Fusion of Remotely Sensed 3DVegetation Structure with a Dynamic Global Terrestrial Ecosystem Model for Improved Estimates of Carbon Stocks and Land-Atmosphere Exchanges

Wenge Ni-Meister, Hunter College of The City University of New York, wenge.ni-meister@hunter.cuny.edu (Presenter)
Nacy Kiang, NASA Goddard Institute for Space Science, nancy.n.kiang@nasa.gov
Gordon Green, The City University of New York, gordongreen@earthlink.net

The overall goal of this project is to use a physical approach to derive vegetation structure from the ICESat (Ice Cloud and Land Elevation Satellite) data and generate structure datasets compatible with the Ent Dynamic Global Terrestrial Ecosystem Model (DGTEM) constrained by MODIS land cover and corrected the slope effect using Shuttle Radar Topography Mission (SRTM)/Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) slope data for improved estimates of terrestrial carbon stocks and fluxes.

We are developing a scheme to derive the distribution of vegetation height and height profiles through fusing ICESat, MODIS land cover and small footprint lidar data. We apply the physical approach described in Yang et al. (2010) to remove the slope effect from ICESat waveform extent to retrieve vegetation height. We will present some of preliminary evaluation results by comparing ICESat vegetation height data using our approach with airborne small-footprint and large footprint Lidar Vegetation Imaging Sensing (LVIS) collected in whole North America and our preliminary N.A. height distribution map.

We are using an analytical foliage-clumped radiative transfer scheme based on the Geometric Optical Radiative Transfer (GORT) theory (Ni-Meister et al., 2010 and Yang et al., 2010) to develop an analytical scheme to transfer the nadir-pointing ICESat lidar waveforms to vertical foliage profiles and then to the profiles of Photosynthetic Active Radiation (APAR) absorbed by green vegetation from different incident angles. This scheme is being evaluated using in deciduous forests of Morgan-Monroe State Forests (MMSF), IN and Havard Forests, MA and boreal conifer forests in central Canada.

Fusion of ICESat and MODIS data provides Ent structure inputs for each Ent model grid. Future work will continue to expand our method to large regions and use these vegetation structure inputs to drive Ent-DGVM to evaluate the improvement of carbon stock and fluxes estimation

Presentation Type:  Poster

Session:  Coupled Processes at Land-Atmosphere-Ocean Interfaces   (Mon 4:00 PM)

Associated Project(s): 

• Related Activity

Poster Location ID: 59