Scale-dependent biases in systematic land cover maps undermine freshwater ecological assessment

作者：Iñaki Fernández de Larrea, Jon Gonzalez-Ibarzabal, Aitor Bastarrika Izaguirre, Aitor Larrañaga · 发表于：Environmental Monitoring and Assessment · 年份：2026 · DOI：10.1007/s10661-026-15882-1 · 研究领域：Remote Sensing in Agriculture、Land Use and Ecosystem Services、Hydrology and Watershed Management Studies

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 (< 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 spati...