Modeling and Estimating Change

Abstract

Airborne laser scanning (ALS) data are a source of spatial information that can be used to assist in the efficient and precise estimation of forest attributes such as biomass and biomass change, particularly for remote and inaccessible forests. This chapter includes an introduction to the use of ALS data for estimating change and a detailed review with tabular summary of the small number of known published reports on the topic. The review proceeds chronologically, noting the progression from exploratory and correlation studies to modeling and mapping and finally to statistically rigorous inference for population parameters. Both direct and indirect approaches for constructing models and mapping change are summarized. Although maps can be used to assist in the estimation procedure, systematic model prediction errors as reflected in maps induce bias into estimators. Thus, if the objective is rigorous inference for population parameters such as mean biomass change per unit area, rather than just maps of the populations, then bias must be estimated and incorporated into the parameter estimates, and uncertainty must be estimated. The design-based, model-assisted estimators that are presented for both independent estimation samples and single samples with repeated observations satisfy these criteria and produce inferences in the form of confidence intervals.