Detecting, Analyzing, and Predicting Land Use/Land Cover (LULC) Changes in Arid Regions Using Landsat Images, CA-Markov Hybrid Model, and GIS Techniques

Understanding the change dynamics of land use and land cover (LULC) is critical for efficient ecological management modification and sustainable land-use planning. This work aimed to identify, simulate, and predict historical and future LULC changes in the Sohag Governorate, Egypt, as an arid region. In the present study, the detection of historical LULC change dynamics for time series 1984–2002, 2002–2013, and 2013–2022 was performed, as well as CA-Markov hybrid model was employed to project the future LULC trends for 2030, 2040, and 2050. Four Landsat images acquired by different sensors were used as spatial–temporal data sources for the study region, including TM for 1984, ETM+ for 2002, and OLI for 2013 and 2022. Furthermore, a supervised classification technique was implemented in the image classification process. All remote sensing data was processed and modeled using IDRISI 7.02 software. Four main LULC categories were recognized in the study region: urban areas, cultivated lands, desert lands, and water bodies. The precision of LULC categorization analysis was high, with Kappa coefficients above 0.7 and overall accuracy above 87.5% for all classifications. The results obtained from estimating LULC change in the period from 1984 to 2022 indicated that built-up areas expanded to cover 12.5% of the study area in 2022 instead of 5.5% in 1984. This urban sprawl occurred at the cost of reducing old farmlands in old towns and villages and building new settlements on bare lands. Furthermore, cultivated lands increased from 45.5% of the total area in 1984 to 60.7% in 2022 due to ongoing soil reclamation projects in desert areas outside the Nile Valley. Moreover, between 1984 and 2022, desert lands lost around half of their area, while water bodies gained a very slight increase. According to the simulation and projection of the future LULC trends for 2030, 2040, and 2050, similar trends to historical LULC changes were detected. These trends are represented by decreasing desert lands and increasing urban and cultivated newly reclaimed areas. Concerning CA-Markov model validation, Kappa indices ranged across actual and simulated maps from 0.84 to 0.93, suggesting that this model was reasonably excellent at projecting future LULC trends. Therefore, using the CA-Markov hybrid model as a prediction and modeling approach for future LULC trends provides a good vision for monitoring and reducing the negative impacts of LULC changes, supporting land use policy-makers, and developing land management.

Авторы
Selmy Salman A.H.1 , Kucher Dmitry E. 2 , Mozgeris Gintautas3 , Moursy Ali R.A.4 , Jimenez-Ballesta Raimundo5 , Kucher Olga D. 6 , Fadl Mohamed E.7 , Mustafa Abdel-rahman A.4
Журнал
Remote Sensing
Издательство
MDPI AG
Номер выпуска
23
Язык
Английский
Страницы
5522
Статус
Опубликовано
DOI
https://doi.org/10.3390/rs15235522
Том
15
Год
2023
Организации
  • 1 Department of Soils and Water, Faculty of Agriculture, Assiut University, Assiut 71526, Egypt
  • 2 Department of Environmental Management, Institute of Environmental Engineering, People’s Friendship University of Russia Named after Patrice Lumumba (RUDN University), 6 Miklukho-Maklaya St., 117198 Moscow, Russia
  • 3 Department of Forest Sciences, Agriculture Academy, Vytautas Magnus University, Studentų Str. 11, LT-53361 Akademija, Kaunas Dstr., Lithuania
  • 4 Department of Soil and Water, Faculty of Agriculture, Sohag University, Sohag 82524, Egypt
  • 5 Department of Geology and Geochemistry, Autónoma University of Madrid, 28049 Madrid, Spain
  • 6 Scientific Department, Rural Urban Framework Center, People’s Friendship University of Russia Named after Patrice Lumumba (RUDN University), 6 Miklukho-Maklaya St., 117198 Moscow, Russia
  • 7 Division of Scientific Training and Continuous Studies, National Authority for Remote Sensing and Space Sciences (NARSS), Cairo 11769, Egypt
Дата создания
18.07.2024
Дата изменения
18.07.2024
Постоянная ссылка
https://repository.rudn.ru/ru/records/article/record/157459/
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