Statistical texture for contour interval choice of 1:50,000 DEMs

MingLiang Luo; Guoan Tang; Shijiang Yan; Youfu Dong

doi:10.1117/12.813200

10 November 2008 Statistical texture for contour interval choice of 1:50,000 DEMs

MingLiang Luo, Guoan Tang, Shijiang Yan, Youfu Dong

Proceedings Volume 7146, Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Advanced Spatial Data Models and Analyses; 71462U (2008) https://doi.org/10.1117/12.813200
Event: Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Geo-Simulation and Virtual GIS Environments, 2008, Guangzhou, China

Abstract

How to choose contour interval in different geomorphologic type areas is a challenging job. In the paper a statistical texture method is used to measure the distance between Loess Hillock and Loess Ridge in Loess Plateau, which geomorphologic types are hills. The result shows that when the two areas classified into 25 classes or so with the contour interval 15 and 19 individually, the class separability seems more distinct than less than 25 classes. The results also shows that when the number of class is bigger than 25, the class separability decrease instead of increasing correspondingly. It seems that the too many classes used may produce more details in cost of decreasing class separability. And in the seven statistical variables, the number of polygons is the most stable while the mean grayscale, the standard variation of grayscale are the most sensitive when the contour interval changes. The result indicates that the contour interval mainly influences by elevation and relative relief without more information which being dominant. By aid of an appropriate contour interval, the landform features can be easily extracted and is very helpful in delaminate the topography.

Citation Download Citation

MingLiang Luo, Guoan Tang, Shijiang Yan, and Youfu Dong "Statistical texture for contour interval choice of 1:50,000 DEMs", Proc. SPIE 7146, Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Advanced Spatial Data Models and Analyses, 71462U (10 November 2008); https://doi.org/10.1117/12.813200

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