Quantitative assessment of spatio-temporal dynamics of land use/land cover and land surface temperature using different algorithms and landsat imagery

Human-induced land use land cover changes have resulted in adverse impacts on the environment at various spatial and temporal scales. Land surface temperature (LST) is an important indicator associated with strong spatio-temporal variability in complex Earth Patterns. Remote sensing is the primary source for various types of thematic data critical to land analysis, including land use data and land surface temperature changes. In this study, first, to analyze the spatial-temporal land surface temperature changes, LST maps were prepared using Radiative Transfer Equation, Single Channel Algorithm, Split Window Algorithm and Two Mono Window Algorithm. Then the performance of Land Surface Temperature (LST) retrieval methods was evaluated. Land use and land cover maps were prepared to investigate the contribution and role of each land use class on the changes of the LST.  Also, the effects Normalized Difference Vegetation Index, Normalized Difference Built-up Index and elevation on LST variability were investigated. There was a positive correlation of LST with Normalized Difference Built-up Index and a negative correlation with Normalized Difference Vegetation Index and elevation for all years. Spatio-temporal analysis shows a 6.64°C increase in LST from 1989 to 2021. The results clearly showed the moderating role of vegetation, regardless of its type, on LST. The findings of the present study serve as basic information for future studies to investigate the impact of different policies on LULC change in the region. Also, the obtained results reveal the importance of studying and examining the mentioned changes in order to achieve sustainable development.