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 greater or equal to 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 plus/minus 0.4 m3 and %RMSE is approximately 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).

Giant sequoia ( Sequoiadendron giganteum ) in the UK: carbon storage potential and growth rates

“…Segmented point clouds of individual trees were extracted using the TLSeparation [27] and TLS2trees [28] Python tools. Tree volumes were calculated by fitting quantitative structural models (QSMs) to the resulting leaf-off point clouds using TreeQSM [29,30] and optimized using the optqsm tool [31].…”

TLS2trees: A scalable tree segmentation pipeline for TLS data

“…All steps use Python as a base programming language, this includes common scientific libraries such as Numpy, Scipy and Pandas (see Wilkes et al. (2023) for full list) which are managed with the conda package and environment manager. In addition, the pre‐processing step also uses the PDAL library (PDAL Contributors, 2020), semantic segmentation uses PyTorch libraries (version 1; Fey & Lenssen, 2019) and instance segmentation uses Networkx (Hagberg et al., 2008).…”

“…The workflow is also modular so new or additional methods can be added or existing steps replaced. For more information and code see Wilkes et al (2023).…”

“…The TLS2trees code is available from Wilkes et al. (2023). Code to run TreeQSM in Octave is available from https://github.com/philwilkes/run-treeqsm-in-octave.…”

Point2Tree(P2T)—Framework for Parameter Tuning of Semantic and Instance Segmentation Used with Mobile Laser Scanning Data in Coniferous Forest

“…Wilkes et al [21] addressed the instance segmentation problem in their tool TLS2Trees, which leverages on the FSCT semantic segmentation model published by Krisanski et al [17]. In their approach, the wood-classified points from the semantic segmentation are used to construct a graph through the point cloud, then use the shortest path analysis to attribute points to individual stem bases.…”