Recent years have seen the parallel emergence of mature carbon markets and of new tools to map forest carbon using earth observation (EO) tools. Projects under the NASA carbon monitoring system…

This federal contract award to Oregon State University (OSU) is part of the NASA Carbon Monitoring System (CMS) program. The $990,606 grant will fund a research project to develop a framework for using Earth observation tools to map forest carbon with spatially explicit uncertainty quantification. The project aims to address challenges in incorporating new Earth observation data sources and extending carbon monitoring over time to support carbon market registration and verification. The award has no set-aside designation. OSU, as a research institution within the Oregon University System, is leveraging its expertise in environmental sciences, forestry, and remote sensing to deliver this work for NASA. The project involves collaborations with other stakeholders from industry, agencies, and carbon registries to ensure the framework developed can be broadly adopted.

Recent years have seen the parallel emergence of mature carbon markets and of new tools to map forest carbon using earth observation (EO) tools. Projects under the NASA carbon monitoring system (CMS) have often been at the forefront of EO-based forest carbon monitoring technology, but innovations have also blossomed in private industry: technology-based startups recognize that EO-based tools can introduce tremendous efficiency into the carbon registration process. If EO-based tools can reduce cost-toentry in carbon-markets, many more landowners can manage for carbon. Agencies, companies, and non-profit conservation groups all have an interest in increased use of EO tools in forest carbon mapping. Adoption has been slow, however. As markets increase in overall capital, the stakes for robust estimation of carbon rise. For trust and transparency, the carbon market registration process depends on clear quantification of uncertainties that can be understood using well-established design-based statistics. Yet the mapping of carbon from remote sensing necessarily depends on model-based estimation and estimates of model-based uncertainty do not necessarily correspond to the uncertainties required by registration bodies. Tools are needed to quantify uncertainty in a manner that is spatially explicit, transparent and scalable. Moreover, as new EO methods emerge, registration and verification bodies need tools to quantify how those methods improve the uncertainty. Through three progressive objectives, we propose a framework to address these challenges. The first objective focuses on building a hierarchical bayesian structure that models spatial dependence and produces spatially explicit estimates of uncertainty that can be quantified for any arbitrary area. The second objective tests and demonstrates the framework in incorporating new EO-derived maps, new data types, and in providing tools to harmonize across existing biomass maps. The third objective extends these tests to the temporal realm, evaluating how the framework can be used to extend monitoring of carbon. We will test the objectives at sites across gradients of forest type and climate in the west coast states of the U.S., and we will incorporate prior CMS biomass data both at the broad and the local scales. As a synthetic effort, our proposing team (both CO-is and collaborators) brings together experts who formerly worked on diverse CMS projects, as well as experts in carbon markets. The origins of this project emerged from discussions with carbon-interested groups, and our work is heavily driven by stakeholder need. Through stakeholders already committed and additional stakeholders we recruit in the project, we will convene a technical advisory group (tag) to help guide implementation of the project goals. Current committed members include those from industry, agencies, and carbon registries. Overall project deliverables include: - baseline maps of forest biomass, tree lists, and associated uncertainty surfaces - demonstrations of improvement in uncertainty surfaces resulting from incorporation of small-footprint lidar maps, gedi data, and potentially from other EO products from photogrammetry and private firms - tools to bring time-series predictions into carbon-market monitoring paradigms - web-based interfaces to allow broad use and adoption of the framework by agencies, registries, and private landowners.