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The establishment of commercial forestry plan- tations in natural grassland vegetation, results in increased transpiration and interception which in turn, results in a streamflow reduction. Methods to quantify this impact typi- cally require LAI as an input into the various equations and process models that are applied. The use of remote sensing technology as a tool to estimate leaf area index (LAI) for use in estimating canopy interception is described in this paper. Remote sensing provides a potential solution to effectively monitor the spatial and temporal variability of LAI. This is illustrated using Hyperion hyperspectral imagery and three vegetation indices, namely the normalized difference vege- tation index (NDVI), soil adjusted vegetation index (SAVI) and Vogelmann index 1 to estimate LAI in a catchment af- forested with Eucalyptus, Pinus and Acacia genera in the KwaZulu-Natal midlands of South Africa. Of the three veg- etation indices used in this study, it was found that the Vogel- mann index 1 was the most robust index with anR 2 and root mean square error (RMSE) values of 0.7 and 0.3 respectively. However, both NDVI and SAVI could be used to estimate the LAI of 12 year old Pinus patula accurately. If the intercep- tion component is to be quantified independently, estimates of maximum storage capacity and canopy interception are re- quired. Thus, the spatial distribution of LAI in the catchment is used to estimate maximum canopy storage capacity in the study area.

Spatial mapping of leaf area index using hyperspectral remote sensing for hydrological applications with a particular focus on canopy interception