Quantifying topographic fabric: eigenvector analysis using digital elevation models

Peter L. Guth

doi:10.1117/12.339825

29 January 1999 Quantifying topographic fabric: eigenvector analysis using digital elevation models

Peter L. Guth

Proceedings Volume 3584, 27th AIPR Workshop: Advances in Computer-Assisted Recognition; (1999) https://doi.org/10.1117/12.339825
Event: The 27th AIPR Workshop: Advances in Computer-Assisted Recognition, 1998, Washington, DC, United States

Abstract

Digital elevation models provide an estimate of the topographic fabric (the tendency for topography to have a preferred orientation) of the earth's surface. The algorithm extracts the eigenvectors and eigenvalues from a 3 X 3 matrix of the sums of the cross products of the directional cosines of the surface normals computed at each point in the DEM. The ratio of eigenvalues S₁ (largest) and S₂ measures the ruggedness of the terrain. The ratio of eigenvalues S₂ and S₃ (smallest) measures the tendency for the terrain to have a preferred orientation, while their orientation reflects the direction of dominant topographic fabric. Sample sizes of about 500 - 2500 points provide robust statistics, allowing sample regions of 1/2 to 1/3 square degree for global data sets and about 600 meters on a side with 30 m US topography. Topographic fabric appears to be a fundamental characteristic of landforms amenable to quantitative study. It should be included in terrain analysis and classification, and may lead to better estimates for cross-country mobility.

Citation Download Citation

Peter L. Guth "Quantifying topographic fabric: eigenvector analysis using digital elevation models", Proc. SPIE 3584, 27th AIPR Workshop: Advances in Computer-Assisted Recognition, (29 January 1999); https://doi.org/10.1117/12.339825

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