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TLS2trees: A scalable tree segmentation pipeline for TLS data

Authors :
Wilkes, Phil
Disney, Mathias
Armston, John
Bartholomeus, Harm
Bentley, Lisa
Brede, Benjamin
Burt, Andrew
Calders, Kim
Chavana-Bryant, Cecilia
Clewley, Daniel
Duncanson, Laura
Forbes, Brieanne
Krisanski, Sean
Malhi, Yadvinder
Moffat, David
Origo, Niall
Shenkin, Alexander
Yang, Wanxin
Wilkes, Phil
Disney, Mathias
Armston, John
Bartholomeus, Harm
Bentley, Lisa
Brede, Benjamin
Burt, Andrew
Calders, Kim
Chavana-Bryant, Cecilia
Clewley, Daniel
Duncanson, Laura
Forbes, Brieanne
Krisanski, Sean
Malhi, Yadvinder
Moffat, David
Origo, Niall
Shenkin, Alexander
Yang, Wanxin
Source :
ISSN: 2041-210X
Publication Year :
2023

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 on 7.5 ha of TLS data captured across 10 plots of seven forest types; from open savanna to dense tropical rainforest. A total of 10,557 trees are segmented with TLS2trees: these are compared to 1281 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%. Segmentation performance decreases for smaller trees, for example where DBH ≤10 cm; a number of reasons are suggested including performance of semantic segmentation step. The volume and scale of TLS data captured in forest plots is increasing. It is suggested that to fully utilise this data for activities such as monitoring, reporting and verification or as reference data for satellite missions an automated processing pipeline, such as TLS2trees, is required. To facilitate improvements to TLS2trees, as well as modification for other l

Details

Database :
OAIster
Journal :
ISSN: 2041-210X
Notes :
application/pdf, Methods in Ecology and Evolution 14 (2023) 12, ISSN: 2041-210X, ISSN: 2041-210X, English
Publication Type :
Electronic Resource
Accession number :
edsoai.on1415728738
Document Type :
Electronic Resource