Optimization of Canopy Height Model Generation Parameters for Precise Forestry

Abstract

The usage of photogrammetric technologies is essential in the concept of precise forestry. Dense unmanned laser scanning (ULS) point clouds are the innovative and precise data source for canopy height model (CHM) generation. It is necessary to choose the CHM generation method and its settings appropriately. This study evaluated different CHM generation methods and aimed to select the optimal parameters for CHM generation based on dense ULS point clouds of a temperate forest in central Europe. The results show that the choice of method and settings influences the quality of parameters describing forest stands, such as tree height or volume, and determining the location of tree tops and 2D tree contours. The most accurate CHMs were generated using the pit-free method. This method provides the lowest differences between the reference values, which were evaluated using the proposed CHM quality index. The cell size of generated rasters had the most significant influence on the quality of CHM, regardless of the method. Among all variants, the optimal variant was selected with a spatial resolution of CHM of 20 cm and a number of height levels of 4 and no interpolation of values for areas without data. For coniferous forest, this variant has a mean tree top location error of 0.1 m, a mean tree top height error of 0.1 m, and a mean tree crown volume error of 8.5 m 3 . For deciduous forest, this variant has a mean tree top location error of 0.3 m, a mean tree top height error of 0.7 m, and a mean tree crown volume error of 40.8 m 3 .

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