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Assessing Climate and Land-Use Change Scenarios on Future Desertification in Northeast Iran: A Data Mining and Google Earth Engine-Based Approach

Author

Listed:
  • Weibo Yin

    (School of Civil Engineeing and Transportation, North China University of Water Resources and Electric Power, Zhengzhou 450046, China)

  • Qingfeng Hu

    (College of Surveying and Geo-Informatics, North China University of Water Resources and Electric Power, Zhengzhou 450046, China)

  • Jinping Liu

    (College of Surveying and Geo-Informatics, North China University of Water Resources and Electric Power, Zhengzhou 450046, China
    Hydraulics and Geotechnics Section, KU Leuven, Kasteelpark Arenberg 40, BE-3001 Leuven, Belgium)

  • Peipei He

    (College of Surveying and Geo-Informatics, North China University of Water Resources and Electric Power, Zhengzhou 450046, China)

  • Dantong Zhu

    (College of Surveying and Geo-Informatics, North China University of Water Resources and Electric Power, Zhengzhou 450046, China)

  • Abdolhossein Boali

    (Department of Arid Zone Management, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan 4913815739, Iran)

Abstract

Desertification poses a significant threat to dry and semi-arid regions worldwide, including Northeast Iran. This study investigates the impact of future climate and land-use changes on desertification in this region. Six remote sensing indices were selected to model desertification using four machine learning algorithms: Random Forest (RF), Support Vector Machine (SVM), Gradient Boosting Machine (GBM), and Generalized Linear Models (GLM). To enhance the model’s reliability, an ensemble model was employed. Future climate and land-use scenarios were projected using the CNRM-CM6 model and Markov chain analysis, respectively. Results indicate that the RF and SVM models performed best in mapping current desertification patterns. The ensemble model highlights a 2% increase in decertified areas by 2040, primarily in the northwestern regions. The study underscores the importance of land-use change and climate change in driving desertification and emphasizes the need for sustainable land management practices and climate change adaptation strategies to mitigate future impacts.

Suggested Citation

  • Weibo Yin & Qingfeng Hu & Jinping Liu & Peipei He & Dantong Zhu & Abdolhossein Boali, 2024. "Assessing Climate and Land-Use Change Scenarios on Future Desertification in Northeast Iran: A Data Mining and Google Earth Engine-Based Approach," Land, MDPI, vol. 13(11), pages 1-16, October.
    • Handle: RePEc:gam:jlands:v:13:y:2024:i:11:p:1802-:d:1511413
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    References listed on IDEAS

    as
    1. Gi-Wook Cha & Hyeun-Jun Moon & Young-Chan Kim, 2021. "Comparison of Random Forest and Gradient Boosting Machine Models for Predicting Demolition Waste Based on Small Datasets and Categorical Variables," IJERPH, MDPI, vol. 18(16), pages 1-16, August.
    2. Esmaeil Silakhori & Mohammad Reza Dahmardeh Ghaleno & Sarita Gajbhiye Meshram & Ehsan Alvandi, 2022. "To assess the impacts of climate change on runoff in Golestan Province, Iran," 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. 112(1), pages 281-300, May.
    Full references (including those not matched with items on IDEAS)

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