Using the full potential of Airborne Laser Scanning (aerial LiDAR) in wildlife research

Species' habitats are strongly influenced by the 3-dimensional (3D) structure of ecosystems. The dominant technique used to measure 3D structure is Airborne Laser Scanning (ALS), a type of LiDAR (Light Detection and Ranging) technology. Airborne Laser Scanning captures fine-scale structural information over large spatial extents and provides useful environmental predictors for habitat modeling. However, due to technical complexities of processing ALS data, the full potential of ALS is not yet realized in wildlife research, with most studies relying on a limited set of 3D predictors, such as vegetation metrics developed principally for forestry applications. Here, we highlight the full potential of ALS data for wildlife research and provide insight into how it can be best used to capture the environmental conditions, resources, and risks that directly determine a species' habitat. We provide a nontechnical overview of ALS data, covering data considerations and the modern options available for creating custom, ecologically relevant, ALS predictors. Options included the following: i) direct point cloud approaches that measure structure using grid, voxel, and point metrics, ii) object-based approaches that identify user-defined features in the point cloud, and iii) modeled environmental predictors that use additional modeling to infer a range of habitat characteristics, including the extrapolation of field acquired measurements over ALS data. By using custom ALS predictors that capture species-specific resources, risks, and environmental conditions, wildlife practitioners can produce models that are tailored to a species' ecology, have greater biological realism, test a wider range of species-environment relationships across scales, and provide more meaningful insights to inform wildlife conservation and management. Airborne Laser Scanning (a LiDAR technology) captures fine-scale structural information over entire landscapes and provides useful environmental predictors for habitat modeling. However, due to the technical complexities of processing ALS data, the full potential of this technology is not yet realized in habitat research. Here, we provide a habitat-oriented and non-technical overview of ALS data, covering data considerations and the modern options available for creating custom, ecologically relevant, habitat predictors. image