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Funded Research

High Resolution Carbon Monitoring and Modeling: A CMS Phase 2 Study

Dubayah, Ralph: University of Maryland (Project Lead)
Hurtt, George: University of Maryland (Co-Investigator)
Swatantran, Anuradha (Anu): University of Maryland (Co-Investigator)
Birdsey, Richard (Rich): Woodwell Climate Research Center (Institution Lead)
DeCola, Philip (Phil): IG3IS (Participant)
Zhao, Maosheng: University of Maryland (Participant)
Abbott, Phillip: Purdue University (Stakeholder)
Canick, Michelle: The Nature Conservancy (Stakeholder)

Project Funding: 2012 - 2015

NRA: 2011 NASA: Carbon Monitoring System   

Funded by NASA

The overall goal of our proposed research is the continuing prototype development of a framework for estimating local-scale carbon stocks and future carbon sequestration potential for the State of Maryland using remote sensing and ecosystem modeling. Specifically, we will address the following objectives: (1) Improve our existing methodology for carbon stock estimation and uncertainty and assess its efficacy across an expanded range of environmental and forest conditions; (2) Provide local-scale estimates of carbon stocks and their uncertainties for the entire state of Maryland representing Eastern U.S. forest types; (3) Initialize and run a prognostic ecosystem model to estimate carbon stocks and their changes, and to estimate carbon sequestration potential; (4) Provide detailed validation of national biomass maps using FIA data and localscale biomass maps.(5) Demonstrate new data acquisition technology (single photon counting) for lowcost, rapid carbon assessments. Our proposed work will greatly expand our coverage from 2 to 24 Maryland counties and extends from the tidewater forests of the Chesapeake Bay through the coastal plains and uplands, to the mountainous forests of Western Maryland and the Appalachians. This gradient in land use, topographic, edaphic, and climatic conditions enables an appropriate expansion of methods, models, data, and assessments consistent with the goals of the second phase of CMS. Our objectives build from our Phase 1 work and lead to a clear set of tasks for the proposed effort. These are divided into seven categories of activities traceable to this framework: (1) Remote sensing data acquisition and processing; (2) Field data collection and analysis; (3) Algorithm development and image processing; (4) Statistical and machine learning model development; (5) County biomass and uncertainty map generation, and end-to-end error analysis; (6) Prognostic ecosystem modeling, and; (7) national biomass map validations. An additional element of our proposed work is a coordinated outreach effort to county and state agencies to inform and promote their activities in CMS and includes a transfer of technology to the State of Vermont. To promote this outreach we will also implement a new, web-based data visualization, query and delivery system, Grid^Intel Online (GIO) that allows any user to call up lidar data, associated imagery, biomass and error estimates for arbitrary map areas. Deliverables for this project expand upon those from Phase 1. In addition to the developed framework the project will produce the following CMS products: (1) tiled and mosaicked canopy height and forest/non-forest maps at 2 m and 30 m resolution for Maryland; (2) AGBM maps at 30 m resolution with associated uncertainty maps; (3) EDmodel based carbon and carbon-flux maps at 90 m resolution; (4) ED-model maps of carbon sequestration potential; (5) web-based data visualization and query system; (6) map of canopy structure and biomass derived from wall-to-wall single photon lidar for Alleghany county; (7) assessment of main sources of error and proposed strategies for reducing errors in future deployment of an operational CMS.


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

O'Neil-Dunne J, MacFaden S, Royar A, Reis M., Dubayah R. and Swatantran A. (2014) An Object-Based Approach to Statewide Land Cover Mapping. Proceedings of the 2014 ASPRS Annual Conference. Louisville, KY

Tang, H., Ma, L., Lister, A., O'Neil-Dunne, J., Lu, J., Lamb, R. L., Dubayah, R. O., Hurtt, G. 2020. High-resolution forest carbon mapping for climate mitigation baselines over the RGGI region, USA. Environmental Research Letters. DOI: 10.1088/1748-9326/abd2ef

Tang, H., Swatantran, A., Barrett, T., DeCola, P., Dubayah, R. 2016. Voxel-Based Spatial Filtering Method for Canopy Height Retrieval from Airborne Single-Photon Lidar. Remote Sensing. 8(9), 771. DOI: 10.3390/rs8090771

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

Huang, W., Swatantran, A., Johnson, K., Duncanson, L., Tang, H., O'Neil Dunne, J., Hurtt, G., Dubayah, R. 2015. Local discrepancies in continental scale biomass maps: a case study over forested and non-forested landscapes in Maryland, USA. Carbon Balance and Management. 10(1). DOI: 10.1186/s13021-015-0030-9

Johnson, K. D., Birdsey, R., Cole, J., Swatantran, A., O'Neil-Dunne, J., Dubayah, R., Lister, A. 2015. Integrating LIDAR and forest inventories to fill the trees outside forests data gap. Environmental Monitoring and Assessment. 187(10). DOI: 10.1007/s10661-015-4839-1

Johnson, K. D., Birdsey, R., Finley, A. O., Swantaran, A., Dubayah, R., Wayson, C., Riemann, R. 2014. Integrating forest inventory and analysis data into a LIDAR-based carbon monitoring system. Carbon Balance and Management. 9(1), 3. DOI: 10.1186/1750-0680-9-3

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

  • Fusing Next-generation Active Remote Sensing Data for Improved Forest Height and Structure Mapping   --   (Wenlu Qi, 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]
  • Integrating LIDAR and Forest Inventories to Fill the Trees Outside Forests Data Gap   --   (Kristofer Johnson, Richard Birdsey, Jason Cole, Anuradha Swatantran, Jarlath O'Neil-Dunne, Ralph Dubayah, Andrew J. Lister)   [abstract]

2013 NASA Terrestrial Ecology Science Team Meeting Poster(s)

  • High-Resolution Ecosystem Modeling as part of Robust Carbon Monitoring System   --   (Maosheng Zhao, George Hurtt, Ralph Dubayah, Justin Fisk, Amanda Armstrong, Anuradha Swatantran, Naira Pinto, Oliver Rourke, Steve Flanagan, Chengquan Huang)   [abstract]
  • Forest Structure and Biomass Mapping Using Time Series Landsat Observations, Small Footprint Lidar, and Field Inventory Data in North Carolina   --   (Chengquan Huang)   [abstract]

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