Forest structure estimation and pattern exploration from discrete-return lidar in subalpine forests of the central Rockies
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Canadian Journal of Forest Research. 38: 2081-2096.
This study evaluates the relative ability of simple light detection and ranging (lidar) indices (i.e., mean and maximum heights) and statistically derived canonical correlation analysis (CCA) variables attained from discrete-return lidar to estimate forest structure and forest biomass variables for three temperate subalpine forest sites. Both lidar and CCA explanatory variables performed well with lidar models having slightly higher explained variance and lower root mean square error. Adjusted R2 values were 0.93 and 0.93 for mean height, 0.74 and 0.73 for leaf area index, and 0.93 and 0.85 for all carbon in live biomass for the lidar and CCA explanatory regression models, respectively. The CCA results indicate that the primary source of variability in canopy structure is related to forest height, biomass, and total leaf area, and the second most important source of variability is related to the amount of midstory foliage and tree density.
Sherrill, K.R.; Lefsky, M.A.; Bradford, J.B.; Ryan, M.G. 2008, Forest structure estimation and pattern exploration from discrete-return lidar in subalpine forests of the central Rockies. Canadian Journal of Forest Research. 38: 2081-2096. https://doi.org/10.1139/X08-059.