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Prediction of drought-driven land use/land cover changes in the Bakhtegan Lake watershed of Iran using Markov chain cellular automata model and remote sensing data

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  • Marzieh Mokarram

    (Shiraz University)

  • Tam Minh Pham

    (Vietnam National University, Hanoi)

Abstract

The southern region of Iran is a dry land zone where the drought is a driving force of land-use changes controlling water resources and aquatic ecosystems, such as lakes and wetlands. This study aimed to predict the 2040 drought condition in the Bakhtegan Lake watershed (Iran) and its correlation with land use changes. Extracting from Landsat and MODIS imagery, we classify land-use classes in 2000–2020 and investigate its relationship with multiple drought indicators through principal components analysis (PCA) analysis. In addition, using the Markov and cellular automata (CA)-Markov chain, the 2040 prediction maps of land-use and drought indices were made. Then, the regression analysis was used to reveal the influence trend of desertification on land-use activities in the future. Finally, the ecosystem services value and changes in agricultural lands between 2000 and 2020 were studied in the study area. The observed results of drought indices and land-use maps in 2000–2020 indicated that the expansion of the drought susceptibility zone causes the increasing areas of salt and bare land in parts of the northwestern and southern region. Based on PCA analysis, Normalized Difference Vegetation Index, Enhanced Vegetation Index, and Vegetation Condition Index were chosen to be input data in the CA–Markov model for drought-indices prediction in 2040. The value of these indices would decrease in 2040, indicating more drought in the region. The land-use prediction results in 2040 found that it will increase the percentage of bare land to 0.91, salt land to 0.89, and decrease 0.93 and 0.9 percentages of agricultural land and water lakes. Regression results showed that land use changes were related to drought indices with high accuracy (R2 = 0.93). In addition, the unprincipled use of agricultural lands and their conversion into saline and barren lands has led to a decrease of more than one million and nine hundred thousand dollars annually in the ecosystem services value.

Suggested Citation

  • Marzieh Mokarram & Tam Minh Pham, 2023. "Prediction of drought-driven land use/land cover changes in the Bakhtegan Lake watershed of Iran using Markov chain cellular automata model and remote sensing data," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 116(1), pages 1291-1314, March.
    • Handle: RePEc:spr:nathaz:v:116:y:2023:i:1:d:10.1007_s11069-022-05721-0
    DOI: 10.1007/s11069-022-05721-0
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    References listed on IDEAS

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    Cited by:

    1. Nan Xu & Ling Cui & Yi Qu & Gongqi Sun & Xingyu Zeng & Hongqiang Zhang & Haiyan Li & Boqi Zhou & Chunyu Luo & Ruoyuan Wu, 2024. "Wetland Distribution Prediction Based on CA–Markov Model under Current Land Use and Protection Policy in Sanjiang Plain," Sustainability, MDPI, vol. 16(13), pages 1-15, July.

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