[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2526":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":57},2526,"Scale-dependent biases in systematic land cover maps undermine freshwater ecological assessment","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs10661-026-15882-1","Abstract Land cover is a key determinant of landscape structure and ecological processes across spatial and temporal scales. In freshwater ecosystems, where riparian and catchment land cover regulate hydrology, nutrient inputs, and habitat quality, most ecological assessments rely on systematically produced land cover maps whose reliability varies with scale and context. Despite their widespread use, the extent to which these products accurately represent landscape patterns and long-term dynamics at ecologically relevant scales remains poorly understood. Here, we evaluate the reliability of systematic land cover maps and assess whether Landsat-based supervised classification (SC) provides more accurate and temporally consistent representations of land cover patterns in draining catchments and riparian areas. We analysed five headwater catchments (\u003C 70 km 2 ) within Natura 2000 sites in northern Spain and reconstructed land cover dynamics over four decades (1984–2023) using Random Forest classification implemented in Google Earth Engine. SC results were compared with CORINE Land Cover, SIOSE, and the Spanish National Forest Inventory across three spatial scales, relevant for freshwater ecological processes: catchment, riparian corridor, and reach. SC achieved consistently high accuracy across all periods (mean overall accuracy = 87.8%), outperforming CORINE and SIOSE and matching NFI only for forest classes. Discrepancies between SC and systematic maps increased at finer spatial scales and varied with landscape context, particularly in heterogeneous riparian environments. Systematic maps also showed limited temporal consistency, frequently misrepresenting land cover trends relative to SC. These scale-dependent and context-specific biases can alter estimates of riparian vegetation and landscape composition and their temporal dynamics, potentially leading to misleading assessments of freshwater ecosystem condition. Our findings demonstrate that reliance on coarse-scale land cover products can distort ecological inference, particularly for freshwater ecosystems where ecological condition depends strongly on catchment and riparian landscape structure. Integrating reproducible, remote sensing–based classifications offers a practical pathway to improving the accuracy and interpretability of land cover indicators in landscape ecological research.","摘要 土地覆盖是跨时空尺度上景观结构和生态过程的关键决定因素。在淡水生态系统中，河岸带和流域土地覆盖调节着水文过程、养分输入和栖息地质量，而大多数生态评估依赖于系统性生产的土地覆盖图，其可靠性随尺度和背景而变化。尽管这些产品被广泛使用，但其在生态相关尺度上准确表征景观格局和长期动态的程度仍鲜为人知。本研究评估了系统性土地覆盖图的可靠性，并检验基于Landsat的监督分类（supervised classification, SC）是否能更准确、更具时间一致性地表征汇水流域和河岸带的土地覆盖格局。我们分析了西班牙北部Natura 2000保护区内五个源头流域（\u003C 70 km²），利用在Google Earth Engine中实现的随机森林分类重建了四十年（1984–2023）的土地覆盖动态。SC结果在三个与淡水生态过程相关的空间尺度——流域、河岸廊道和河段——上与CORINE土地覆盖、SIOSE和西班牙国家森林清查进行了比较。SC在所有时期均达到了持续较高的精度（平均总体精度 = 87.8%），优于CORINE和SIOSE，仅在森林类别上与NFI相当。SC与系统性地图之间的差异在更精细的空间尺度上增大，并随景观背景而变化，尤其是在异质性河岸环境中。系统性地图还表现出有限的时间一致性，相对于SC频繁错误表征土地覆盖趋势。这些尺度依赖性和背景特异性的偏差可能改变对河岸植被和景观组成及其时间动态的估计，从而可能导致对淡水生态系统状况的误导性评估。我们的研究结果表明，依赖粗尺度土地覆盖产品可能扭曲生态推断，尤其是对于生态状况强烈依赖于流域和河岸景观结构的淡水生态系统。整合可重复的、基于遥感的分类为提升景观生态研究中土地覆盖指标的准确性和可解释性提供了一条实用途径。",null,"Environmental Monitoring and Assessment","2026-09-14T00:00:00Z","论文",10,false,78,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},16,22,18,13,9,1,"该研究揭示系统性土地覆盖图存在尺度依赖偏差，提出基于遥感的可复现分类方法，对农业面源污染评估和流域生态管理有实质参考价值，但属细分领域学术进展，公共影响有限。",[25],{"name":10,"url":6},[27,28,29,30,31],"农业信息化","遥感监测","土地覆盖","淡水生态","生态评估",0,"10.1007\u002Fs10661-026-15882-1",{"doi":33,"openalex_id":35,"authors":36,"venue":10,"cited_by_count":32,"oa_url":49,"card":50,"direction":54,"ingested_from":56},"W7212458932",[37,40,43,46],{"name":38,"orcid":39},"Iñaki Fernández de Larrea","https:\u002F\u002Forcid.org\u002F0000-0002-5910-6905",{"name":41,"orcid":42},"Jon Gonzalez-Ibarzabal","https:\u002F\u002Forcid.org\u002F0009-0001-2278-1245",{"name":44,"orcid":45},"Aitor Bastarrika Izaguirre","https:\u002F\u002Forcid.org\u002F0000-0001-8454-7289",{"name":47,"orcid":48},"Aitor Larrañaga","https:\u002F\u002Forcid.org\u002F0000-0002-0185-9154","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs10661-026-15882-1.pdf",{"tldr":51,"method":52,"finding":53,"direction":54,"opportunity":55},"评估系统性土地覆盖图在淡水生态评估中的尺度偏差，并用Landsat监督分类重建四十年土地覆盖动态。","随机森林分类与Google Earth Engine，对比CORINE、SIOS","监督分类精度更高，系统图在更细尺度偏差增大且时间一致性差，可能误导淡水生态评估。","农业遥感与作物表型","可研究多尺度遥感分类产品在河岸带等异质景观中的偏差校正，并开发面向淡水生态的尺度自适应土地覆盖指标。","openalex","2026-09-15T23:30:20.336621Z"]