Leaf Area Index, Biomass Carbon and Growth Rate of Radiata Pine Genetic Types and Relationships with LiDAR

Forests

Peter N. Beets²  Stephen Reutebuch¹  Mark O. Kimberley²  Graeme R. Oliver²  Stephen H. Pearce² 

[1] USDA, Forest Service, Pacific Northwest Research Station, Suite 201, 400 North 34th Street, Seattle, WA 98103, USA; E-Mails:;New Zealand Forest Research Institute, Ltd, Private Bag 3020, Rotorua 3046, New Zealand; E-Mails:

关键词: carbon stock;    forest carbon sink;    sequestration;    Kyoto Protocol;    LiDAR;    aspect;    genetic improvement;   

DOI  :  10.3390/f2030637

来源: mdpi

【 摘 要 】

Relationships between discrete-return light detection and ranging (LiDAR) data and radiata pine leaf area index (LAI), stem volume, above ground carbon, and carbon sequestration were developed using 10 plots with directly measured biomass and leaf area data, and 36 plots with modelled carbon data. The plots included a range of genetic types established on north- and south-facing aspects. Modelled carbon was highly correlated with directly measured crown, stem, and above ground biomass data, with r = 0.92, 0.97 and 0.98, respectively. LiDAR canopy percentile height (P30) and cover, based on all returns above 0.5 m, explained 81, 88, and 93% of the variation in directly measured crown, stem, and above ground live carbon and 75, 89 and 88% of the modelled carbon, respectively. LAI (all surfaces) ranged between 8.8–19.1 in the 10 plots measured at age 9 years. The difference in canopy percentile heights (P95–P30) and cover based on first returns explained 80% of the variation in total LAI. Periodic mean annual increments in stem volume, above ground live carbon, and total carbon between ages 9 and 13 years were significantly related to (P95–P30), with regression models explaining 56, 58, and 55%, respectively, of the variation in growth rate per plot. When plot aspect and genetic type were included with (P95–P30), the R² of the regression models for stem volume, above ground live carbon, and total carbon increment increased to 90, 88, and 88%, respectively, which indicates that LiDAR regression equations for estimating stock changes can be substantially improved by incorporating supplementary site and crop data.

【 授权许可】

CC BY   
© 2011 by the authors; licensee MDPI, Basel, Switzerland.