TLS2trees: a scalable tree segmentation pipeline for TLS data

Abstract

Above Ground Biomass (AGB) is an important metric used to quantify the mass of carbon stored in terrestrial ecosystems. For forests, this is routinely estimated at the plot scale (typically [≥]1 ha) using inventory measurements and allometry. In recent years, Terrestrial Laser Scanning (TLS) has appeared as a disruptive technology that can generate a more accurate assessment of tree and plot scale AGB; however, operationalising TLS methods has had to overcome a number of challenges. One such challenge is the segmentation of individual trees from plot level point clouds that are required to estimate woody volume, this is often done manually (e.g. with interactive point cloud editing software) and can be very time consuming. Here we present TLS2trees, an automated processing pipeline and set of Python command line tools that aims to redress this processing bottleneck. TLS2trees consists of existing and new methods and is specifically designed to be horizontally scalable. The processing pipeline is demonstrated across 10 plots of 7 forest types; from open savanna to dense tropical rainforest, where a total of 10,557 trees are segmented. TLS2trees segmented trees are compared to 1,281 manually segmented trees. Results indicate that TLS2trees performs well, particularly for larger trees (i.e. the cohort of largest trees that comprise 50% of total plot volume), where plot-wise tree volume bias is {+/-}0.4 m3 and %RMSE is ~60%. To facilitate improvements to the presented methods as well as modification for other laser scanning modes (e.g. mobile and UAV laser scanning), TLS2trees is a free and open-source software (FOSS).