Urbanization and Land Surface Temperature Dynamics in Tangail Pourashava: A Spatiotemporal Study
DOI:
https://doi.org/10.69728/jst.v11.77Keywords:
Urbanization, Land Surface Temperature (LST), Land Use and Land Cover (LULC), Spatiotemporal Analysis, Geographic Information System (GIS), Remote SensingAbstract
In this study, the researchers analyzed the changes in urbanization and Land Surface Temperature (LST) in Tangail Paurashava from 2003 to 2023. With the help of Landsat data and GIS tools, this study evaluates changes in LULC, checks temperature variations, and explores how they are linked. It was noted that the inhabited areas grew 58% more, but agricultural and vegetated lands shrank almost one-third and one-fifth each. Meanwhile, the LST increased from 29°C to 33°C , with the LST closely linked to urban built up areas and bare land and negatively connected to vegetated, agricultural, and water areas. The directional growth of cities in this area is most noticeable to the northeast and southwest, caused by roads, real estate markets, and other factors. It has been confirmed that there is a growing UHI effect, putting stress on cities' environment and people's health. The results highlight the importance of planning, building green areas, and enforcing zoning rules in expanding secondary cities to combat climate change. Together, these approaches provide a reliable method for assessing urban environmental changes and prepare these regions to tackle climate change.
References
Anselin, L. (1995). Local indicators of spatial association—LISA. Geographical Analysis, 27(2), 93–115. https://doi.org/10.1111/j.1538-4632.1995.tb00338.x
BBS. (2023). Statistical Yearbook of Bangladesh 2023. Bangladesh Bureau of Statistics.
Batty, M. (2007). Cities and complexity: Understanding cities with cellular automata, agent-based models, and fractals. MIT Press.
Bonna, S., & Akter, L. (2023). Socio-economic status of pineapple growers in Bangladesh: A study on Tangail district. Asian Journal of Social Sciences and Legal Studies, 5(3), 67–76.
Broto, V. CBroto, V. C. (2017). Urban governance and the politics of climate change. World Development, 93, 1–15. https://doi.org/10.1016/j.worlddev.2016.12.004
Cao, X., Weng, Q., & Wang, Y. (2019). Empirical analysis of urban expansion and its spatiotemporal patterns in China using remotely sensed data. Remote Sensing, 11(15), 1752. https://doi.org/10.3390/rs11151752
Chander, G., Markham, B. L., & Helder, D. L. (2009). Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Remote Sensing of Environment, 113(5), 893–903. https://doi.org/10.1016/j.rse.2009.01.007
Congalton, R. G. (1991). A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment, 37(1), 35–46. https://doi.org/10.1016/0034-4257(91)90048-B
Congalton, R. G., & Green, K. (2009). Assessing the accuracy of remotely sensed data: Principles and practices (2nd ed.). CRC Press.
Creswell, J. W., & Plano Clark, V. L. (2017). Designing and conducting mixed methods research (3rd ed.). SAGE Publications.
Dewan, A., Kiselev, G., Botje, D., Mahmud, G. I., Bhuian, M. H., & Hassan, Q. K. (2021). Surface urban heat island intensity in five major cities of Bangladesh: Patterns, drivers and trends. Sustainable Cities and Society, 71, 102926. https://doi.org/10.1016/j.scs.2021.102926
Fattah, M. A., Morshed, S. R., & Morshed, S. Y. (2021). Impacts of land use-based carbon emission pattern on surface temperature dynamics: Experience from the urban and suburban areas of Khulna, Bangladesh. Remote Sensing Applications: Society and Environment, 22, 100508. https://doi.org/10.1016/j.rsase.2021.100508
Foody, G. M. (2002). Status of land cover classification accuracy assessment. Remote Sensing of Environment, 80(1), 185–201. https://doi.org/10.1016/S0034-4257(01)00295-4
Foody, G. M. (2003). Remote sensing of tropical forest environments: Towards the monitoring of environmental resources for sustainable development. International Journal of Remote Sensing, 24(20), 4035–4046. https://doi.org/10.1080/0143116031000070949
Gao, C., Feng, Y., Wang, R., Lei, Z., Chen, S., Tang, X., & Xi, M. (2023). 50-year urban expansion patterns in Shanghai: Analysis using impervious surface data and simulation models. Land, 12(11), 2065. https://doi.org/10.3390/land12112065
IPCC. (2021). Climate change 2021: The physical science basis. Cambridge University Press. https://doi.org/10.1017/9781009157896
Imran, H. M., Hossain, A., Islam, A. S., Rahman, A., Bhuiyan, M. A. E., Paul, S., & Alam, A. (2021). Impact of land cover changes on land surface temperature and human thermal comfort in Dhaka city of Bangladesh. Earth Systems and Environment, 5, 667–693. https://doi.org/10.1007/s41748-021-00222-5
Islam, M. S., & Hasan, M. M. (2019). Urban land use change and temperature variations in Bangladesh. Environment and Urbanization ASIA, 10(1), 55–69. https://doi.org/10.1177/0975425319832431
Johansson, E., & Emmanuel, R. (2006). The influence of urban design on outdoor thermal comfort in the hot, humid city of Colombo, Sri Lanka. International Journal of Biometeorology, 51, 119–133. https://doi.org/10.1007/s00484-006-0047-6
Jones, T., & Clark, D. (2019). Participatory GIS and climate adaptation planning. Climate Policy, 19(2), 243–257. https://doi.org/10.1080/14693062.2018.1494530
Kim, T., & Noh, Y. (2024). Planning factors affecting carbon footprints of residents: Density, land use, and suburbanization. Environment and Planning B: Urban Analytics and City Science, 51(1), 157–173.
Liu, J., & Niyogi, D. (2019). Meta-analysis of urbanization impact on rainfall modification. Scientific Reports, 9(1), 7301. https://doi.org/10.1038/s41598-019-43507-1
Liu, X., Wang, N., Li, Z., Jia, R., & Qiao, Z. (2023). Research on time series and spatial gradient of urban heat island expansion from the perspective of urban renewal. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 16, 8680–8688.
Long, W., & Srihann, S. (2004, September). Land cover classification of SSC image: unsupervised and supervised classification using ERDAS Imagine. In IGARSS 2004. 2004 IEEE International Geoscience and Remote Sensing Symposium (Vol. 4, pp. 2707-2712). IEEE.
MODIS. (2023)MODIS. (2023). MODIS land products. NASA.
Mamun, S. A., Runa, H. A., Hoque, M. M. M., Sheikh, S., & Arif, R. H. (2018). Assessment of spatio-temporal changes of land use in Tangail Municipality using GIS. Journal of Environmental Science and Natural Resources, 11(1–2), 245–252.
Marolla, C. (2024). Urban climate health risks and resilience. American Journal of Preventive Medicine.
Mi, Z., Guan, D., Liu, Z., Liu, J., Viguié, V., Fromer, N., & Wang, Y. (2019). Cities: The core of climate change mitigation. Journal of Cleaner Production, 207, 582–589. https://doi.org/10.1016/j.jclepro.2018.10.034
Municipal Records. (2023). Tangail Pourashava urban development plan. (Unpublished report)
Panwar, M., & Jindal, S. (2024). Urban heat island and building energy consumption. IOP Conference Series: Earth and Environmental Science, 1326(1), 012080.
Rahman, M. A., Gain, A. K., & Giupponi, C. (2022). Mapping the urban heat island in a tropical city. Urban Climate, 41, 101066. https://doi.org/10.1016/j.uclim.2021.101066
Rashid, N., Alam, J. M., Chowdhury, M. A., & Islam, S. L. U. (2022). Impact of land use change and urbanization on urban heat island effect in Narayanganj city, Bangladesh: A remote sensing-based estimation. Environmental Challenges, 8, 100571. https://doi.org/10.1016/j.envc.2022.100571
Roy, S., Pandit, S., Eva, E. A., Bagmar, M. S. H., Papia, M., Banik, L., ... & Razi, M. A. (2020). Examining the nexus between land surface temperature and urban growth in Chattogram Metropolitan Area of Bangladesh using long term Landsat series data. Urban Climate, 32, 100593.
Sarker, B. C., Shutradhar, S. C., Khan, A., Saifullah, A. S. M., & Ruma, A. B. (2015). Urban growth and its impact on Tangail municipal area. Journal of Environmental Science and Natural Resources, 8(2), 163–166.
Sentinel-2. (2023)Sentinel-2. (2023). Copernicus Open Access Hub. European Space Agency. https://scihub.copernicus.eu
Smith, L., & Lee, D. (2021) Smith, L., & Lee, D. (2021). Urban temperature disparities and planning strategies. Landscape and Urban Planning, 208, 104038. https://doi.org/10.1016/j.landurbplan.2021.104038
Sobrino, J. A., Jimenez-Munoz, J. C., & Paolini, L. (2004). Land surface temperature retrieval from LANDSAT TM 5. Remote Sensing of Environment, 90(4), 434–440. https://doi.org/10.1016/j.rse.2004.02.003
Tusher, T. R., Zaman, M. R., & Nasrin, S. (2020). Exploring the growth dynamics and spatial pattern of Tangail Pourashava using geospatial tools. Journal of Geographic Research, 3(2), 101–113.
UN-Habitat. (2022). World cities report 2022: Envisaging the future of cities. United Nations Human Settlements Programme. https://unhabitat.org
Weng, Q., Lu, D., & Schubring, J. (2004). Estimation of land surface temperature–vegetation abundance relationship for urban heat island studies. Remote Sensing of Environment, 89(4), 467–483. https://doi.org/10.1016/j.rse.2003.11.005
Yan, Y., Ju, H., Zhang, S., & Jiang, W. (2019). Spatiotemporal patterns and driving forces of urban expansion in coastal areas: A study on urban agglomeration in the Pearl River Delta, China. Sustainability, 12(1), 191. https://doi.org/10.3390/su12010191
Yaun, F., Sawaya, K. E., Loeffelholz, B. C., & Bauer, M. E. (2005). Land cover classification and change analysis of the Twin Cities (Minnesota) metropolitan area by multitemporal Landsat remote sensing. Remote Sensing of Environment, 98(2–3), 317–328. https://doi.org/10.1016/j.rse.2005.08.006
Zhang, Y., Balzter, H., & Wang, C. (2017). Urban heat islands in global climate change. Environmental Research Letters, 12(9), 094011.
Zhou, D., Zhang, L., & Sun, G. (2017). Land cover changes and temperature rise in China. Remote Sensing, 9(4), 327. https://doi.org/10.3390/rs9040327
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