Land-cover mapping in the Nujiang Grand Canyon: integrating spectral, textural, and topographic data in a random forest classifier

The integration of spectral, textural, and topographic information using a random forest classifier for land-cover mapping in the rugged Nujiang Grand Canyon was investigated in this study. Only a few land-cover categories were accurately discriminated using spectral information exclusively, with an overall accuracy of 0.56 and a kappa coefficient of 0.51. The inclusion of topographic information as additional bands provided higher overall accuracy (0.69) and kappa coefficient (0.65) than topographic correction (overall accuracy, 0.57-0.58; kappa coefficient range, 0.52-0.53), which failed to markedly improve classification accuracy. In contrast with the exclusive use of spectral bands, most of the included land-cover categories were correctly classified using textural features exclusively (overall accuracy, 0.67-0.88; kappa coefficient, 0.63-0.87). In particular, classification based on geostatistical features led to slightly more accurate results than did grey-level co-occurrence matrix parameters. The window size selected for texture calculation markedly affected the texture-based classification accuracy: larger window size yielded higher classification accuracy. However, no optimal window size exists. The inclusion of the topographic bands in the texture images led to an increase in the overall accuracy of 1.1-9.0%, and to an increase in the kappa coefficient of 0.0-10.9%. Thus, for the Nujiang Grand Canyon, topographic information was more important for the discrimination of some land-cover types than spectral and textural information. Among the Landsat Thematic Mapper (TM) spectral bands, bands 6 and 4 were of greatest importance. The relative importance of textural features generally increased with window size, and a few textural features were of consistently high importance. Although a random forest classifier does not overfit, undertaking feature selection analysis prior to classification may still be valuable.