Bi-temporal characterization of terrestrial temperature in relation to urban land use land cover dynamics and policies in Kozhikode Urban Area, India

The pattern of Land Use Land Cover (LULC) is shifting in an unbalanced manner due to urban growth. When cities flourish, vegetated regions are altered into impervious concrete masses. This along with other factors increases the Land Surface Temperature (LST) in urban areas, leading to the emergence of Surface Urban Heat Islands (SUHIs). This study seeks to examine how the evolving patterns of LULC impact the intensity of LST and its correlation with the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Built-up Index (NDBI) in the rapidly expanding Kozhikode Urban Area (KUA). This is studied using a combination of geographic information systems, satellite remote sensing, and statistical analysis. Satellite images of Landsat 5 (TM) and Landsat 8 (OLI & TIRS) of the years 1993 and 2018 were used, respectively. ArcGIS software has been used to map the LULC pattern, LST, NDVI and NDBI. The supervised technique with the maximum likelihood algorithm is used to derive the LULC pattern, while the mono-window algorithm has been used to derive the LST. MS Excel, SPSS, and OrginPro were used for statistical analysis. The results reflect a decrease of 73.88 per cent in total vegetated areas from 1993 to 2018, while the built-up area expanded from 19.95 km2 to 132.96 km2. The mean LST increased by 2.65 degrees C from 1993 to 2018. Pearson correlation revealed LST is negatively correlated with NDVI (r =-0.541, P < 0.5 in 1993, r =-0.616, P < 0.5 in 2018) and positively correlated with NDBI (r = 0.664, P < 0.5 in 1993, r = 0.612, P < 0.5 in 2018). As a result, this paper urges urban planners and policymakers in Kozhikode to urgently address the disproportionate LULC patterns emerging in the city and implement more sustainable development measures and policies like urban greening with proper planning and effective stakeholder engagement to control the rising LST.