article · Journal of Water and Climate Change
This research assesses how changing land cover influenced land surface temperatures in Adama City, Ethiopia, over a twenty-year span from 2002 to 2022. Utilizing Landsat satellite imagery alongside geographic information system tools, the analysis evaluated relationships between surface temperatures, vegetation density, and built-up land. Between 2002 and 2022, combined dense and sparse vegetation fell from 24.14 square kilometres, representing 17.47 percent of the area, to 18.17 square kilometres, or 13.15 percent. Over the same timeframe, the average land surface temperature rose from 28.25 to 31.78 degrees Celsius. The study found a negative correlation between temperature and vegetation presence, but a positive relationship with built-up areas. Built-up indices proved to be a more effective predictor of surface temperature than vegetation indices, showing that fast-paced urban expansion drives local temperature increases.
Expanding cities often replace natural cooling greenery with heat-absorbing structures and roads, driving up local temperatures. Understanding how specific types of urban development alter local climate allows municipal authorities to design smarter urban plans. This evidence helps city planners recognise the thermal consequences of converting green spaces into buildings, supporting efforts to mitigate harmful microclimate warming.
The research provides early-stage observational evidence that can inform municipal authorities and urban planning software developers seeking to model urban heat risks. While it demonstrates the utility of remote sensing datasets for tracking surface warming against infrastructure growth, the abstract does not indicate a direct commercial application pathway or product development framework.
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ABSTRACT Land surface temperature (LST) is becoming a serious environmental issue, since it is an essential controller of city climate. Currently, addressing this challenge in urban planning has gained worldwide importance. The study aims to analyze the effect of land cover dynamics on LST using geographic information system and remote sensing techniques. In this study, Landsat 7 ETM+ (2002) and Landsat 8 TIRS (2022) data were used. The LST was retrieved from Landsat datasets. The correlation analyses were conducted on LST, normalized difference vegetation index (NDVI), and normalized difference built-up index (NDBI). The findings suggested that there was a negative correlation between LST and the NDVI, while a positive correlation existed with the NDBI. Moreover, NDBI was found to be a better predictor of LST than NDVI. Additionally, it was revealed that the proportion of dense and sparse vegetation cover was reduced from 24.14 km2 (17.47%) in 2002 to 18.17 km2 (13.15%) in 2022. Similarly, the average LST increased from 28.25 to 31.78°C. This indicated that the rapid growth of urban development was the primary factor behind the rise in LST. Therefore, it was deemed crucial to create a smart urban land use plan to mitigate the impacts of microclimate change.
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DOI: 10.2166/wcc.2024.067
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