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County-level Aboveground Biomass Estimation Implications of Allometric Equation Selection

Laura Duncanson, University of Maryland, (Presenter)
Kristofer Johnson, USDA Forest Service,
Wenli Huang, University of Maryland, College Park,
Ralph Dubayah, University of Maryland,

Quantifying forest aboveground biomass is critical for constraining estimates of carbon cycle stocks and fluxes. Airborne LiDAR has emerged as the gold standard technology for modeling forest aboveground biomass, but LiDAR-based estimates are calibrated with field AGBM estimates, which are generated through the application of allometric equations. These allometric equations, relating individual tree Diameter at Breast Height (DBH), species, and sometimes height, are developed through the destructive sampling of small, locally clustered samples. The goal of this paper is to determine the implications of selecting different allometric equations for estimating field biomass. Wall-to-wall LiDAR data were collected over Sonoma County in the summer of 2014, in tandem with a field campaign collecting biomass samples for 189 field plots. We applied three allometric equations to field datasets (Jenkins, FIA’s Component Ratio Method, and local species-specific equations). We then used random forest regression to relate LiDAR and topographic metrics to field estimated biomass, producing three aboveground biomass models (one for each allometric approach). Finally, we apply these three models to predict biomass over Sonoma County. This research analyzes the implications of allometric equation selection both at the modeling level (i.e. which allometric approach yields the highest correlation with LiDAR metrics) and at a county-level.

Presentation Type:  Poster

Session:  Carbon Monitoring System (CMS) Posters   (Mon 1:30 PM)

Associated Project(s): 

  • Dubayah, Ralph: Development of a Prototype MRV System to Support Carbon Ecomarket Infrastructure in Sonoma County ...details

Poster Location ID: 111


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