A workflow for extracting ungulate trails in wetlands using 3D point clouds obtained from airborne laser scanning

Jinhu Wang^1*Perry Cornelissen²W. Daniel Kissling¹

• ¹Department Theoretical and Computational Ecology, Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam, Amsterdam, Netherlands
• ²Staatsbosbeer, Amersfoort, Netherlands

Ungulates and other mammalian herbivores can create trails in dense vegetation by trampling and browsing. This can affect vegetation structure and result in the fragmentation of closed, high vegetation, with subsequent impacts on biodiversity. Manually mapping trails in the field or from aerial photographs can be challenging and time-consuming, especially in inaccessible or difficult-to-access habitats such as wetlands and if trails occur beneath the canopy of woody plants (i.e. trees and shrubs) or in other tall vegetation (e.g. reed). Airborne laser scanning (ALS) provides an alternative method because Light Detection and Ranging (LiDAR) can record returns from both the canopy and the ground, as some laser pulses pass through gaps in the vegetation, resulting in highly accurate and dense threedimensional (3D) point clouds. Here, we present a workflow for extracting ungulate trails using 3D point clouds obtained from country-wide ALS surveys, illustrated by red deer trampling in reedbeds within a 36 km 2 marsh area of a Dutch nature reserve. The workflow starts by pre-processing to retile the LiDAR point clouds to designated tiles and removes outliers from the raw data. The (near-)terrain points are then segmented using an iterative filtering algorithm, and digital terrain models are generated with a user-defined resolution. Finally, trail cells are extracted by thresholding the residuals from iterative Laplacian smoothing and then refined by sparse 3D structure tensor voting. The parameters of the workflow were optimized with comprehensive sensitivity analyses. Applying the workflow resulted in a classification of trail and non-trail grid cells at 10 cm resolution across the study area. Compared to manually labeled ground truths, the results showed an overall accuracy of 93% and 90% in regions of red deer grazing only and both geese and red deer grazing, respectively. To test its transferability, the workflow could be applied to other LiDAR data (e.g. ALS surveys from another flight campaign in the same study area or in a different country), to other nature areas (e.g. other rewilding sites or other wetlands), and to other ungulate species (e.g. domesticated livestock or other native large herbivores).