Towards small-footprint airborne LiDAR-assisted large scale operational forest inventory - A case study of integrating LiDAR data into forest inventory and analysis in Kenai Peninsula, Alaska

Many studies have already demonstrated that small-footprint airborne LiDAR has the capacity to measure forest biophysical characteristics and the accuracy of the results is relatively consistent and independent of specific LiDAR systems. However, most previous studies were conducted in small research areas. To date, there have been relatively few examples of applying LiDAR to large area operational forest inventory because of the high cost and lack of methodology and expertise. The main objective of this research is to develop processing and analysis techniques to facilitate the use of small-footprint LiDAR data for large-scale Forest Inventory and Analysis (FIA) on the Kenai Peninsula of Alaska. Results from this study indicate that it is possible to develop parsimonious regression models for different forest types using three primary LiDAR metrics---mean height, coefficient of variation of height and canopy point density. LiDAR mean height represents canopy height in the field, coefficient of variation of height represents canopy depth, and canopy point density represents canopy cover. These three LiDAR metrics succinctly describe the 3D canopy structure and have clear biological interpretation. Forest aboveground biomass models using these three LiDAR metrics have R2 values ranging from 0.68 to 0.87 for three different forest types. This research also assessed plot position error and plot size on these three LiDAR metrics and predicted forest biomass through simulation. Results show that the accuracy of plot position and plot size are important factors affecting the accuracy and precision of LiDAR metrics and predicted biomass in heterogeneous forest stands. Results suggested that small position error is acceptable in homogeneous forest stands, but accurate field plot positions are necessary in heterogeneous forest stands. In the context of FIA, acquiring accurate coordinates for the subplots is not currently part of the standard plot protocol. If it is not possible to obtain accurate GPS locations for each subplot, linking LiDAR data with field measurements using larger plots, which encompass four subplots, may provide a way to characterize forest condition at similar scale as the combination of the four subplots. Finally, maps of predicted plot-level forest height over the whole study region were produced from both LiDAR data and field measurements, and the distribution of predicted stand height from field data is very similar to the distribution of predicted LiDAR mean height.;In conclusion, the methodology and results presented in this dissertation demonstrate that it is feasible to integrating LiDAR data with existing FIA field plot network.