Estimating the density of raccoon dogs (Nyctereutes procyonoides) and water deer (Hydropotes inermis) using thermal real-time drone surveys and distance sampling

Accurate estimation of population density and spatial distribution is essential for wildlife conservation and management, yet conventional survey methods face substantial limitations. Estimating the density of medium- to large-sized mammals remains particularly challenging due to difficulties in direct observation and methodological constraints. This study qualitatively evaluated the detection performance and species identification capability of real-time drone surveys and assessed the applicability of distance sampling for density estimation. Surveys were conducted in a reedbed in Yeonsu, Incheon, and a plantation forest in Chungju, South Korea. Real-time drone surveys combined with line-transect sampling were applied in Yeonsu, whereas point-transect sampling was used in Chungju. Across nine line-transect surveys, 270 raccoon dogs ( Nyctereutes procyonoides ) were recorded, yielding an estimated density of 31.8 ind/km 2 with a relatively high detection probability (73.7%). In contrast, four point-transect surveys detected 35 water deer ( Hydropotes inermis ), resulting in an estimated density of 30.6 ind/km 2 but with low detection probability (34.4%) and wide confidence intervals due to limited sample size and complex terrain. These results indicate that real-time drone surveys, when integrated with distance sampling, can effectively correct for detection bias in density estimation of medium- to large-sized mammals. Future studies should apply advanced approaches such as Multiple Covariate Distance Sampling to account for variability in detection probability across survey conditions.