Abstract
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.
Highlights
The fate of live forest biomass is largely controlled by growth and disturbance processes, both natural and anthropogenic
A common image framework for biomass estimation and for change detection allows for consistent comparison of both state and change processes controlling biomass dynamics
Overview The biomass monitoring system is built from three methodological components: (1) regionalscale time-series image analysis using LandTrendr (Kennedy et al 2010, 2015); (2) statistical linkage of that imagery with field plot data using the gradient nearest neighbor (GNN) methodology (Ohmann et al 2012); and (3) local-scale assessment using airborne lidar data (Kane et al 2010)
Summary
Predicting the fate of carbon in forests under future climates is a fundamental scientific and management challenge, as these systems contain large reservoirs of carbon, provide many essential ecosystem services, and represent a potentially critical feedback in global climate change (Birdsey et al 2007). Though FIA measurements are recorded consistently, cover the contiguous US, and are statistically defensible, the goal of any such inventory program is an estimate of growing stock or volume for large areas (McRoberts et al 2014). This result may not be sufficient for managers and modelers who need spatially explicit maps of carbon and carbon change at the scale of management (Sannier et al 2016). The decadal repeat period of FIA plot measurements (in the western US) is longer than that needed to capture anthropogenic and natural processes that significantly alter carbon pools every year (Houghton 2005)
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