Assessing variations in land cover-land use and surface temperature dynamics for Dehradun, India, using multi-time and multi-sensor landsat data

Abstract

Rapid urbanisation and industrialisation coupled with overpopulation have altered land cover/land use (LCLU) and surface temperature (ST) patterns in Dehradun. Monitoring these changes through satellite-based remote sensing is required to ensure the sustained development of this ecologically fragile region. Here, LU and ST dynamics of the Dehradun municipal area have been estimated using Landsat-5 datasets for 1991, 1998, and 2008 and Landsat-8 dataset for 2018. LU maps have been extracted using a Gaussian Maximum Likelihood classifier with an overall accuracy of over 88% and Kappa coefficients above 0.85. Results reveal that the urban region expanded by 80.6% in the 27 years while dense vegetation and dry river bed classes have declined sharply. Sparse vegetation has risen by 3 km 2 , whereas bare ground has decreased by about 4.3 km 2 . Mean ST has increased above 9 °C from 1991 to 2018 in every season. A seasonal influence is evident on the mean ST per LU class’s trend, which rose between 8 °C and 12 °C for every LU class, indicating significant warming across each LU class. ST probably has non-linear relationships with its causal factors represented by spectral indices, elevation, and population density. Urban heat island (UHI) formation is thus evinced, promulgating the administration’s urgent action to save the environment and redrawing policies for ambitious projects like smart cities.

Assessing the Cooling Effect of Blue-Green Spaces: Implications for Urban Heat Island Mitigation

“…Instead of field measurements, a cross-validation approach was employed to examine the Landsat-estimated LST's reliability using a Moderate Resolution Imaging Spectroradiometer (MODIS) MOD11A1 V6.1 LST product [54] available on GEE. MOD11A1 provides daily LST values with a 1000 m resolution.…”