Forest Change Detection in Lidar Data Based on Polar Change Vector Analysis

Monitoring forest dynamics is of critical importance for both sustainable forest management and conservation purposes. Light detection and ranging (lidar) data provide a detailed representation of the 3-D structure of forest stands that can be used to analyze a number of trees and stand characteristics. Recently, multiple lidar acquisitions over the same area are becoming more common allowing changes in stand attributes to be assessed over time. In order to effectively utilize such multitemporal data sets for forest dynamics monitoring, we propose a method for unsupervised change detection (CD) of lidar data based on polar change vector analysis (CVA). The proposed method involves extracting relevant lidar point cloud metrics for a given area over time. Pixel-wise difference vectors of the metrics are then converted from Cartesian to polar coordinates to represent the magnitude and direction of change. Finally, the change vectors are analyzed in the polar domain to automatically discriminate between the different classes of change. The method is applied to a multitemporal lidar data set of coniferous forest on Vancouver Island, British Columbia, Canada, impacted by various types of land cover change. The experimental results demonstrate that the proposed method is capable of automatically discriminating between different classes of lidar change.