Evaluating land and tree cover datasets for the identification of agroforestry in temperate Europe

作者：Arina Machine, Moya Burns, H. Balzter · 年份：2026 · DOI：10.5194/egusphere-egu26-12956 · 研究领域：Remote Sensing in Agriculture、Agroforestry and silvopastoral systems、Remote Sensing and LiDAR Applications

Agroforestry, the integration of trees on productive agricultural land (Mosquera-Losada et al., 2018), can be identified through remote sensing methods by the combination of land cover and tree cover maps. Previous work has classified agroforestry as agricultural land with greater than 5% tree cover (Lawson et al., 2025; Zomer et al., 2016).However, there exist several regional, European, and global land cover and tree cover products that could be suitable for agroforestry identification, but these products vary in resolution, data inputs, and methodology of production. Our work benchmarked the performance of four land cover maps(Büttne et al., 2021; Karvatte et al., 2021; Schultz et al., 2025; UKCEH, 2022) and nine tree cover maps(Brandt et al., 2024; Copernicus, 2023, 2025; Hunter et al., 2025; Lang et al., 2023; Tolan et al., 2024; Weinstein et al., 2020) that were capable of mapping trees outside of woodlands. We evaluated the datasets’ ability to identify agroforestry on 25 agroforestry sites across the United Kingdom, including a mix of silvoarable and silvopastoral systems, as well as planting ages, densities, and species, as well as nearby agricultural (no trees) and woodland (no agriculture) control fields.We found that a number of datasets used in previous studies underperformed when distinguishing agroforestry from control fields as well as the previously utilised pixel-based approaches being unsuitable to identify agroforestry fields as a whole. Datasets with coar...