Geomorphological signatures: classification of aggregated slope unit objects from digital elevation and remote sensing data

The concept of a geomorphological signature is developed for classifying and mapping slope units with an automated procedure for analysing digital elevation and remote sensing data. Slope units are extracted from a digital elevation model (DEM) using a break of slope rule on downslope profiles. Each slope unit is an aggregated object of contiguous pixels and is summarized with five suites of variables: shape, topography, topographic variability, spectral characteristics, and variability in spectral characteristics. The variables are derived from the DEM and a corresponding SPOT satellite image. A ten-class scheme is used to classify slope units for a study area in southwest Yukon, Canada. Discriminant analysis results show the power of various combinations of variables to distinguish the classes, with a maximum classification accuracy of 90 per cent. Training signatures are employed for classifying the entire study area to produce a map with 88·5 per cent accuracy. The study shows that generating extensive geomorphological signatures for aggregated slope unit objects is a valuable exercise for discrimination and mapping. © 1998 John Wiley & Sons, Ltd.

[1]  Richard J. Pike,et al.  The geometric signature: Quantifying landslide-terrain types from digital elevation models , 1988 .

[2]  Vincent B. Robinson,et al.  Classification of higher order topographic objects on digital terrain data , 1992 .

[3]  L. D. Miller,et al.  An automated land-use mapping comparison of the Bayesian maximum likelihood and linear discriminant analysis algorithms , 1984 .

[4]  Susan L. Ustin,et al.  Vegetation mapping of forested ecosystems in interior Central Alaska , 1994, Proceedings of IGARSS '94 - 1994 IEEE International Geoscience and Remote Sensing Symposium.

[5]  K. O. Niemann Landscape drainage modelling to enhance Landsat classification accuracies , 1991 .

[6]  Danielle J. Marceau,et al.  Remote sensing and the measurement of geographical entities in a forested environment. 1. The scale and spatial aggregation problem , 1994 .

[7]  W. Ahmad,et al.  Land cover mapping in a rugged terrain area using Landsat MSS data , 1992 .

[8]  J. B. Adams,et al.  Geologic mapping using Landsat MSS and TM images - Removing vegetation by modeling spectral mixtures , 1985 .

[9]  T. M. Lillesand,et al.  Remote Sensing and Image Interpretation , 1980 .

[10]  S. Franklin,et al.  Incorporation of a digital elevation model derived from stereoscopic satellite imagery in automated terrain analysis , 1994 .

[11]  Jean Chorowicz,et al.  A new technique for recognition of geological and geomorphological patterns in digital terrain models , 1989 .

[12]  R. Young,et al.  Satellite imagery analysis of landforms: Illustrations from southeastern Australia , 1994 .

[13]  W. G. Collins,et al.  Integration of Context Classifiers with GIS , 1989, 12th Canadian Symposium on Remote Sensing Geoscience and Remote Sensing Symposium,.

[14]  Steven E. Franklin,et al.  Comparison of Derivative Topographic Surfaces of a DEM Generated from Stereoscopic SPOT Images with Field Measurements , 1996 .

[15]  Timothy A. Warner,et al.  Rule-based geobotanical classification of topographic, aeromagnetic, and remotely sensed vegetation community data , 1994 .

[16]  G. Pickup,et al.  CORRELATIONS BETWEEN DEM-DERIVED TOPOGRAPHIC INDICES AND REMOTELY-SENSED VEGETATION COVER IN RANGELANDS , 1996 .

[17]  Russell G. Congalton,et al.  A review of assessing the accuracy of classifications of remotely sensed data , 1991 .

[18]  O L Hughes,et al.  Surficial geology and geomorphology, Aishihik Lake, Yukon Territory , 1990 .

[19]  Joaquin Melia,et al.  A study on the utilization of sir‐a data for population estimation in the eastern part of Spain , 1987 .

[20]  Steven E. Franklin,et al.  An automated approach to the classification of the slope units using digital data , 1998 .

[21]  M. D. Fleming An integrated approach for automated cover-type mapping of large inaccessible areas in Alaska , 1988 .

[22]  John R. Dymond,et al.  Automated mapping of land components from digital elevation data , 1995 .