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TLS2trees: A scalable tree segmentation pipeline forTLSdata

Urheber*innen

Wilkes,  Phil
External Organizations;

Disney,  Mathias
External Organizations;

Armston,  John
External Organizations;

Bartholomeus,  Harm
External Organizations;

Bentley,  Lisa
External Organizations;

/persons/resource/benbrede

Brede,  Benjamin
1.4 Remote Sensing, 1.0 Geodesy, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

Burt,  Andrew
External Organizations;

Calders,  Kim
External Organizations;

Chavana‐Bryant,  Cecilia
External Organizations;

Clewley,  Daniel
External Organizations;

Duncanson,  Laura
External Organizations;

Forbes,  Brieanne
External Organizations;

Krisanski,  Sean
External Organizations;

Malhi,  Yadvinder
External Organizations;

Moffat,  David
External Organizations;

Origo,  Niall
External Organizations;

Shenkin,  Alexander
External Organizations;

Yang,  Wanxin
External Organizations;

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Volltexte (frei zugänglich)

5024638.pdf
(Verlagsversion), 15MB

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Zitation

Wilkes, P., Disney, M., Armston, J., Bartholomeus, H., Bentley, L., Brede, B., Burt, A., Calders, K., Chavana‐Bryant, C., Clewley, D., Duncanson, L., Forbes, B., Krisanski, S., Malhi, Y., Moffat, D., Origo, N., Shenkin, A., Yang, W. (2023): TLS2trees: A scalable tree segmentation pipeline forTLSdata. - Methods in Ecology and Evolution, 14, 12, 3083-3099.
https://doi.org/10.1111/2041-210X.14233


Zitierlink: https://gfzpublic.gfz-potsdam.de/pubman/item/item_5024638
Zusammenfassung
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 laser scanning modes (e.g. mobile and UAV laser scanning), TLS2trees is a free and open-source software.