Seasonal variations and challenges in estimating populations and identifying species of Korean ungulates using drone‐derived thermal orthomosaic maps

Drones equipped with thermal infrared (TIR) cameras offer significant time and labor savings in estimating wild ungulate populations. However, accurately monitoring forest‐dwelling ungulates remains challenging due to their elusive behavior and complex habitat. This study evaluated the feasibility of using TIR orthomosaic maps derived from drone surveys to estimate the population size of three Korean ungulate species: water deer Hydropotes inermis, roe deer Capreolus pygargus and long‐tailed goral Naemorhedus caudatus in a semi‐controlled environment. We generated 15 paired TIR and RGB orthomosaic maps from drone flights conducted in March and June 2024. Ungulate counts from TIR imagery were cross‐verified using red, green and blue (RGB) maps. Two error metrics – counting error and detection error – were calculated using the maximum verified count per month as a proxy for the true population size. Regression analysis indicated that ground sample distance (GSD) was positively associated with counting error, while no clear relationship was found between GSD and detection error. These results suggest that environmental and behavioral factors may influence detection reliability more strongly than image resolution alone. In addition, we analyzed thermal body measurements to explore the potential for species identification. While TIR orthomosaic maps were generally effective for estimating body size, variation in posture – particularly lying positions – substantially affected measurement accuracy and limited their usefulness for distinguishing species. This study highlights both the capabilities and limitations of using TIR orthomosaic maps for ungulate monitoring and offers practical considerations for applying drone‐based surveys in more complex natural settings.