Estimating stem volume and biomass of Pinus koraiensis using LiDAR data

 

Doo-Ahn Kwak • Woo-Kyun Lee • Hyun-Kook Cho •
Seung-Ho Lee • Yowhan Son • Menas Kafatos •
So-Ra Kim

 

Abstract: The objective of this study was to estimate the stem volume and biomass of individual trees using the
crown geometric volume (CGV), which was extracted from small-footprint light detection and ranging (LiDAR) data.
Attempts were made to analyze the stem volume and biomass of Korean Pine stands (Pinus koraiensis Sieb. et
Zucc.) for three classes of tree density: low (240 N/ha), medium (370 N/ha), and high (1,340 N/ha). To delineate
individual trees, extended maxima transformation and watershed segmentation of image processing methods were
applied, as in one of our previous studies. As the next step, the crown base height (CBH) of individual trees has to be determined; information for this was found in the LiDAR point cloud data using k-means clustering. The LiDARderived CGV and stem volume can be estimated on the basis of the proportional relationship between the CGV and stem volume. As a result, low tree-density plots had the best performance for LiDAR-derived CBH, CGV, and stem volume (R2 = 0.67, 0.57, and 0.68, respectively) and accuracy was lowest for high tree-density plots (R2 = 0.48, 0.36, and 0.44, respectively). In the case of medium treedensity plots accuracy was R2 = 0.51, 0.52, and 0.62,
respectively. The LiDAR-derived stem biomass can be predicted from the stem volume using the wood basic density of coniferous trees (0.48 g/cm3), and the LiDARderived above-ground biomass can then be estimated from
the stem volume using the biomass conversion and expansion factors (BCEF, 1.29) proposed by the Korea Forest Research Institute (KFRI).

 

Keywords: Above-ground biomass, Crown base height, Crown geometric volume, k-means clustering, LiDAR, Stem volume