Modelling Terrain Complexity
Terrain complexity is an important terrain feature in digital terrain analysis; however, unlike aspect or slope, terrain complexity is an ambiguous terrain feature that until now has had no optimal index to quantify it. The traditional terrain complexity
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Abstract Terrain complexity is an important terrain feature in digital terrain analysis; however, unlike aspect or slope, terrain complexity is an ambiguous terrain feature that until now has had no optimal index to quantify it. The traditional terrain complexity definitions can be classified as statistical, geometrical, and semantic indices. These indices evaluate terrain complexity only from one perspective of geomorphometry, and will cause more or less prejudice when modelling the real world. This chapter wants to seeks an optimal terrain complexity index (TCI) based grid DEM. Firstly, we select four traditional indices (total curvature, rugosity, local relief, local standard deviation) that can easily be evaluated by a local kernel window, then deduce the compound terrain complexity index (CTCI) using the normalization factor. In order to validate the CTCI, four study areas with typical terrain characteristics of plane, gully, hill and hybrid landforms are selected for experimentation. The results show CTCI to be a sound terrain parameter to evaluate terrain complexity. Terrain complexity is a regional feature, while CTCI is a local index, so the statistics (Mean CTCI, Maximum CTCI, and SD CTCI) are proper indicators to statistically evaluate terrain complexity. Keywords: DEM, terrain complexity, terrain complexity index.
1 Introduction Digital terrain data are useful for all kinds of applications in digital terrain analysis (DTA). Recently, terrain feature extraction methods have generally been based on grid DEMs because most terrain data are organized in a raster format. Terrain complexity, which describes turbulence and complexity of the terrain surface, is not only an important terrain parameter in digital terrain analysis, but also widely applied in the fields of reduction of topographic data, terrain classification and visualization, mapping and
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LU Huaxing
surveying, landuse, soil erosion, surface turbulence and biological richness assessment, and DEM accuracy modelling. Terrain complexity is involved to varying extents in many studies. For example, Chou et al. (1999) discuss the reduction of topographic data based on terrain complexity. Gao (1998) studied the sampling intervals on the reliability of topographic variables from DEMs with terrain complexity of valleys, peaks, and ridges. Jie et al. (2003) studied the accuracy of the digital elevation model in terms of topographic complexity. Parth and Mukunda (2005) analysed the relationship of the biological richness with terrain complexity in the eastern Himalayas. Cary et al. (2006) studied the sensitivity of areas burned to variations of landform (flat, undulating and mountainous). Fesquet et al. (2006) studied the impact of terrain heterogeneity on near-surface turbulence. In these studies, terrain complexity was only regarded as an influencing factor or analysis condition. Only in the research by Hsu (2002) was the indicator of the terrain complexity discussed quantitatively. Unfortunately, the indicator of terrain complexity is still a stati
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