Advancing Wildlife Monitoring: Drone-Based Sampling for Roe Deer Density Estimation

TL;DR

This study demonstrates that drone-based thermal and RGB imaging can efficiently and non-intrusively estimate roe deer densities over large areas, providing comparable or higher results than traditional camera trap methods.

Contribution

The paper introduces a drone-based approach for wildlife density estimation that is scalable, less labor-intensive, and compares favorably with established methods like camera traps and REM.

Findings

Drone methods yielded similar or higher density estimates than REM.

Drones enabled large-area surveys in a single day with minimal disturbance.

Different methods reflect different aspects of wildlife activity.

Abstract

We use unmanned aerial drones to estimate wildlife density in southeastern Austria and compare these estimates to camera trap data. Traditional methods like capture-recapture, distance sampling, or camera traps are well-established but labour-intensive or spatially constrained. Using thermal (IR) and RGB imagery, drones enable efficient, non-intrusive animal counting. Our surveys were conducted during the leafless period on single days in October and November 2024 in three areas of a sub-Illyrian hill and terrace landscape. Flight transects were based on predefined launch points using a 350 m grid and an algorithm that defined the direction of systematically randomized transects. This setup allowed surveying large areas in one day using multiple drones, minimizing double counts. Flight altitude was set at 60 m to avoid disturbing roe deer (Capreolus capreolus) while ensuring detection. Animals were manually annotated in the recorded imagery and extrapolated to densities per square kilometer. We applied three extrapolation methods with increasing complexity: naive area-based extrapolation, bootstrapping, and zero-inflated negative binomial modelling. For comparison, a Random Encounter Model (REM) estimate was calculated using camera trap data from the flight period. The drone-based methods yielded similar results, generally showing higher densities than REM, except in one area in October. We hypothesize that drone-based density reflects daytime activity in open and forested areas, while REM estimates average activity over longer periods within forested zones. Although both approaches estimate density, they offer different perspectives on wildlife presence. Our results show that drones offer a promising, scalable method for wildlife density estimation.