An automated approach to the classification of the slope units using digital data

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Geomorphology21 (1998) 251-264

An automated approach to the classification of the slope units using digital data
Philip T. Giles a, *, Steven E. Franklin b a Department of Geography, Saint Mary's University, Halifax, NS B3H 3C3, Canada b Department of Geography, University of Calgary, Calgary, AB T2N 1N4, Canada

Received 15 June 1996; revised 12 May 1997; accepted 23 May 1997

Abstract
Digital elevation and remote sensing data sets contain different, yet complementary, information related to geomorphological features. Digital elevation models (DEMs) represent the topography, or land form, whereas remote sensing data record the reflectance/emittance, or spectral, characteristics of surfaces. Computer analysis of integrated digital data sets can be exploited for geomorphological classification using automated methods developed in the remote sensing community. In the present study, geomorphological classification in a moderate- to high-relief area dominated by slope processes in southwest Yukon Territory, Canada, is performed with a combined set of geomorphometric and spectral variables in a linear discriminant analysis. An automated method was developed to find the boundaries of geomorphological objects and to extract the objects a~,~ groups of aggregated pixels. The geomorphological objects selected are slope units, with the boundaries being breaks of slope on two-dimensional downslope profiles. Each slope unit is described by variables summarizing the shape, topographic, and spectral characteristics of the aggregated group of pixels. Overall discrimination accuracy of 90% is achieved for the aggregated slope units in ten classes. © 1998 Elsevier Science B.V.
Keywords: automated analysis; classification; data processing; geomorphology

1. Introduction
Digital elevation models (DEMs) and remote sensing data contain important geomorphological information about characteristics of the surface. A grid DEM stores elevation values at regularly