Counting of Plantation Trees Based on Line Detection of Point Cloud Data

Objectives When performing plantation surveys using laser point cloud data, there are missing points in the scanned point cloud data due to the occlusion and self-occlusion of trees during laser scanning, the felling of trees and other reasons. So, the locations of the missing trees are inaccurate, and the forest survey results have large errors. The key to solving this problem is to realize the filling of the missing tree point cloud.

Methods This paper defines a concept named degree-of-collinearity, and constructs a method based on degree-of-collinearity combined with straight line detection to fill in missing data.

Results For the experimental results of simulated data, the average accuracy of the proposed algorithm is 97.28%; for the experimental results of plantation data, the proposed algorithm detects the location of 9 missing trees, and the degree-of-collinearity rises from 0.219 3 to 0.270 5.

Conclusions The experimental results show that this method can realize the optimal inference of missing location, strengthen the collinear relationship of filled data and can also be applied to count the missing trees in the artificial forest.