Combining Lidar and Stereo Imagery for Mapping Forest Structure Prior to Wildfire

Abstract:

Lidar has become an established tool for estimating forest structure attributes from observations of forest canopy height and variability. These attributes include ones relevant to fire behavior and effects, but due to the sparse availability of lidar data, it is unlikely to be available to document pre-fire conditions. In contrast, aerial photography is regularly collected in many countries and advances in stereo image matching have improved the accuracy of photogrammetric point clouds. As part of a study of the physical and ecological impacts of the 2012 High Park Fire, we generated a photogrammetric point cloud from pre-fire aerial imagery collected in 2008 and combined it with a digital terrain model generated from a 2013 post-fire lidar collection. We then generated a suite of canopy height and density indices from the pre-fire photogrammetry and post-fire lidar point clouds and compared them to each other and to forest structure attributes measured in the field immediately post-fire.

For unburned areas, indices from the photogrammetric point cloud tended to be higher for height percentiles, lower for height variability, and higher for canopy density when compared to the corresponding lidar indices. However, in regression analyses combining canopy height and density indices derived from photo and lidar sources, a single equation could estimate field measured forest structure attributes without significant bias from the source of the indices. Models of attributes such as aboveground biomass on unburned plots had similar root mean square errors for lidar (27.5%), photogrammetry (28.2%), and both data sources (RMSE = 29.4% and source bias = -0.69). Similar results were obtained for Lorey’s height, basal area, and canopy bulk density. In the context of wildfire, pre-fire forest structure information could aid assessments of contributing factors such as canopy fuels and fire effects such as loss of biomass. The wide spatial and temporal coverage of aerial photos and growing coverage of lidar could enable many other applications of lidar-photogrammetry fusion, including assessments of changes in forest carbon storage.