Prediction of surface urban heat island based on predicted consequences of urban sprawl using deep learning: A way forward for a sustainable environment

The present work aimed at the spatiotemporal analysis of Land Surface Temperature (LST) and several land-use land-cover spectral indices, namely Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), Soil Adjusted Vegetation Index (SAVI), and Modified Normalized Difference Water Index (MNDWI) and to develop a Convolutional Neural Network (CNN) model to predict the LST and the indices for Surface Urban Heat Island (SUHI) evaluation based on the impervious surface area. The predictions were made only for LST and two indices (NDVI and NDBI) because of their strong correlation with LST. Landsat-8 satellite imageries acquired for 2013, 2015, 2017, 2019, and 2021 were utilized, and the Sialkot district, a swiftly urbanizing and advancing industrial city, was selected as a case study area. In the study, first, the indices (NDVI and NDBI) were predicted for 2019, 2021, and 2023 following a sequential procedure, and then based on the predicted indices, the LST for the mentioned years was predicted. Ultimately, to analyze the impact of impervious area on SUHI, impervious and SUHI were extracted for 2019, 2021, and 2023 using the predicted NDBI and LST, respectively, by employing Otsu's Thresholding technique. The predicted LST and indices for 2019 and 2021 were compared with the obtained 2019 and 2021 LST and indices through several statistical measures, such as the kappa index, to evaluate the performance of the CNN model. The kappa index of different kappa statistics for 2019 and 2021 varied between 0.81 and 0.96, which resembles several previous studies, thus indicating sufficient future predictions. SUHI evaluation was conducted based on the observed (2016) and simulated (2021) impervious area and normalized LST images. The results showed that the distribution of the SUHI was highly related to the impervious area. Suppose the urbanization rate in the study area continues at its current pace. In that case, this expansion will cause a dramatic increase in SUHI distribution unless decision-makers consider any proper urban planning model for the study area.