Roberts, Dar: UC Santa Barbara (Project Lead)
Dennison, Philip (Phil): University of Utah (Co-Investigator)
Dubayah, Ralph: University of Maryland (Co-Investigator)
Parker, Geoffrey: Smithsonian Environmental Research Center (Co-Investigator)
Project Funding:
2005 - 2008
NRA: 2004 NASA: Carbon Cycle Science
Funded by NASA
Abstract:
We propose a multi-site study across a range of ecosystems, climates and disturbance histories using combined LIDAR and hyperspectral data. Our broad objective is to evaluate the added value of the combined analysis of LIDAR-and hyperspectral data for carbon cycle science. Specific objectives include: 1) to evaluate LIDAR and hyperspectral-derived metrics both individually and in combination to determine their ability to reduce uncertainty in estimates of carbon stocks and accumulation rates; 2) to assess the extent to which forest type, environmental factors and disturbance history influence the choice and importance of specific LIDAR and hyperspectral metrics; 3) to evaluate the importance of spatial variation and representativeness of field plots and flux towers within heterogeneous landscapes, quantifying sources of aggregation error. As part of this component, we will produce maps of carbon stocks and fluxes with estimates of uncertainty; and, 4) to inform carbon cycle science and future missions by evaluating which LIDAR and hyperspectral metrics are likely to perform best in a future spaceborne mission. We have identified six study sites possessing high quality biophysical measurements and diverse disturbance histories. Sites include three Western US sites (Wind River, Sierra Nevada, and Yellowstone), two Eastern US sites (Harvard Forest, SERC), and a site in Central America (La Selva). Each of these sites has existing hyperspectral data (AVIRIS, HYMAP or HYDICE), LIDAR data (small and/or large footprint) and a wealth of existing reference data needed for development and validation. Our general strategy will be to calculate a series of standardized remotely sensed measures of structure and composition from LIDAR and hyperspectral data at the six sites. These remotely sensed measures will be evaluated individually, and in combination against extensive, multiscale field data to establish statistical relationships between remote sensing data, above-ground biomass and biomass accumulation rates. Statistical relationships derived at each site will be compared across sites to determine whether a common set of metrics function across forest types, climates and disturbance regimes. Field plots and flux towers will be evaluated within the context of landscape heterogeneity, to determine how well they represent the region. We have assembled a team with expertise in all components of the research, including the PI Roberts and Co-I Dennison (hyperspectral analysis, prior research experience working with LIDAR and hyperspectral data at Wind River, Harvard Forest, Yellowstone and La Selva), Co-I Dubayah (LIDAR analysis, experience working with LIDAR data at Sierra Nevada, and La Selva) and Co-I Parker (forest structure, prior experience at SERC, Wind River, La Selva and Harvard Forest). This research is most responsive to categories 1 (North American Carbon) and 3 (Regional studies to reduce major uncertainties about the carbon cycle) of the RFP, and contributes by evaluating the potential of new remotely sensed systems for improving estimates of carbon stocks and fluxes. Our specific focus is on a unique set of airborne LIDAR and hyperspectral data acquired over six extremely well-characterized sites in North and Central America. All remote sensing products will be intensively validated through the use of existing ground data.
Publications:
Clark, M. L., Roberts, D. A., Ewel, J. J., Clark, D. B. 2011. Estimation of tropical rain forest aboveground biomass with small-footprint lidar and hyperspectral sensors. Remote Sensing of Environment. 115(11), 2931-2942. DOI: 10.1016/j.rse.2010.08.029
Swatantran, A., Dubayah, R., Roberts, D., Hofton, M., Blair, J. B. 2011. Mapping biomass and stress in the Sierra Nevada using lidar and hyperspectral data fusion. Remote Sensing of Environment. 115(11), 2917-2930. DOI: 10.1016/j.rse.2010.08.027
Bergen, K. M., Goetz, S. J., Dubayah, R. O., Henebry, G. M., Hunsaker, C. T., Imhoff, M. L., Nelson, R. F., Parker, G. G., Radeloff, V. C. 2009. Remote sensing of vegetation 3-D structure for biodiversity and habitat: Review and implications for lidar and radar spaceborne missions. Journal of Geophysical Research: Biogeosciences. 114(G2). DOI: 10.1029/2008JG000883
Sims, D.A., Rahman, A.F., Roberts, D.A., Cordova, V.D., Ogunjemiyo, S., and Prentiss, D.E., 2006. Use of Hyperspectral reflectance indices for estimation of gross carbon flux and light use efficiency across diverse vegetation types, International Journal of Geoinformatics 2(1), 15-30.
CLARK, M., ROBERTS, D., CLARK, D. 2005. Hyperspectral discrimination of tropical rain forest tree species at leaf to crown scales. Remote Sensing of Environment. 96(3-4), 375-398. DOI: 10.1016/j.rse.2005.03.009
2008 NASA Carbon Cycle & Ecosystems Joint Science Workshop Posters
- Spectral and Structural Differences Between Coniferous and Broadleaf Forest derived from LIDAR and AVIRIS
-- (Dar Alexander Roberts, Keely L Roth, Eliza S Bradley, Parker G Geoffrey, Dennison E Philip, Natour Bothaina)
[abstract]
[poster]
More details may be found in the following project profile(s):
