Mapping Global Forest Carbon Stock

Yifan Yu, UCLA, yifan@atmos.ucla.edu (Presenter)
Sassan Saatchi, CALTECH/JPL, sassan.saatchi@jpl.nasa.gov
Ulrike Seibt, UCLA, ulli@atmos.ucla.edu
Michael Lefsky, Colorado State, lefsky@cnr.colostate.edu

Current studies on the global carbon cycle as well as the UN-REDD initiatives place a great emphasis on the need for a global spatially explicit distribution of terrestrial carbon stock. Both the quantitative distribution and the errors associated are important for constraining the uncertainties in the carbon cycle and for reporting purposes. We use a combination of satellite data including optical sensors on MODIS (MODerate-resolution imaging Spectroradiometer), radar sensor on QSCAT (QuikSCAT/SeaWinds Scatterometer), topography from SRTM (Shuttle Radar Topography Mission), and forest structure and height from GLAS (Geoscience Laser Altimeter System) in a non-parametric statistical model to estimate global aboveground biomass distribution at 3 minute (~5km) resolution. The statistical model employed for this study is based on the Maximum Entropy optimization approach to quantify the forest biomass at the landscape scale with the aid of spatial data from remote sensing sensors. Forest Lorey's height derived from GLAS and its conversion to aboveground biomass via allometric equations serve as the global training and validation data in mapping the forest biomass. The model provide an error propagation scheme to quantify the uncertainty of the forest carbon at the pixel level.

Presentation Type:  Poster

Session:  Other   (Tue 11:30 AM)

Associated Project(s): 

• Saatchi, Sassan: Requirements for Spaceborne Fusion of Lidar and Radar Measurements of Forest 3-D Structure and Above-ground Biomass ...details

Poster Location ID: 296