Share Email Print
cover

Proceedings Paper

The extraction and quantitative analysis of channel junctions based on DEMs
Author(s): Youfu Dong; Guoan Tang; Mingliang Luo
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

Channel junctions are the intersection points of different gullies in a drainage area. Base on DEM data and GIS process, an effective extraction method of channel junctions is proposed at first in this paper. Then with the definition of channel junction density being introduced, the rationality and validity that it is used to describe erosion intensity at macro-scale is explored. Meanwhile, the quantitative difference of channel junction density in the Loess Plateau in North Shaanxi Province of China is analyzed. The experiment results show that there is a strong correlation of channel junction density and gully density. Moreover, channel junction density keeps more sensitive than gully density when proper threshold values are applied at different grid resolution scales. In addition, channel junction density and the loess landform types correlate intensively, which reveals the great potential significance of channel junctions on geomorphology research. At the same time, the variations of channel junction density at different order levels in the typical watersheds are discussed. So, the research is hopeful in deepening our understanding on landform characteristics and evolutions of the Loess Plateau.

Paper Details

Date Published: 11 November 2008
PDF: 9 pages
Proc. SPIE 7146, Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Advanced Spatial Data Models and Analyses, 71462N (11 November 2008); doi: 10.1117/12.813191
Show Author Affiliations
Youfu Dong, Nanjing Normal Univ. (China)
Nanjing Univ. of Technology (China)
Guoan Tang, Nanjing Normal Univ. (China)
Mingliang Luo, Nanjing Normal Univ. (China)
Institute of Mountain Hazards and Environment (China)


Published in SPIE Proceedings Vol. 7146:
Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Advanced Spatial Data Models and Analyses
Lin Liu; Xia Li; Kai Liu; Xinchang Zhang, Editor(s)

© SPIE. Terms of Use
Back to Top
PREMIUM CONTENT
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?
close_icon_gray