Nathaniel Anderson, Rocky Mountain Research Station, nathanielmanderson@fs.fed.us (Presenter)
John Hogland, Rocky Mountain Research Station, jshogland@fs.fed.us
Woodam Chung, The University of Montana, woodam.chung@umontana.edu
J. Greg Jones, Rocky Mountain Research Station, jgjones@fs.fed.us


The 1.5 million acre Uncompahgre Plateau in Colorado faces significant management challenges related to fire, insects, disease, and invasive species, which are affecting diverse ecosystems, including pinyon-juniper, aspen and mixed conifer forests. In response, the US Forest Service is intensifying silvicultural treatments through a Collaborative Forest Landscape Restoration Project. Over the next decade, these treatments are expected to produce biomass that could be cofired with coal in a nearby 110-megawatt power plant or delivered to other end-users. However, the yield and costs for harvesting, processing, and transporting biomass produced by these treatments is unknown. To map above ground biomass and model potential biomass flows resulting from alternative treatment intensities and market conditions, the authors developed spatial methods that integrate remote sensing imagery, field plot data, operations research, and a heuristic algorithm for transportation optimization. Biomass estimation is based on a novel two-stage classification and estimation approach employing principle component analysis, polytomous logistic regression, and multivariate regression applied to NAIP orthoimagery and FIA data. Ground-truthed raster surfaces depicting basal area, trees, and above ground biomass per acre at one square meter resolution are used as inputs into spatial economic models that predict economic flows based on recovery factors, marginal forest operations costs, transportation costs, and market prices. Results include isocost contour maps that display the source and predicted biomass that would be supplied to specific locations at defined incremental prices. The methods and results of this study are useful to foresters, managers and investors interested in quantifying and predicting biomass supply.