Characterization of SilviLaser 2021 Benchmark Data Set

Abstract

During SilviLaser 2021, a benchmark was organized to demonstrate the capability of different 3D data acquisition techniques for capturing various forest parameters. In total, 9 groups of participants revealed their advanced techniques and setups to acquire point clouds in the designated sites. Based on the applied equipment and platform, all approaches are categorized into 3 groups: mobile laser scanning, terrestrial laser scanning, and photogrammetry (plus others). In order to efficiently and accurately extract basic forest parameters (e.g., stem position, tree species, DBH) from this data set, understanding the data behavior of different approaches is the main key to maximizing their strength. This study aims to characterize each method in terms of spatial distribution and coverage of point clouds, extra attributes, as well as pros and cons for different usage purposes. The output of this study is valuable for selecting an adequate method to fulfill the requirements of user-specific forestry applications. Also, it is beneficial for solving the existing or future problems of multi-source point cloud processing in forests, e.g., co-registration of various data sources. This study is done within the framework of the project 4Map4Health.