Forest structure estimation and pattern exploration from discrete-return lidar in subalpine forests of the central Rockies

• Published in: Canadian journal of forest research Vol. 38; no. 8; pp. 2081 - 2096
• Main Authors: Sherrill, K.R, Lefsky, M.A, Bradford, J.B, Ryan, M.G
• Format: Journal Article
• Language: English
• Published: Ottawa, ON National Research Council of Canada 01.08.2008; NRC Research Press; Canadian Science Publishing NRC Research Press
• Subjects: age structure; Alpine flora; Biological and medical sciences; Biomass; canonical correlation analysis; canopy; Carbon; Carbon cycle; coniferous forests; correlation; Correlation (Statistics); Correlation analysis; Distribution; Environmental aspects; equations; estimation; Evaluation; Foliage; Forest biomass; forest stands; forest trees; Forestry; Forestry research; Forests; Fundamental and applied biological sciences. Psychology; height; Leaves; Lidar; Mean square errors; Measuring instruments; Optical radar; principal component analysis; regression analysis; spatial distribution; stand structure; vegetation structure; Rocky Mountain region; United States; Rocky Mountains; Forestry
• ISSN: 0045-5067; 1208-6037
• DOI: 10.1139/X08-059

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. When stand age is graphed as a function of individual plot scores for canonicals one and two, there is a clear relationship with stand age and the development of stand structure. Lidar-derived biomass and related estimates developed in this work will be used to parameterize decision-support tools for analysis of carbon cycle impacts as part of the North American Carbon Program.