Mapping vegetation, soils, and geology in semiarid shrublands using spectral matching and mixture modeling of SWIR AVIRIS imagery

Spectral matching and linear mixture modeling techniques have been applied to synthetic imagery and AVIRIS SWIR imagery of a semiarid rangeland in order to determine their effectiveness as mapping tools, the synergism between the two methods, and their advantages, and limitations for rangeland resource exploitation and management. Spectral matching of pure library spectra was found to be an effective method of locating and identifying endmembers for mixture modeling although some problems were found with the false identification of gypsum. Mixture modeling could accurately estimate proportions for a large number of materials in synthetic imagery; however, it produced high variance estimates and high error estimates when presented with all nine AVIRIS endmembers because of high noise levels in the imagery. The problem of which endmembers to select was addressed by implementing a mixture model that allowed estimation of the errors on the proportions estimates, discarding the endmembers with the highest errors, recomputing the errors, and the proportions estimates, and iterating this process until the mixture maps were relatively free from noise. This methodology ensured that the lowest contrast materials were discarded. The inevitable confusion that followed was monitored the using the maps produced by spectral matching. Spectral matching was more effective than mixture modeling for geological mapping because it allowed identification and mapping of the relatively pure regions of all the surficial materials that exert an influence on the spectral response. The maps of the different clay minerals were of considerable value for mineral exploration purposes. Conversely, spectral matching was less useful than mixture modeling for rangeland vegetation studies because a classification of all pixels is needed and abundance estimates are required for many applications. Mixture modeling allowed identification of both nonphotosynthetic and green vegetation cover and thus total cover. Though the green vegetation mixture map appears to be very precise, the nonphotosynthetic vegetation estimates were poor. (C)Elsevier Science Inc., 1999.