Terrain relief periods of loess landforms based on terrain profiles of the Loess Plateau in northern Shaanxi Province, China View Full Text


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Article Info

DATE

2018-12-29

AUTHORS

Jianjun Cao, Guoan Tang, Xuan Fang, Jilong Li, Yongjuan Liu, Yiting Zhang, Ying Zhu, Fayuan Li

ABSTRACT

The Loess Plateau is densely covered by numerous types of gullies which represent different soil erosion intensities. Therefore, research on topographic variation features of the loess gullies is of great significance to environmental protection and ecological management. Using a 5 m digital elevation model and data from a national geographic database, this paper studies different topographical areas of the Loess Plateau, including Shenmu, Suide, Yanchuan, Ganquan, Yijun, and Chunhua, to derive representative gully terrain profile data of the sampled areas. First, the profile data are standardized in MATLAB and then decomposed using the ensemble empirical mode decomposition method. Then, a significance test is performed on the results; the test confidence is 95% to 99%. The most reliable decomposition component is then used to calculate the relief period and size of the gullies. The results showed that relief periods of the Chunhua, Shenmu, Yijun, Yuanchuan, Ganquan, and Suide gullies are 1110.14 m, 1096.85 m, 1002.49 m, 523.48 m, 498.12 m, and 270.83 m, respectively. In terms of gully size, the loess landforms are sorted as loess fragmented tableland, aeolian and dune, loess tableland, loess ridge, loess hill and loess ridge, and loess hill, in descending order. Taken together, the gully terrain features of the sample areas and the results of the study are approximately consistent with the actual terrain profiles. Thus, we conclude that ensemble empirical mode decomposition is a reliable method for the study of the relief and topography of loess gullies. More... »

PAGES

1-12

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11707-018-0732-x

DOI

http://dx.doi.org/10.1007/s11707-018-0732-x

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1110953303


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