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Quantitatively Assessing the Future Land-Use/Land-Cover Changes and Their Driving Factors in the Upper Stream of the Awash River Based on the CA–Markov Model and Their Implications for Water Resources Management

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  • Mekonnen H. Daba

    (Institute of Environment and Sustainable Development in Agriculture (IEDA), Chinese Academy of Agricultural Sciences (CAAS), Beijing 100081, China
    Bako Agricultural Research Center, Oromia Agricultural Research Institute, Bako P.O. Box 03, Ethiopia
    Graduate School of Chinese Academy of Agricultural Sciences, Beijing 100081, China)

  • Songcai You

    (Institute of Environment and Sustainable Development in Agriculture (IEDA), Chinese Academy of Agricultural Sciences (CAAS), Beijing 100081, China)

Abstract

Despite the rapid economic and population growth, the risks related to the current dynamics of land use and land cover (LULC) have attracted a lot of attention in Ethiopia. Therefore, a complete investigation of past and future LULC changes is essential for sustainable water resources and land-use planning and management. Since the 1980s, LULC change has been detected in the upper stream of the Awash River basin. The main purpose of this research was to investigate the current dynamics of LULC and use the combined application of the cellular automata and the Markov chain (CA–Markov) model to simulate the year 2038 LULC in the future; key informant interviews, household surveys, focus group discussions, and field observations were used to assess the consequences and drivers of LULC changes in the upstream Awash basin (USAB). This research highlighted the importance of remote sensing (RS) and geographic information system (GIS) techniques for analyzing the LULC changes in the USAB. Multi-temporal cloud-free Landsat images of three sequential data sets for the periods (1984, 2000, and 2019) were employed to classify based on supervised classification and map LULC changes. Satellite imagery enhancement techniques were performed to improve and visualize the image for interpretation. ArcGIS10.4 and IDRISI software was used for LULC classification, data processing, and analyses. Based on Landsat 5 TM-GLS 1984, Landsat 7 ETM-GLS 2000, and Landsat 8 2019 OLI-TIRS, the supervised maximum likelihood image classification method was used to map the LULC dynamics. Landsat images from 1984, 2000, and 2019 were classified to simulate possible LULC in 2019 and 2038. The result reveals that the maximum area is covered by agricultural land and shrubland. It showed, to the areal extent, a substantial increase in agricultural land and urbanization and a decrease in shrubland, forest, grassland, and water. The LULC dynamics showed that those larger change rates were observed from forest and shrubland to agricultural areas. The results of the study show the radical changes in LULC during 1984–2019; the main reasons for this were agricultural expansion and urbanization. From 1984 to 2019, agriculture increased by 62%, urban area increased by 570.5%, and forest decreased by 88.7%. In the same year, the area of shrubland decreased by 68.6%, the area of water decreased by 65.5%, and the area of grassland decreased by 57.7%. In view of the greater increase in agricultural land and urbanization, as well as the decrease in shrubland, it means that the LULC of the region has changed. This research provides valuable information for water resources managers and land-use planners to make changes in the improvement of future LULC policies and development of sub-basin management strategies in the context of sustainable water resources and land-use planning and management.

Suggested Citation

  • Mekonnen H. Daba & Songcai You, 2022. "Quantitatively Assessing the Future Land-Use/Land-Cover Changes and Their Driving Factors in the Upper Stream of the Awash River Based on the CA–Markov Model and Their Implications for Water Resources," Sustainability, MDPI, vol. 14(3), pages 1-29, January.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:3:p:1538-:d:736957
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    Citations

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    Cited by:

    1. Chenyu Du & Peihao Song & Kun Wang & Ang Li & Yongge Hu & Kaihua Zhang & Xiaoli Jia & Yuan Feng & Meng Wu & Kexin Qu & Yangyang Zhang & Shidong Ge, 2022. "Investigating the Trends and Drivers between Urbanization and the Land Surface Temperature: A Case Study of Zhengzhou, China," Sustainability, MDPI, vol. 14(21), pages 1-16, October.
    2. Ayşe Çağlıyan & Dündar Dağlı, 2022. "Monitoring Land Use Land Cover Changes and Modelling of Urban Growth Using a Future Land Use Simulation Model (FLUS) in Diyarbakır, Turkey," Sustainability, MDPI, vol. 14(15), pages 1-24, July.
    3. Seyd Teymoor Seydi & Reza Shah-Hosseini & Meisam Amani, 2022. "A Multi-Dimensional Deep Siamese Network for Land Cover Change Detection in Bi-Temporal Hyperspectral Imagery," Sustainability, MDPI, vol. 14(19), pages 1-17, October.
    4. Selamawit Haftu Gebresellase & Zhiyong Wu & Huating Xu & Wada Idris Muhammad, 2023. "Scenario-Based LULC Dynamics Projection Using the CA–Markov Model on Upper Awash Basin (UAB), Ethiopia," Sustainability, MDPI, vol. 15(2), pages 1-27, January.
    5. Samuel Che Nde & Sammy Kipyego Bett & Manny Mathuthu & Lobina Palamuleni, 2022. "Anthropogenic Land Use and Land Cover Change as Potential Drivers of Sediment Sources in the Upper Crocodile River, North West Province, South Africa," IJERPH, MDPI, vol. 19(20), pages 1-19, October.
    6. Yongjun Du & Xiaolong Li & Xinlin He & Xiaoqian Li & Guang Yang & Dongbo Li & Wenhe Xu & Xiang Qiao & Chen Li & Lu Sui, 2022. "Multi-Scenario Simulation and Trade-Off Analysis of Ecological Service Value in the Manas River Basin Based on Land Use Optimization in China," IJERPH, MDPI, vol. 19(10), pages 1-31, May.
    7. Rungruang Janta & Laksanara Khwanchum & Pakorn Ditthakit & Nadhir Al-Ansari & Nguyen Thi Thuy Linh, 2022. "Water Yield Alteration in Thailand’s Pak Phanang Basin Due to Impacts of Climate and Land-Use Changes," Sustainability, MDPI, vol. 14(15), pages 1-19, July.
    8. Zahra Allahdad & Saeed Malmasi & Morvarid Montazeralzohour & Seyed Mohammad Moein Sadeghi & Mohammad M. Khabbazan, 2022. "Presenting the Spatio-Temporal Model for Predicting and Determining Permissible Land Use Changes Based on Drinking Water Quality Standards: A Case Study of Northern Iran," Resources, MDPI, vol. 11(11), pages 1-14, November.
    9. Nuaman Ejaz & Mohamed Elhag & Jarbou Bahrawi & Lifu Zhang & Hamza Farooq Gabriel & Khalil Ur Rahman, 2023. "Soil Erosion Modelling and Accumulation Using RUSLE and Remote Sensing Techniques: Case Study Wadi Baysh, Kingdom of Saudi Arabia," Sustainability, MDPI, vol. 15(4), pages 1-14, February.
    10. Leizhou Zhu & Yaping Huang, 2022. "Multi-Scenario Simulation of Ecosystem Service Value in Wuhan Metropolitan Area Based on PLUS-GMOP Model," Sustainability, MDPI, vol. 14(20), pages 1-19, October.
    11. Jian Zhou & Shan Jiang & Sanjit Kumar Mondal & Jinlong Huang & Buda Su & Zbigniew W. Kundzewicz & Ziyan Chen & Runhong Xu & Tong Jiang, 2022. "China’s Socioeconomic and CO 2 Status Concerning Future Land-Use Change under the Shared Socioeconomic Pathways," Sustainability, MDPI, vol. 14(5), pages 1-17, March.

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