Abstract: The potentials of the compact airborne spectrographic imager(CASI) have been studied for coniferous forest LAI estimation by using three types of modelling techniques: univariate regression, multiple regression and vegetation index (VI) based LAI estimation. Four study sites have been selected along a forest transect in Oregon, USA. LAI measurements were colected from these study sites. CASI data of two imaging modes: spatial and spectral modes had been calibrated and corrected. The LAI measurements and the corrected CASI data were then used to study their relationships. Results indicate that the two imaging modes CASI data have similar effectiveness for LAI estimation. The multiple regression method resulted in higher accuracies of predicted LAI as compared to the univariate regression and VI based LAI estimation methods. The use of normalized difference vegetation index (NDVI) produced better LAI estimation than the use of other forms of variates for both univariate regression and VI based LAI estimation methods. For the univariate regression, a non-linear hypebola relationship between the LAI and the NDVI was the most apprepriate for LAI estimation. In this study, the VI based LAI estimation method has proved to be simple to use and very effective.