Gully erosion has the potential to cause significant land degradation, yet the scale of gully features means that changes
are difficult to map. Here we describe the application of ASTER imagery, surface modelling and land cover information
to detect gully erosion networks with maximum obtainable accuracy. A grey level co-occurrence matrix (GLCM) texture
analysis method was applied to ASTER bands as one of the input layers. GLCM outputs were combined with
geomorphological input layers such as flow accumulation, slope angle and aspect, which were derived from an ASTER-based
digital elevation model (DEM). The ASTER-based DEM with 15-meter resolution was prepared from L1A.
Artificial neural networks (ANN) and decision tree (DT) approaches have been used to classify input layers for five
sample areas. This differentiates gullies from landscape areas with no gullies. We found that DT methods classified the
image with the highest accuracy (85% overall) in comparison with the ANN.