Adaptive hierarchical cell sub-division (AHCS) method for enhanced surface radiance temperature variability analysis

Abstract

The issues with global warming and heat island effect raises the global inclination to elucidate the concerns towards the study of surface temperature variations in the urban environment. Surface radiance temperature (SRT) are considered undeniably one of the most substantial parameters to evaluate the impact of temperature variability in an urban environment. The work tried to present the thermal image processing techniques for retrieving the SRT from Landsat ETM+ data. Afterward, an innovative method namely adaptive hierarchical cell sub-division (AHCS) method in conjunction with statistical techniques were attempted to characterize the spatial variability for each of the directional attributes. These techniques also helped in detecting and quantifying the spatial variability at major and minor scales. The spatial variability through AHCS were analyzed to illustrate their native spatial distribution over urban–rural (Rurban) areas contributing to heat island. The results apparently revealed the aggregate spatial thermal configuration along Rurban areas and vice versa. The key fraction of urban landscape patches with high, sub high and low densities specified their inherent characteristics. The high-temperature type was the prevailing class in the urban core while sub medium temperature and low-temperature type were the main contributors in the rural landscape. Whereas, urban fringe exhibited very complex results of temperature variability for the various land use. The study also authenticated the approach for assimilation of spatial variability techniques with a powerful statistical approach as a reliable instrument to monitor the thermal dynamics.