Publication

Inasmuch as LiDAR is becoming an increasingly prominent tool for forest inventory, it is timely to develop a framework to understand the statistical properties of LiDAR-based estimates. A model-assisted approach to estimation and inference when using LiDAR as a tool to inventory aboveground forest biomass is presented. An empirical example is also presented, yet the article's focus is largely methodological. The sampling plan in the example is viewed as a two-stage design, with slightly different primary sampling units between the profiling and scanning laser surveys. A regression estimator is presented that uses biomass data from the Norwegian National Forest Inventory as the response variable and laser-derived variables as covariates. A major thrust of this article is the presentation of the variance of the estimators of total biomass and biomass per hectare as well as variance estimators.