Estimation of Leaf Area Index (LAI) Through the Acquisition of Ground Truth Data in Yosemite National Park
Galli
Basson, San Jose State University, n/a
Bettina
Schiffman, Foothill College, n/a
Anjanette
Hawk, Southwest Indian Polytechnic Institute, n/a
Dustin
Ottman, University of Wisconsin, Milwaukee, n/a
Evan
Lue, University of California, Santa Barbara, n/a
Forrest
Melton, California State University, Monterey Bay, n/a
Ramakrishna
Nemani, NASA Ames Research Center, n/a
Cindy
Schmidt, San Jose State University, cynthia.l.schmidt@nasa.gov
(Presenting)
J. W.
Skiles, NASA Ames Research Center, joseph.w.skiles@nasa.gov
(Presenting)
Leaf area index (LAI) is an important indicator of ecosystem health. Remote sensing offers the only feasible method of estimating LAI at global and regional scales. Land managers can efficiently monitor changes in vegetation by using NASA data products such as the MODIS LAI 1km product. To increase confidence in use of the MODIS LAI product in Yosemite National Park, we investigated the accuracy of remotely sensed LAI data and created LAI maps using three optical in-situ instruments: the LAI-2000 instrument, digital hemispheric photography (DHP), and the Tracing Radiation and Architecture of Canopies (TRAC) instrument. We compared our in-situ data with three spectral vegetation indices derived from Landsat Thematic Mapper imagery: Reduced Simple Ratio (RSR), Simple Ratio (SR), and Normalized Difference Vegetation Index (NDVI) to produce models which created LAI maps at 30m and 1km resolution. The strongest correlations occurred between DHP LAI values and RSR. Pixel values from the 1km LAI map were then compared to pixel values from a MODIS LAI map. A strong correlation exists between our in-situ data and MODIS LAI values which confirms its accuracy for use by the National Park Service as a decision support tool in Yosemite. The MODIS LAI product is particularly useful because of its high temporal resolution of 1-2 days and can be used to monitor current and future vegetation changes. The model created using the in-situ data can also be applied to Landsat data to provide thirty years of historical LAI values.
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