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Modelling Terrain Complexity

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Advances in Digital Terrain Analysis

Part of the book series: Lecture Notes in Geoinformation and Cartography ((LNGC))

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

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References

  • Byers, J.A., (1992), Grid Cell Contour Mapping of Point Densities: Bark Beetle Attacks, Fallen Pine Shoots, and Infested Trees, Oikos, 63: 233–243.

    Article  Google Scholar 

  • Carlisle, B.H., (2005), Modelling the Spatial Distribution of DEM Error, Transactions in GIS, (9)4: 521–540.

    Article  Google Scholar 

  • Cary, G.J., Keane, R.E., Gardner, R.H., Lavorel, S., Flannigan, M.D., Davies, I.D., Li, C., Lenihan, J.M., Rupp, T.S. and Mouillot F., (2006), Comparison of the Sensitivity of Landscape-fire-succession Models to Variation in Terrain, Fuel pattern, Climate and Weather, Landscape ecology, (21)1: 121–137.

    Article  Google Scholar 

  • Chou, Y., Liu, P. and Dezzani, R.J., (1999), Terrain Complexity and Reduction of Topographic Data, Journal of Geographical Systems, (1)2: 179–198.

    Article  Google Scholar 

  • Ehlschlaeger, C.R. and Shortridge, A., (1997), Modelling Elevation Uncertainty in Geographical Analyses, In Kraak, M.J. and Molenaar, M., (eds): Advances in GIS Research: Proceedings of the Seventh International Symposium on Spatial Data Handling, London: Taylor and Francis: 585–95.

    Google Scholar 

  • Evans, I.S., (1972), General Geomorphometry, Derivatives of Altitude and Descriptive Statistics, In Chorley, R.J. (ed.):Spatial Analysis in Geomorphology, London: Methuen and Co.: 17–90.

    Google Scholar 

  • Evans, I.S., (1980), An Integrated System of Terrain Analysis and Slope Mapping, Zeitschrift Fiir Geomorphologie. Supplement Band, 36: 274–95.

    Google Scholar 

  • Evans, I.S., (1990), General geomorphometry, In Goudie, A., (ed.): Geomophological techniques, London: Allen and Unwin.

    Google Scholar 

  • Fesquet, C., Barthlott, C., Drobinski, P., Dubos, T., Pietras, C. and Haeffelin, M., (2006), Impact of Terrain Heterogeneity on Near-surface Turbulence: Long Term Investigation at Sirta Observation, URL: http://ams.confex.com/ams/BLTAgFBioA/techprogram/paper_111039.

    Google Scholar 

  • Fisher, P., (1998), Improve Modelling of Elevation Error with Geostatistics, Geoinformatica, (2)3: 215–233.

    Article  Google Scholar 

  • Florinsky, I.V., (1998), Accuracy of Local Topographic Variables Derived From Digital Elevation Models, International Journal of Geographical Information Science, 12: 47–61.

    Article  Google Scholar 

  • Gao, J., (1997), Resolution and Accuracy of Terrain Representation by Grid DEMs at a Micro-scale, International Journal of Geographic Information Science, 11: 199–212.

    Google Scholar 

  • Gao, J., (1998), Impact of Sampling Intervals on the Reliability of Topographic Variables Mapped from Grid DEMs at a Micro-scale, Geographical Information Science, 12(8): 875–890.

    Article  Google Scholar 

  • Guth, P., (1992), Spatial analysis of DEM error, Proceedings of the ASPRS/ACSM Annual Meeting, Washington D.C.: 187–96.

    Google Scholar 

  • Hengl, T., Gruber, S. and Shrestha, D.P., (2003), Digital Terrain Analysis in ILWIS, Netherlands: ITC.

    Google Scholar 

  • Hobson, R.D., (1967), FORTRAN IV programs to determine the surface roughness in topography for the CDC 3400 computer, Computer Contribution State Geol. Survey Kansas, 14: 1–28.

    Google Scholar 

  • Hobson, R.D., (1972), Surface roughness in topography: quantitative approach, In Chorley, R.J., (ed.): Spatial analysis in geomorphology: 225–245.

    Google Scholar 

  • Holmgren, P., (1994), Multiple flow direction algorithm for runoff modelling in grid based elevation models: an empirical evaluation, Hydrology Processes, 8: 327–334.

    Article  Google Scholar 

  • Hsu Chiu-Ling, (2002), An Indicator Research of the Terrain Complexity A Classification of Gully Scale Based on DEM, Taiwan: Taiwan University, Master Dissertation.

    Google Scholar 

  • Hu Peng, Wu Yanlan and Hu Hai, (2005), A New Research on Fundamental Theory of Assessing the Accuracy of DEMs, Geo-Information Science, 7: 28–33 (in Chinese).

    Google Scholar 

  • Hunter, G.J. and Goodchild, M.F., (1997), Modelling the Uncertainty of Slope and Aspect Estimates Derived From Spatial Database, Geographical Analysis, 29: 35–49.

    Article  Google Scholar 

  • Jenness, J. (2002), Surface Areas and Ratios from Elevation Grid (surfgrids.avx) Extension for ArcView3.x, v.1.2, Jenness Enterprises, URL: http://www. jennessent.com/arcview/surface_areas.htm .

    Google Scholar 

  • Jie Shan, Muhammad Zaheer and Ejaz Hussain, (2003), Study on Accuracy of 1-Degree DEM Versus Topographic Complexity Using GIS Zonal Analysis, Journal of Surveying Engineering, (129)2: 85–89.

    Google Scholar 

  • Kyriakidis, P.C., Shortridge, A.M. and Goodchild, M.F., (1999), Geostatistics for Conflation and Accuracy Assessment of Digital Elevation Models, International Journal of Geographical Information Science, 13: 677–707.

    Article  Google Scholar 

  • Li, Z. and Zhu, Q., (2003), Digital Elevation Model, Beijing: Science Press (in Chinese).

    Google Scholar 

  • Liu Xuejun, (2002), On the Accuracy of the Algorithms for Interpreting Grid-based Digital Terrain Model, Ph.D. Dissertation (in Chinese).

    Google Scholar 

  • Lu Huaxing, Liu Xuejun and Bian Lu, (2007), Terrain Complexity: Definition, Index and DEM Resolution, Geoinformatics 2007: Geospatial Information Science, Jingming Chen and Yingxia Pu (eds.), Proc. of SPIE, Vol. 6753 675323-1.

    Google Scholar 

  • Mendicino, G. and Sole, A., (1997), The Information Content Theory for the Estimation of the Topographic Index Distribution Used in Topmodel, Hydrological Processes, 11: 1099–1114.

    Article  Google Scholar 

  • Parth Sarathi Roy and Mukunda Dev Behera Arunachal Pradesh, (2005), Assessment of biological richness in different altitudinal zones in the Eastern Himalayas, Current Science, (88)2: 250–257.

    Google Scholar 

  • Shary, P.A., Sharaya, L.S. and Mitusov, A.V., (2002), Fundamental Quantitative Methods of Land Surface Analysis, Geoderma, 107: 1–32.

    Article  Google Scholar 

  • Shary, P.A., (2006), Predictable and terrain –Specific Landform Classification. Proceeding for International Symposium on Terrain Analysis and Digital Terrain Modelling, CD

    Google Scholar 

  • Tang Guo’an, (2000), A Research on the Accuracy of DEMs, Beijing, New York: Science Press.

    Google Scholar 

  • Tsai Tsung-Hsiun, (1994), A Morphometric Study of Digital Elevation Models, Taiwan University, Masters Dissertation.

    Google Scholar 

  • Tucker, G.E. and Bras, R.L., (1998), Hillslope Processes, Drainage Density, and Landscape Morphology, Water Resources Research, (34)10: 2751–2764.

    Article  Google Scholar 

  • Wilson, J.P., Repetto, P.L. and Snyder, R.D., (2000), Effect of Data Source, Grid Resolution, and Flow-routing Method on Computed Topographic Attributes, In Wilson, J.P. and Gallant, J.C. (eds): Terrain Analysis: Principles and Applications, New York: John Wiley & Sons: 133–161.

    Google Scholar 

  • Yanalak, M., (2003), Effect of Gridding Method on Digital Terrain Model Profile Data Based on Scattered Data, Journal of Computing in Civil Engineering, (17)1: 58–68.

    Article  Google Scholar 

  • Zhang Chao, Chen Bingxian and Wu Lun, (1999), Geographic Information Systems, Beijing: High Education Press (in Chinese).

    Google Scholar 

  • Zhou Qiming and Liu Xuejun, (2006), Digital Terrain Analysis, Beijing: Science Press (in Chinese).

    Google Scholar 

  • Zhou Tong and Long Yi, (2006), A Fractal Method to Describe the Terrain Complexity Reflected by the Raster DEM, Geography and Geo-Information Science, (22)1: 26–30 (in Chinese).

    Google Scholar 

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Huaxing, L. (2008). Modelling Terrain Complexity. In: Zhou, Q., Lees, B., Tang, Ga. (eds) Advances in Digital Terrain Analysis. Lecture Notes in Geoinformation and Cartography. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77800-4_9

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