Assessing spatiotemporal variations in land surface temperature and SUHI intensity with a cloud based computational system over five major cities of India

Abstract

• The study showed that all cities have undergone a change in LULC pattern. • Concurrent data from space-based sensors for effective disaster response. • The impact of LST and SUHI over the vegetation cover with a cloud-based computing. • Biophysical parameters like NDVI, NDBI, and MNDWI are used for analysis and results derivations. The surface urban heat island (SUHI) is one of the most evident climate phenomena in low- and mid-latitude cities. Extensive growth and intense urbanization has brought a phenomenal change to the urban landscape. The rapid expansion of cities have the potential to generate distinctive local urban climates by increasing land surface temperature (LST) and the intensities of surface urban heat islands (SUHI). The study uses Landsat data in a cloud based computational system to investigate the spatiotemporal variation of land use land cover (LULC) for five major cities (Mumbai, New Delhi, Bangalore, Hyderabad, and Ahmadabad) from 2007 to 2020. Selected biophysical parameters like Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), and Modified Normalized Difference Water Index (MNDWI) are used. The interrelationship with the dynamics of LST to analyze the SUHI profiles reveals that the mean LST has a positive correlation for impermeable surfaces and a negative correlation for green surfaces, which are common for all Indian cities and results in degradation of environmental quality. The results obtained from this study showed that all cities have undergone a change in LULC pattern, which induced a rise in overall temperature and SUHI intensity. The expansion of the urban landscape along with the changing vegetation and cropping patterns due to the rise in anthropogenic activities has resulted in susceptible climatic conditions and necessitates further environmental investigations. The findings of the study can be recommended to monitor the impacts of SUHI intensity at different locations for future studies to adapt sustainable urban planning of cities.