[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2424":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":60},2424,"Nonlinear relationships between vegetation dynamics and ecosystem services in the Yangtze River Basin: insights from random forest and GTWR analyzes","https:\u002F\u002Fdoi.org\u002F10.1080\u002F17538947.2026.2722459","Understanding the nonlinear relationships between vegetation dynamics and ecosystem services (ES) is essential for sustainable ecosystem management in the Yangtze River Basin (YRB). Using the Google Earth Engine platform, we analyzed NDVI dynamics in the YRB from 2000 to 2020. The InVEST model was used to assess four key ES, including water yield (WY), carbon storage (CS), habitat quality (HQ), and soil retention (SR). Random forest and spatiotemporal geographically weighted regression models were further applied to explore the nonlinear relationships and spatial heterogeneity between NDVI and ES. The results showed that: (1) mean NDVI in the YRB was 0.692, 0.697, and 0.724 in 2000, 2010, and 2020, respectively, exhibiting clear spatiotemporal heterogeneity. (2) WY showed a non-monotonic trajectory of initial increase followed by decline, SR increased markedly, HQ remained relatively stable, and CS showed a slight decrease. (3) CS and SR showed more pronounced positive associations with NDVI, whereas WY and HQ exhibited stronger interval variability or weaker localized effects. (4) NDVI generally had positive local effects on CS, HQ, and SR, while the NDVI–WY relationship exhibited both positive associations and localized negative effects. These findings provide valuable insights for ecological restoration and ecosystem service management in the YRB.","理解植被动态与生态系统服务（ES）之间的非线性关系，对长江流域（YRB）生态系统的可持续管理至关重要。本研究基于Google Earth Engine平台，分析了2000—2020年长江流域NDVI的动态变化，并利用InVEST模型评估了四项关键生态系统服务，包括产水量（WY）、碳储量（CS）、生境质量（HQ）和土壤保持（SR）。进一步采用随机森林模型和时空地理加权回归模型，探讨NDVI与生态系统服务之间的非线性关系及空间异质性。结果表明：（1）长江流域NDVI均值在2000年、2010年和2020年分别为0.692、0.697和0.724，呈现出明显的时空异质性。（2）产水量呈先增后减的非单调变化轨迹，土壤保持显著增加，生境质量相对稳定，碳储量略有下降。（3）碳储量和土壤保持与NDVI的正相关关系更为显著，而产水量和生境质量则表现出更强的区间变异性或较弱的局部效应。（4）NDVI总体上对碳储量、生境质量和土壤保持具有正向局部效应，而NDVI与产水量之间既存在正相关关系，也存在局部负效应。这些发现可为长江流域生态修复与生态系统服务管理提供有价值的参考。",null,"International Journal of Digital Earth","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,"基于GEE与随机森林、GTWR方法揭示长江流域植被与生态系统服务非线性关系，方法新颖、数据扎实，对流域生态修复有参考价值，但属学术论文、公共政策影响有限。",[25],{"name":10,"url":6},[27,28,29,30,31],"遥感","长江流域","生态系统服务","植被动态","数字地球",0,"10.1080\u002F17538947.2026.2722459",{"doi":33,"openalex_id":35,"authors":36,"venue":10,"cited_by_count":32,"oa_url":51,"card":52,"direction":58,"ingested_from":59},"W7212379230",[37,39,41,44,47,49],{"name":38,"orcid":9},"Jinhuang Lin",{"name":40,"orcid":9},"Jixing Huang",{"name":42,"orcid":43},"Yongwu Dai","https:\u002F\u002Forcid.org\u002F0000-0002-8562-5733",{"name":45,"orcid":46},"Guoqing Li","https:\u002F\u002Forcid.org\u002F0000-0003-1767-3052",{"name":48,"orcid":9},"Yuanrui Zang",{"name":50,"orcid":9},"Yan Huang","https:\u002F\u002Fwww.tandfonline.com\u002Fdoi\u002Fpdf\u002F10.1080\u002F17538947.2026.2722459?needAccess=true",{"tldr":53,"method":54,"finding":55,"direction":56,"opportunity":57},"用随机森林与GTWR揭示长江流域2000—2020年NDVI与四项生态系统服务的非线性关系。","GEE提取NDVI，InVEST评估四项ES，随机森林与GTWR分析非线性及空间","NDVI与碳储、土壤保持正相关更强，与产水呈先增后减且存在局部负效应。","农业绿色发展与碳","可引入多源遥感与作物物候变量，探究NDVI-ES非线性阈值及跨尺度驱动机制。","农业遥感与作物表型","openalex","2026-09-14T23:30:24.653682Z"]