Project Funding: 2014 - 2018

NRA: 2014 NASA: Carbon Monitoring System   

Funded by NASA

Abstract:
The overall goal of our project is the continuing development of a framework for estimating high-resolution carbon stocks and dynamics and future carbon sequestration potential using remote sensing and ecosystem modeling linked with existing field observation systems such as the USFS Forest Inventory. In particular, we seek to demonstrate an approach that provides the basis for the rapid expansion from Maryland to nearby states, and which additionally enables the monitoring of annualized changes in stocks through time at fine spatial resolution. We believe this build-out is possible today and is a critical step in the development of a national CMS. Specifically we will address the following objectives: (1) Improve our existing methodology for carbon stock estimation and uncertainty based on lessons learned from our Phase 2 study; (2) Provide wall-to-wall, high-resolution estimates of carbon stocks and their uncertainties for the 3-state region of Pennsylvania, Delaware and Maryland; (3) Initialize and run a prognostic ecosystem model for carbon at high-spatial resolution over multiple eastern states; (4) Validate national biomass maps using Forest Inventory and Analysis (FIA) data and high-resolution biomass maps over an expanded domain; (5) Develop and test methods for monitoring changes in carbon stocks through time using repeat lidar data, satellite imagery, forest inventory data, and remote sensing driven mechanistic modeling; (6) Demonstrate MRV efficacy to meet stakeholder needs in our 3-state region, and a vision for future national-scale deployment. Our work has followed a logical expansion of effort, from proof-of concept starting with just two counties in our Phase 1 pilot study, to an entire state (24 counties) in Phase 2. This research has emphatically demonstrated the feasibility of large-scale mapping using airborne lidar. We propose to build on these efforts to encompass another qualitative increase in spatial extent, new MRV-relevant product prototyping, and a vision for future operational deployment of MRV systems that are responsive to local, national and international interests in management and policy.

Publications:

Datta, A., Banerjee, S., Finley, A. O., Hamm, N. A. S., Schaap, M. 2016. Nonseparable dynamic nearest neighbor Gaussian process models for large spatio-temporal data with an application to particulate matter analysis. The Annals of Applied Statistics. 10(3). DOI: 10.1214/16-AOAS931

Dolan, K. A., Hurtt, G. C., Flanagan, S. A., Fisk, J. P., Sahajpal, R., Huang, C., Page, Y. L., Dubayah, R., Masek, J. G. 2017. Disturbance Distance: quantifying forests' vulnerability to disturbance under current and future conditions. Environmental Research Letters. 12(11), 114015. DOI: 10.1088/1748-9326/aa8ea9

Finley, A. O., Banerjee, S., E.Gelfand, A. 2015. spBayesfor Large Univariate and Multivariate Point-Referenced Spatio-Temporal Data Models. Journal of Statistical Software. 63(13). DOI: 10.18637/jss.v063.i13

Finley, A. O., Banerjee, S., Weiskittel, A. R., Babcock, C., Cook, B. D. 2014. Dynamic spatial regression models for space-varying forest stand tables. Environmetrics. 25(8), 596-609. DOI: 10.1002/env.2322

Huang, W., Dolan, K., Swatantran, A., Johnson, K., Tang, H., O'Neil-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. 14(9), 095002. DOI: 10.1088/1748-9326/ab2917

Hurtt, G., Zhao, M., Sahajpal, R., Armstrong, A., Birdsey, R., Campbell, E., Dolan, K., Dubayah, R., Fisk, J. P., Flanagan, S., Huang, C., Huang, W., Johnson, K., Lamb, R., Ma, L., Marks, R., O'Leary, D., O'Neil-Dunne, J., Swatantran, A., Tang, H. 2019. Beyond MRV: high-resolution forest carbon modeling for climate mitigation planning over Maryland, USA. Environmental Research Letters. 14(4), 045013. DOI: 10.1088/1748-9326/ab0bbe

Itter, M. S., Finley, A. O., D'Amato, A. W., Foster, J. R., Bradford, J. B. 2017. Variable effects of climate on forest growth in relation to climate extremes, disturbance, and forest dynamics. Ecological Applications. 27(4), 1082-1095. DOI: 10.1002/eap.1518

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). DOI: 10.1186/1750-0680-9-3

Johnson, K. D., Domke, G. M., Russell, M. B., Walters, B., Hom, J., Peduzzi, A., Birdsey, R., Dolan, K., Huang, W. 2017. Estimating aboveground live understory vegetation carbon in the United States. Environmental Research Letters. 12(12), 125010. DOI: 10.1088/1748-9326/aa8fdb

Riemann, R., Liknes, G., O'Neil-Dunne, J., Toney, C., Lister, T. 2016. Comparative assessment of methods for estimating tree canopy cover across a rural-to-urban gradient in the mid-Atlantic region of the USA. Environmental Monitoring and Assessment. 188(5). DOI: 10.1007/s10661-016-5281-8

Tang, H., Ma, L., Lister, A., O'Neill-Dunne, J., Lu, J., Lamb, R. L., Dubayah, R., Hurtt, G. 2021. High-resolution forest carbon mapping for climate mitigation baselines over the RGGI region, USA. Environmental Research Letters. 16(3), 035011. DOI: 10.1088/1748-9326/abd2ef

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. WIREs 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

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

Flanagan, S., Hurtt, G., Fisk, J., Sahajpal, R., Hansen, M., Dolan, K., Sullivan, J., Zhao, M. 2016. Potential Vegetation and Carbon Redistribution in Northern North America from Climate Change. Climate. 4(1), 2. DOI: 10.3390/cli4010002

Hurtt, G. C., Thomas, R. Q., Fisk, J. P., Dubayah, R. O., Sheldon, S. L. 2016. The Impact of Fine-Scale Disturbances on the Predictability of Vegetation Dynamics and Carbon Flux. PLOS ONE. 11(4), e0152883. DOI: 10.1371/journal.pone.0152883

Babcock, C., Finley, A. O., Bradford, J. B., Kolka, R., Birdsey, R., Ryan, M. G. 2015. LiDAR based prediction of forest biomass using hierarchical models with spatially varying coefficients. Remote Sensing of Environment. 169, 113-127. DOI: 10.1016/j.rse.2015.07.028

Hamm, N. A. S., Finley, A. O., Schaap, M., Stein, A. 2015. A spatially varying coefficient model for mapping PM10 air quality at the European scale. Atmospheric Environment. 102, 393-405. DOI: 10.1016/j.atmosenv.2014.11.043

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

Junttila, V., Kauranne, T., Finley, A. O., Bradford, J. B. 2015. Linear Models for Airborne-Laser-Scanning-Based Operational Forest Inventory With Small Field Sample Size and Highly Correlated LiDAR Data. IEEE Transactions on Geoscience and Remote Sensing. 53(10), 5600-5612. DOI: 10.1109/TGRS.2015.2425916

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

• High-Resolution Carbon Monitoring and Modeling: Continuing Prototype Development and Deployment   --   (George Hurtt, Richard Birdsey, Molly Elizabeth Brown, Philip DeCola, Katelyn Dolan, Ralph Dubayah, Vanessa Marie Escobar, Andrew Finley, Chang Huang, Kristofer Johnson, Jarlath O'Neil-Dunne, Maosheng Zhao)   [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):

• NASA Terrestrial Ecology (TE) Project Profile
• North American Carbon Program (NACP) Project Profile
• NASA Carbon Monitoring System (CMS) Project Profile