Preprints
https://doi.org/10.5194/egusphere-2022-1055
https://doi.org/10.5194/egusphere-2022-1055
 
14 Oct 2022
14 Oct 2022
Status: this preprint is open for discussion.

Quantifying vegetation indices using TLS: methodological complexities and ecological insights from a Mediterranean forest

WIlliam Rupert Moore Flynn1, Harry Jon Foord Owen2, Stuart William David Grieve1,3, and Emily Rebecca Lines2 WIlliam Rupert Moore Flynn et al.
  • 1School of Geography, Queen Mary University of London, Mile End Rd, Bethnal Green, London E1 4NS
  • 2Department of Geography, University of Cambridge, Downing Place, Cambridge, CB2 3EN
  • 3Digital Environment Research Institute, Queen Mary University of London, New Road, London, E1 1HH

Abstract. Accurate measurement of vegetation density metrics including plant, wood and leaf area indices (PAI, WAI and LAI) is key to monitoring and modelling carbon storage and uptake in forests. Traditional passive sensor approaches, such as Digital Hemispherical Photography (DHP), cannot separate leaf and wood material, nor individual trees, and require many assumptions in processing. Terrestrial Laser Scanning (TLS) data offer new opportunities to improve understanding of tree and canopy structure. Multiple methods have been developed to derive PAI and LAI from TLS data, but there is little consensus on the best approach, nor are methods benchmarked as standard.

Using TLS data collected in 33 plots containing 2472 trees of five species in Mediterranean forests, we compare three TLS methods (LiDAR Pulse, 2D Intensity Image and Voxel-Based) to derive PAI and compare with co-located DHP. We then separate leaf and wood in individual tree point clouds to calculate wood to total plant area (α), a metric to correct for non-photosynthetic material in LAI estimates. We use individual tree TLS point clouds to estimate how α varies with species, tree height and stand density.

We find the LiDAR Pulse method agrees most closely with DHP, but is limited to single scan data so cannot determine individual tree α. The Voxel-Based method shows promise for ecological studies as it can be applied to individual tree point clouds. Using the Voxel-Based method, we show that species explain some variation in α, however, height and density were stronger predictors.

Our findings highlight the value of TLS data to improve fundamental understanding of tree form and function, but also the importance of rigorous testing of TLS data processing methods at a time when new approaches are being rapidly developed. New algorithms need to be compared against traditional methods, and existing algorithms, using common reference data. Whilst promising, our results show that metrics derived from TLS data are not yet reliably calibrated and validated to the extent they are ready to replace traditional approaches for large scale monitoring of PAI and LAI.

WIlliam Rupert Moore Flynn et al.

Status: open (until 22 Dec 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

WIlliam Rupert Moore Flynn et al.

WIlliam Rupert Moore Flynn et al.

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Short summary
Quantifying vegetation indices is crucial for ecosystem monitoring and modelling. TLS has the potential to accurately measure vegetation indices, but multiple methods exist, with little consensus on best practice. We compare three methods and extract wood to plant ratio, a metric used to correct for wood in leaf indices. We show corrective metrics vary with tree structure and variation between methods, highlighting the value TLS data, but also the importance of rigorous testing.