Estimating leaf area index at multiple heights within the understorey component of Loblolly pine forests from airborne discrete-return lidar

ABSTRACT

Airborne discrete-return light detection and ranging (lidar) can be used to estimate leaf area index (LAI) with relatively high accuracy. This capacity was explored with regard to assessing the capability of estimating LAI at different heights at the plot level, in the presence of understorey vegetation, within intensively managed Loblolly pine forest in North Carolina, USA. Field measurements utilized the LI-COR LAI-2200 plant canopy analyser for field-based estimates of effective LAI at three elevations within each plot; these were on the ground (0.0 m) and 1.0 m and 2.5 m above the ground within the various understorey heights and densities. A number of new and previously existing lidar metrics and indices were calculated from the distribution of return heights, which have been identified as potentially strong predictors of LAI. A bivariate and stepwise regression approach was then applied to create models for the estimation of LAI from lidar-derived height distribution metrics. The results show that specific logarithm transformed laser penetration indices calculated using a height threshold (e.g. the number of returns below 2.5 m ratioed against all returns) as close to field LAI measurement height (e.g. 2.5 m) was more effective than other lidar metrics. LAI can be estimated for each of the three measurement heights within the understorey component explaining 67 to 76% of the variance (root mean square error 0.42–0.57). The indices that produced the highest correlations and which were selected in stepwise regression analysis were calculated using all returns. The results indicate that LAI can be estimated accurately using lidar data in pine plantation forest over a variety of stand conditions.

Acknowledgements

This research was possible thanks to support from the Forest Productivity Cooperative, and the help in field data collection provided by Beth Stein. We gratefully acknowledge the support and lidar data supplied by NASA and also to the staff members of the Duke forest teaching and research laboratory, Northern Research Station Strategic Foresight Group of the North Carolina Forest Service, and the land owner Weyerhaeuser.

Additional information

Funding

Funding for this work was provided in part by the Virginia Agricultural Experiment Station and the Program McIntire Stennis of the National Institute of Food and Agriculture, U.S. Department of Agriculture.