Spatial Variability of Urban Heat Islands in Cairo City, Egypt using Time Series of Landsat Satellite Images

Islam Abou El-Magd, Ahmed Ismail, Naglaa Zanaty, (doi: 10.23953/cloud.ijarsg.48)

Abstract


Global warming has obtained more attention because of increasing global mean surface temperature since the late 19th century. Urbanization is considered an important contributor to global warming in big cities. Cairo is one of the heavily populated cities in the world, which has rapid urbanization that resulted in remarkable temperature variation compared to the rural surrounding areas. This phenomenon is known as "Urban Heat Islands" (UHIs) that influence both local and regional climate, environment, and socio-economic development. In this research time series of satellite images were used to map the spatial variability of Land Surface Temperature (LST) and Heat Islands in Cairo city. Historical and contemporary record of Landsat images nearly 16 dates from 1990 to 2014 were used to retrieve LST and land cover categories. Results showed that LST was highly influenced by Land use and land cover changes; heat island effect was dominant in urbanized areas, bare/desert land and industrial zones (e.g. Shobra El-khema). The industrial zones, in particular, were possibly high due to the aluminum roof material plus the thermal energy from production activities and high emissions of air pollutants. The pattern of spatial distribution of heat islands has significantly changed from bare/desert land and built up areas (as warmer) to cultivated land and water bodies (as colder). To analyze the relationship between Heat Islands and land-cover changes a quantitative correlation between LST and Normalized Difference Vegetation Index (NDVI), Modified Normalized Difference Water Index (MNDWI) and Normalized Difference Build-up Index (NDBI) was employed. It was found that the correlation between LST and NDVI and MNDWI was negative with r2 of 0.8 and 0.57 respectively; however, the correlation with NDBI was strongly positive with r2 of 0.81. It is anticipated that the outcomes of this research will contribute to GEOSS for global integration and correlation.


Keywords


Remote Sensing; Land Surface Temperature; Mono Window Algorithm; Cairo

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