Time-series analysis of open data for studying urban heat island phenomenon: a geospatial approach

Abstract

Urbanization is increasing with a faster pace in almost all the cities of India, which has somehow prompted the anthropogenic activities eventually influencing the environment in this climate change scenario. This has led to the more prominent urban heat island (UHI) effect in urban areas, significantly raising the surface temperature of urban built-up. To focus on this issue, a spatio-temporal analysis of UHI over the Jaipur site of Rajasthan has been performed using moderate-resolution imaging spectroradiometer (MODIS) land surface temperature (LST) and MODIS Normalized Difference Vegetation Index (NDVI) for 16 years i.e., from 2003 to 2018 wherein NDVI has been used to analyze the status of vegetation in the urban and non-urban area w.r.t. UHI phenomenon. Study of UHI phenomenon has been done by computing the difference of mean LST between urban area and urban periphery (at a buffer distance of 2 km from urban boundary) both for day and night time. The difference of LST day time observed between urban and non-urban (buffer) is 0.06 °C in May, 2003 and 0.51 °C in May, 2018 whereas, difference of LST night time between urban and area surrounding to urban is 0.58 °C in May, 2003 and 1.41 °C in May, 2018. From the quantitative analysis of land use land cover it has been observed that there is a rapid increase of urban area from 63% in 2003 to 69% in 2018 and vegetation cover decreased from 36% in 2003 to 31% in 2018. The percentage decrease of vegetation cover from 2003 to 2018 is 5% and the percentage increase in urban area is 6% which is still continuing at an even faster rate. Data preparation tasks can be greatly minimized by using analysis ready open data available in the public domain for carrying out such studies. The approach followed in the study will assist researchers to carry out a quick spatio-temporal analysis for the identification of areas susceptible to increasing variability of LST. Also, it provides a basis to understand and manage urban stress, one of the major causes of unalterable damages to the environment.