An Evaluation and Comparison of LiDAR Remote Sensing Technology and Large Scale Digital Photography for Landscape Level Forest Management Applications in Complex Multi-Aged Coniferous Forests

In light of an anticipated shift in timber extraction that focuses on non-pine forest types in the Northern Interior forest region, the evaluation of new remote sensing tools in these areas provide an important opportunity to examine methods, models and processes for improved inventories and resource analyses. This report summarizes the application of the LiDAR system to estimate understory vegetation and secondary structure. Airborne LiDAR data collections were gathered at the Aleza Lake Research Forest in the SBS wk1 mixed spruce subalpine fir forests east of Prince George, BC on August 30th , 2006. LiDAR analyses incorporated the use of customized algorithms and field data to assess its accuracy in quantifying understory vegetation biomass and secondary forest structure. Three models were developed to describe secondary structure, one model describing all secondary structure (r = 0.49), another describing coniferous secondary structure (r = 0.52), and the final model describing deciduous secondary structure (r = 0.23). The effect of overstory on LiDAR accuracy was also tested with the results indicating no significant impact. The LiDAR data produced very good results for high biomass areas as well as for describing understory vegetation, in both open and closed canopy conditions. Further work could be done to incorporate predictive ecosystem mapping with finer detailed LiDAR secondary structure mapping to produce operational maps of secondary structure in areas requiring rehabilitation, such as the Mountain Pine Beetle affected areas in the Interior, and to improve inventories for mid-term timber supply estimates in nonpine forest types. This and other research increasingly suggests that the high cost of LiDAR could be offset by the range of forest values that can be accurately inventoried and modeled from the LiDAR data.