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Cook-B-01 Project Profile   (updated 03-Jan-2017)
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Project Title:Improving Forest Biomass Mapping Accuracy with Optical-LiDAR Data and Hierarchical Bayesian Spatial Models

Science Team
Members:

Bruce Cook, NASA GSFC (Project Lead)
Andrew (Andy) Finley, Michigan State University

Project Duration: 2012 - 2015
Solicitation:NASA: Carbon Monitoring System (2011)
Abstract: We propose to implement a novel approach for mapping forest biomass and associated errors using the fusion of airborne LiDAR, passive optical and thermal data and a Bayesian hierarchical model that accounts for spatial variances between ground observations and remotely sensed data. This method will be compared with the more traditional appr ... [more]
Measurement Approaches:
  • Remote Sensing
  • Airborne Sampling
  • In Situ Measurements
CMS:
  • Land Biomass
Other Keywords:  LiDAR

Participants:

Chad Babcock, University of Washington
Bruce Cook, NASA GSFC
Lawrence Corp, SSAI
Andrew (Andy) Finley, Michigan State University
Hank Margolis, NASA Headquarters
Jamon Van Den Hoek, NASA Goddard Space Flight Center

Contact Support to request an email list of project participants.

Project URL(s): http://gliht.gsfc.nasa.gov/
 
Data
Products:
Product Title:  Forest biomass estimation using individual tree crown information
Spatial Extent:  Smithsonian Environmental Research Center of Maryland and Sierra Nevada Mountains (Teakettle) of California
Spatial Resolution:  Greater than or equal to 1 m (tree-scale)
Time Period:  2008-2012
Temporal Frequency:  Single point time observation
Status:  Available
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Product Title:  Forest biomass maps and associated uncertainties generated with hierarchical Bayesian spatial models.
Spatial Extent:  Penobscot Experimental Forest of Maine
Spatial Resolution:  10 20 m (plot-scale)
Time Period:  2009-2012
Temporal Frequency:  Every 5 years for Maine and sampling snapshots for other sites
Status:  Archived
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Publications: 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

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

Finley, A.O., S. Banerjee, Y., Zhou, B.D. Cook. 2016. Process-based hierarchical models for coupling high-dimensional LiDAR and forest variables over large geographic domains. arXiv: 1603.07409

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

Duncanson, L. I., Dubayah, R. O., Cook, B. D., Rosette, J., Parker, G. 2015. The importance of spatial detail: Assessing the utility of individual crown information and scaling approaches for lidar-based biomass density estimation. Remote Sensing of Environment. 168, 102-112. DOI: 10.1016/j.rse.2015.06.021

Rosette, J., Cook, B., Nelson, R., Huang, C., Masek, J., Tucker, C., Sun, G., Huang, W., Montesano, P., Rubio-Gil, J., Ranson, J. 2015. Sensor Compatibility for Biomass Change Estimation Using Remote Sensing Data Sets: Part of NASA's Carbon Monitoring System Initiative. IEEE Geoscience and Remote Sensing Letters. 12(7), 1511-1515. DOI: 10.1109/LGRS.2015.2411262

Goetz, S. J., Hansen, M., Houghton, R. A., Walker, W., Laporte, N., Busch, J. 2015. Measurement and monitoring needs, capabilities and potential for addressing reduced emissions from deforestation and forest degradation under REDD+. Environmental Research Letters. 10(12), 123001. DOI: 10.1088/1748-9326/10/12/123001

Finley, A. O., Banerjee, S., Cook, B. D. 2014. Bayesian hierarchical models for spatially misaligned data in R. Methods in Ecology and Evolution. 5(6), 514-523. DOI: 10.1111/2041-210X.12189

White, J. C., Wulder, M. A., Varhola, A., Vastaranta, M., Coops, N. C., Cook, B. D., Pitt, D., Woods, M. 2013. A best practices guide for generating forest inventory attributes from airborne laser scanning data using an area-based approach. The Forestry Chronicle. 89(06), 722-723. DOI: 10.5558/tfc2013-132

White, J.C., M. A. Wulder, A. Varhola, M. Vastaranta, N. C. Coops, B. D. Cook, D. Pitt, and M. Woods. 2013. A best practices guide for generating forest inventory attributes from airborne laser scanning data using the area-based approach. Information Report FI-X-10. Natural Resources Canada, Canadian Forest Service, Canadian Wood Fibre Centre, Pacific Forestry Centre, Victoria, BC. 50 p. http://cfs.nrcan.gc.ca/pubwarehouse/pdfs/34887.pdf

Duncanson, L. I., Cook, B. D., Hurtt, G. C., Dubayah, R. O. 2014. An efficient, multi-layered crown delineation algorithm for mapping individual tree structure across multiple ecosystems. Remote Sensing of Environment. 154, 378-386. DOI: 10.1016/j.rse.2013.07.044

Cook, B., Corp, L., Nelson, R., Middleton, E., Morton, D., McCorkel, J., Masek, J., Ranson, K., Ly, V., Montesano, P. 2013. NASA Goddard's LiDAR, Hyperspectral and Thermal (G-LiHT) Airborne Imager. Remote Sensing. 5(8), 4045-4066. DOI: 10.3390/rs5084045

Huang, W., Sun, G., Dubayah, R., Cook, B., Montesano, P., Ni, W., Zhang, Z. 2013. Mapping biomass change after forest disturbance: Applying LiDAR footprint-derived models at key map scales. Remote Sensing of Environment. 134, 319-332. DOI: 10.1016/j.rse.2013.03.017

Babcock, C., Matney, J., Finley, A. O., Weiskittel, A., Cook, B. D. 2013. Multivariate Spatial Regression Models for Predicting Individual Tree Structure Variables Using LiDAR Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 6(1), 6-14. DOI: 10.1109/JSTARS.2012.2215582

Finley, A. O., Banerjee, S., Cook, B. D., Bradford, J. B. 2013. Hierarchical Bayesian spatial models for predicting multiple forest variables using waveform LiDAR, hyperspectral imagery, and large inventory datasets. International Journal of Applied Earth Observation and Geoinformation. 22, 147-160. DOI: 10.1016/j.jag.2012.04.007

Montesano, P. M., Cook, B. D., Sun, G., Simard, M., Nelson, R. F., Ranson, K. J., Zhang, Z., Luthcke, S. 2013. Achieving accuracy requirements for forest biomass mapping: A spaceborne data fusion method for estimating forest biomass and LiDAR sampling error. Remote Sensing of Environment. 130, 153-170. DOI: 10.1016/j.rse.2012.11.016

2015 NASA Carbon Cycle & Ecosystems Joint Science Workshop Poster(s)
  • G-LiHT: Multi-Sensor Airborne Image Data from Denali to the Yucatan   --   (Bruce Cook, Lawrence A Corp, Douglas Morton, Joel McCorkel)   [abstract]   [poster]
5th NACP All-Investigators Meeting Posters (2015):
  • Application of Airborne Remote Sensing to Define Terrestrial Ecosystem Form & Function -- (Lawrence A Corp, Bruce Cook, Elizabeth M. Middleton, Petya Krasteva Entcheva Campbell, Karl Fred Huemmrich) [abstract]
4th NACP All-Investigators Meeting Posters (2013):
  • Examining the Carbon Sequestration Potential of Recently Disturbed Trees in a Managed Northern Wisconsin Forest -- (Jamon Van Den Hoek, Bruce Cook, Jeffrey Masek, Robert E Kennedy, Compton Tucker) [abstract]   [poster]
2013 NASA Terrestrial Ecology Science Team Meeting Poster(s)
  • Lidar derived canopy height models of Harvard Forest   --   (Ian Paynter, Edward Saenz, Xiaoyuan Yang, Yan Liu, Zhuosen Wang, Crystal Schaaf, Zhan Li, Alan Strahler, Bruce Cook, Keith Krause, Nathan Leisso, Courtney Meier, Darius Culvenor, Glenn Newnham, David Jupp, Jenny Lovell, Ewan Douglas, Jason Martel, Supriya Chakrabarti, Timothy Cook, Glenn Howe, Kuravi Hewawasam, Jeffrey Thomas, Jihyun Kim, Shabnam Rouhani, Yun Yang, Nima Pahlevan, Qingsong Sun, Francesco Peri, Angela Erb)   [abstract]
  • G-LiHT: Goddard’s LiDAR, Hyperspectral and Thermal Airborne Imager   --   (Bruce Cook, Lawrence Corp, Ross Nelson, Douglas Morton, Kenneth J Ranson, Jeffery Masek, Elizabeth Middleton)   [abstract]

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