Post-stratified estimation of forest area and growing stock volume using lidar-based stratifications

National forest inventories report estimates of parameters related to forest area and growing stock volume for geographic areas ranging in size from municipalities to entire countries. Landsat imagery has been shown to be a source of auxiliary information that can be used with stratified estimation to increase the precision of estimates, although the increase is greater for estimates of forest area than for estimates of growing stock volume. The objective of the study was to assess the utility of lidar-based stratifications for increasing the precision of mean proportion forest area and mean growing stock volume per unit area. Stratifications based on nonlinear logistic regression model predictions of volume obtained from lidar data reduced variances of mean growing stock volume estimates by factors as great as 3.2 and variances of mean proportion forest area estimates by factors as great as 1.5.

► Stratified estimation was used to reduce variances of inventory estimates ► Stratifications were derived from a lidar-based map of volume per unit area ► Maps were constructed using linear and nonlinear models and the k-NN technique ► The best results were obtained using a nonlinear logistic model ► Variances of estimates of mean volume were reduced by factors as great as 3