Evaluation of the potential of various spectral indices and textural features derived from satellite images for surficial deposits mapping

The present paper deals with the relevance of spectral and textural indices to surficial deposits identification and mapping. The study area is located in the Cochabamba valley in central Bolivia. Potential of SPOT‐4, Landsat‐7 and Radarsat‐1 data were compared for surficial deposits mapping. Different spectral indices including NDVI (normalized difference vegetation index) and TSAVI (transformed soil adjusted vegetation index) and textural features (mean, standard deviation, angular second moment, entropy, etc.) were extracted from these datasets and used in the mapping process. The results showed that indices exhibit different level of sensitivities according to surficial deposit types. A discriminant analysis was conducted to extract the most significant indices, which were then used in a three‐step linear combination mathematical model to map surficial deposits. We achieved an overall classification rate of 74% using spectral data of land use map in step 1. By adding information on vegetation and soils obtained from evaluation of spectral indices, this rate was improved to 82% during step 2. Finally, it was further slightly improved to 83% by adding textural data in the final step.