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.
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The datasets used or analyzed during the current study are available from the corresponding author on reasonable request.
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MM contributed to conceptualization, funding acquisition, visualization, data curation, formal analysis, methodology, visualization, data curation, formal analysis, methodology. TMP contributed to conceptualization, visualization, supervision, writing—review and editing.
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Mokarram, M., Pham, T.M. 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. Nat Hazards 116, 1291–1314 (2023). https://doi.org/10.1007/s11069-022-05721-0
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DOI: https://doi.org/10.1007/s11069-022-05721-0