A Voxel-based Method for Forest Change Detection after Fire Using LiDAR Data

Abstract:

A Voxel-based Method for Forest Change Detection after Fire Using LiDAR Data

Zewei Xu and Jonathan A. Greenberg

Traditional methods of forest fire modeling focus on the patterns of burning in two-dimensions at relatively coarse resolutions. However, fires spread in complex, three-dimensional patterns related to the horizontal and vertical distributions of woody fuel as well as the prevailing climate conditions, and the micro-scale patterns of fuel distributions over scales of only meters can determine the path that fire can take through a complex landscape. One challenge in understanding the full three-dimensional (3D) path that a fire takes through a landscape is a lack of data at landscape scales of these burns. Remote sensing approaches, while operating at landscape scales, typically focus on two-dimensional analyses using standard image-based change detection techniques. In this research, we develop a 3D voxel-based change detection method applied to multitemporal LiDAR data collected before and after forest fires in California to quantify the full 3D pattern of vegetation loss. By changing the size of the voxel, forest loss at different spatial scales is revealed. The 3D tunnel of fuel loss created by the fire was used to examine ground-to-crown transitions, firebreaks, and other three-dimensional aspects of a forest fire.