Canadian Forest Service Publications
Implications of differing input data sources and approaches upon forest carbon stock estimation. 2010. Wulder, M.A.; White, J.C.; Stinson, G.; Hilker, T.; Kurz, W.A.; Coops, N.C.; St-Onge, B.A.; Trofymow, J.A. Environmental Monitoring and Assessment 166(1-4): 543-561.
Available from: Pacific Forestry Centre
Catalog ID: 31515
CFS Availability: PDF (download)
Available from the Journal's Web site. †
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Site index is an important forest inventory attribute that relates productivity and growth expectation of forests over time. In forest inventory programs, site index is used in conjunction with other forest inventory attributes (i.e., height, age) for the estimation of stand volume. In turn, stand volumes are used to estimate biomass (and biomass components) and enable conversion to carbon. In this research, we explore the implications and consequences of different estimates of site index on carbon stock characterization for a 2,500-ha Douglas-fir-dominated landscape located on Eastern Vancouver Island, British Columbia, Canada. We compared site index estimates from an existing forest inventory to estimates generated from a combination of forest inventory and light detection and ranging (LIDAR)-derived attributes and then examined the resultant differences in biomass estimates generated from a carbon budget model (Carbon Budget Model of the Canadian Forest Sector (CBM-CFS3)). Significant differences were found between the original and LIDAR-derived site indices for all species types and for the resulting 5-m site classes (p<0.001). The LIDAR-derived site class was greater than the original site class for 42% of stands; however, 77% of stands were within ±1 site class of the original class. Differences in biomass estimates between the model scenarios were significant for both total stand biomass and biomass per hectare (p<0.001); differences for Douglas-fir-dominated stands (representing 85% of all stands) were not significant (p=0.288). Overall, the relationship between the two biomass estimates was strong (R 2=0.92, p<0.001), suggesting that in certain circumstances, LIDAR may have a role to play in site index estimation and biomass mapping.
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