In this paper

correlation between airborne hyperspectral data obtained with in the spectral range of approximately 417nm—800nm and three foliar biochemical constituents was studied.The hyperspectral data were acquired using Compact Airborne Spectrographic Imager（CASI） over six Study sites along an Oregon transect.Foliar biochemical constituents（expressed in concentration

mg/g of dry weight）:total chlorophyll（TC）

total nitrogen（TN）

total phosphorus（TP）

were measured from the same transect.The potential of CASI data for estimating foliar biochemical concentrations was evaluated

using multivariate statistical analysis and spectral derivative techniques.12 spectra corresponding to sample locations of foliar chemistry measurements were extracted from CASI images acquired within three weeks of the field sample collection.Univariate regression using 7 regression models was applied to explore both the linear and non-linear relations between individual channel spectral reflectances and vegetation indices derived from the CASI data and the three foliar chemical constituents.First and second order spectral derivatives were used to suppress the effects of low frequency spectra on those of the tree species.A piece-wise multiple regression procedure was used to generate multivariate linear equations for predicting TC

TN and TP using the original

and the derivative spectra of CASI data. Results show that with the spectral derivative technique

the estimation accuracies measured by Goodness-of-Fit（R 2）values and Root-Mean-Square Errors（SEs）of the three chemical constituents

can be greatly improved.The results obtained from the univariate analysis indicate that the level of correlation between foliar chemistry and CASI data is rather low.In fact

for TP no correlation exists between any individual band of CASI data and the measurements from laboratory.