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

2025-08-05

AI Summary

Traditional wildlife density monitoring methods—including capture–recapture, distance sampling, and camera trapping—suffer from labor intensity, limited spatial coverage, or bias toward nocturnal activity. To address these limitations, this study proposes a non-intrusive, drone-based dual-modality (thermal infrared and RGB) remote sensing approach for roe deer density estimation in the southeastern Austrian Alps. We introduce an innovative sampling framework: a 350-m grid overlaid with randomized flight headings, enabling multi-drone coordination and large-scale, low-altitude (60 m) surveys within a single day. Density estimation integrates manual annotation, naive area-based extrapolation, bootstrapping, and a zero-inflated negative binomial model, validated against independent camera trap data and random encounter modeling. Results indicate that drone-derived densities consistently exceed camera trap estimates, better capturing diurnal activity in open and ecotonal habitats. The method demonstrates high temporal resolution, broad spatial coverage, and strong scalability.

Technology Category

• Intelligent Robots: State Estimation
• Computer Vision: Remote Sensing / Geospatial AI
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Security and Privacy: Large-scale security measurements

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.

Problem

• Estimating roe deer density using drone-based sampling
• Comparing drone methods with traditional camera trap techniques
• Developing efficient non-intrusive wildlife monitoring approaches

Innovation

• Drones with thermal and RGB imagery
• Systematic randomized transect algorithm
• Zero-inflated negative binomial modeling