High-resolution forest carbon monitoring and modeling (Hurtt-CMS20) utilizes field data and advanced remote sensing and modeling to provide consistent baseline maps, future carbon sequestration potentials, and annual monitoring to meet state needs for tracking forest carbon and developing climate mitigation plans. Initially developed in MD, then applied to the Regional Greenhouse Gas (RGGI) Region, the project is now under development at national-global scales.

This is critical to provide an annual geospatial estimate of forest carbon flux that can be used to evaluate the progress reforestation and reductions in deforestation have achieved over the period due to policies and investments. Successes includes creating a monitoring prototype with wall to wall coverage and increased temporal resolution that can be used together with US Forest Service data. These data will be extended to have national and global coverage.

Cropland carbon monitoring system (CCMS) was developed using the EPIC (Environmental Policy Integrated Climate Model) process-based agroecosystem model. The model was implemented over the contiguous United States, to estimate annual net ecosystem balance under corn, soybeans and winter wheat production systems cultivated in 2012 (drought year) and 2015 (normal year). Both growing and non-growing season fluxes were accounted for when estimating net ecosystem carbon balance (Bandaru CMS2020).

Understanding how agricultural crops contribute to the nation’s carbon balance is essential as we manage for both food security and climate adaptation in these managed ecosystems. The CCMS estimates will produced annual estimates of carbon flux for corn, soybean, wheat, cotton, and rice crops grown in the conterminous US at a spatial resolution of 500 m, and can contribute to a better understanding of the impact of agriculture on the US carbon balance.

Understanding Amazon forest degradation in South America is important to improve our understanding of the long-term carbon consequences of forest degradation. The level of emphasis on forest degradation in monitoring, reporting, and verification (MRV) of forest monitoring activities in Amazonia fundamentally depends on the magnitude of net carbon emissions from logging, fire, and forest fragmentation. Morton (CMS2014) created maps using contemporary forest inventory data and extensive airborne lidar surveys with time series of Landsat data to evaluate landscape patterns of forest carbon stocks.

The project contributes to understanding the contribution to greenhouse gas emissions of deforestation, forest degradation and forest regrowth across the diverse Amazon ecosystem. The project has been extended to produce high quality maps across Alaska and other regions where deforestation and land degradation due to a variety of causes are poorly characterized.