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NASA's Arctic-Boreal Vulnerability Experiment

ABoVE

Morton-02 Project Profile
  (updated 03-Jan-2017)
Project Title:A Joint USFS-NASA Pilot Project to Estimate Forest Carbon Stocks in Interior Alaska by Integrating Field, Airborne and Satellite Data
Project Lead: Douglas (Doug) Morton, NASA GSFC
Project Funding: 2013 - 2017
NASA (agency representative: Diane Wickland)
Project Type:NASA -- joined ABoVE in 2015
Solicitation:NASA: Carbon Monitoring System (2013)
Successor Projects: Cook-B-03  
Abstract: Monitoring U.S. forest carbon stocks is critical for natural resource management and national greenhouse gas reporting activities. The USFS Forest Inventory and Analysis (FIA) program 'the largest network of permanent forest inventory plots in the world' covers most U.S. forestlands. However, more than 450,000 km2 of forests in interior Alaska (15% ... [more]
ABoVE Science Questions
  • Carbon Pools
ABoVE Disciplinary Theme
  • Vegetation Structure and Function
Participants: Project Lead(s):Co-Investigator(s):Post-Doc(s):Participant(s):

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Measurements:
  • Airborne Remote Sensing
    • Airborne Lidar
  • Vegetation
    • FIA plots [details in USFS]
Sites/Measurements Map:
Select a feature on the map to display information about that location.
 
Research
Contributions:
Status: In-Progress Date When Available: 9/30/2016
Product Title:  Maps of carbon stocks with pixel-level carbon estimates and pixel-level uncertainties.
Description:  - Quantify Forest carbon stocks and uncertainties in a region with sparse Ground-based data for inventory and management purposes.
Expected Users:  USFS in Alaska, NASA CMS and ABoVE science teams
Metadata URL(s):

http://forest.gsfc.nasa.gov

http://gliht.gsfc.nasa.gov
Data Server URL(s):

http://forest.gsfc.nasa.gov

http://gliht.gsfc.nasa.gov
Archived Data Citation:  

Status: In-Progress Date When Available: 9/30/2016
Product Title:  Statistical estimates of carbon stocks at stratum level.
Description:  - Provide statistical estimates of Forest carbon stocks with uncertainties for Comparison purposes.
Expected Users:  USFS in Alaska, NASA CMS and ABoVE science teams
Metadata URL(s):

http://forest.gsfc.nasa.gov

http://gliht.gsfc.nasa.gov
Data Server URL(s):

http://forest.gsfc.nasa.gov

http://gliht.gsfc.nasa.gov
Archived Data Citation:  
 
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

Junttila, V., Finley, A. O., Bradford, J. B., Kauranne, T. 2013. Strategies for minimizing sample size for use in airborne LiDAR-based forest inventory. Forest Ecology and Management. 292, 75-85. DOI: 10.1016/j.foreco.2012.12.019

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

Guhaniyogi, R., Finley, A. O., Banerjee, S., Kobe, R. K. 2013. Modeling Complex Spatial Dependencies: Low-Rank Spatially Varying Cross-Covariances With Application to Soil Nutrient Data. Journal of Agricultural, Biological, and Environmental Statistics. 18(3), 274-298. DOI: 10.1007/s13253-013-0140-3

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

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]
  • Large-area inventory of boreal forest carbon stocks in interior Alaska using G-LiHT data and forest inventory plots   --   (Douglas Morton, Bruce Cook, Hans Erik Andersen, Robert Pattison, Ross Nelson, Andrew Finley, Chad Babcock, Lawrence A Corp, Matthew E Fagan, Laura Duncanson)   [abstract]

This project also has a Project Profile for the following program(s):


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