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 ForestSAT: Corvallis, Oregon, September 2012

Regional Carbon Mapping Yearly ( ) 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 National-scale biomass estimators for United States tree species. Forest Science 49(1): 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 Investigation into calculating tree biomass and carbon in the FIADB using a biomass expansion factor approach. In: McWilliams, W., Moisen, G., Czaplewski, R., comps Forest Inventory and Analysis (FIA) Symposium; October 21-23, 2008: Park City, UT. Proc. RMRS-P-56CD. 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 meta- analysis 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(β0 + β1 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

Height Class

Age Class Distribution Ratio Difference

Vegetation Class Distribution

Vegetation Class Comparison Ratio of Jenkins/CRMDifference Jenkins-CRM

Ownership Class Distribution

Ownership Class Comparison Ratio of Jenkins/CRMDifference 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 HeightStand 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.

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