The extraction and quantitative analysis of channel junctions based on DEMs

Youfu Dong; Guoan Tang; Mingliang Luo

doi:10.1117/12.813191

11 November 2008 The extraction and quantitative analysis of channel junctions based on DEMs

Youfu Dong, Guoan Tang, Mingliang Luo

Proceedings Volume 7146, Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Advanced Spatial Data Models and Analyses; 71462N (2008) https://doi.org/10.1117/12.813191
Event: Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Geo-Simulation and Virtual GIS Environments, 2008, Guangzhou, China

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.

Citation Download Citation

Youfu Dong, Guoan Tang, and Mingliang Luo "The extraction and quantitative analysis of channel junctions based on DEMs", Proc. SPIE 7146, Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Advanced Spatial Data Models and Analyses, 71462N (11 November 2008); https://doi.org/10.1117/12.813191

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