Calibrating airborne vegetation data for hydrological applications under dry conditions

Accurate spatial vegetation data are essential for hydrological modelling s ince vegetation processes directly influence biomass production and affect the distribution of surface water. Spatially distributed vegetation data ar e difficult and expensive to collect on the ground. Ground-collected data r arely provide complete spatial coverage at a single time. Remotely sensed d ata provide spatially extended maps of the surface cover in catchments, but require calibration. In this study, values of the airborne Normalized Diff erence Vegetation Index (NDVI), obtained with the Compact Airborne Spectrog raphic Imager (CASI), were calibrated with ground biomass samples in a 27km (2) catchment consisting of 65% partially grazed pastures and grasses and 3 5% open and medium density woodland. Linear, quadratic and exponential regr essions were applied to six waveband combinations of CASI NDVI and the best result was an exponential correlation of r(2) = 0.62. This suggests that C ASI NDVI has an exponential relationship with biomass. Calibration was affe cted by vegetation type and height, grazing, possible saturation of the nea r-infrared (NIR) bands and the narrow swathe width of aircraft data. Ground validation between Leaf Area Index (LAI) and biomass gave an r2 = 0.80. No statistically significant correlation was found between LAI and airborne N DVI. Significant fractions of non-green biomass at some sites, due to dry c onditions, were seen as a contributing factor.