Mapping of snow-damaged trees based on bitemporal airborne LiDAR data

The use of multitemporal LiDAR data in forest-monitoring applications has been so far largely unexplored. In this work, we aimed to develop and test a simple method for the detection of snow-induced canopy changes by employing bitemporal LiDAR data acquired in 2006-2010. Our study area was located in southern Finland (62A degrees N, 24A degrees E), where snow-induced damage occurred in 10 permanent Scots pine ()-dominated plots in winter 2009-2010. For the detection of snow-damaged crowns, we developed a a dagger CHM method by contrasting bitemporal LiDAR canopy height models (CHMs) and analyzing the resulting difference image, using binary image operations to extract the damaged crowns. Furthermore, we examined the structural and spatial factors that could explain snow damage at the individual tree level. The a dagger CHM method developed is based on two threshold parameters, i.e., the required height difference in the contrasted CHMs and the minimum plausible area of damage. When testing the performance of a dagger CHM method, we found that the plot-level omission error rates were 19-75%, while the commission error rates were 0-21%. Furthermore, the relative estimation accuracy of the damaged crown projection area (DCPA) ranged from -16.4 to 5.4%. The observed damage could be explained at tree level by stem tapering, relative tree size, and local stand density. To conclude, a dagger CHM method developed constitutes a potential tool for the monitoring of structural canopy changes in the dominant tree layer if dense multitemporal LiDAR data are available.