Quantifying Aboveground Biomass for Common Shrubs in Northeastern California

Graduation date: 2017Quantifying biomass is important for sustainable forest management. The purpose of this study is to obtain allometric relationships for seven species of shrubs common in northeastern California so that estimates of carbon and fuel loading may be better realized. Although some shrub biomass equations exist, such equations are limited in sample size and geographic scope. In this study, predictive equations were developed for shrub species using available metrics that relate crown area, basal area, plant height, and stem diameter to the dried weight of total, leaf, 1-hour, 10-hour, 100-hour, and 1,000-hour biomass components. Nonlinear least squares regression was used to obtain parameter estimates via an allometric (power) model. Seemingly unrelated regression (SUR) was used to formulate biomass equations for each shrub species. The resulting system of equations offer a reference for land managers and researchers interested in biomass assessment. Multinomial log-linear regression (MLR) was used to obtain predicted proportions of each shrub species biomass component and was consistent in producing low values of RMSE%. Nonlinear mixed effects models (NMEM) were used to estimate biomass component by species in Chapter 3. The use of NMEM in this setting was beneficial in explaining within subject biomass and allometry in the seven shrub species. The equations resulting from this research are applicable to areas within northeastern California where similar climate, vegetation associations, and soils may be found in relation to the study area for which this research occurred