LiDAR based individual tree localization for sample plot inventory

  Objective  The LiDAR with high scanning frequency was used to assist the ground verification process in individual tree localization, quickly and accurately corresponding to the sample plot observation results and ground verification data, aiming to provide technical support for precision forestry survey.

  Method  Taking the planted forest in Sun Yat-sen University as the research object, with the help of laser pairs LiDAR with high scanning frequency (defined as monitor LiDAR) to assist tree localization during the ground verification of individual tree inventory, we firstly solved the problem of registration with backpack and monitoring point clouds, and obtained the base map of individual tree localization in sample plots; then, based on the method of background difference, the monitor LiDAR was used to track the dynamic target in real time and obtain its positions. Combined with the individual tree localization base map, the position of the individual tree measured by the ground investigator was judged indirectly, so as to realize the correspondence between the sample plot observation and the validation data.

  Result  Using our method to register the backpack and monitor point clouds, RMSE of coarse registration was all less than 0.2 m, and which was less than the minimum spacing of standing trees and met the corresponding requirements; using the dynamic target tracking and corresponding method in this paper, taking the positions of investigators as seed points, the serial number of tree which was closest to the seed points in the base map was obtained through the nearest search, so as to realize the data correspondence, its accuracy was higher than 95%, the average processing time of per frame was less than 0.1 s, which met the requirements of real-time.

  Conclusion  Our method proposed in this paper uses LiDAR to assist tree localization in the process of ground verification, which can improve the accuracy and efficiency of correspondence to sample plot observation results and verification data, and helps to promote the development of precision forestry inventory technology.