LiDAR as a rapid tool to predict forest habitat types in Natura 2000 networks

Management strategies for the conservation of biodiversity can be developed only with precise information on the spatial distribution of organisms on relevant, mostly regional, spatial scales. Current surrogates for approximating the distribution of biodiversity are habitats mapped within a number of national and international frameworks (e.g., Natura 2000), even though conventional habitat mapping is time consuming and requires well-trained personnel. Here we evaluated the use of light detection and ranging (LiDAR) to map forest habitat types to simplify the process. We used available data of habitat types for the Bavarian Forest National Park as a basis to predict habitat types with LiDAR-derived variables. Furthermore, we compared these results with predictions based on extensive ground-based climate, soil and vegetation data. Using linear and flexible discriminant analyses, we found that LiDAR is able to predict forest habitat types with the same overall accuracy as the extensive ground data for climate, soil and vegetation composition. Subtle differences in the vegetation structure between habitat types, particularly in the vertical and horizontal vegetation profiles, were captured by LiDAR. These differences in the physiognomy were in part caused by changes in altitude, which also influence tree species composition. We propose that the most-efficient way to identify forest habitat types according Natura 2000 is to combine remote-sensing LiDAR data with well-directed field surveys.