Extraction of Forest Density Based on Airborne LiDAR and Mean Shift Algorithms

To extract forest density, Mean Shift segmentation algorithm is explored for point clouds processing. The first step is to use the Mean Shift algorithm for the point clouds initial segmentation depended on feature vectors, bandwidth and thresholds. Feature vectors consist of three-dimensional coordinates and normal vector of the point clouds and bandwidth and thresholds are determined by statistical analysis method. The second step is to obtain canopy point clouds through the analysis of the segmented point clouds added with filtering conditions such as shrubs and weeds. The third step is to use Mean Shift algorithm to calculate forest density. With the statistical analysis of segmented the canopy point clouds in each category, the steady state points are marked as the rough locations for individual trees. Finally, the results are verified by field measured data. It is found that the average accuracy of this method could be more than 90.0%, which meets the requirements of forestry industry. In comparison with the Watershed method, the accuracy of the Mean Shift method is 92.5%, which is higher than Watershed method with 70.0% accuracy, and it can avoid the over segmentation in the Watershed method.