A reference airborne LiDAR dataset for forest research

This repository contains the dataset presented in Parkan et al. (2018).

Abstract:

The benefits of Airborne Laser Scanning (ALS) to efficiently monitor and manage forests are widely accepted. Products derived from ALS have been successfully used in a range of different domains including ecosystem characterization, habitat modeling, timber volume estimation, forest fire management and territorial planning. Many of these applications are dependent on the estimation of biophysical parameters at the canopy and/or individual tree scale. These parameters are generally computed with area (stand) or object (tree) centric approaches. In particular, the development of processing chains directly or indirectly involving individual tree crown segmentation, tree species classification and allometric modeling constitute the bulk of scientific activity in the domain. However, the diversity of ALS data characteristics and non-standard error assessment procedures means that the results reported in different studies are often difficult to compare. In order to support standardization and benchmark studies, this article presents a reference ALS dataset, an error assessment framework and provides several example workflows to illustrate its potential use in forest research.