Automated tree detection and crown delineation using airborne laser scanner data in heterogeneous East-Central Europe forest with different species mix

Abstract

Identifying tree locations is a basic step in the derivation of other tree parameters using remote sensing techniques, particularly when using airborne laser scanning. There are several techniques for identifying tree positions. In this paper, we present a raster-based method for determining tree position and delineating crown coverage. We collected data from nine research plots that supported different mixes of species. We applied a raster-based method to raster layers with six different spatial resolutions and used terrestrial measurement data as reference data. Tree identification at a spatial resolution of 1.5 m was demonstrated to be the most accurate, with an average identification ratio (IR) of 95% and average detection ratio of 68% being observed. At a higher spatial resolution of 0.5 m, IR was overestimated by more than 600%. At a lower spatial resolution of 3 m, IR was underestimated at less than 44% of terrestrial measurements. The inventory process was timed to enable evaluation of the time efficiency of automatic methods.