Forest cover mapping in Central Spain with IRS-WIFS images and multi-extent textual-contextual measures
An area of 95 200 km2 in central Spain was mapped using IRS-WiFS data to test the potential use of this imagery for regional cover type mapping. In addition to the original WiFS red and NIR spectral bands, textural-contextual images were computed as means and variances of the NDVI and the NIR and red bands over windows of different sizes (3×3 to 50×50 pixels) and included in the analysis. An iterative classification of the imagery was performed using a maximum likelihood classifier with feature selection by spectral separability indices. Results obtained in this study show that IRS-WiFS is a valuable source of information for forest cover mapping at regional scales. Semi-natural areas (comprising forests, shrubs and grasslands) and forests were classified with 93% and 83% mean accuracy, respectively. The classification accuracy of most cover types increased when textural-contextual information computed over large windows (larger than those reported in the literature) were used for classification in combination with spectral data.