Combining remote sensing applications and local knowledge in understanding urban heat in a semi-arid region: a case study of Tamale’s thermal landscape

Landscape features influence Land Surface Temperature (LST) as they affect the thermal properties of the earth's surface. This makes the use of Remote Sensing (RS) technologies such as Landsat images very instrumental in analysing the alterations in temperature in urban settings with high spatial and temporal precision. Thus, in this paper, we analyse the distribution of LST in urban areas to unravel the spatial patterns using spectral indices like the Normalised Difference Vegetation Index, Normalised Difference Water Index, Normalised Difference Built-up Index, and Normalised Difference Bareness Index. We incorporated local knowledge by interviewing 10 elderly people who have lived more than 20 years and correlated with the results from the RS techniques. The indices were derived from Landsat 7 data covering the Tamale Metropolitan Area from 2004 to 2020. ENVI 5.3 software was used to process the images or datasets while Excel and R were used to analyse the correlations. The calculated LST showed changes in LST from 2004 to 2020 ranging from -5.6 to 14.80°C in the Metropolis. The analysis of the in-depth interviews revealed that individuals in areas with high vegetation perceived rising temperatures, but to a lesser extent than those in densely built-up areas. The findings highlight the usefulness of RS for studying urban environments, and thus, provide a foundation for decision-making in sustainable urban planning. This is novel as it shows how local people’s knowledge can be used to validate LST from RS and this proves very useful in areas that lack in-situ temperature data.