Leaf area index （LAI） measurements collected from a Ponderosa pine stand in Oregon have been correlated with the hyperspectral data acquired using a Compact Airborne Spectro-graphic Imager （CASI）. Eight LAI values ranging from 0.87 to 2.72 have been measured using an LAI-2000 Plant Canopy Analyzer at the study site. First-order and second-order spectral derivatives have been taken to the CASI data to suppress the effects of the soil background on the forest spectral reflectances. A piece-wise multiple regression procedure has been used to explore the relationships between the LAIs and the CASI data. This procedure produces multivariate linear equations and their associated goodness-of-fit （GOF） values and standard errors （SE） for LAI estimation.Results show that the spectral derivative technique can increase the correlations between the LAIs and the CASI data and thus lead to improved accuracies of LAI estimation. For instance

the highest GOF obtained for single-channel LAI prediction is 0.681 with a SE of 0.345. These have been considerably improved to 0.904 and 0.189

and 0.898 and 0.195 after taking the first-order and the second-order derivatives

respectively.