Assessment of gully erosion in relation to lithology in the southwestern Zagros Mountains, Iran using ASTER data, GIS and stochastic modeling

Authors

  • Reza Zakerinejad Faculty of Geographical Sciences and Planning, University of Isfahan, Isfahan, Iran Author
  • Adel Omran Department of Physical Geography, University of Tübingen, Tübingen, Germany; Department of Science and Mathematical Engineering, Faculty of Petroleum and Mining Engineering, Suez University, Egypt Author
  • Volker Hochschild Department of Physical Geography, University of Tübingen, Tübingen, Germany Author
  • Michael Maerker Department of Earth and Environmental Sciences, Pavia University, Italy Author

DOI:

https://doi.org/10.4461/GFDQ.2018.41.15

Keywords:

Gully erosion, ASTER multispectral data, lithology, topographic indices

Abstract

Soil erosion in arid areas is a major environmental threat. Gullies, as one of the most intensive soil erosion processes, are very common in the southwest of Iran. Lithology, vegetation density and climate change, as well as land use and land cover change are effective drivers of soil loss in general, and gully erosion in particular. The overall objective of this research is to assess the relation between, lithology and the spatial distribution of gullies in the Mazayjan basin. Data were collected by field survey, interpreting aerial photos and analyzing ASTER multispectral images. Modeling of spatial gully susceptibility was performed with a GIS-based statistical mechanics approach (Maxent). The analysis of ASTER bands ratios yields valuable results in terms of the mineral differentiation of the Zagros Mountain substrates and hence, can be utilized as a tool for lithological mapping. Additionally, the statistical mechanics approach used to assess the relation between existing gully locations and the combinations of lithologic predictor variables show that gullies have a high probability in areas showing substrates with high amounts of salt, gypsum and marl, especially in the plain areas. The model performance shows a very high accuracy both for train and test data. The spatial prediction highlights concentrated gully erosion in areas with aeolian sediments on top of alluvial substrates.

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Published

2024-05-28

Issue

Section

Research and review papers

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