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Funded Research

Annual GHG Inventory and MRV System for the US Forestlands

Saatchi, Sassan: Jet Propulsion Laboratory / Caltech (Project Lead)
Domke, Grant: USDA Forest Service (Co-Investigator)
Hagen, Stephen (Steve): Applied Geosolutions (Co-Investigator)
Park, Taejin: NASA Ames Research Center / BAERI (Co-Investigator)
Woodall, Christopher (Chris): USDA Forest Service (Co-Investigator)
Yu, Yifan: UCLA (Co-Investigator)
Bowman, Kevin: JPL (Collaborator)
Bloom, Alexis (Anthony): Jet Propulsion Lab, California Institute of Technology (Post-Doc)

Project Funding: 2016 - 2019

NRA: 2015 NASA: Carbon Monitoring System   

Funded by NASA

Abstract:
We propose to use the CMS infrastructure developed in our earlier pilot project and MRV prototypes to perform an updated annual Green House Gas (GHG) inventory of the US forestlands and contribute to the existing national MRV and the US Forest Service and the Environmental Protection Agency (EPA) national reporting to the United Nation Framework Convention on Climate Change (UNFCCC). The proposed work will produce spatial products on carbon stocks and fluxes that include stakeholder requirements on attributions and uncertainty and deliver at low-latency in order to be integrated in the national carbon management, decision making, and the official national MRV system. With the participation of stakeholders in the process of developing the products, for the first time, NASA CMS program will have the opportunity to directly contribute in the national GHG inventory. The overall objectives of the proposed work are: 1. Develop spatial products on carbon pools and fluxes over the US forestlands including Alaska with the low latency to be used for annual reporting 2. Quantify all sources and sinks and attributions by combining spatial data on forest cover change, pools, and fluxes into the CARDAMOM model data fusion framework 3. Quantify and report uncertainty for all components of sources and sinks in the US forestlands 4. Benchmark the methodology and products for integration in the national MRV system and future stakeholder’s activities. The proposed CMS activity will advance the remote sensing techniques and product by: 1) quantifying changes of forest cover with all natural and anthropogenic attributions at the annual cycle with low-latency delivery, 2) integrating remote sensing and in-situ observations on carbon pools and fluxes in a diagnostic ecosystem carbon balance model to improve carbon sinks and sources for different attributions associated with annual changes in the US forestlands, 3) improve characterization and quantification of errors and uncertainty following the IPCC good practice guidelines, and 4) including stakeholders interests and requirements by directly involving the user community and allowing the evaluation of CMS products for decision making and integration in the national MRV system. By including Alaska, the proposed work will use satellite and airborne and existing in- situ observations to compensate for the lack of extensive forest inventory data and provide, for the first time, the GHG inventory including all pools and fluxes, for both managed and unmanaged forests of the region. The methodology, including the CMS infrastructure for data processing, analysis, uncertainty assessment and data products will be benchmarked to allow integration in national MRV system. The benchmarking will also provide transparency in the entire performance of the carbon monitoring infrastructure for reporting and verification in future carbon trading protocols.

Publications:

Hogan, J. A., Domke, G. M., Zhu, K., Johnson, D. J., Lichstein, J. W. 2024. Climate change determines the sign of productivity trends in US forests. Proceedings of the National Academy of Sciences. 121(4). DOI: 10.1073/pnas.2311132121

Yu, Y., Saatchi, S., Domke, G. M., Walters, B., Woodall, C., Ganguly, S., Li, S., Kalia, S., Park, T., Nemani, R., Hagen, S. C., Melendy, L. 2022. Making the US national forest inventory spatially contiguous and temporally consistent. Environmental Research Letters. 17(6), 065002. DOI: 10.1088/1748-9326/ac6b47


More details may be found in the following project profile(s):