Impacts of Human Activities on Urban Sprawl and Land Surface Temperature in Rural Areas, a Case Study of El-Reyad District, Kafrelsheikh Governorate, Egypt

Wael Mostafa, Zenhom Magd, Saif M. Abo Khashaba, Belal Abdelaziz, Ehab Hendawy, Abdelaziz Elfadaly, Mohsen Nabil, Dmitry E. Kucher, Shuisen Chen and Elsayed Said Mohamed ()
Additional contact information
Wael Mostafa: Geography Department, Faculty of Arts, Kafrelsheikh University, Kafrelsheikh 33516, Egypt
Zenhom Magd: Geography Department, Faculty of Arts, Kafrelsheikh University, Kafrelsheikh 33516, Egypt
Saif M. Abo Khashaba: Geology Department, Faculty of Science, Kafrelsheikh University, Kafrelsheikh 33516, Egypt
Belal Abdelaziz: National Authority for Remote Sensing and Space Sciences (NARSS), Cairo 11769, Egypt
Ehab Hendawy: National Authority for Remote Sensing and Space Sciences (NARSS), Cairo 11769, Egypt
Abdelaziz Elfadaly: National Authority for Remote Sensing and Space Sciences (NARSS), Cairo 11769, Egypt
Mohsen Nabil: National Authority for Remote Sensing and Space Sciences (NARSS), Cairo 11769, Egypt
Dmitry E. Kucher: Department of Environmental Management, Institute of Environmental Engineering, RUDN University, 6 Miklukho-Maklaya St, Moscow 117198, Russia
Shuisen Chen: Joint Laboratory on Low-Carbon Digital Monitoring, Guangdong Institute of Carbon Neutrality (Shaoguan), Shaoguan 512029, China
Elsayed Said Mohamed: National Authority for Remote Sensing and Space Sciences (NARSS), Cairo 11769, Egypt

Sustainability, 2023, vol. 15, issue 18, 1-19

Abstract: Anthropogenic activities affect the surrounding environment dynamically in different ways. In the arid and hyper arid, agriculture is concentrated in rural communities, which are cooling surfaces that help mitigate surface temperature increases. Recently, rural communities are suffering from increasing urban sprawl. The current work focuses on evaluating the changes in land cover and their impacts on land surface temperature (LST) during (1988–2022) and predicting the changes until 2056 in El-Reyad District, Kafrelsheikh Governorate, Egypt. For achieving this purpose, Landsat images (TM, ETM+, and OLI) were used. The support vector machine (SVM) was applied using Google Earth Engine (GEE) to monitor changes in land use/cover and LST. The prediction of land use until 2056 was achieved using the CA-Markov simulation model. The results showed six land cover classes: agricultural lands, bare lands, urban areas, natural vegetation, Lake Burullus, and fish farms. The results showed the effects of human activity on the conversion of agricultural land to other activities, as agricultural lands have decreased by about 3950.8 acres, while urban areas have expanded by 6283.2 acres, from 1988 to 2022. Fish farms have increased from 3855.6 to 17,612 acres from 1988 to 2022. While the area of bare land decreased from 28.3% to 0.7% of the total area, it was converted to urban, agricultural, and fish farms. The spatiotemporal change in land cover affected the balance of LST in the study area, although the average temperature increased from 32.4 ± 0.5 to 33.6 ± 0.2 °C. In addition, it is expected to reach 36 ± 0.5 °C in 2056, and there are some areas with decreased LST where it is converted from bare areas into fish farms and agricultural uses. The prediction results show that the agricultural area will decrease by −11.38%, the urban area will increase by 4.6%, and the fish farms area will increase by 6.1%. Thus, preserving green spaces and reducing urban sprawl in rural communities are very important objectives.

Keywords: remote sensing; land cover; Markov model; land surface temperature; Google Earth Engine (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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