LiDAR-based detection of field hamster (Cricetus cricetus) burrows in agricultural fields

Abstract

Farmers face increasing pressure to maintain vital populations of the critically endangered field hamster (Cricetus cricetus) while managing crop damage caused by field mice. This challenge is linked to the UN Sustainable Development Goals (SDGs) 2 and 15, addressing food security and biodiversity. Consequently, the reliable detection of hamster activity in agricultural fields is essential. While remote sensing offers potential for wildlife monitoring, commonly used RGB imagery has limitations in detecting small burrow entrances in vegetated areas. This study investigates the potential of drone-based Light Detection and Ranging (LiDAR) data for identifying field hamster burrow entrances in agricultural landscapes. A geostatistical method was developed to detect local elevation minima as indicators of burrow openings. The analysis used four datasets captured at varying flight altitudes and spatial resolutions. The method successfully detected up to 20 out of 23 known burrow entrances and achieved an F1-score of 0.83 for the best-performing dataset. Detection was most accurate at flight altitudes of 30 m or lower, with performance decreasing at higher altitudes due to reduced point density. These findings demonstrate the potential of UAV-based LiDAR to support non-invasive species monitoring and habitat management in agricultural systems, contributing to sustainable conservation practices in line with the SDGs.