Assessing Forest Gap Dynamics and Growth Using Multi-temporal Laser-scanner Data

Research on lidar change detection is at its inception with a few studies to monitor coastal erosion and almost none for forest dynamics. While long-term installations and dendrochronology are cost and time intensive, this study highlights potential use of multi-temporal medium density lidar data for studying forest dynamics in a spatially explicit manner, particularly in identifying new canopy gaps and assessing height growth. It also underlines some of the challenges of co-registering multi-temporal lidar datasets, working with large differences in return densities, and developing methodological approaches to compute growth. Two laser-scanner datasets, acquired in 1998 and 2003 over a 6 km area of the mixed boreal forest in Quebec, Canada, were analysed. After coregistration, an automated method to accurately identify new gaps was developed which showed an overall accuracy of 96% when compared with high resolution images. Mean gap size, gap density and rate of gap openings have been in accordance with the reported statistics for the boreal forests. Forest growth was assessed by comparing various lidar statistics for hardwoods and softwoods in three height classes. The measured growth was in general consistent with expected height growth for the concerned species, however, improvements will be needed to increase the accuracy and reliability of results.