Geospatial quantification and analysis of environmental changes in urbanizing city of Kolkata (India)

Abstract

Over the past five decades, the fragile wetland ecosystem surrounding the city of Kolkata has witnessed extensive changes in the name of urban development. In this study, we elaborate relationships among biophysical parameters and land surface temperature (LST) in Kolkata city and nearby surrounding areas where rapid urbanization has occurred. LST and associated surface physical characteristics were assessed using Landsat images acquired for the years 1989, 2006, and 2010. The satellite data was used to study the spatiotemporal urban footprint and correlation among normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), normalized difference water index (NDWI) and LST. Land use land cover (LULC) maps prepared using supervised classification had overall accuracy of 90, 88, and 86 % and kappa coefficient of 0.8726, 0.8455, and 0.8212 for 1989, 2006, and 2010, respectively. The spatial expansion as a consequence of increasing urban population is 108.94 km(2) over past two decades. The urban built-up in and around the city extends up to 88.71 km(2) in 1989, 144.64 km(2) in 2006, and 197.65 km(2) in 2010. These changes have attributed in elevating surface temperature in the study region. Analysis of biophysical parameters shows LST and NDBI having a positive correlation, LST and NDVI having negative correlation, while NDBI and NDWI having a perfectly negative correlation. Satellite estimated temperatures of the surface show a warming trend evident from increasing mean surface temperature values from 27.36 °C in 1989 to 30.025 °C in 2006 and 33.023 °C in 2010. The magnitude and extent of the estimates of LST are consistent with the urbanization pattern throughout the city and adjoining areas.

Impact of COVID-19 Induced Lockdown on Environmental Quality in Four Indian Megacities Using Landsat 8 OLI and TIRS-Derived Data and Mamdani Fuzzy Logic Modelling Approach

“…This indicator is the combination of reflectance of green plants, dry and plant absent areas, and soil parts through two near-infrared (NIR) bands [36]. It is computed using Equation (10).…”

Spatiotemporal Analysis of Land Use/Land Cover and Its Effects on Surface Urban Heat Island Using Landsat Data: A Case Study of Metropolitan City Tehran (1988–2018)

“…FOR PEER REVIEW 3 of 26 dynamics were generated to obtain the quantized information about the land cover for the sake of sustainable development [2,8,[27][28][29][30][31][32][33][34]. Previously, the relationship of land surface temperature (LST) vs. normal difference vegetation index (NDVI) [7,8,[35][36][37][38], and LST vs. normal difference built-up index (NDBI) [39][40][41] have been taken into consideration to visualize the actual status of the local landscape [39,42,43]. In this article, spatiotemporal datasets of Landsat (1988-2018) have been thoroughly used for the analysis of the variation of LU/LC and SUHI dynamics phenomena over the city of Tehran using summer dry season (July-August) data.…”

“…The observed higher temperature over the urban than the rural areas led to the creation of an urban heat island (UHI) [5,6]. This difference in temperature over urban-rural landscapes has been raised mainly because of the depletion of green space, shrinking of water bodies, lessening of farmland, and the growth of impervious surface [4,5,7].…”

Modeling an Indian megalopolis– A case study on adapting SLEUTH urban growth model

“…Especially in tropical countries like India, the high heat challenges become multiplied in urban areas with overflowing population, poverty and poor informal infrastructure (Kovats & Akhtar, 2008). This rapid growth has resulted in uncontrolled and inefficient infrastructural development that threatens the sustainability of the environment due to a loss of biodiversity (Nagendra et al, 2013), deforestation and fragmentation (Nagendra, Sudhira, Katti, Tengö, & Schewenius, 2012), an increase in surface temperature and heat fluxes, degradation of the air quality, an increase in greenhouse gas emissions, variability in rainfall (Kishtawal, Niyogi, Tewari, Pielke, & Shepherd, 2010;Mitra, Shepherd, & Jordan, 2012;Niyogi et al, 2007), and water quality (Aggarwal & Butsch, 2011;Mohan & Kandya, 2015;Nagendra et al, 2013;Rao, Jaswal, & Kumar, 2004;Sharma, Chakraborty, & Joshi, 2015;Sharma, Ghosh, & Joshi, 2013;WHO, 2014) and an increase the vulnerability of the urban area to extreme weather related events (Parikh, Jindal, & Sandal, 2013;Revi, 2008;Suriya & Mudgal, 2012).…”