Development of a Monitoring Method Using UAVs That Can Detect the Occurrence of Bark Stripping by Deer

Abstract

The occurrence of bark stripping associated with increased deer densities can severely damage forests. Identifying trends in bark stripping is crucial for forest management, but such data are often difficult to obtain through field surveys. Therefore, this study aimed to develop an efficient monitoring method using unmanned aerial vehicles (UAVs) that can detect the occurrence of bark stripping and enable long-term monitoring. The area around the Ochiai Pass in Higashi-Iya Ochiai, Miyoshi City, Tokushima Prefecture, Japan, was selected as the study area for the survey of Abies homolepis, which was found to be significantly bark-stripped by deer in the field. The location and root diameter of A. homolepis were measured, and the percentages of bark stripping and tree growth were visually determined. Simultaneously, normalized difference vegetation index (NDVI) and visible light orthomosaic images were produced using a UAV. A canopy polygon of A. homolepis was created, and the average value of the NDVI within the polygon was calculated. Where the bark stripping rate at the root edge was greater than 75%, the number of “partially dead” and “dead” trees increased significantly, indicating that bark stripping by deer was the primary cause of the death of A. homolepis in Ochiai Pass. In addition, the mean value of the NDVI was significantly lower, with a bark stripping rate of 75% or higher, indicating that the NDVI of the canopy of A. homolepis can be used to estimate individuals with a high bark stripping rate at the root tips, that is, those with a high probability of mortality. Furthermore, by extrapolating the results of the tree-by-tree survey to the nontarget A. homolepis, we detected 46 (8%) A. homolepis with an average NDVI value of 0.8 or less (i.e., those with a bark stripping ratio of 75% or higher and a high probability of mortality). Therefore, the utilization of remote sensing technology via UAVs, as demonstrated in this study, proves to be a potent means for monitoring the incidence of bark stripping.