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

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 as 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.

[1]  P. Bolstad,et al.  An evaluation of DEM accuracy: elevation, slope, and aspect , 1994 .

[2]  T. Frank.  Mapping dominant vegetation communities in the Colorado Rocky Mountain Front Range with Landsat Thematic Mapper and digital terrain data , 1988 .

[3]  D. Civco.  Topographic normalization of landsat thematic mapper digital imagery , 1989 .

[4]  D. C. Brockelbank,et al.  STEREO ELEVATION DETERMINATION TECHNIQUES FOR SPOT IMAGERY , 1991 .

[5]  D. Tempelman-Kluit.  Reconnaissance geology of Aishihik lake, Snag and Part of Stewart River map-areas, west-central Yukon , 1973 .

[6]  Rachael McDonnell,et al.  Including the spatial dimension: using geographical information systems in hydrology , 1996 .

[7]  M. Ehlers,et al.  Stereocorrelation of Landsat TM images , 1987 .

[8]  B. M. Evans,et al.  Accuracy of SPOT digital elevation model and derivatives: utility for Alaska's North slope , 1992 .

[9]  K. Beven,et al.  THE PREDICTION OF HILLSLOPE FLOW PATHS FOR DISTRIBUTED HYDROLOGICAL MODELLING USING DIGITAL TERRAIN MODELS , 1991 .

[10]  Daniel E. Friedman,et al.  The automatic generation of digital terrain models from satellite images by stereo , 1987 .

[11]  H. H. Madden.  Comments on the Savitzky-Golay convolution method for least-squares-fit smoothing and differentiation of digital data , 1976 .

[12]  Jan-Peter Muller,et al.  Digital elevation model production by stereo-matching spot image-pairs: a comparison of algorithms , 1989, Image Vis. Comput..

[13]  Jaroslav Hofierka,et al.  Modelling Topographic Potential for Erosion and Deposition Using GIS , 1996, Int. J. Geogr. Inf. Sci..

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

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

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

[17]  T Day,et al.  QUALITY ASSESSMENT OF DIGITAL ELEVATION MODELS PRODUCED BY AUTOMATIC STEREOMATCHERS FROM SPOT IMAGE PAIRS , 2006 .

[18]  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.

[19]  Harold A. Mooney,et al.  Remote Sensing of Biosphere Functioning , 1990 .

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

[21]  S. E. Franklin,et al.  Terrain analysis from digital patterns in geomorphometry and Landsat MSS spectral response , 1987 .

[22]  G. Pickup.  Remote Sensing of Landscape Processes , 1990 .

[23]  R. Blong,et al.  An hypothetical nine unit landsurface model , 1968 .

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

[25]  A. Skidmore.  Terrain position as mapped from a gridded digital elevation model , 1990 .

[26]  Lawrence W. Martz,et al.  Using cesium-137 and landform classification to develop a net soil erosion budget for a small Canadian Prairie watershed , 1991 .

[27]  Steven E. Franklin,et al.  Radiometric processing of aerial and satellite remote-sensing imagery , 1995 .

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

[29]  I. Moore,et al.  Digital terrain modelling: A review of hydrological, geomorphological, and biological applications , 1991 .

[30]  E. L. Usery,et al.  Automated classification of generic terrain features in digital elevation models , 1993 .

[31]  Steven E. Franklin,et al.  Classification of permafrost active layer depth from remotely sensed and topographic evidence , 1993 .

[32]  Steven E. Franklin,et al.  Geomorphometric processing of digital elevation models , 1987 .

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

[34]  John R. G. Townshend,et al.  Terrain Analysis and Remote Sensing , 1981 .

[35]  B. Tabachnick,et al.  Using Multivariate Statistics , 1983 .

[36]  M. J. Norušis,et al.  SPSS advanced statistics user's guide , 1990 .

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

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