A best practices guide for generating forest inventory attributes from airborne laser scanning data using an area-based approach

A best practices guide for the use of airborne laser scanning data (ALS; also referred to as Light Detection and Ranging or LiDAR) in forest inventory applications is now available for download from the Canadian Forest Service bookstore (White et al ., 2013; http://cfs.nrcan.gc.ca/publications?id= 34887 ). The guide, produced by the Canadian Forest Service, Natural Resources Canada, brings together state-of-the-art approaches, methods, and data to enable readers interested in using ALS data to characterize large forest areas in a costeffective manner. The best practices presented in the guide are based on more than 25 years of scientific research on the application of ALS data to forest inventory. The guide describes the entire process for generating forest inventory attributes from ALS data and recommends best practices for each step of the process—from ground sampling through to metric generation and model development. The collection of ground plot data for model calibration and validation is a crit ical component of the recommended approach and is described in detail in the guide. Appendices to the guide pro vide additional details on ALS data acquisition and metric generation. The area-based approach is typically accomplished in two steps (Fig. 1). In the first step, ALS data are acquired for the entire area of interest (wall-to-wall coverage), tree-level meas ures are acquired from sampled ground plots and summa rized to the plot level, and predictive models are developed (e.g., using regression or non-parametric methods). For the purposes of model development, the ALS data is clipped to correspond to the area and shape of each ground plot. A set of descriptive statistics (referred to as “metrics”) are calculated from the clipped ALS data and include measures such as mean height, height percentiles, and canopy cover (Woods et al . 2011). Inventory attributes of interest are either measured by ground crews (i.e., height, diameter) or modelled (i.e., vol ume, biomass) for each ground plot. It is critical that ground plots represent the full range of variability in the attribute(s) of interest and to accomplish this, the use of a stratified sampling approach is recommended, preferably with strata that are defined using the ALS metrics themselves. Thus, the ALS data must be acquired and processed prior to ground sampling. Finally, predictive models are constructed using the ground plot attributes as the response variable and the ALS-derived metrics as predictors. In the second step of the area-based approach, models that were developed using co-located ground plots and ALS data are then applied to the entire area of interest to generate the desired wall-to-wall estimates and maps of specific forest inventory attributes. The same metrics that are calculated for the clipped ALS data (as described above) are generated for the wall-to-wall ALS data and the predictive equations devel oped from the modelling in the first step are applied to the entire area of interest using the wall-to-wall metrics. The pre diction unit for this application is a grid cell, the size of which relates to the size of the ground-measured plot. Once the pre dictive equations are applied to the wall-to-wall ALS data, each grid cell will have an estimate for the attribute of interest. The primary advantage of the area-based approach is hav ing complete (i.e., wall-to-wall) spatially explicit measures of canopy height, associated metrics, and all modelled attributes for an area of interest (Fig. 2). The area-based approach described in the guide also enables more precise estimates of certain forest variables and the calculation of confidence intervals for stand-level estimates (Woods et al . 2011).