Comparison of biomass allometric approaches for regional scale carbon mapping

Scott Powell – Montana State UniversityRobert Kennedy – Boston UniversityJanet Ohmann – USDA Forest ServiceWarren Cohen – USDA Forest Service

Matthew Gregory – Oregon State UniversityHeather Roberts – Oregon State University

Van Kane – University of WashingtonJim Lutz – University of Washington

ForestSAT: 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 genericJenkins, 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 consistentHeath, 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.

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/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:spowell@montana.edu

(406) 994-5017