Advancing Wildlife Monitoring: Drone-Based Sampling for Roe Deer Density Estimation
Stephanie Wohlfahrt, Christoph Praschl, Horst Leitner, Wolfram Jantsch, Julia Konic, Silvio Schueler, Andreas St\"ockl, David C. Schedl

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.…
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