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The effect of leaf-on and leaf-off forest canopy conditions on LiDAR derived estimations of forest structural diversity.

Davison, Sophie and Donoghue, Daniel N.M. and Galiatsatos, Nikolaos (2020) 'The effect of leaf-on and leaf-off forest canopy conditions on LiDAR derived estimations of forest structural diversity.', International journal of applied earth observation and geoinformation., 92 . p. 102160.

Abstract

Forest structural diversity metrics describing diversity in tree size and crown shape within forest stands can be used as indicators of biodiversity. These diversity metrics can be generated using airborne laser scanning (LiDAR) data to provide a rapid and cost effective alternative to ground-based inspection. Measures of tree height derived from LiDAR can be significantly affected by the canopy conditions at the time of data collection, in particular whether the canopy is under leaf-on or leaf-off conditions, but there have been no studies of the effects on structural diversity metrics. The aim of this research is to assess whether leaf-on/leaf-off changes in canopy conditions during LiDAR data collection affect the accuracy of calculated forest structural diversity metrics. We undertook a quantitative analysis of LiDAR ground detection and return height, and return height diversity from two airborne laser scanning surveys collected under leaf-on and leaf-off conditions to assess initial dataset differences. LiDAR data were then regressed against field-derived tree size diversity measurements using diversity metrics from each LiDAR dataset in isolation and, where appropriate, a mixture of the two. Models utilising leaf-off LiDAR diversity variables described DBH diversity, crown length diversity and crown width diversity more successfully than leaf-on (leaf-on models resulted in R² values of 0.66, 0.38 and 0.16, respectively, and leaf-off models 0.67, 0.37 and 0.23, respectively). When LiDAR datasets were combined into one model to describe tree height diversity and DBH diversity the models described 75% and 69% of the variance (R² of 0.75 for tree height diversity and 0.69 for DBH diversity). The results suggest that tree height diversity models derived from airborne LiDAR, collected (and where appropriate combined) under any seasonal conditions, can be used to differentiate between simple single and diverse multiple storey forest structure with confidence.

Item Type:Article
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Available under License - Creative Commons Attribution Non-commercial No Derivatives.
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Status:Peer-reviewed
Publisher Web site:https://doi.org/10.1016/j.jag.2020.102160
Publisher statement:© 2020 Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license.(http://creativecommons.org/licenses/BY-NC-ND/4.0/).
Date accepted:14 May 2020
Date deposited:11 June 2020
Date of first online publication:09 June 2020
Date first made open access:11 June 2020

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