[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3559":3,"related-3559":58},{"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":18,"tags":20,"primary_topic":9,"secondary_topics":21,"classification":22,"search_phrases":38,"slug":39,"view_count":40,"doi":41,"paper":42,"created_at":57},3559,"Multiscale dynamics and nonlinear associations of ecological quality in the Hangzhou Bay urban agglomeration, China","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffevo.2026.1955721","Ecological quality change in rapidly urbanizing regions is spatially heterogeneous, but assessments based on a single analytical scale may obscure local changes and alter the apparent factor importance. This study constructed a 2000–2024 Remote Sensing Ecological Index (RSEI) series for the Hangzhou Bay Urban Agglomeration using Google Earth Engine and Landsat imagery. Ecological changes were compared across 1, 3, 5, and 10 km grids and county units with XGBoost–SHAP model fitted to pooled observations from six benchmark years. Mean RSEI showed an early decline followed by sustained recovery, yielding a net increase of 0.0423. High values were concentrated in southwestern forested areas, whereas low values occurred mainly in the northern plain, densely urbanized bay areas, and urban-expansion corridors. The proportion of improved area increased from 75.34% at 1 km to 84.25% at the county scale. During 2015–2020, county-level aggregation increased the stable proportion while reducing the improved proportion, indicating that coarse units can mask opposing local changes. Land-use intensity had the largest relative mean absolute SHAP contribution at all scales (44.59%–55.43%), while the contribution of nighttime light intensity rose with aggregation and reached 20.33% at the county scale. These results demonstrate that ecological-change detection and interpretation of modeled associations are scale dependent, supporting a nested multiscale assessment framework.","快速城市化地区的生态质量变化具有空间异质性，但基于单一分析尺度的评估可能掩盖局部变化并改变因子重要性的表观结果。本研究利用Google Earth Engine和Landsat影像构建了2000—2024年杭州湾城市群遥感生态指数（RSEI）序列。在1、3、5和10 km网格及县级单元上比较了生态变化，并采用XGBoost–SHAP模型对六个基准年的汇总观测数据进行拟合。RSEI均值呈先下降后持续恢复的趋势，净增加0.0423。高值集中在西南部森林地区，低值主要出现在北部平原、高度城市化的湾区及城市扩张廊道。改善面积比例从1 km尺度的75.34%增至县级尺度的84.25%。2015—2020年期间，县级聚合提高了稳定比例而降低了改善比例，表明粗尺度单元可掩盖相反的局部变化。土地利用强度在所有尺度上均具有最大的相对平均绝对SHAP贡献（44.59%—55.43%），而夜间灯光强度的贡献随聚合程度上升，在县级尺度达到20.33%。这些结果表明，生态变化检测和模型关联解释具有尺度依赖性，支持构建嵌套多尺度评估框架。",null,"Frontiers in Ecology and Evolution","2026-09-24T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"该文为杭州湾城市群生态质量遥感评估的生态学论文，与三农、农业信息化、智慧农业、数字乡村等主题无直接关联，不建议进入每日精选。",[19],{"name":10,"url":6},[12],[],{"status":23,"locked":14,"version":24,"method":25,"evidence":26},"needs_review","2026-10-22.2","rules",{"candidates":27,"agricultural_evidence":14,"reason":37},[28],{"code":29,"score":30,"supported":31,"title":32,"abstract":33,"tags":36},"I3",6,true,[],[34,35],"遥感","remote sensing",[],"农业相关性需要复核",[],"Multiscaledynamicsandnonlinearassociatio-3559",2,"10.3389\u002Ffevo.2026.1955721",{"doi":41,"openalex_id":43,"authors":44,"venue":10,"cited_by_count":15,"oa_url":49,"card":50,"direction":54,"ingested_from":56},"W7214202972",[45,47],{"name":46,"orcid":9},"Zhiyuan Xu",{"name":48,"orcid":9},"Fuyan Ke","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fecology-and-evolution\u002Farticles\u002F10.3389\u002Ffevo.2026.1955721\u002Fpdf",{"tldr":51,"method":52,"finding":53,"direction":54,"opportunity":55},"构建杭州湾城市群2000-2024年RSEI序列，揭示生态质量的多尺度动态与非线性驱动关联。","Google Earth Engine与Landsat构建RSEI，结合XGBo","生态质量先降后升净增0.0423，改善区比例随尺度由75.34%升至84.25%，驱动因子重要性具尺","农业遥感与作物表型","可将多尺度嵌套框架与非线性归因迁移至农田生态质量评估，探究耕地利用强度与夜间灯光的尺度效应。","openalex","2026-09-26T23:30:31.485495Z",{"total":15,"page":59,"page_size":59,"items":60},1,[]]