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Yardangs, an exclusive landform due to intensive wind erosion, cover a large area in the hyper-arid Lut desert of Iran. This paper presents a new approach using Self Organizing Map (SOM) as unsupervised algorithm of artificial neural networks for analysis and characterization of yardangs.

Nowadays, the Shuttle Radar Topography Mission (SRTM) with 3 arc sec data (approximately 90 m resolution) and nearly world wide coverage provides uniform good quality data.

The SRTM 3 arc sec data were re-projected to a 90 m UTM grid. Bivariate quadratic surfaces with moving window size of 5 x 5 were fitted to this DEM. The first derivative, slope steepness and the second derivatives minimum, maximum curvature and cross-sectional curvatures were calculated as geomorphometric parameters used as input to the SOMs. 42 SOMs with different learning parameter settings, e.g. initial and final radius, number of iterations, and the effect of random initial weights on average quantization error were investigated. A SOM with a low average quantization error (0.1040) was used for further analysis. Feature space analysis, morphometric signatures, three-dimensional inspection, auxiliary data like Landsat ETM+ and high resolution satellite imagery from QuickBird facilitated the assignment of semantic meaning to the output classes in terms of geomorphometric features. Results are provided in a geographic information system as thematic maps of landform entities based on form and slope, e.g. yardangs (ridge), corridors (valley) or planar areas.

The results showed that all yardangs and corridors were clearly recognized and classified by this method when their width was larger than the DEM resolution but became unrecognizable if their width is much smaller than the grid resolution. The identified yardangs and corridors are aligned NNW-SSE parallel to the prevailing direction of the strong local 120 days wind and cover about 31% and 42% of the study area respectively. The results demonstrate that SOM is a very efficient tool for analyzing aeolian landforms in hyper-arid environments that provides very useful information for terrain feature analysis in remote regions. (c) 2008 Elsevier Inc. All rights reserved.