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Funded Research

Development of a Prototype MRV System to Support Carbon Ecomarket Infrastructure in Sonoma County

Dubayah, Ralph: University of Maryland (Project Lead)
Boyd, Matthew: Watershed Sciences Inc. (Co-Investigator)
Finley, Andrew (Andy): Michigan State University (Co-Investigator)
Galik, Christopher: North Carolina State University (Co-Investigator)
Hurtt, George: University of Maryland (Co-Investigator)
O'Neil-Dunne, Jarlath: University of Vermont (Co-Investigator)
Swatantran, Anuradha (Anu): University of Maryland (Co-Investigator)
Zhao, Maosheng: University of Maryland (Co-Investigator)
Birdsey, Richard (Rich): Woodwell Climate Research Center (Institution Lead)
Brown, Molly: University of Maryland (Institution Lead)
Duncanson, Laura: University of Maryland (Participant)
Escobar, Vanessa: NASA GSFC / SSAI (Participant)
Johnson, Kristofer (Kris): USDA Forest Service (Participant)
Gaffney, Karen: Sonoma County Agricultural Preservation and Open Space District (Stakeholder)
Robinson, Tom: Sonoma County Agricultural Preservation and Open Space District (Stakeholder)
Schichtel, Allison: Sonoma County Agricultural Preservation and Open Space District (Stakeholder)

Project Funding: 2013 - 2016

NRA: 2013 NASA: Carbon Monitoring System   

Funded by NASA

National and international programs have an increasing need for precise and accurate estimates of forest carbon and structure to support greenhouse gas reduction plans, climate initiatives, and other international climate treaty frameworks such as REDD++. Central to these activities is the development of MRV (measurement, reporting and verification) systems that provide an accounting of forest carbon emission and sequestration at high spatial resolution with appropriate temporal frequencies. Such systems can be used to support and sustain the development of an 'ecomarket' infrastructure centered on carbon, along with other ecosystem services, such as biodiversity, water resources, and the like. Central to ecomarkets is the creation of financial incentives that reward the preservation and enhancement of ecosystem services through time, as enabled from robust MRV systems. NASA has recognized the urgent need for the development of MRV through its initiation of the Carbon Monitoring System (CMS) program. The University of Maryland, working with NASA centers, the USFS, and commercial entities has led research efforts in Phase I and Phase II that have laid the basic groundwork for MRV. Our Phase II project uses existing, wall-to-wall airborne lidar coverage and in-situ field data collection to produce high-resolution maps of carbon stocks for all of Maryland. These same data are also used to drive a prognostic ecosystem model to predict carbon fluxes and carbon sequestration potential. This work has demonstrated the feasibility of large-scale mapping using airborne lidar, an important first step, and suggests logical follow-on activities that should be undertaken towards the realization of operational MRV systems that are responsive to local, national and international interests in management and policy. The overall goal of this project is the continuing development of a prototype MRV system based on commercial off-the-shelf (COTS) remote sensing and analysis capabilities to support ecomarket infrastructure in Sonoma County, California. Building on our East Coast county-level work as part of CMS I and CMS II, we seek to address the following questions: - What accuracies are achievable using predominantly COTS-based approaches to high-resolution MRV for forest carbon? - What is the 'price-of-precision' for MRV systems and how does this vary as a function of sample design, ground data, remote sensing data acquisition and analysis costs? - How can stakeholder needs and requirements be integrated during the creation and implementation of MRV systems to provide effective decision support and compliance capabilities, and with better-informed policy decisions? Can a cloud-based architecture be used to facilitate the initiation and use of MRV systems to enable their implementation domestically and abroad? We have identified five objectives to answer our research questions: (1) Integration of Sonoma County stakeholder needs and requirements into the MRV system design. (2) High-resolution wall-to-wall estimation of carbon stocks and their uncertainties for Sonoma County and mapping of sequestration potential under various development scenarios using the Ecosystem Demography model. (3) Development of the key components of an end-to-end MRV system that includes data acquisition, warehousing, baseline quantification, data accessibility, accounting, reporting and stakeholder communication. (4) Analysis of the 'price-of-precision' through a cost-benefit analysis of data resolution relative to accuracy achievable at particular spatial scales e.g. United Nations Framework Conference on Climate Change (UNFCCC) Tier 1 vs. Tier 3. (5) Demonstration of a functional prototype MRV platform with visualization, and analytical capabilities for addressing Sonoma County initiatives. Our basic approach to high-resolution carbon stock mapping has been established in our CMS Phase 1 (two Maryland counties) and Phase 2 (23 Maryland counties) efforts.


Duncanson, L., Rourke, O., Dubayah, R. 2015. Small Sample Sizes Yield Biased Allometric Equations in Temperate Forests. Scientific Reports. 5(1). DOI: 10.1038/srep17153

Huang, W., Dolan, K. A., Swatantran, A., Johnson, K. D., Tang, H., ONeil-Dunne, J., Dubayah, R., Hurtt, G. 2019. High-resolution mapping of aboveground biomass for forest carbon monitoring system in the Tri-State region of Maryland, Pennsylvania and Delaware, USA. Environmental Research Letters. DOI: 10.1088/1748-9326/ab2917

Huang, W., Swatantran, A., Duncanson, L., Johnson, K., Watkinson, D., Dolan, K., O'Neil-Dunne, J., Hurtt, G., Dubayah, R. 2017. County-scale biomass map comparison: a case study for Sonoma, California. Carbon Management. 8(5-6), 417-434. DOI: 10.1080/17583004.2017.1396840

Finley, A. O., Banerjee, S., Zhou, Y., Cook, B. D., Babcock, C. 2017. Joint hierarchical models for sparsely sampled high-dimensional LiDAR and forest variables. Remote Sensing of Environment. 190, 149-161. DOI: 10.1016/j.rse.2016.12.004

Datta, A., Banerjee, S., Finley, A. O., Gelfand, A. E. 2016. On nearest-neighbor Gaussian process models for massive spatial data. Wiley Interdisciplinary Reviews: Computational Statistics. 8(5), 162-171. DOI: 10.1002/wics.1383

Salazar, E., Hammerling, D., Wang, X., Sanso, B., Finley, A. O., Mearns, L. O. 2016. Observation-based blended projections from ensembles of regional climate models. Climatic Change. 138(1-2), 55-69. DOI: 10.1007/s10584-016-1722-1

Swatantran, A., Tang, H., Barrett, T., DeCola, P., Dubayah, R. 2016. Rapid, High-Resolution Forest Structure and Terrain Mapping over Large Areas using Single Photon Lidar. Scientific Reports. 6, 28277. DOI: 10.1038/srep28277

Babcock, C., Finley, A. O., Cook, B. D., Weiskittel, A., Woodall, C. W. 2016. Modeling forest biomass and growth: Coupling long-term inventory and LiDAR data. Remote Sensing of Environment. 182, 1-12. DOI: 10.1016/j.rse.2016.04.014

Datta, A., Banerjee, S., Finley, A. O., Gelfand, A. E. 2016. Hierarchical Nearest-Neighbor Gaussian Process Models for Large Geostatistical Datasets. Journal of the American Statistical Association. 111(514), 800-812. DOI: 10.1080/01621459.2015.1044091

2015 NASA Carbon Cycle & Ecosystems Joint Science Workshop Poster(s)

  • County-level Aboveground Biomass Estimation Implications of Allometric Equation Selection   --   (Laura Duncanson, Kristofer Johnson, Wenli Huang, Ralph Dubayah)   [abstract]
  • Integrating Lidar Canopy Height and Landsat-based Forest Disturbance History with Ecosystem Demography Model for Carbon Change Estimation, A Case in Charles County, Maryland   --   (Maosheng Zhao, Chengquan Huang, George Hurtt, Ralph Dubayah, Justin Fisk, Anu Swatantran, Wenli Huang, Hao Tang)   [abstract]

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