Project Funding: 2012 - 2014

NRA: 2011 NASA: Carbon Monitoring System   

Funded by NASA

Abstract:
Under phase I of the Carbon Monitoring System (CMS) Biomass Pilot Project, we developed a map of aboveground carbon stocks at 100-m spatial resolution using a combination of remote sensing products combined with ground inventory data. In phase II, we propose to build upon phase I by developing similar spatial products for carbon stocks in all pools (belowground biomass, dead wood, forest floor, soil organic carbon) for three points in time so that net annual carbon stock changes (fluxes) over time may be estimated spatially over US forestlands. Additionally, we propose to test a methodology for separating net flux into its component parts of gross emissions and gross removals to enable a better understanding of how forests should be managed to decrease emissions and increase removals. We will use remote sensing products to quantify areas of forest disturbance and change and develop a fully spatial framework for estimating GHG dynamics (i.e., gross emissions and removals). Our proposed methodology will follow the IPCC Good Practice Guidelines for national GHG accounting from the forest/land-use sector. The expected spatial framework will enable future integration of the proposed activities and products with the CMS Flux Pilot Project. It will also demonstrate a method by which spatial data and models can be integrated with ground data to prototype IPCC recommended Monitoring, Reporting and Verification (MRV) systems for reducing emissions from deforestation and forest degradation and increasing removals from enhancement of forest carbon stocks.

Publications:

Zhang, G., Ganguly, S., Nemani, R. R., White, M. A., Milesi, C., Hashimoto, H., Wang, W., Saatchi, S., Yu, Y., Myneni, R. B. 2014. Estimation of forest aboveground biomass in California using canopy height and leaf area index estimated from satellite data. Remote Sensing of Environment. 151, 44-56. DOI: 10.1016/j.rse.2014.01.025

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

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

• The New Forest Carbon Accounting Framework of the US and NASA Carbon Cycle Science: Identifying Concomitant Knowledge Gaps and Research Opportunities   --   (Sean P Healey, Christopher W. Woodall, Grant M Domke, John Coulston, Brian F Walters, James A Smith, Andy Gray)   [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