Data and resolution requirements in mapping biodiversity and ecosystem services in boreal drained peatland forests

Boreal drained-peatland forests provide a wide range of interlinked and overlapping biodiversity and ecosystem services (BES). Mapping BES is essential for informed decision-making to manage and conserve forest resources effectively. Centimeter-level resolution is often deemed necessary for mapping BES in complex landscapes like northern boreal drained peatland forests. However, systematic tests on the optimal spatial resolution, data combinations, and the impact of including or omitting specific datasets on mapping accuracy remain limited. In this study, we mapped several indices of BES, including biodiversity conservation, habitat suitability, non-timber forest products (e.g., bilberry and cowberry yield), scenic beauty, timber production and carbon storage, using multiple remote sensing (RS) data. These data sources included airborne laser scanning, unmanned aerial vehicle (UAV) data, and optical satellite data from PlanetScope, Sentinel-2, and Landsat 8-9. The specific objectives were: (1) to identify the best RS predictors for individual BES; (2) to develop random forest regression models for predicting BES; (3) to compare the performance of difference RS data and (4) to upscale the pixel-level distribution of BES across different canopy covers closed, partial, and open. Our preliminary findings indicate that BES can be accurately predicted using a set of height, density, and multispectral predictors, with explained variance ranging from 13% to 90% for individual BES. Model performance varied among individual BES and across different RS data sources. Furthermore, we successfully upscaled the BES predictions to map the spatial distribution of BES across the entire study area. Areas with closed canopies exhibited higher BES potential compared to partial and open canopies. These results demonstrate that RS data can be effectively used to predict BES on a spatial scale, providing a valuable tool for sustainable forest management.