Managing forests to increase carbon stocks or reduce emissions requires knowledge of how management practices effect carbon pools over time, and inexpensive techniques to monitor activities.  This study develops methodology to integrate the multi-tier monitoring data from the North American Carbon Program (NACP) with management decisions by linking bottom-up and top-down ecosystem models with decision-support tools.  Monitoring systems for carbon stocks and fluxes in the NACP include a multi-tier hierarchy of observation methods: remote sensing, inventories, landscape biometrics, and flux towers.  We use the GIS version of PnET-CN to scale up and map observations from flux towers, landscape biometrics, and inventories to areas of approximately 2500 km² around flux tower sites. The NASA-CASA model is used to derive estimates for the same areas from remote sensing observations by the MODIS sensor, and biophysical maps.  We compare and reconcile the top-down and bottom-up approaches, then use the mapped estimates of productivity and biomass that embed consequences of land disturbances and forest age structure as input to decision-support tools.  Key information for the decision-support tools includes (1) estimates of carbon stocks and quantified impacts of management activity; (2) estimates of net ecosystem production (NEP) and changes in carbon pools; and (3) estimates of forest/atmosphere carbon fluxes and relevant effects from various environmental controls. With a network of sites that represent different managed forests of the U.S., we envision a set of “benchmark” estimates that can simply and reliably document the expected effects of management decisions and separate these effects from natural factors such as climate variability. This work is relevant to land managers and climate change policy because it supports a need to estimate and report carbon stocks and changes in carbon stocks to state, regional, national, and private greenhouse gas registries.