Comparing echo-based and canopy height model-based metrics for enhancing estimation of forest aboveground biomass in a model-assisted framework

Among the forestry-related applications for which airborne laser scanning (ALS) data have been shown to be beneficial, forest inventory has been investigated as much if not more than other applications. Metrics extracted from ALS data for spatial units such as plots and grid cells are typically of two forms: echo-based metrics derived directly from the three-dimensional distribution of the point cloud data and metrics derived from a canopy height model (CHM). For both cases, a large number of metrics can be calculated and used to construct parametric and non-parametric models to predict forest variables. We compared model-assisted estimates of total forest aboveground biomass (AGB) obtained using echo-based and CHM-based height metrics with two prediction methods: (i) a parametric linear model, and (ii) the non-parametric k-Nearest Neighbors (k-NN) technique. Model-assisted (MA) estimators were used with sample data obtained using a two-phase, tessellation stratified sampling (TSS) framework to estimate population parameters. The study was conducted in Molise Region in central Italy. For the four combinations of metrics and prediction techniques, estimates of total biomass were similar, in the range 1.96–2.1 million t, with standard error estimates that were also similar, in the range 0.20–0.21 t. Thus, the CHM-based metrics produced AGB estimates that were similar to and as accurate as those for the echo-based metrics, regardless of whether the parametric or the non-parametric prediction method was used. Additionally, the proposed MA estimator was more accurate than the estimator that did not use auxiliary data.