With the recognition of the critical role that they play in the global climate system, managing forests to enhance carbon stores as a strategy for mitigating future climate change has become an…

This federal contract award is for a grant to the University of Maine System, through its University of Maine division, to develop an integrated data and modeling framework for monitoring, reporting, and verifying forest carbon stocks and fluxes in the Acadian Forest region of the northeastern U.S. and eastern Canada. The $940,308 grant, awarded by the National Aeronautics and Space Administration (NASA), aims to improve regional-scale carbon budget estimates by assimilating remote sensing data into a process-based landscape model. The approach builds on previous NASA Carbon Monitoring System research and will provide explicit measures of uncertainty, a critical requirement for evaluating the utility of monitoring, reporting, and verification (MRV) products. The refined model-data framework can be applied to other regions, contributing to scientifically sound and reliable MRV systems for quantifying forest carbon dynamics and supporting climate change mitigation policy and management decisions. The contract has no set-aside designation.

With the recognition of the critical role that they play in the global climate system, managing forests to enhance carbon stores as a strategy for mitigating future climate change has become an important goal of resource policy and decision-making across local, national and international levels. Policy and management decisions depend on scientifically sound and reliable monitoring, reporting, and verification (MRV) systems for quantifying - and assessing the uncertainty in - stocks and transfers within and among the major forest carbon pools. However, regional and national estimates of forest carbon sinks differ significantly among assessments depending on the measurement or scaling approach used and the budget components considered. Although there are benefits in retaining independence among these different approaches for constraining and comparing regional-scale carbon budget estimates, we posit that more significant progress in MRV will be made by developing and employing explicitly integrated data and methodologies. Therefore, we propose an updated approach that integrates remotely sensed data along with inventory-based information in a framework for mechanistic representation of forest carbon dynamics using process-based landscape modeling. The proposed approach uses the landis-II forest landscape model as the framework for assimilating remote sensing data to quantify carbon stocks and fluxes at high spatial resolution and annual time steps. The landis-II framework allows for localized, dynamic, and remote sensing-based parameterization of the model that will provide explicit measures of uncertainty, which is a critical requirement in evaluating the value of MRV products for the user community. The remote sensing methods build on existing prototype activities developed from previous NASA carbon monitoring system projects by integrating multi-sensor, multi-scale remote sensing for characterizing regional forest composition, structure, and disturbance. These data sets will be assimilated into the landis-II framework through both serving as the target values in a state-of-the-art bayesian inverse parameterization as well as initializing site- and regional-scale model applications. This approach is based on established methods used by this investigator team and its collaborators for (a) species-specific aboveground biomass estimation using spectral-hyperspectral scaling of forest composition, (b) spatial modeling of forest structure metrics from airborne and space-borne laser scanning (lidar) data, and (c) disturbance history metrics derived from segmentation and classification of the satellite time-series record. The refined model-data framework will be developed and calibrated for application in the acadian forest region of the northeastern USA and maritime canada. Aggregated estimates of forest carbon stocks and transfers across maine, USA, and new brunswick, canada, will be compared against reported estimates from the national forest carbon accounting systems in the two countries. The result will be a verified MRV system that can be applied to other regions since IT is both comprehensive of the major carbon pools for the managed forest sector as well as consistent in its spatially and temporally explicit accounting of stocks and fluxes, and their uncertainties.