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The fate of live forest biomass is largely controlled by growth and disturbance processes, both natural and anthropogenic. Thus, biomass monitoring strategies must characterize both the biomass of the forests at a given point in time and the dynamic processes that change it. Here, we describe and test an empirical monitoring system designed to meet those needs. Our system uses a mix of field data, statistical modeling, remotely-sensed time-series imagery, and small-footprint lidar data to build and evaluate maps of forest biomass. It ascribes biomass change to specific change agents, and attempts to capture the impact of uncertainty in methodology. We find that:

A common image framework for biomass estimation and for change detection allows for consistent comparison of both state and change processes controlling biomass dynamics. Regional estimates of total biomass agree well with those from plot data alone.

The system tracks biomass densities up to 450-500 Mg ha(-1) with little bias, but begins underestimating true biomass as densities increase further.

Scale considerations are important. Estimates at the 30 m grain size are noisy, but agreement at broad scales is good. Further investigation to determine the appropriate scales is underway.

Uncertainty from methodological choices is evident, but much smaller than uncertainty based on choice of allometric equation used to estimate biomass from tree data.

In this forest-dominated study area, growth and loss processes largely balance in most years, with loss processes dominated by human removal through harvest. In years with substantial fire activity, however, overall biomass loss greatly outpaces growth.

Taken together, our methods represent a unique combination of elements foundational to an operational landscape-scale forest biomass monitoring program.