Development of Vegetation Structure Inputs From ICESat, SRTM and MODIS Satellite Data for a Mixed Canopy Dynamic Global Terrestrial Ecosystem Model

State of the Problem Lidar remote sensing provides measurements of horizontal and vertical vegetation structure of ecosystems which will be critical for estimating global carbon storage and assessing ecosystem response to climate change and natural and anthropogenic disturbances. However, no consistent approach currently exists to derive the lidar based vegetation structure information required by terrestrial ecosystem models. Further, when available, this information has rarely been incorporated into ecosystem models. This goal of this study is to derive vegetation structure from the ICESat (Ice Cloud and Land Elevation Satellite) and Shuttle Radar Topography Mission (SRTM) data and generate structure datasets compatible with the Ent Dynamic Global Terrestrial Ecosystem Model (DGTEM) constrained by MODIS land cover for improved estimates of terrestrial carbon stocks and fluxes. Methodology We developed a physical approach to derive accurate vegetation structure including height and foliage profile from archived ICESat and National Elevation Data (NED, US only) or SRTM, (globally) slope data for Ent Dynamic Global Terrestrial Ecosystem Model (DGTEM) constrained by MODIS land cover. We will present the methodology, the comparison results of ICESat footprint level vegetation height with airborne lidar data. We will also show the use of ICESat fullwaveforms for photosynthesis/conductance estimation by comparing ICESat-based Photosynthetic Active Radiation profiles absorbed by green vegetation with field measurements in different vegetation biomes. We used 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