This study investigated how lidar-derived vegetation indices, disturbance history from Landsat time series (LTS)
imagery, plot location accuracy, and plot size influenced accuracy of statistical spatial models (nearest-neighbor
imputation maps) of forest vegetation composition and structure. Nearest-neighbor (NN) imputation maps were
developed for 539,000 ha in the central Oregon Cascades,...
This study investigated how lidar-derived vegetation indices, disturbance history from Landsat time series (LTS)
imagery, plot location accuracy, and plot size influenced accuracy of statistical spatial models (nearest-neighbor
imputation maps) of forest vegetation composition and structure. Nearest-neighbor (NN) imputation maps were
developed for 539,000 ha in the central Oregon Cascades,...
This study investigated how lidar-derived vegetation indices, disturbance history from Landsat time series (LTS)
imagery, plot location accuracy, and plot size influenced accuracy of statistical spatial models (nearest-neighbor
imputation maps) of forest vegetation composition and structure. Nearest-neighbor (NN) imputation maps were
developed for 539,000 ha in the central Oregon Cascades,...
Previous studies have demonstrated that light detection and ranging (LiDAR)-derived variables can be used to model forest yield variables, such as biomass, volume, and number of stems. However, the next step is underrepresented in the literature: estimation of forest yield with appropriate confidence intervals. It is of great importance that...