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Comparison of biomass allometric approaches for regional scale carbon mapping Scott Powell – Montana State University Robert Kennedy – Boston University Janet Ohmann – USDA Forest Service Warren Cohen – USDA Forest Service Matthew Gregory – Oregon State University Heather Roberts – Oregon State University Van Kane – University of Washington Jim Lutz – University of Washington Forest. SAT: Corvallis, Oregon, September 2012
Regional Carbon Mapping • Yearly (1990 -2010) maps of aboveground live biomass • Sources of uncertainty – Spectral data • 3 permutations – Modeling parameters • 3 permutations – Biomass allometrics • 2 permutations
Biomass Allometric Equations • Enable scaling of tree-level measurements to biomass. • Variety of approaches ranging from generic to site-specific. – Different scales, assumptions, uses, and interpretations. – Carbon accounting vs. carbon mapping
Objectives • Compare mapped predictions of aboveground biomass based on two common allometric approaches. • Improve understanding of the range of uncertainty introduced into carbon mapping from selection of biomass allometric approach. • Assess differences in estimated biomass based on forest structure, composition, and land ownership.
Methods Allometric approaches: 1. Jenkins Equations: Nationally generic Jenkins, J. C. , D. C. Chojnacky, L. S. Heath, and R. A. Birdsey. 2003. National-scale biomass estimators for United States tree species. Forest Science 49(1): 12 -35. 2. Component Ratio Method (CRM): Regionally-tailored but nationally consistent Heath, L. S. , M. H. Hansen, J. E. Smith, W. B. Smith, and P. D. Miles. 2009. Investigation into calculating tree biomass and carbon in the FIADB using a biomass expansion factor approach. In: Mc. Williams, W. , Moisen, G. , Czaplewski, R. , comps. 2009. 2008 Forest Inventory and Analysis (FIA) Symposium; October 21 -23, 2008: Park City, UT. Proc. RMRS-P-56 CD. Fort Collins, CO: U. S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 1 CD.
Jenkins Equations • 10 national-level generalized biomass equations based on metaanalysis of published equations. • Current basis for U. S. greenhouse gas inventories. • Based solely on DBH measurements, and do not include tree height measurements. Aboveground Biomass = Exp(β 0 + β 1 ln DBH)
Component Ratio Method (CRM) • Basis for current FIA biomass estimates • Nationally-consistent method that relies on regional FIA volume equations and specific gravity to estimate biomass. • Volume equations incorporate tree height (or surrogate)
Previous Studies • Zhou and Hemstrom, 2009 – PNW-RP-584 – CRM biomass estimates were 17% lower than Jenkins biomass estimates for aboveground softwood biomass in Oregon. • Domke et al. , 2012 – Forest Ecology and Management. – CRM biomass estimates were 16% lower than Jenkins biomass estimates for the 20 most common species in the U. S.
Results: Overall Difference
Differences by Vegetation Class
Spatial Variation: Relative Differences by Height and Age Ratio = Jenkins/CRM
Spatial Variation: Absolute Differences by Height and Age Difference = Jenkins - CRM
Exceptions: Forest types where Jenkins < CRM • 0. 4% of study area - (19, 026 ha) • Abies amabilis/Chamaecyparis nootkatensis (384 ha) • Populus tremuloides/Acer macrophyllum (2, 330 ha) • Alnus rubra/Tsuga heterophylla (4, 967 ha) • Arbutus menziesii (4, 818 ha) • Larix occidentalis/Pinus ponderosa (168 ha) • Pinus monticola (494 ha) • Pseudotsuga menziesii/Fraxinus latifolia (1, 944 ha) • Pinus lambertiana/Pseudotsuga menziesii (3, 920 ha)
Height Class Distribution Ratio Difference
Age Class Distribution Ratio Difference
Vegetation Class Distribution
Vegetation Class Comparison Ratio of Jenkins/CRM Difference Jenkins-CRM
Ownership Class Distribution
Ownership Class Comparison Ratio of Jenkins/CRM Difference Jenkins-CRM
Conclusions • Overall difference between methods is 18% but there is significant spatial variation (up to 31% in young, open stands). • Jenkins biomass > CRM biomass, especially in younger, shorter, more open stands on private lands.
Conclusions • Absolute differences are smaller in these lower biomass locations, but contribution is important due to large area. Stand Height Stand Age
Conclusions • Neither approach is inherently “correct”. – Incorporation of regionally-tailored volume equations within a nationally-consistent framework is an improvement for spatially explicit purposes. • Need additional scales of validation, including Lidar-derived biomass estimates (with “local” allometric equations).
Conclusions • Implications for strict accounting purposes AND mapping applications. • Careful equation selection in highly disturbed landscapes (young, short, open stands). • Temporal considerations: Jenkins would potentially over-estimate biomass (relative to CRM) in post-disturbance, regenerating stands.
Thank You. Questions? Contact me at: [email protected] edu (406) 994 -5017