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Comparison of biomass allometric approaches for regional scale carbon mapping

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

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Comparison of biomass allometric approaches for regional scale carbon mapping

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  1. 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

  2. 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

  3. 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

  4. 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.

  5. 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: McWilliams, W., Moisen, G., Czaplewski, R., comps. 2009. 2008 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.

  6. 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)

  7. 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)

  8. 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.

  9. Results: Overall Difference

  10. Differences by Vegetation Class

  11. Spatial Variation: Relative Differences by Height and Age Ratio = Jenkins/CRM

  12. Spatial Variation: Absolute Differences by Height and Age Difference = Jenkins - CRM

  13. 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)

  14. Height Class Distribution Ratio Difference

  15. Height Class

  16. Age Class Distribution Ratio Difference

  17. Vegetation Class Distribution

  18. Vegetation Class Comparison Ratio of Jenkins/CRM Difference Jenkins-CRM

  19. Ownership Class Distribution

  20. Ownership Class Comparison Ratio of Jenkins/CRM Difference Jenkins-CRM

  21. 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.

  22. Conclusions • Absolute differences are smaller in these lower biomass locations, but contribution is important due to large area. Stand Height Stand Age

  23. 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).

  24. 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.

  25. Thank You.Questions?Contact me at:spowell@montana.edu(406) 994-5017

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