Pansharpened landsat 8 thermal-infrared data for improved Land Surface Temperature characterization in a heterogeneous urban landscape

Reliable urban Land Use Land Cover (LULC) and Land Surface Temperature (LST) characterization are important for land use planning as well as climate and thermal impact analysis in cities. High resolution LULC and LST datasets are required for detailed spatial analysis such as understanding the contribution of small LULC fragments to thermal characteristics of the entire urban landscape. To date, detailed analysis of urban surfaces using satellite imagery has been hindered by low to moderate spatial resolution of freely available sensors of past and existing missions. Hence, this study sought to investigate the potential of pansharpening Landsat 8 data in improving LULC mapping and LST characterization in Bulawayo metropolitan city, Zimbabwe. Six LULC classes namely; bare area, built-up, low density, dense forest, grasslands and water bodies were considered for the study and respective spatially-explicit locational information (GPS coordinates) class samples identified. Supervised image classification using the Support Vector Machines algorithm was adopted for the un-pansharpened (30m) and separately using different pansharpened (i.e. Brovey, Simple Mean, ESR, IHS and Gram-Schmidt) datasets (15m) as input. Results indicated improved discriminability of LULC and LST features as well as increased classification accuracy from all pansharpening methods when compared to the 30m resolution data. Average inter-class separability increased from 1.577 using original data to 1.863 after pansharpening. The simple mean method showed the highest classification accuracy (92.13%), outperforming un-pansharpened data (89.77%) and other pansharpening methods (89.45–91.75%). Pansharpened data showed better accuracy in LST spatial characterization than un-pansharpened thermal data. For each LULC class, mean LST was slightly higher before, than after pansharpening, by between 0.1 and 1 °C. The study further established that pansharpening based on simple mean method did not compromise the spatial correlation between LULC and LST. These findings present a great improvement towards accurately mapping LULC and LST in heterogeneous urban landscapes.