Three-dimensional Habitat Characterisation for European Forest Bats Using High-resolution Point Clouds

The loss of biodiversity has been recorded globally at unprecedented rates. Among the various organisms under threat are the European forest dwelling bats, which experienced a significant population decline. One of the causes of this decline is the alteration and destruction of their habitat. To understand how bats interact with forests, a detailed characterisation of their habitat can help target conservation efforts. The common habitat characterisation approach in forests is to carry out field surveys. During these surveys the forest is visually described by qualitative indices of complexity and structure. This detailed surveying requires extensive time investment and highly depends on the field crew who conducts the survey. Naturally, an observer bias is inevitable. Some characteristic parameters, like the volume of gaps or foliage density, cannot be determined by conventional measuring approaches.

In recent years, LiDAR-based 3D point clouds are increasingly used to characterise habitats.  In forest environments, different vegetation density and layering, as well as the changing terrain, make the point cloud-based characterisation particularly challenging. Existing approaches resort to 2.5D raster data, disregarding the full potential of the three-dimensionality that point clouds provide. Given that bat species utilise both tree crowns and the ground, such information is of the utmost importance.

In this work, we present a full 3D point cloud analysis for forest habitats. We quantitatively characterise the habitat and provide a characterisation approach for complex environments. By analysing the acquired point cloud in 3D, we infer the forest structure as a whole. Such a characterisation allows us to assess how much area is potentially used by bats for flying and foraging. The quantitative nature of the characterisation enables the comparison between vegetation structures in different forest stands.

We demonstrate the proposed characterisation on different forest stand types, i.e., beech and mixed forests, in the Vienna Biosphere Reserve. Designated areas were captured with a handheld mobile laser scanner. We show that both for dense and sparse stands the proposed characterisation approach was successfully applied. Therefore, our analysis can be applied to all forested ecosystems, encompassing orchards as well as avenues. The analysis is performed in R and is easy to use. In this way, we can establish better conservation strategies for endangered forest species worldwide.