Keywords
Abstract
Rapid urbanization processes taking place in small and medium-sized Indian towns have influenced the local thermal conditions and atmospheric composition; however, empirical evidence on this subject is lacking. Within the framework of this research, co-evolution of the UHI intensity with carbon emissions in Jhargram Municipality, West Bengal (21.40 km²), for the years 2020 to 2025 was estimated. Multi-temporal Landsat 8 and Landsat 9 satellite imagery is utilized to calculate the LST, and the following biophysical surface indices were calculated along with identification of UHI intensity zones: Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), Built-up Index (BI) and Modified Normalized Difference Water Index (MNDWI). Simultaneously, air quality was assessed in 15 georeferenced points, and ambient CO₂, CO and surface temperature were recorded in early mornings in April 2020 and July 2025. In the examined period, the built-up area increased rapidly (+3.93 km²), expanding up to 47%; in contrast, vegetation cover decreased by 21% and water surfaces by nearly 49%. The land with LST > 28°C increased by 56% and correlated with the increase of Very High UHI concentration by 51%. The ambient concentration of CO₂ at the nodes increased from 340–360 ppm (2020) to 410–560 ppm (2025). There was a progressive increase in positive correlation (r) between UHI magnitude and built-up surface characteristics (NDBI: r = 0.21; BI: r = 0.24) in 2025, and a negative correlation with vegetation cover (NDVI: r = −0.22). The study thus highlights that there is a significant feedback relationship between changes in land use, surface temperature, and atmospheric CO₂ concentrations in an emerging semi-urban town located adjacent to ecologically fragile forest areas of India.
References
1) Akbari, H., Pomerantz, M. and Taha, H. (2001) ‘Cool surfaces and shade trees to reduce energy use and improve air quality in urban areas’, Solar Energy, 70(3), pp. 295–310.
2) Canty, M.J., Nielsen, A.A. and Schmidt, M. (2004) ‘Automatic radiometric normalization of multitemporal satellite imagery’, Remote Sensing of Environment, 91(3–4), pp. 441–451.
3) Chakraborty, P., Ghosh, T. and Basu, M. (2021) ‘Spatio-temporal analysis of land use change and its impact on LST in Midnapore Municipality’, GeoJournal, 86(6), pp. 2617–2630.
4) Choudhury, D., Das, K. and Das, A. (2019) ‘Assessment of land use land cover changes and its impact on variations of land surface temperature in Asansol–Durgapur Development Region’, Egyptian Journal of Remote Sensing and Space Sciences, 22(2), pp. 203–218.
5) Congalton, R.G. (1991) ‘A review of assessing the accuracy of classifications of remotely sensed data’, Remote Sensing of Environment, 37(1), pp. 35–46.
6) DDMA Jhargram (2022) District Environment and Climate Profile. Government of West Bengal.
7) Demirel, H., Ozcinar, C. and Anbarjafari, G. (2010) ‘Satellite image contrast enhancement using discrete wavelet transform and singular value decomposition’, IEEE Geoscience and Remote Sensing Letters, 7(2), pp. 333–337.
8) Forest Survey of India (2021) India State of Forest Report 2021. Ministry of Environment, Forest and Climate Change, Government of India.
9) Gill, S.E., Handley, J.F., Ennos, A.R. and Pauleit, S. (2007) ‘Adapting cities for climate change: The role of green infrastructure’, Built Environment, 33(1), pp. 115–133.
10) Grimm, N.B., Faeth, S.H., Golubiewski, N.E., Redman, C.L., Wu, J., Bai, X. and Briggs, J.M. (2008) ‘Global change and the ecology of cities’, Science, 319(5864), pp. 756–760.
11) Guan, D., Li, H., Inohae, T., Su, W., Nagaie, T. and Hokao, K. (2011) ‘Modeling urban land use change and its impact on surface temperature in the Fukuoka metropolitan area’, Landscape and Urban Planning, 100(1–2), pp. 28–42.
12) Guha, S., Govil, H. and Mukherjee, S. (2020) ‘Monitoring spatio-temporal urban expansion and land surface temperature in Bhubaneswar city’, Geocarto International, 35(2), pp. 173–195.
13) He, C., Shi, P., Xie, D. and Zhao, Y. (2010) ‘Improving the normalized difference built-up index to map urban built-up areas using a semiautomatic segmentation approach’, Remote Sensing Letters, 1(4), pp. 213–221.
14) India Meteorological Department (IMD) (2023) Climatological Normals: West Bengal and Jhargram District. Government of India.
15) IPCC (2021) Climate Change 2021: The Physical Science Basis. Cambridge: Cambridge University Press.
16) Kjellstrom, T., Freyberg, C., Lemke, B., Otto, M. and Briggs, D. (2016) ‘Estimating population heat exposure and impacts on working people in conjunction with climate change’, International Journal of Biometeorology, 60(1), pp. 79–91.
17) Kundu, S., Roy, A. and Ghosh, S. (2021) ‘Assessment of Urban Heat Island in Asansol Municipal Corporation using geospatial techniques’, Egyptian Journal of Remote Sensing and Space Sciences, 24(3), pp. 615–624.
18) Li, Z. and Zhou, B. (2019) ‘Mapping urban heat islands using quantile regression’, Urban Climate, 29, 100488.
19) Liang, S., Fang, H. and Chen, M. (2001) ‘Atmospheric correction of Landsat ETM+ land surface imagery’, IEEE Transactions on Geoscience and Remote Sensing, 39(11), pp. 2490–2498.
20) Mondal, S., Paul, A. and Das, A. (2019) ‘Urban expansion and its impact on land surface temperature in Durgapur, West Bengal’, Urban Climate, 29, 100505.
21) Narumi, D., Levinson, R. and Shimoda, Y. (2021) ‘Effect of urban heat island and global warming countermeasures on heat release and CO₂ emissions from a detached house’, Atmosphere, 12(5), 572.
22) Nichol, J.E. (1994) ‘A GIS-based approach to microclimate monitoring in Singapore’s high-rise housing estates’, Photogrammetric Engineering and Remote Sensing, 60(10), pp. 1225–1232.
23) Oke, T.R. (1982) ‘The energetic basis of the urban heat island’, Quarterly Journal of the Royal Meteorological Society, 108(455), pp. 1–24.
24) Patz, J.A., Campbell-Lendrum, D., Holloway, T. and Foley, J.A. (2005) ‘Impact of regional climate change on human health’, Nature, 438(7066), pp. 310–317.
25) Phiri, D., Simwanda, M., Salekin, S., Nyirenda, V.R., Murayama, Y. and Ranagalage, M. (2020) ‘Sentinel-2 data for land cover/use mapping: A review’, Remote Sensing, 12(14), 2291.
26) Radočaj, D., Obhodšaš, J., Rastija, M. and Jurišić, M. (2020) ‘Global open data remote sensing satellite missions for land use and land cover (LULC) classification’, Land, 9(11), 402.
27) Richter, R. (1997) ‘Correction of atmospheric and topographic effects for high spatial resolution satellite imagery’, International Journal of Remote Sensing, 18(5), pp. 1099–1111.
28) Sahoo, P. and Mahata, S. (2021) ‘A pandemic situation and positive impact on environment in 21st century: A case study in City of Nature, West Bengal, India’, International Journal of Advanced Research, 9(3), pp. 811–826.
29) Sahoo, P. and Mahata, S. (2021) ‘Geology and soil characteristics of Jhargram district, West Bengal’, Indian Journal of Geoscience, 75(2), pp. 88–97.
30) Sailor, D.J. (2011) ‘A review of methods for estimating anthropogenic heat and moisture emissions in the urban environment’, International Journal of Climatology, 31(2), pp. 189–199.
31) Santamouris, M. (2015) ‘Analyzing the heat island magnitude and characteristics in one hundred Asian and Australian cities and regions’, Science of the Total Environment, 512, pp. 582–598.
32) Seto, K.C., Güneralp, B. and Hutyra, L.R. (2012) ‘Global forecasts of urban expansion to 2030 and direct impacts on biodiversity and carbon pools’, Proceedings of the National Academy of Sciences, 109(40), pp. 16083–16088.
33) Sobrino, J.A., Jiménez-Muñoz, J.C. and Paolini, L. (2004) ‘Land surface temperature retrieval from LANDSAT TM 5’, Remote Sensing of Environment, 90(4), pp. 434–440.
34) Sun, Q., Wu, Z. and Tan, J. (2021) ‘The relationship between land surface temperature and land use/land cover in Guangzhou, China’, Environmental Earth Sciences, 65(6), pp. 1687–1694.
35) Tucker, C.J., Slayback, D.A., Pinzon, J.E., Los, S.O., Myneni, R.B. and Taylor, M.G. (2001) ‘Higher northern latitude NDVI and growing season trends from 1982 to 1999’, International Journal of Biometeorology, 45(4), pp. 184–190.
36) UN DESA (2018) World Urbanization Prospects: The 2018 Revision. New York: United Nations Department of Economic and Social Affairs.
37) Voogt, J.A. and Oke, T.R. (2003) ‘Thermal remote sensing of urban climates’, Remote Sensing of Environment, 86(3), pp. 370–384.
38) WHO (2018) Ambient (outdoor) air quality and health. World Health Organization. Available at: World Health Organization (Accessed: 8 September 2026).
39) Xu, H. (2006) ‘Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery’, International Journal of Remote Sensing, 27(14), pp. 3025–3033.
40) Young, N.E., Anderson, R.S., Chignell, S.M., Vorster, A.G., Lawrence, R. and Evangelista, P.H. (2017) ‘A survival guide to Landsat preprocessing’, Ecology, 98(4), pp. 920–932.
41) Yuan, F. and Bauer, M.E. (2007) ‘Comparison of impervious surface area and NDVI as indicators of surface urban heat island effects in Landsat imagery’, Remote Sensing of Environment, 106(3), pp. 375–386.
42) Zha, Y., Gao, J. and Ni, S. (2003) ‘Use of normalized difference built-up index in automatically mapping urban areas from TM imagery’, International Journal of Remote Sensing, 24(3), pp. 583–594.
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright (c) 2026 Prahallad Barman, Dr. Pronob Sahoo (Author)