Analysis of the Relationship between Land Surface Temperature and Vegetation and Built-Up Indices in Upper-Hill, Nairobi
Mwangi, Patricia Wanjiku
Karanja, Faith Njoki
Kamau, Peter Kariuki
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Designing “liveable” cities as climate change effects are felt all over the world has become a priority to city authorities as ways are sought to reduce rising temperatures in urban areas. Urban Heat Island (UHI) effect occurs when there is a difference in temperature between rural and urban areas. In urban areas, impervious surfaces absorb heat during the day and release it at night, making urban areas warmer compared to rural areas which cool faster at night. This Urban Heat Island effect is particularly noticeable at night. Noticeable negative effects of Urban Heat Islands include health problems, air pollution, water shortages and higher energy requirements. The main objective of this research paper was to analyze the spatial and temporal relationship between Land Surface Temperature (LST) and Normalized Density Vegetation Index (NDVI) and Built-Up Density Index (BDI ) in Upper-Hill, Nairobi Kenya. The changes in land cover would be represented by analyzing the two indices NDVI and BDI . Results showed the greatest increase in temperature within Upper-Hill of up to 3.96˚C between the years 2015 and 2017. There was also an increase in impervious surfaces as indicated by NDVI and BDI within Upper-Hill and its surroundings. The linear regression results showed a negative correlation between LST and NDVI and a positive correlation with BDI , which is a better predictor of Land Surface Temperature than NDVI . Data sets were analyzed from Landsat imagery for the periods 1987, 2002, 2015 and 2017 to determine changes in land surface temperatures over a 30 year period and it’s relation to land cover changes using indices. Visual comparisons between Temperature differences between the years revealed that temperatures decreased around the urban areas. Minimum and maximum temperatures showed an increase of 1.6˚C and 3.65˚C respectively between 1987 and 2017. The comparisons between LST, NDVI and BDI show the results to be significantly different. The use of NDVI and BDI to study changes in land cover due to urbanization, reduces the time taken to manually classify moderate resolution satellite imagery.