A comparison of forest height prediction from FIA field measurement and LiDAR data via spatial models

Previous studies have shown a high correspondence between tree height measurements acquired from airborne LiDAR and that those measured using conventional field techniques. Though these results are very promising, most of the studies were conducted over small experimental areas and tree height was measured carefully or using expensive instruments in the field, which is not feasible in a practical forest inventory context. In this study, 105 plots located west of the Kenai Mountains, Kenai Peninsula, Alaska were measured and LiDAR data over the same set of field plots were acquired. Plot tree height, stand height, LiDAR mean height and LiDAR 90th percentile height were computed. Using the Matern covariance model for constant mean Gaussian spatial process, ordinary kriging was implemented and contour maps of predicted plot-level height from field height measurements and from LiDAR data were produced over the entire region along with maps of estimated standard error. Results indicate that at 300m by 300m pixel resolution, the spatial trends of predicted plot-level height are similar between field measurements and LiDAR measurements. The distribution of predicted stand height is very similar to the distribution of predicted LiDAR mean height with mean difference of only 0.28m. The mean of predicted plot tree height is comparable to the mean of predicted LiDAR 90th percentile height, but the distribution of predicted LiDAR 90th percentile height has much heavier tails.