The measurement of wind erosion through field survey and remote sensing: a case study of the Mu Us Desert, China

Yaojie Yue, Peijun Shi, Xueyong Zou, Xinyue Ye, A. xing Zhu, Jing ai Wang

Research output: Contribution to journalArticlepeer-review

15 Scopus citations

Abstract

The measurement of wind erosion is not only important to understand wind erosion itself, but also an important scientific step in efforts to reverse the process of desertification. This study develops a model that predicts sand transport flux at pixel level based on the quantitative relationship between remotely sensed vegetation coverage and observed sand transport rate data. The data were collected from field surveys in the sandy area of Yuyang County, which is located in the Mu Us Desert. The study found that the sand transport flux was 9,643 kilograms per meter annually (kga−1 m−1) for a mobile dune, 6,394 kga−1 m−1 for a semi-mobile dune, 2,634.5 kga−1 m−1 for bare cultivated land, and 127.7 kga−1 m−1 for a semi-fixed dune. Using the sand flux derived by our model, the wind erosion modulus in the Yuyang sandy area was found to be 1,673.18 tons per square kilometer annually [ta−1 (km2)−1] in 1986, 1,568.10 in 1996, and 1,685.04 in 2005. Mean vegetation coverage in the sandy area of Yuyang in 1986, 1996, and 2005 was 42.39, 48.07, and 48.03 %, respectively, indicating that overall, vegetation coverage increased in the region, while decreasing in some areas, and that the vegetation coverage incensement failed to reduce the quantity of regional wind erosion. This sand flux retrieval method has the potential for successful application in other, similar areas.

Original languageEnglish (US)
Pages (from-to)1497-1514
Number of pages18
JournalNatural Hazards
Volume76
Issue number3
DOIs
StatePublished - Apr 2015
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Water Science and Technology
  • Atmospheric Science
  • Earth and Planetary Sciences (miscellaneous)

Keywords

  • Field survey
  • Mu Us Desert
  • Quantitative retrieval model
  • Remote sensing
  • Wind erosion

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