REMOTE-SENSING OF SEASONAL LEAF-AREA INDEX ACROSS THE OREGON TRANSECT

Remotely sensed data acquired from four remote-sensing instruments on three different aircraft platforms over a transect of coniferous fores t stands in Oregon were analyzed with respect to seasonal leaf area in dex (LAI). Data from the four instruments were corrected for the varyi ng seasonal and geographic atmospheric conditions present along the tr ansect. Strong logarithmic relationships were observed between seasona l maximum and minimum LAI and the simple ratio (SR) (near infrared/red reflectance) calculated from the broad-spectral-band Thematic Mapper Simulator (TMS), as well as from the narrow-spectral-band Airborne Vis ible/Infrared Imaging Spectrometer (AVIRIS), the Compact Airborne Spec trographic Imager (CASI), and a Spectron SE590 spectro-radiometer (R2 = 0.82-0.97). The TMS SR reached an asymptote at an LAI of almost-equa l-to 7-8. However, the SE590 and the CASI SR continued to increase up to the maximum LAI of 10.6. The variability of the relationship betwee n the AVIRIS SR and LAI increased at stands with LAIs >7, making a tre nd in the AVIRIS SR-LAI relationship at LAIs >7 difficult to discern. The SRs of the coniferous forest stands measured by the narrow-spectra l-band instruments were higher than they were from the broad-spectral- band TMS. This is attributed partially to the integration of the TMS o ver a broad wavelength region in the red and more strongly to calibrat ion differences between the sensors. Seasonal TMS SR trends for four t ime periods for some of the stands deviated from the expected seasonal LAI trends, possibly because of smoke and very low sun angles during some of the acquisition periods. However, the expected SR differences for the seasonal minimum and maximum LAI were observed for all of the sensors for nearly all of the forest stands. This study demonstrates t hat remotely sensed data from both broad- and narrow-spectral-band ins truments can provide estimates of LAI for use in forest ecosystem simu lation models to estimate evapotranspiration, photosynthesis, canopy t urnover, and net primary production over large areas.