Investigation of the potential of drone observations for detection of forest disturbance caused by heavy snow damage in a Japanese cedar (Cryptomeria japonica) forest

Forest disturbance by heavy snow seriously affects ecosystem functions and provision of ecosystem services. To evaluate the spatial distribution of this disturbance over large areas, it is necessary to develop a flexible, inexpensive, and generalizable method based on remote sensing. Here, we examined the ability of an unmanned drone to detect the disturbance caused by heavy snow in a Japanese cedar (Cryptomeria japonica) forest, which is a typical landscape species in Japan's mountainous areas. We obtained aerial photographs in late October 2016 using the drone in a research plot where many individuals were damaged by moist, heavy snow in mid-December 2014. The forest disturbance rate was estimated by visually inspecting the structure from motion (SfM) point clouds generated from the drone's aerial photographs. We detected 90 to 96% of healthy individuals, but many tilted trees and trees with broken stems but an intact canopy were misidentified as healthy individuals. The estimated forest disturbance rate (33%) obtained from the SfM point clouds coincided well with the actual forest disturbance rate (35%) obtained from tree surveys. Consequently, this approach can potentially be used to detect narrow and patchy disturbances in Japanese cedar forest, although further observations at multiple points will be required to develop the accuracy of this approach.