Monte Carlo Simulation of Map Error in Carbon Assessments

Design-based field inventories provide important, well-constrained estimates of the quantity of forest carbon in particular areas, but their capacity to depict the effects of management and forest disturbance is limited by the relative rarity of disturbed conditions on the landscape. The temporal depth and spatial breadth of observations from platforms such as Landsat provide unique perspective on ecosystem dynamics, but the integration of these observations into formal decision support will rely upon improved uncertainty accounting. Monte Carlo (MC) simulations offer a practical, empirical method of accounting for potential map errors in broader ecosystem assessments. We describe a technique called PDF (probability density function) Weaving, which constrains MC simulations of vegetation map error using population estimates derived independently from design-based inventories. This approach is based on constructing systems of linear equations and inequalities which incorporate initial standard inventory results and initial map. Solution of these systems provides parameters for probability functions that may be used to simulate different levels of error in every MC simulation. We illustrated this approach, using error assessments calibrated with forest inventory data, in an assessment of the effects of wildfire and harvest on carbon storage over 20 years on a forested landscape in the western United States (US). This assessment utilized the Forest Carbon Management Framework (ForCaMF) approach, which is being implemented by the US National Forest System (NFS). Results showed that systematic map error can contribute significant uncertainty in a Monte Carlo analysis, but that impacts of fire and harvest on landscape-level carbon storage may nevertheless be clearly identified and differentiated using remotely sensed maps of vegetation and disturbance.