PARENT SESSION
Poster Session #44: Remote Sensing and GIS.
Wednesday, August 7. Presentation from 5:00 PM to 6:30 PM. Exhibit Hall B & C, TCC
120
Estimating Leaf Area Index from Landsat ETM+ data in the Hudson River Valley of New York State.
Killilea, Mary*,1, DeGloria, Stephen1, Riha, Susan1, Philpot, William1, 1 Cornell University, Ithaca, NY
ABSTRACT- Leaf area index (LAI) is an important forest characteristic for studying carbon dynamics. Physiologically, the leaf surface of a tree is where CO2, water, and energy exchange occur. As a result LAI is directly related to net primary production. The relationship between LAI and net primary production, as well as the potential to estimate LAI from remotely sensed data, makes LAI important to regional and global carbon modeling. This study will use Landsat ETM+ imagery and measurements of LAI from several sites in the Hudson River Valley to estimate LAI for New York State. Leaf area measurements were made using the CI-110 Digital Plant Canopy Imager at 39 sites located throughout the Hudson River Valley. Vegetation indices were calculated from Landsat ETM+ imagery. The vegetation indices of interest for this study are primarily ratios of the Landsat ETM+ red and near infrared bands, which have been found to provide good estimates of LAI. After the vegetation indices were calculated from the Landsat ETM+ data, the relationships between the LAI site measurement and vegetation index values at those locations were examined using least squares regression analysis. The regression equations describing the LAI-vegetation indices relationships were then used to create a map of LAI for the lower Hudson River Valley. Validation of the LAI map will be done using additional LAI site measurements that will be collected during the summer of 2002. These LAI values can then be uses as input to a biophysical process model that will estimate carbon sequestration in Hudson River Valley forests.
KEY WORDS: leaf area index, vegetation indices, Landsat ETM+, remote sensing