The objectives of this research were: (1) to quantify indicators of riparian condition; and (2) to assess these indicators for detecting change in riparian condition. Two multi-spectral QuickBird images were acquired in 2004 and 2005 for a section of the Daly River in north Australia. These data were collected coincidently with vegetation and geomorphic field data. Indicators of riparian condition, including percentage canopy cover, organic litter, canopy continuity, bank stability, flood damage, riparian zone width and vegetation overhang, were then mapped. Field measurements and vegetation indices were empirically related using regression analysis to develop algorithms for mapping organic litter and canopy cover (R2 = 0.59-0.78). Using a standard nearest-neighbour algorithm, object-oriented supervised image classification provided thematic information (overall accuracies 81-90%) for mapping riparian zone width and vegetation overhang. Bank stability and flood damage were mapped empirically from a combination of canopy cover information and the image classification products (R2 = 0.70-0.81). Multi-temporal image analysis of riparian condition indicators (RCIs) demonstrated the advantages of using continuous and discrete data values as opposed to categorical data. This research demonstrates how remote sensing can be used for mapping and monitoring riparian zones in remote tropical savannas and other riparian environments at scales from 1 km to 100s km of stream length.