A Robust Method for Detecting Wind-Fallen Stems from Aerial RGB Images Using a Line Segment Detection Algorithm

Increased frequencies and windspeeds of storms may cause disproportionately high increases in windthrow damage. Storm-felled trees provide a surplus of breeding material for bark beetles, often resulting in calamities in the subsequent years. Thus, the timely removal of fallen trees is regarded as a good management practice that requires strategic planning of salvage harvesting. Precise information on the number of stems and their location and orientation are needed for the efficient planning of strip roads and/or cable yarding lines. An accurate assessment of these data using conventional field-based methods is very difficult and time-consuming; remote sensing techniques may be a cost-efficient alternative. In this research, a methodology for the automatic detection of fallen stems from aerial RGB images is presented. The presented methodology was based on a line segment detection algorithm and proved to be robust regarding image quality. It was shown that the method can detect frequency, position, spatial distribution and orientation of fallen stems with high accuracy, while stem lengths were systematically underestimated. The methodology can be used for the optimized planning of salvage harvesting in the future and may thus help to reduce consequential bark beetle calamities after storm events.

Automated summary

Increased frequencies and windspeeds of storms can cause disproportionately high increases in windthrow damage. Fallen trees from storms provide breeding material for bark beetles, leading to calamities in subsequent years. Thus, timely removal of fallen trees is considered a good practice, which requires strategic planning. Remote sensing techniques can be a cost-efficient alternative in obtaining precise information about the number, location, and orientation of fallen trees. This research introduces a methodology using aerial RGB images for automatic detection of fallen stems, which has shown high accuracy in detecting various parameters while underestimating stem lengths systematically. It can be used for optimized planning of salvage harvesting to reduce bark beetle calamities.