PARENT SESSION
Poster Session # 5: GIS and Remote Sensing.
Monday, August 4 Presentation from 5:00 PM to 6:30 PM. SITCC Exhibit Hall B.
Indicators of phytodiversity in hyperspectral imagery of the Konza Prairie LTER site.
Carter, Gregory*,1, Knapp, Alan2, Smith, Melinda3, Hoch, Greg2, Anderson, Jim4, 1 University of Southern Mississippi, Ocean Springs, MS2 Kansas State University, Manhattan, KS3 National Center for Ecological Analysis and Synthesis, Santa Barbara, CA4 NASA, Stennis Space Center, MS
ABSTRACT- Spectral radiance reflected from 12 watersheds on the Konza Prairie Long-Term Ecological Research site was evaluated to test the hypothesis that its spatial variability would serve as a generic indicator of plant species diversity. A 224 channel hyperspectral data set gathered in June 2000 by the Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) provided complete coverage of the 400 to 2,500 nm range at 10 nm per channel. The flight altitude of 20 km yielded a ground spatial resolution of 20 m. The data were radiometrically corrected for atmospheric absorption and georeferenced to ground coordinates of 94 long-term transects. For each transect location, a mean spectral reflected radiance was computed from values within 10-20 pixels. These mean spectra, their standard deviations and coefficients of variation corresponded with mean vegetation parameters that were determined previously for each transect and obtained through the Konza LTER 2000 data archive. Linear regression analysis at each spectral band indicated no significant relationships between within-transect spatial variability in reflected radiance and species richness or the Shannon-Weiner diversity index. Thus, the initial hypothesis was rejected. However, several band ratios of radiance per se in the 760-950 nm range correlated significantly (p=0.01) with species richness (maximum r-squared=0.42). Further research addressing diversity-productivity interactions and temporal variation in landscape spectra will expectedly yield greater efficacy for remote sensing in the estimation of phytodiversity.
Key words: biodiversity, remote, hyperspectral, grassland