Landscape-land surface temperature relation in an arid hot city
Description
The research hypothesis is that detailed landscape metrics when correlated with diurnal and nocturnal land surface temperature (LST) and analysed with linear regression can help identify heat reduction policies for hot arid cities. The data show that in Africa, cities in hot arid zones are poorly investigated, that the urban-rural gradient of LST is controversial, landscape features most related to urban heat are highly variable, and that recommended policies are mainly oriented towards increasing vegetation but remain generic. In Niamey (Niger) road width, tree and shrub cover are the most correlated landscape metrics with diurnal LST. Road width is highly correlated with nocturnal LST. The other metrics are not statistically significant. The road width-nocturnal LST relationship is linear. The dataset contains six tables and three figures. The first three tables cover (1) published studies of land surface temperature (LST) in African cities with more than 100,000 inhabitants per climate zone, (2) the main Landscape-LST dynamics observed, and (3) the policies recommended by published literature to reduce LST. Full references to the studies considered are provided. The last three tables contain data on the Landscape-LST relationship in the city of Niamey, Niger. The remaining three tables show (4) the eight landscape metrics and the average daytime and nighttime LST observed in 29 neighbourhood samples in May 2020, (5) the correlation matrix, and (6) the stepwise linear regression results are provided. The three figures represent (1) the location of the neighbourhood samples considered and (2-3) the normal probability plot of residuals. The eight landscape metrics are collected by visual photointerpretation of very high-resolution Google Earth Pro images from May 2020. Daytime and nighttime LST are extracted from ECOSTRESS data from May 2 and 6, 2020. This dataset can be used as a baseline against which to ascertain changes at later dates or to compare landscape features and LST with those of other cities in the same arid hot climate (Bsh, according to the Köppen-Geiger classification).
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Steps to reproduce
The published literature considered is identified through keywords (LST, urban heat, name of individual African cities) by querying Google Scholar and Scopus. Landscape metrics are extracted by visual photointerpretation of very high resolution satellite imagery freely accessible from Google Earth Pro supplemented by terrain inspections. Diurnal and nighttime LST is extracted from data captured by the ECOSTRESS sensor installed on the International Space Station. QGIS was used to extract LST data on the 29 neighbourhood samples.