Statistical properties of mean stand biomass estimators in a LIDAR-based double sampling forest survey design.

Abstract

Airborne laser scanning (LIDAR) can be a valuable tool in double-sampling forest survey designs. LIDAR-derived forest structure metrics are often highly correlated with important forest inventory variables, such as mean stand biomass, and LIDAR-based synthetic regression estimators have the potential to be highly efficient compared to single-stage estimators, which could lead to increased precision for inventory estimates. However, when a limited sample is available to develop the regression model, an estimate based solely on the synthetic regression estimator can yield biased results for stands within a forest area where the regression model was unrepresentative. A number of modified (approximately) design-unbiased regression estimators have been proposed that serve to reduce this model-induced bias while also maintaining the efficient, variance-reducing properties of the synthetic regression estimator. In this study, we use a simulation approach to explore the statistical properties of several LIDAR-based regression estimators of mean stand biomass, using LIDAR and field plot data collected at a study site in a conifer forest in western Washington State, USA.

Citation

Anderson, H.E.; Breidenbach, J. 2007. Statistical properties of mean stand biomass estimators in a LIDAR-based double sampling forest survey design. International Society for Photogrammetry and Remote Sensing: Istanbul, Turkey 8 p