EGU21-10677, updated on 04 Mar 2021
https://doi.org/10.5194/egusphere-egu21-10677
EGU General Assembly 2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

A new drone laser scanning benchmark dataset for characterization of single-tree and forest biophysical properties

Puliti Stefano1, Grant D. Pears2, Michael S. Watt2, Edward Mitchard3, Iain McNicol3, Magnus Bremer4, Martin Rutzinger4, Peter Surovy5, Luke Wallace6, Markus Hollaus7, and Rasmus Astrup1
Puliti Stefano et al.
  • 1Norwegian Institute for Bioeconomy Research (NIBIO), Division of Forest and Forest Resources, National Forest Inventory. Høgskoleveien 8, 1433 Ås, Norway
  • 2Scion, 49 Sala Street, Private Bag 3020, Rotorua 3046 and 10 Kyle St, Christchurch 8011, New Zealand
  • 3University of Edinburgh, School of GeoSciences, Crew Building, the King's Buildings, Edinburgh, Scotland
  • 4University of Innsbruck, Institut für Geographie Innrain 52f A - 6020 Innsbruck
  • 5Faculty of Forestry and Wood Sciences, Czech University of Life Sciences, Prague, Kamýcká 129, 165 00 Praha, Czech Republic
  • 6School of Geography, Planning and Spatial Sciences, University of Tasmania, Hobart, Australia
  • 7TU Wien, Department of Geodesy and Geoinformation, E120-07, Wiedner Hauptstraße 8, 1040 Vienna, Austria

Survey-grade drone laser scanners suitable for unmanned aerial vehicles (UAV-LS) allow the efficient collection of finely detailed three-dimensional information of tree structures. This data type allows forests to be resolved into discrete individual trees and has shown promising results in providing accurate in-situ observations of key forestry variables. New and improved approaches for analyzing UAV-LS point clouds have to be developed to transform the vast amounts of data from UAV-LS into actionable insights and decision support. Many different studies have explored various methods for automating single tree detection, segmentation, parsing into different tree components, and measurement of biophysical variables (e.g., diameter at breast height). Despite the considerable efforts dedicated to developing automated ways to process UAV-LS data into useful data, current methods tend to be tailored to small datasets, and it remains challenging to evaluate the performance of different algorithms based on a consistent validation dataset. To fill this knowledge gap and to further advance our ability to measure forests from UAV-LS data, we present a new benchmarking dataset. This data is composed of manually labelled UAV-LS data acquired a number of continents and biomes which span tropical to boreal forests. The UAV-LS data was collected exclusively used survey-grade sensors such as the Riegl VUX and mini-VUX series which are characterized by a point density in the range 1 – 10 k points m2. Currently, such data represent the state-of-the-art in aerial laser scanning data. The benchmark data consists of a library of single-tree point clouds, aggregated to sample plots, with each point classified as either stem, branch, or leaves. With the objective of releasing such a benchmark dataset as a public asset, in the future, researchers will be able to leverage such pre-existing labelled trees for developing new methods to measure forests from UAV-LS data. The availability of benchmarking datasets represents an important driver for enabling the development of robust and accurate methods. Such a benchmarking dataset will also be important for a consistent comparison of existing or future algorithms which will guide future method development.

How to cite: Stefano, P., Pears, G. D., Watt, M. S., Mitchard, E., McNicol, I., Bremer, M., Rutzinger, M., Surovy, P., Wallace, L., Hollaus, M., and Astrup, R.: A new drone laser scanning benchmark dataset for characterization of single-tree and forest biophysical properties, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10677, https://doi.org/10.5194/egusphere-egu21-10677, 2021.

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