[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2423":3},{"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,"view_count":32,"doi":33,"paper":34,"created_at":52},2423,"Spatio-temporal evolution and driving force analysis of carbon storage coupled with PLUS-InVEST-GeoDetector in the Fen River Basin of China","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-71784-9","Abstract Against the backdrop of China’s “Dual Carbon” (carbon peak and carbon neutrality) strategic initiative, terrestrial ecosystem carbon sequestration has become a core pillar of climate mitigation and ecological governance. This study systematically quantifies the spatiotemporal distribution of carbon storage and and projects long-term carbon sink trajectories under divergent socioecological development pathways across the Fen River Basin—a representative ecologically fragile tributary catchment of the Yellow River. Four scenarios based on the basin’s ecological landscape planning were established: Natural Development, Economic Priority, Cropland Protection, and Ecological Protection. By coupling the PLUS and InVEST models, an analytical framework linking land use change with ecosystem carbon storage was constructed to simulate and predict the spatial distribution and dynamics of carbon storage under different scenarios. The Geodetector was used to identify the key driving factors. The results indicate that: (1) From 2005 to 2020, cropland remained the dominant land use type but showed a continuous decreasing trend alongside grassland degradation. Forest land area initially decreased before recovering slightly, while built-land expanded consistently. (2) The total carbon storage in the basin showed a declining trend, decreasing from 700.32 Tg in 2005 to 683.48 Tg in 2020, with a cumulative loss of 16.84 Tg. Soil organic carbon was the major carbon pool. Spatially, areas with high carbon storage were primarily distributed in the eastern and western parts of the basin, while the central-southern regions generally had lower carbon storage. Carbon storage exhibited significant positive spatial autocorrelation, with High-High clusters (hot spots) being more extensive than Low-Low clusters (cold spots). The area experiencing carbon loss was substantially larger than the area with carbon gain. (3) Multi-scenario simulations revealed that different development pathways significantly impact carbon storage. The Ecological Protection scenario yielded the highest carbon storage (683.57 Tg), highlighting the effectiveness of ecological protection in enhancing regional carbon sink capacity. (4) NDVI was the dominant factor influencing the spatial heterogeneity of carbon storage, possessing strong independent explanatory power. Its interactive effects with other factors further enhanced the ability to explain variations in carbon storage. This study elucidates the coupling mechanism between land use change and carbon storage, providing a scientific reference for ecological protection, land structure optimization, and carbon sink enhancement in the Fen River Basin.","在中国“双碳”（碳达峰与碳中和）战略倡议背景下，陆地生态系统碳汇已成为气候减缓与生态治理的核心支柱。本研究系统量化了汾河流域——黄河具有代表性的生态脆弱支流流域——碳储量的时空分布，并预测了不同社会生态发展路径下的长期碳汇轨迹。基于流域生态景观规划，设立了四种情景：自然发展、经济优先、耕地保护和生态保护。通过耦合PLUS与InVEST模型，构建了土地利用变化与生态系统碳储量关联的分析框架，以模拟和预测不同情景下碳储量的空间分布与动态变化。运用地理探测器识别关键驱动因子。结果表明：（1）2005年至2020年，耕地仍为主要土地利用类型，但呈持续下降趋势，草地亦退化。林地面积先减少后略有恢复，建设用地持续扩张。（2）流域碳储量总量呈下降趋势，从2005年的700.32 Tg降至2020年的683.48 Tg，累计损失16.84 Tg。土壤有机碳为主要碳库。空间上，高碳储量区主要分布于流域东西部，中南部地区碳储量普遍较低。碳储量表现出显著的正空间自相关，高-高集聚区（热点）范围大于低-低集聚区（冷点）。碳损失面积显著大于碳增益面积。（3）多情景模拟揭示，不同发展路径对碳储量影响显著。生态保护情景碳储量最高（683.57 Tg），凸显了生态保护在提升区域碳汇能力方面的有效性。（4）NDVI是影响碳储量空间分异的主导因子，具有较强独立解释力。其与其他因子的交互作用进一步增强了对碳储量变化的解释能力。本研究阐明了",null,"Scientific Reports","2026-09-12T00:00:00Z","论文",10,false,77,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},16,21,18,14,8,1,"耦合PLUS-InVEST-GeoDetector量化汾河流域碳储时空演变并模拟多情景碳汇路径，方法新颖、数据扎实，对黄河流域生态保护与土地结构优化有参考价值。",[25],{"name":10,"url":6},[27,28,29,30,31],"遥感","生态保护","黄河流域","土地利用","碳汇",0,"10.1038\u002Fs41598-026-71784-9",{"doi":33,"openalex_id":35,"authors":36,"venue":10,"cited_by_count":32,"oa_url":43,"card":44,"direction":50,"ingested_from":51},"W7212371101",[37,39,41],{"name":38,"orcid":9},"Jie Chen",{"name":40,"orcid":9},"Pengxiang Gao",{"name":42,"orcid":9},"Yi Hou","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-71784-9_reference.pdf",{"tldr":45,"method":46,"finding":47,"direction":48,"opportunity":49},"耦合PLUS-InVEST-GeoDetector模拟汾河流域多情景碳储量演变并识别驱动因子。","PLUS与InVEST耦合模拟四情景土地利用，GeoDetector识别驱动因子","2005-2020年碳储量下降16.84 Tg，生态保护情景碳储量最高，NDVI为主导驱动因子。","农业绿色发展与碳","可引入农业管理措施与政策情景，细化耕地保护对碳汇的贡献机制研究。","农业遥感与作物表型","openalex","2026-09-14T23:30:24.574426Z"]