Burrowing mammals are widespread and contribute significantly to soil ecosystem services. However, how to conduct a non-invasive estimation of their actual population size has remained a challenge. Results support that the number of burrow entrances is positively correlated with population abundance and burrows’ location indicates their area of occupancy consequently it provides a benchmark for estimating population size. European souslik is an endangered burrowing species in decline across its range. We present an imagery-based method to identify and count animals’ burrows semi-automatically by combining remotely recorded RGB images, pixel-based imagery (PBI) and Random Forest (RF) classification.
Field images recorded in four colonies were collected, combined and then processed by histogram matching and spectral band normalisation to improve the spectral distinction between the categories BURROW, SOIL, TREE, GRASS. Raw or processed images were analysed by RF classification to compare the change in accuracy metrics as a result of processing.
From accuracy metrics kappa of precision (κBURROWP) and sensitivity (κBURROWS) for BURROW were 95 and 90% respectively. A 10-time bootstrapping of the final model resulted in coefficients of variation (CV%) of κBURROWS and κBURROWP lower than 5%, moreover CV% values were not significantly different between precision and sensitivity scores. The consistency of classification results and balanced precision and sensitivity confirmed the applicability of this approach.
Our method provides an accurate and user-friendly tool to count the number of burrow openings and delineate the areas of occupancy as compared to traditional, more invasive approaches or other computer capacity and end-user expertise demanding methods.