Many areas of the boreal forest of Alaska contain deep layers of moss, duff, and peat, resulting in a large pool of biomass that potentially can burn and smolder for long periods of time creating hazardous smoke episodes for local residents and communities and causing detrimental landscape impacts. Research to quantify fuel consumption, flammability thresholds, and smoke production in boreal forest types is critical for effective modeling of fire effects (e.g. smoke emissions, regional haze, permafrost melting, erosion, plant succession, etc) and landscape management if prescribed burning is to become an important land management technique in the future. Preliminary research has generated a hypothesis of the controlling variables that govern the fuel consumption in the moss and duff layers, but this hypothesis needs to be verified and tested through field-based experimentation. Very limited smoke emissions characterization has been completed. The purpose of this study is to collect fuel consumption data and characterize smoke emissions on active wildfires and prescribed fires. The data will be used to develop new and modify existing forest floor fuel consumption models and develop emission rate equations for the boreal forest fuelbed type. The fuel consumption and emission factors and rate equations will be implemented into the software program Consume 3.0 to better predict moss/peat/duff fuel consumption and smoke production during wildland fires in Alaska. This research will make Consume 3.0 and other fuel consumption, fire effects, and smoke production models more robust and aid managers, planners, and researchers in developing environmentally, socially, and legally responsible land management plans. This research will also allow for a more effective and informed use of emission production and wildfire/prescribed fire trade-off models providing improved wildland fire emissions accounting and planning at the local, regional, and global scales. The fuel consumption and smoke characterization module will be a scientifically based support tool that can be used to improve fire management decision processes (AFP-2003-2, task #1 and linkages with AFP-2003-1, task 3).