Estimating leaf area index in intensively managed pine plantations using airborne laser scanner data

• DOI: 10.1016/j.foreco.2011.12.048
• Publication Date: 2012-02-08T08:17:13Z
• Authors: Alicia Peduzzi; Randolph H. Wynne; Thomas R. Fox; Ross F. Nelson; Valerie A. Thomas
• Keywords: Loblolly pine; Forest management; 0401 agriculture, forestry, and fisheries; Forest mensuration; Forestry; 04 agricultural and veterinary sciences; Remote sensing; Management, Monitoring, Policy and Law; 15. Life on land; Silviculture; Nature and Landscape Conservation

AbstractThe objective of this study was to determine whether leaf area index (LAI) can be accurately estimated in intensively managed pine plantations using multiple-return airborne laser scanner (lidar) data. In situ measurements of LAI were made using the LiCor LAI-2000 Plant Canopy Analyzer on 109 plots under a variety of stand conditions (i.e., stand age, nutritional regime, and stem density) in North Carolina and Virginia, USA in late summer, 2008. Distributional metrics were calculated for each plot using small footprint lidar data (average pulse density 5 pulses per square meter; up to four returns per pulse) acquired in the month preceding the field measurements. Distributional metrics were calculated for each plot using all vegetation returns, as well as using ten 1m deep crown density slices (a new technique introduced in this study), five above and five below the mode of the vegetation returns for each plot. These metrics were used as independent variables in best subsets regressions with LAI (measured in situ) as the dependent variable. The best resulting models had an R2 ranging from 0.61 (for a 2-variable model) to 0.83 (for a 6-variable model). The laser penetration index (LPI) was an important variable regardless of the number of variables used. Other important variables included the mean intensity value, the mean and 20th percentile of the vegetation returns, and various crown density slice metrics. These results indicate that LAI can be estimated accurately using lidar data in intensively managed pine plantations over a wide variety of stand conditions.