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

编号 030025703

推送时间 20200921

研究领域 森林经理 

年份 2020 

类型 期刊 

语种 英语

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

来源期刊 INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION

期 第257期

发表时间 20200609

关键词 Biodiversity;  Forest structural diversity;  Airborne LiDAR;  Multivariate linear regression; 

摘要 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 R-2 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 (R-2 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 confidenc

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