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Biogeosciences An interactive open-access journal of the European Geosciences Union
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https://doi.org/10.5194/bg-2020-121
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-2020-121
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 30 Apr 2020

Submitted as: research article | 30 Apr 2020

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This preprint is currently under review for the journal BG.

Robust processing of airborne laser scans to plant area density profiles

Johan Arnqvist1, Julia Freier2, and Ebba Dellwik3 Johan Arnqvist et al.
  • 1Johan Arnqvist, Uppsala University, department of Earth Sciences, Uppsala, Sweden
  • 2Fraunhofer Institute for Energy Economics and Energy System Technology, Kassel, Germany
  • 3Technical University of Denmark, Roskilde, Denmark

Abstract. We present a new algorithm for the estimation of plant area density (PAD) profiles and plant area index (PAI) for forested areas based on data from airborne lidar.

The new element in the algorithm is to scale and average returned lidar intensities for each lidar pulse, whereas other methods either do not use the intensity information at all, only use average intensity values or do not scale the intensity information, which can cause problems for heterogeneous vegetation. We compare the performance of the new and three previously published algorithms over two contrasting types of forest: a boreal coniferous forest with a relatively open structure and a dense beech forest. For the beech forest site, both summer (full leaf) and winter (bare trees) scans are analyzed, thereby testing the algorithm over a wide spectrum of PAIs.

Whereas all tested algorithms give qualitatively similar results, absolute differences are large (up to 400 % for the average PAI at one site). A comparison with ground-based estimates shows that the new algorithm performs well for the tested sites, and further and more importantly – it never produces clearly dubious results. Specific weak points for estimation of PAD from airborne lidar data are addressed; the influence of ground reflections and the effect of small-scale heterogeneity, and we show how the effect of these points is minimized using the new algorithm. We further show that low-resolution gridding of PAD will lead to a negative bias in the resulting estimate according to Jensen’s inequality for concave functions, and that the severity of this bias is method-dependent. As a result, PAI magnitude as well as heterogeneity scales should be carefully considered when setting the resolution for PAD gridding of airborne lidar scans.

Johan Arnqvist et al.

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Johan Arnqvist et al.

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Latest update: 03 Jun 2020
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Short summary
Data generated by airborne laser scans enables characterization of the surface vegetation for any application that might need it, such as forest management, modelling for numerical weather prediction or wind energy estimation. In this work we present a new algorithm for calculating the vegetation density with data from airborne laser scans. The new routine is proven more robust than earlier methods and implementation in popular programming languages accompany the article to support new users.
Data generated by airborne laser scans enables characterization of the surface vegetation for...
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