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Proceedings Paper

Terrain complexity: definition, index, and DEM resolution
Author(s): Huaxing Lu; Xuejun Liu; Lu Bian
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Paper Abstract

Digital terrain data are useful for all kinds of applications in digital terrain analysis (DTA). Recently, terrain feature extraction are generally based on grid DEM because most terrain data are organized in a raster format. Terrain complexity is very important terrain feature in digital terrain analysis, however, unlike aspect or slope, terrain complexity is an ambiguous conception that till now no optimal index to quantify it. The traditional terrain complexity definitiones can be classified as statistical, geometrical and semantic indices, these indices can quantify terrain complexity to some extent, but can not evaluate some special terrain. This paper wants to seek an optimal Terrain complexity index (TCI) to evaluate the terrain complexity. The total curvature is a synthesis idex of latitude derivative fxx, longitude derivative fyy, and diagonal derivative fxy, it is a sound solution to the terrain anisotropy. In order to test this index, 3 study area with typical terrain of plain, gully, and hill are selected for experimentation, the result shows total curvature is a sound terrain parameter to evaluate terrain complexity. Terrain complexity is a regional feature, while total cuvature is a local index, so the statistic (Mean TCI, Maximum TCI and SD TCI) are proper indicator to evaluate terrain complexity. The derivative of specific points on the mathematic curve is the ratio of the change in the angle of a tangent that moves over a given arc to the length of the arc, the shorter the arc is, the more arcurate the ratio curvature is. As to grid DEM, the length of arc can be consier as the DEM resolution. Result shows, the Mean TCI, Maximum TCI and SD of TCI have strong correlation with DEM resolution according to regression analysis, the R2 is higher than 0.96.

Paper Details

Date Published: 25 July 2007
PDF: 11 pages
Proc. SPIE 6753, Geoinformatics 2007: Geospatial Information Science, 675323 (25 July 2007); doi: 10.1117/12.761899
Show Author Affiliations
Huaxing Lu, Nanjing Normal Univ. (China)
Xuejun Liu, Nanjing Normal Univ. (China)
Lu Bian, Nanjing Normal Univ. (China)


Published in SPIE Proceedings Vol. 6753:
Geoinformatics 2007: Geospatial Information Science

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