A classification method of unmanned-aerial-systems-derived point cloud for generating a canopy height model of Farm Forest

During the last decade unmanned aerial systems (UAS) have been intensively applied to create 3-dimensional models of land surface features for various applications. Although, UAS data can be jointly used with airborne LiDAR data to generate a canopy height model (CHM) of forest stands, it is rare to find research concerned the generation of forest CHM using only UAS data. This paper investigate a suitable method to classify UAS point cloud to create CHM data for forest inventory. Results showed that ground points over building areas, open land, and forestland can be successfully collected by appropriate terrain angles which define a threshold value of the angle between a point, its projection on the plane of a triangle, and the closest vertex of a TIN surface model. A conservative threshold value of 5 degrees was suggested due to its allowing critical ground points whilst excluding crown points being collected. The UAS-derived CHM was evaluated with an RMSE accuracy of 0.01, 0.20, and 0.42 m for road, buildings, and trees respectively.