Laserchicken—A tool for distributed feature calculation from massive LiDAR point cloud datasets

Abstract

Point cloud datasets provided by LiDAR have become an integral part in many research fields including archaeology, forestry, and ecology. Facilitated by technological advances, the volume of these datasets has steadily increased, with modern airborne laser scanning surveys now providing high-resolution, (super-)national scale, multi-terabyte point clouds. However, their wider scientific exploitation is hindered by the scarcity of open source software tools capable of handling the challenges of accessing, processing, and extracting meaningful information from massive datasets, as well as by the domain-specificity of existing tools. Here we present Laserchicken, a user-extendable, cross-platform Python tool for extracting statistical properties of flexibly defined subsets of point cloud data, aimed at enabling efficient, scalable, distributed processing of multi-terabyte datasets. We demonstrate Laserchicken’s ability to unlock these transformative new resources, e.g. in macroecology and species distribution modelling, where it is used to characterize the 3D vegetation structure at high resolution (<10 m) across whole countries or regions. We further discuss its potential as a domain agnostic, flexible tool that can also facilitate novel applications in other research fields.

Keywords

• LiDAR
• Point cloud
• Feature extraction
• Airborne Laser Scanning (ALS)
• Biodiversity
• Ecology