Exploring the Relationship Between Spatio-temporal Land Cover Dynamics and Surface Temperature Over Dehradun Urban Agglomeration, India

In present study, using artificial neural network (ANN), the land cover maps for three years (i.e. 2000, 2010 and 2019) were derived from Landsat optical data and the decadal spatio-temporal land cover dynamics was analysed. The classes delineated were built-up (urban and suburban), cultivated, vegetation, bare soil and river courses. Subsequently, the land cover change patterns were correlated with the LST values, which were retrieved from Landsat thermal data using mono-widow algorithm. The spatio-temporal clustering of high and low LST values (i.e. LST hot and cold spots) over different land covers, with special emphasis on built-up areas, was carried out. The variation in human thermal comfort levels during the period 2000-2019 was also investigated using thermal field variance index. The domain of the present study was Dehradun urban agglomeration.

Automated summary

This study used artificial neural network to derive land cover maps for three years from Landsat optical data and analysed decadal spatio-temporal land cover dynamics. The correlation between land cover change patterns and LST values was investigated, along with the variations in human thermal comfort levels. The study focused on the Dehradun urban agglomeration.