Assessing the impact of COVID-19-induced lockdown on land surface temperatures in heterogeneous cities

Urbanisation has brought about significant changes in the land use/land cover (LULC) and land surface temperatures (LSTs) of cities. The impact of the COVID-19 lockdown on LSTs in Polokwane and Johannesburg, South Africa, was analysed over the years 2020 to 2022, using Landsat 8 and 9 images. The relevant LSTs were retrieved by applying the temperature/emissivity separation algorithm. The normalized difference built-up index (NDBI) and the normalized difference vegetation index (NDVI) threshold techniques were used to evaluate the LSTs for the two cities in terms of five LULC classes: bare land, built-up area, road, vegetation, and waterbody. The LST patterns were estimated per LULC class and the Pearson’s correlation analysis was performed to examine the relationship between NDVI, NDBI, and LST. The findings revealed that throughout the lockdown period (i.e., from January 2020 to March 2021, there was a decrease in LST values across all LULC categories − by 1°C for vegetation and 2°C for the bare land, built-up, road and waterbody classes. The LST values showed an increase from 2021 (during lockdown) to 2022 (post-lockdown), thereby demonstrating in particular their association with the bareland class, where the most notable increases of 1.9°C and 0.6°C were observed in Polokwane and Johannesburg, respectively. There were strong negative correlations between the NDVI and LST (i.e., -0.62, -0.58, and -0.71 for Polokwane and -0.69, -0.69, and -0.73 for Johannesburg) in 2020, 2021, and 2022, respectively. Strong positive correlations between NDBI and LST (i.e., 0.81, 0.79, and 0.85, and 0.70, 0.59, and 0.77) were recorded in Polokwane and Johannesburg respectively for the same period. The decline in LSTs was due to the cessation of industrial activities, transportation, and other human activities, resulting in improved air quality that in turn reduced the effects of the respective urban heat islands (UHIs). The findings offer valuable information that is vital for decision-making procedures that are in line with the aims of SDG 11 which are to improve the sustainability of cities.