":14,"version":35,"method":36,"evidence":407},{"candidates":408,"agricultural_evidence":42},[409,413,417],{"code":127,"score":398,"supported":42,"title":410,"abstract":411,"tags":412},[128],[129,128],[129],{"code":160,"score":22,"supported":14,"title":414,"abstract":415,"tags":416},[],[],[123],{"code":40,"score":22,"supported":14,"title":418,"abstract":419,"tags":420},[],[],[27],[422,423],"Phenomobile 激光雷达 小麦","植物面积指数 三维点云","Phenomobile激光雷达小麦-4078","10.1016\u002Fj.rse.2026.115711",{"doi":425,"openalex_id":427,"authors":428,"venue":392,"cited_by_count":94,"oa_url":389,"card":475,"direction":100,"ingested_from":102},"W7215681984",[429,432,434,437,439,441,444,446,449,452,455,457,459,461,463,465,467,469,472],{"name":430,"orcid":431},"Raúl López‐Lozano","https:\u002F\u002Forcid.org\u002F0000-0001-6499-4743",{"name":433,"orcid":9},"Tian Ma",{"name":435,"orcid":436},"Maxime Soma","https:\u002F\u002Forcid.org\u002F0000-0002-9242-3283",{"name":438,"orcid":9},"Marie Weiss",{"name":440,"orcid":9},"Aurélien Ausset",{"name":442,"orcid":443},"Philippe Burger","https:\u002F\u002Forcid.org\u002F0000-0003-3662-7924",{"name":445,"orcid":9},"Bruno Berthon",{"name":447,"orcid":448},"Romain Chapuis","https:\u002F\u002Forcid.org\u002F0000-0001-7196-4622",{"name":450,"orcid":451},"Marie-Pia D'argaignon","https:\u002F\u002Forcid.org\u002F0009-0004-6568-9213",{"name":453,"orcid":454},"Antonin Grau","https:\u002F\u002Forcid.org\u002F0000-0002-5366-9360",{"name":456,"orcid":9},"Florian Larue",{"name":458,"orcid":9},"Romane Le-Roy",{"name":460,"orcid":9},"Rémy Marandel",{"name":462,"orcid":9},"Vincent Mercier",{"name":464,"orcid":9},"Mathieu Roy",{"name":466,"orcid":9},"Gilles Tison",{"name":468,"orcid":9},"Frédéric Venault",{"name":470,"orcid":471},"Pierre Martre","https:\u002F\u002Forcid.org\u002F0000-0002-7419-6558",{"name":473,"orcid":474},"Frédéric Baret","https:\u002F\u002Forcid.org\u002F0000-0002-7655-8997",{"tldr":476,"method":477,"finding":478,"direction":100,"opportunity":479},"利用车载三LiDAR点云反演小麦冠层植物面积指数与三维分布，实现高通量表型分析。","体素间隙率结合Beer-Lambert数值反演，用AdelWheat模型仿真与1","PAI估算R²=0.82，加入正则项可减少叶倾角差异影响并描述垂直分布变异。","可探索多角度LiDAR与正则化反演结合，提升不同基因型叶倾角垂直分布的鲁棒表型提取。","2026-10-03T23:30:24.403056Z",{"id":482,"title":483,"url":484,"summary":485,"summary_zh":486,"content":9,"source_name":487,"source_url":484,"published_at":488,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":489,"score_detail":490,"sources":492,"tags":494,"primary_topic":498,"secondary_topics":502,"classification":504,"search_phrases":541,"slug":544,"view_count":22,"doi":545,"paper":546,"created_at":573},3830,"Current breakthroughs in biomass and carbon modelling of rubber plantations: A bibliometric analysis and literature review","https:\u002F\u002Fdoi.org\u002F10.14719\u002Fpst.15821","Rubber plantations (Hevea brasiliensis (Willd. ex A.Juss.) Müll.Arg.) are expanding tropical carbon (C) stores, storing an estimated 160–300 mg C ha-1 of total ecosystem C at maturity, but precise biomass and soil organic carbon (SOC) assessment remains difficult due to age disparities, historical land-use variations and data gaps. This review systematically analyses trends and research gaps in biomass accumulation of rubber systems. PRISMA 2020 guidelines were used to examine 104 studies in Scopus published between 1999 and 2025. The literature revealed a marked increase in research studies in China and Thailand post-2013 using a bibliometric approach presented by Biblioshiny. The most important methods are species-specific allometry (coefficient of determination (R2) > 0.9), age-based growth curves, remote sensing (RS) regressions (R2 0.2–0.7) and machine learning (ML) models like Random Forest\u002FXGBoost (R2 0.8–0.97, root mean square error (RMSE) 7–40 mg\u002Fha), which outperform simpler parametric models. Deep learning (DL) yields the highest accuracies (RMSE ~6 mg ha-1). Gaps persist in standardised protocols, South Indian datasets (e.g., Tamil Nadu\u002FWestern Ghats) and integrated lifecycle carbon accounting, including latex\u002FSOC. Forest-to-rubber conversion incurs net losses, while cropland-to-rubber\u002Fagroforestry offers gains. The authors recommend adopting harmonised Tier 2\u002F3 IPCC models, multi-decadal RS archives for reducing emissions from deforestation and forest degradation (REDD+) and ML extension to Indian smallholders. Policy integration with offset markets requires avoiding deforestation to realise climate-smart benefits.","橡胶林（Hevea brasiliensis (Willd. ex A.Juss.) Müll.Arg.）正在扩大热带碳（C）储量，据估计，成熟期生态系统总碳储量可达160–300 mg C ha⁻¹，但由于林龄差异、历史土地利用变化及数据缺口，精确评估生物量和土壤有机碳（SOC）仍然困难。本综述系统分析了橡胶系统生物量积累的趋势与研究空白。采用PRISMA 2020指南，对Scopus数据库中1999年至2025年间发表的104篇研究进行了审查。通过Biblioshiny文献计量方法分析发现，2013年后中国和泰国的研究数量显著增加。最重要的方法包括物种特异性异速生长方程（决定系数（R²）> 0.9）、基于林龄的生长曲线、遥感（RS）回归（R² 0.2–0.7）以及机器学习（ML）模型如随机森林\u002FXGBoost（R² 0.8–0.97，均方根误差（RMSE）7–40 mg\u002Fha），后者优于简单的参数模型。深度学习（DL）精度最高（RMSE约6 mg ha⁻¹）。在标准化方案、南印度数据集（如泰米尔纳德邦\u002F西高止山脉）以及涵盖乳胶\u002FSOC的综合生命周期碳核算方面仍存在空白。森林转橡胶林会导致净碳损失，而农田转橡胶林\u002F农林复合经营则带来碳增益。作者建议采用统一的IPCC第2\u002F3层级模型、多年代际遥感档案以支持减少毁林和森林退化所致排放（REDD+），并将机器学习应用扩展至印度小农户。与抵消市场的政策整合需要避免毁林，以实现气候智慧型效益。","Plant Science Today","2026-09-28T00:00:00Z",80,{"impact":19,"substance":118,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":491},"系统综述橡胶林生物量与碳建模研究进展，方法学与数据缺口梳理扎实，对农业碳汇与遥感监测领域有参考价值。",[493],{"name":487,"url":484},[156,27,495,29,496,497],"农业碳汇","橡胶林","碳汇计量",{"code":153,"slug":499,"name":500,"path":501,"parent_code":137},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",[503],{"code":40,"slug":134,"name":135,"path":136,"parent_code":137},{"status":139,"locked":14,"version":35,"method":36,"evidence":505},{"candidates":506,"agricultural_evidence":42},[507,514,520,524,531,537],{"code":508,"score":203,"supported":42,"title":509,"abstract":511,"tags":513},"I7",[510],"bibliometric",[512,510],"文献计量",[],{"code":153,"score":13,"supported":42,"title":515,"abstract":516,"tags":519},[],[517,156,157,518],"深度学习","deep learning",[156],{"code":40,"score":41,"supported":42,"title":521,"abstract":522,"tags":523},[],[45,46],[29,27],{"code":525,"score":105,"supported":42,"title":526,"abstract":527,"tags":530},"H2",[],[528,529],"土壤","soil",[],{"code":532,"score":50,"supported":42,"title":533,"abstract":534,"tags":536},"G2",[],[535],"小农户",[],{"code":56,"score":22,"supported":14,"title":538,"abstract":539,"tags":540},[],[],[495],[542,543],"橡胶林 生物量 碳储量","橡胶园 遥感 机器学习","橡胶林生物量碳储量-3830","10.14719\u002Fpst.15821",{"doi":545,"openalex_id":547,"authors":548,"venue":487,"cited_by_count":94,"oa_url":567,"card":568,"direction":384,"ingested_from":102},"W7214623646",[549,552,555,558,561,564],{"name":550,"orcid":551},"R M Dhinu","https:\u002F\u002Forcid.org\u002F0009-0008-3471-1093",{"name":553,"orcid":554},"M. Vijayabhama","https:\u002F\u002Forcid.org\u002F0000-0001-9437-9477",{"name":556,"orcid":557},"Balaji Kannan","https:\u002F\u002Forcid.org\u002F0000-0003-2330-0893",{"name":559,"orcid":560},"K Baranidharan","https:\u002F\u002Forcid.org\u002F0000-0001-9371-8588",{"name":562,"orcid":563},"M Kalpana","https:\u002F\u002Forcid.org\u002F0000-0002-0201-6968",{"name":565,"orcid":566},"M Sankavi","https:\u002F\u002Forcid.org\u002F0009-0006-1493-1123","https:\u002F\u002Fhorizonepublishing.com\u002Fjournals\u002Findex.php\u002FPST\u002Farticle\u002Fdownload\u002F15821\u002F16343",{"tldr":569,"method":570,"finding":571,"direction":100,"opportunity":572},"综述橡胶林生物量与碳建模研究进展，指出机器学习与深度学习精度最高但标准化和区域数据仍不足。","PRISMA 2020 文献计量分析 104 篇 Scopus 文献，比较异速生","机器学习\u002F深度学习精度优于传统模型，但标准协议、印度南部数据和全生命周期碳核算仍存空白。","可面向印度小农户构建多年代遥感与机器学习融合的橡胶林碳核算与REDD+监测体系。","2026-09-30T23:30:12.585164Z"]