Data from: 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.