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Remote sensing-based detection of agricultural land losses around Greater Cairo since the Egyptian revolution of 2011

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  • Salem, Muhammad
  • Tsurusaki, Naoki
  • Divigalpitiya, Prasanna

Abstract

Over the last four decades, the loss of agricultural land has been observed in Egypt at high rates. However, the highest rates of losses have occurred since the January 25th revolution in 2011. Greater Cairo (GC), which is the largest metropolitan in Egypt, has witnessed a massive loss of agricultural land since the 2011 revolution. However, until now, no study or official report has revealed the volume of agricultural land losses in this region. This study quantifies agricultural land losses around the GC using Landsat satellite images from 2010 and 2018. Supervised classification was performed using the maximum likelihood algorithm in QGIS software. A post-classification comparison method was applied to detect the land use/land cover changes between the classified images; then, the loss of agricultural land was quantified using Arc GIS software. Visualizations of the gains and losses in agricultural land and the spatial trends of agricultural land losses were created using TerrSet software. The results show that 9600 ha of agricultural land were converted to urban use during 2010–2018, which means that the annual rate of agricultural land loss has tripled and now reaches approximately 1200 ha per year. Decay of executive authority, rapid population growth, real estate market speculation and fragmentation of agricultural land were the main driving factors of agricultural land losses during this period. The results of this research may help decision makers understand the current high rate of agricultural land loss. Hence, appropriate strategies may be adopted to prevent future losses of this valuable land.

Suggested Citation

  • Salem, Muhammad & Tsurusaki, Naoki & Divigalpitiya, Prasanna, 2020. "Remote sensing-based detection of agricultural land losses around Greater Cairo since the Egyptian revolution of 2011," Land Use Policy, Elsevier, vol. 97(C).
    • Handle: RePEc:eee:lauspo:v:97:y:2020:i:c:s0264837719315170
    DOI: 10.1016/j.landusepol.2020.104744
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    References listed on IDEAS

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    1. Chen Liping & Sun Yujun & Sajjad Saeed, 2018. "Monitoring and predicting land use and land cover changes using remote sensing and GIS techniques—A case study of a hilly area, Jiangle, China," PLOS ONE, Public Library of Science, vol. 13(7), pages 1-23, July.
    2. Taher Osman & Prasanna Divigalpitiya & Takafumi Arima, 2016. "Driving factors of urban sprawl in Giza governorate of the Greater Cairo Metropolitan Region using a logistic regression model," International Journal of Urban Sciences, Taylor & Francis Journals, vol. 20(2), pages 206-225, July.
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    Cited by:

    1. Amr E. Keshta & J. C. Alexis Riter & Kamal H. Shaltout & Andrew H. Baldwin & Michael Kearney & Ahmed Sharaf El-Din & Ebrahem M. Eid, 2022. "Loss of Coastal Wetlands in Lake Burullus, Egypt: A GIS and Remote-Sensing Study," Sustainability, MDPI, vol. 14(9), pages 1-16, April.
    2. Omar Hamdy & Hanan Gaber & Mohamed S. Abdalzaher & Mahmoud Elhadidy, 2022. "Identifying Exposure of Urban Area to Certain Seismic Hazard Using Machine Learning and GIS: A Case Study of Greater Cairo," Sustainability, MDPI, vol. 14(17), pages 1-24, August.
    3. Ayman Alhejji & Alban Kuriqi & Jakub Jurasz & Farag K. Abo-Elyousr, 2021. "Energy Harvesting and Water Saving in Arid Regions via Solar PV Accommodation in Irrigation Canals," Energies, MDPI, vol. 14(9), pages 1-24, May.
    4. Muhammad Salem & Arghadeep Bose & Bashar Bashir & Debanjan Basak & Subham Roy & Indrajit R. Chowdhury & Abdullah Alsalman & Naoki Tsurusaki, 2021. "Urban Expansion Simulation Based on Various Driving Factors Using a Logistic Regression Model: Delhi as a Case Study," Sustainability, MDPI, vol. 13(19), pages 1-17, September.

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