UAV-based dead wood mapping in a natural deciduous forest in mid-Germany

The utilization of UAVs for the acquisition of ultra-high resolution imagery has heavily increased during the past decade. Once the hardware is purchased, images can be recorded almost at any time and at low cost. The image parameters can be determined in terms of spectral channels, image overlap, and geometric resolution. The overlap between the images enables stereoscopic image processing, the delineation of point clouds, and the generation of seamless image mosaics. UAV image data products have gathered high interest in the forestry community, as structural and spectral features can be delineated. Accordingly, regular forest monitoring and inventory can be supported using UAV data.

In this study, the potential of DJI Phantom 4 Pro RTK imagery based orthomosaics and point clouds to map dead wood on the forest floor is investigated. The test site is located in the center of the Hainich national park. The Hainich national park is an unmanaged forest comprising deciduous tree species such as Fagus sylvatica (beech), Fraxinus excelsior (ash), Acer pseudoplatanus (sycamore maple), and Carpinus betulus (hornbeam). The flight campaigns were controlled from the Hainich flux tower in the central part of the park area. RGB image data was captured in March 2019 during leaf-off conditions. Agisoft Metashape was used for processing the imagery to orthomosaics and point clouds. The living/standing trees were virtually removed from the point clouds as follows: 1.) normalizing the point cloud for topography, 2.) dropping all points above 5 m height. The remaining points were converted to an orthorectified RGB raster file, which solely contains the forest floor including the deadwood (lying stems) and tree stumps of the virtually cut trees. This raster was eventually used for dead wood mapping. The mapping task was accomplished using the OBIA software eCognition using the line extraction function as major method. The detection rate of the automatic mapping was approximately 70%. The dead wood mapping was complicated dead wood of several years of age featuring almost the same color and elevation level as the surrounding forest floor. Due to the latter, no elevation information was used. For regular monitoring considering recent dead wood only elevation information can be implemented and higher detection rates are feasible.