Measuring individual tree crown diaer with lidar and assessing its influence on estimating forest volume and biomass

Abstract

The main objective of this study was to develop reliable processing and analysis techniques to facilitate the use of small-footprint lidar data for estimating tree crown diameter by measuring individual trees identifiable on the threedimensional lidar surface. In addition, the study explored the importance of the lidar-derived crown diameter for estimating tree volume and biomass. The lidar dataset was acquired over deciduous, coniferous, and mixed stands of varying age classes and settings typical of the southeastern United States. For identifying individual trees, lidar processing techniques used data fusion with multispectral optical data and local filtering with both square and circular windows of variable size. The crown diameter was calculated as the average of two values measured along two perpendicular directions from the location of each tree top by fitting a fourth-degree polynomial on both profiles. The lidar-derived tree measurements were used with regression models and cross-validation to estimate plot level field-measured crown diameter. Linear regression was also used to compare plot level tree volume and biomass estimation with and without lidar-derived crown diameter measures from individual trees. Results for estimating crown diameter were similar for both pines and deciduous trees, with R2 values of 0.62–0.63 for the dominant trees (root mean square error (RMSE) 1.36 to 1.41 m). Lidar-measured crown diameter improved R2 values for volume and biomass estimation by up to 0.25 for both pines and deciduous plots (RMSE improved by up to 8 m3/ha for volume and 7 Mg/ha for biomass). For the pine plots, average crown diameter alone explained 78% of the variance associated with biomass (RMSE 31.28 Mg/ha) and 83% of the variance for volume (RMSE 47.90 m3/ha).