Open AccessUHI SPATIO-TEMPORAL ANALYSIS WITH GEOSPATIAL TECHNIQUES: A CASE OF AHMEDABAD CITY.
Author(s) -
D. Rawal,
Vishal Gupta
Publication year - 2021
Publication title -
paripex indian journal of research
Language(s) - English
DOI - 10.36106/paripex/1912691
Subject(s) - normalized difference vegetation index , land cover , urban heat island , urbanization , environmental science , vegetation (pathology) , remote sensing , satellite , geography , land use , geospatial analysis , scale (ratio) , physical geography , brightness temperature , climatology , meteorology , cartography , climate change , brightness , geology , medicine , oceanography , civil engineering , pathology , aerospace engineering , economic growth , engineering , economics , optics , physics
Spatio-temporal changes in land use land cover (LULC) have been relevant factors in causing the changes in Urban HeatIsland (UHI) pattern across rural and urban areas all over the world. Studies conducted have shown that the relationbetween LULC on scale of the UHI can be an important factor assessing the condition not only for a country but forenvironment of a city also. Over the years it is reflected in health of vegetation and urbanization pattern of cities. As thethermal remote sensing has been evolved, the measurement of the temperature through satellite products hasbecome possible. Thermal data derived through remote sensing gives us birds-eye-view to see how the thermal datavaries in the entire city.In this study such relations are shown over Ahmedabad city of India for the period of 2007 to 2020 using Landsat seriessatellite data. Land Surface Temperature (LST) is calculated using Google Earth Engine Platform Surface BrightnessTemperature for Landsat data and using Radiative Transfer Equation for Landsat data. LST is correlated with land useland cover mainly Built-up, Vegetation, Barren land, Water & Other and corresponding Land Use and Land Coverrespectively, and it is found that LST is positively related with all indices except for Normalize Difference VegetationIndex (NDVI) with strong negative correlation and R 2 of 0.51.