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Land cover classification and change detection analysis of Qaroun and Wadi El-Rayyan lakes using multi-temporal remotely sensed imagery

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Abstract

The Qaroun Lake, Wadi El-Rayyan, and Wadi El-Hitan are some of the most promising ecotourism destinations in Egypt due to their natural mineral resources, wildlife, and biodiversity in addition to their historic heritage that dates back to the age of The Pharos. These natural resources should be managed and maintained without affecting the needs of future generations. Land use/land cover change is the most important factor in causing biodiversity loss. Accordingly, the objectives of this study are to identify, quantify, and model future land cover changes using remote sensing and GIS techniques. To fulfill the objectives of the study, a hybrid image classification is employed using the combination of unsupervised and supervised classification methods to detect land cover types. Post-classification comparison is used to map changes in land cover between 2000 and 2017. Markov model is applied to analyze, predict, and simulate future land cover changes from 2017 to 2050. This is in order to safeguard against the adverse effects and negative consequences of land cover changes, preserve the natural resources, and consequently achieve goals of sustainable development. The outcome of this study can provide policy makers and urban planners with the required information regarding the status of the environment and subsequently reduce pressure on natural resources in order to facilitate conservation planning and sustainable development.

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Authors and Affiliations

  1. The High Institute of Tourism, Hotels & Computer, Egyptian Ministry of Higher Education, Cairo, Egypt

    Soha A. Mohamed

  2. University of Alexandria, Alexandria, Egypt

    Mohamed E. El-Raey

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  1. Soha A. Mohamed
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  2. Mohamed E. El-Raey
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Correspondence to Soha A. Mohamed.

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Mohamed, S.A., El-Raey, M.E. Land cover classification and change detection analysis of Qaroun and Wadi El-Rayyan lakes using multi-temporal remotely sensed imagery. Environ Monit Assess 191, 229 (2019). https://doi.org/10.1007/s10661-019-7339-x

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