Temperate forests of high conservation value are successfully identified by satellite and LiDAR data fusion

Abstract

Forest ecosystems will play a critical role in achieving policy targets for biodiversity and conservation, such as those set out in the EU Biodiversity strategy for 2030. However, practitioners need to know where forests of high conservation value are to make the best-informed decisions about which forests to prioritize. Here, we combine airborne LiDAR (airborne laser scanning/ALS), optical satellite imagery, and gridded datasets on soil and water availability with machine learning models to predict forests' conservation value across Denmark. We then use change-detection algorithms to identify forests that had been disturbed since the collection of the LiDAR data to produce up-to-date estimates for the year 2020. Our models reached a high predictive capacity (82% accuracy) and suggested that 1982 km² (~31%) of Denmark's forests were of potential high conservation value. Our study demonstrates the utility of data fusion approaches to identify forest areas of high value for conservation at fine spatial resolutions (~10–100 m) and nationwide extents. However, uncertainties remain in our approach. Hence, our findings should be used to guide field-based assessments to confirm the in situ conservation value of the forests. Only in combination with such in situ data will approaches like ours enable decision makers to better protect forest biodiversity.