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Proceedings Paper

Determination of suitable cell size for grid based digital elevation model
Author(s): Xuejun Liu; Yanfang Wang; Bei Jin
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Paper Abstract

Horizontal resolution, one of dominant variables for grid based Digital Elevation Model (DEM), directly determines topographic expression, the accuracy of terrain parameters and geosciences simulations based on DEM. Cell size is determined on relationship between resolution and terrain parameters traditionally, without taking terrain variance information content of the raw data into account. This paper puts forward two methods for suitable DEM horizontal resolution by mining the input contour data based on geostatistics. One is a direct method considering internal and external variance. Regularization variables of serial resolutions are calculated from the sampled data by regularization theory in geostatistics. After the variance comparison between point and serial resolutions, the grid at which the external variance between adjacent grids is larger than average internal variance in grid is named suitable resolution. The other method, which combines macro-topographical variance with micro-topographical variance, is an indirect way. Various large-scale supports and their regularization variables are made by dividing the sampled data using regularization theory. In order to ascertain an optimal support size to express macroscopic spatial variance of terrain, semivariograms of regularization variables are analyzed on various support sizes. Estimation of the optimal bin size that can estimate the probability density function in non-parametric density estimation is referred to decide the microcosmic appropriate resolution in the optimal support. Both methods were experimented in practice, and gave relatively consistent results. The latter was commended considering the computational efficiency.

Paper Details

Date Published: 16 October 2009
PDF: 9 pages
Proc. SPIE 7492, International Symposium on Spatial Analysis, Spatial-Temporal Data Modeling, and Data Mining, 74924U (16 October 2009); doi: 10.1117/12.838600
Show Author Affiliations
Xuejun Liu, Nanjing Normal Univ. (China)
Yanfang Wang, Nanjing Normal Univ. (China)
Bei Jin, Nanjing Normal Univ. (China)


Published in SPIE Proceedings Vol. 7492:
International Symposium on Spatial Analysis, Spatial-Temporal Data Modeling, and Data Mining

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