Estimation of the Relationship between Vegetation Pattern and Land Surface Temperature in Asansol Municipal Corporation
DOI:
https://doi.org/10.55863/ijees.2024.0062Abstract
Urbanization is the obvious reason for the fluctuation of land surface temperature phenomena that leads to a distressing urban environment. Urban vegetation has the potential to minimize the land surface temperature intensity. The study has investigated the relationship between Land Surface Temperature (LST) and vegetation patterns in AMC. The Landsat 5 TM and the Landsat 8 OLI satellite images for the years 1991 and 2021 have been considered for analysis. The four Landscape metrics including Class Area, Patch Density, Edge Density, and Mean Shape Index have been selected to determine the spatial pattern of green spaces. The correlation techniques have been applied to depict the association between the variables. The study has found that vegetation configuration has no significant relationship with LST within the aforesaid period. But the vegetation amount is slightly associated with LST in comparison to vegetation configuration. Therefore vegetation amount may play a crucial role to mitigate the LST phenomenon. However, the relationship is very complex and varies spatially and scale-wise.
References
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 :467–483.
Taha, H. 1997. Urban climates and heat islands: albedo, evapotranspiration, and anthropogenic heat. Energy and Building. 25: 99–103
Zhibin, R. Haifeng, Z. Xingyuan, H. Dan, Z. Xingyang, Y. 2014. Estimation of the relationship between urban vegetation configuration and land surface temperature with remote sensing. Journal of the Indian Society of Remote Sensing, 43(1): 89-100
Shashua-Bar, L. Hoffman, M. E. 2000. Vegetation as a climatic component in the design of an urban street-an empirical model for predicting the cooling effect of urban green areas with trees. Energy and Building. 31: 221–235.
Maroni, D. Cardoso, G.T. Neckel, A. Maculan, L.S. Oliveria, M.L.S. Bodah, E.T. Bodah, B.W. Santosh, M. 2021. Land surface temperature and vegetation index as a proxy to microclimate. Journal of Environmental Chemical Engineering.9
Chen, X. Zhao,H. Li, P.Yin, Z. 2006. Remote sensing image-based analysis of the relationship between urban heat island and land use/cover changes. Remote Sensing of Environment. 104(2): 133–146.
Raynolds, M.K. Comiso, J.C. Walker, D.A. Verbyla, D. 2008. Relationship between satellite-derived land surface temperatures, arctic vegetation types, and NDVI. Remote Sensing of Environment. 112: 1884–1894
Naeem, S. Cao, C. Qazi, W.A. Zamani, M. Wei, C. Acharya, B.K. Rehman, A.U. 2018. Studying the Association between Green Space Characteristics and Land Surface Temperature for Sustainable Urban Environments: An Analysis of Beijing and Islamabad. International Journal of Geo-Information. 7: 38
Landsberg, H.E. 1981. The Urban Climate. Academic Press. New York.
Guha, S. Govil, H. 2020. An assessment on the relationship between land surface temperature and normalized difference vegetation index. Environment, Development and Sustainability.
Barakat, A. Ouargaf, Z. Khellouk, Rida. Jazouli, Aafaf El. Touhami, F. 2019. Land Use/Land Cover Change and Environmental Impact Assessment in Beni‑Mellal District (Morocco) Using Remote Sensing and GIS. Earth Systems and Environment.
Zhang, F. Kung, H. Johnson V.C. LaGrone, B.L.Wang, J. 2017. Change Detection of Land Surface Temperature (LST) and some Related Parameters Using Landsat Image: a Case Study of the Ebinur Lake Watershed, Xinjiang, China. Wetlands.
Sun, Q. Wu, Z. Tan, J. 2012. The relationship between land surface temperature and land use/land cover in Guangzhou, China. Environ Earth Sci. 65: 1687–1694
Li, X. Zhou, W. Ouyang, Z. 2012. Spatial pattern of green space affects land surface temperature: evidence from the heavily urbanized Beijing metropolitan area, China. Landscape Ecology. 27: 887–898.
Turner, M.G. 1990. Spatial and temporal analysis of landscape patterns. Landscape Ecology. 4(1): 21–30
Zhang, X. Zhong, T. Feng, X. Wang, K. 2009. Estimation of the relationship between vegetation patches and urban land surface temperature with remote sensing. International Journal of Remote Sensing. 30(8): 2105–2118.
Turner, M. G. Robert, V. O’Neill, R. H. 1989. Effects of changing spatial scale on the analysis of landscape pattern. Landscape Ecology. 3: 153–162.
Hess, G. R. Bartel, R. A. Leidner, A. K. Rosenfeld, K. M. 2006. Effectiveness of biodiversity indicators varies with extent, grain, and region. Biological Conservation. 132(4): 448–457.
Weng, Q.- Liu, H.- Lu, D. 2007. Assessing the effects of land use and land cover patterns on thermal conditions using landscape metrics in city of Indianapolis, United States. Urban Ecosystem. 10(2): 203–219.
Chakravarti, J. 1998. Evolution of Asansol Durgapur Industrial Complex and its Impact on Regional Economy. Doctoral dissertation, University of Calcutta, Kolkata.
Chatterjee, S. Bisai, D. Khan, A. 2014. Detection of Approximate Potential Trend Turning Points in Temperature Time Series (1941-2010) for Asansol Weather Observation Station, West Bengal, India. Atmospheric and Climate Sciences. 4: 64-69.
Ghoshdastidar, M. 2011. An Interaction between Spatial and Economic Changes in a City: A Case Study of Asansol. Doctoral dissertation, University of Calcutta, Kolkata.
Government of West Bengal. 2015. Land-Use Development and Control Plan Vision 2025 for Asansol Subdivision: Final Report. Asansol: Asansol Durgapur Development Authority.
Asansol Municipal Corporation 2022. http://www.asansolmunicipalcorporation.com.
Das, S. 2018. Impact of Industrialization & Urbanization on the Environment of Asansol Durgapur planning Area. Doctoral dissertation Department of Geography, University of Calcutta.
Choudhury, D. Das, K. Das, A. 2019. Assessment of land use land cover changes and its impact on variations of land surface temperature in Asansol-Durgapur Development Region. The Egyptian Journal of Remote Sensing and Space Sciences. 22(2): 203–218.
Das, N. Mondal, P. Sutradhar, S. Ghosh, R. 2020. Assessment of variation of land use/land cover and its impact on land surface temperature of Asansol subdivision. The Egyptian Journal of Remote Sensing and Space Sciences.
Foody, G. M. 1992. On the compensation for chance agreement in image classification accuracy assessment. Photogrammetric Engineering and Remote Sensing. 58(10): 1459–1460.
Landis, J. R. Koch, G. G. 1977. The measurement of observer agreement for categorical data. Biometrics. 33(1): 159–174.
Young, N.E. Anderson, R.S. Chignel, S.M. Vorster, A.G.- Lawrence, R.- Evangelista1, P.H. 2017. A survival guide to Landsat preprocessing. Ecology. 98(4): 920–932
Sobrino, J.A. Jime´nez-Mun˜oza, J.C- Paolini, L. 2004. Land surface temperature retrieval from LANDSAT TM 5. Remote Sensing of Environment. 90, 434-440.
Cohen, J.A. 1960. Coefficient of agreement for nominal scales. Educ. Psychol. Meas, 20: 37–46.
McGarigal, Kevin.- Marks, B. J. 1995. Fragstats: spatial pattern analysis program for quantifying landscape structure.
Wu, J. 2013. Landscape Sustainability Science: Ecosystem Services and Human Well-Being in Changing Landscapes. Landscape Ecology. 28: 999-1023.
Amanollahi, J. Abdullah, A.H. Ramli, M.F. Pirasteh, S. 2012. Land surface temperature assessment in semi-arid residential area of Tehran, Iran using Landsat imagery. World Applied Sciences Journal. 20 (2): 319-326.
Zareie, S. Khosrav, H. Nasiri, A. Dastoran, M. 2016. Using Landsat Thematic Mapper (TM) sensor to detect change in land surface temperature in relation to land use change in Yazd, Iran .Solid Earth. 7: 1551–1564
Givoni, B. 1994. Urban design for hot humid region. Renewable Energy. 5: 1047-1053
Yokohari, M. Brown, R. Kato, Y. Moriyama, H. 1997. Effects of paddy fields on summertime air and surface temperatures in urban fringe areas of Tokyo, Japan. Landscape and Urban Planning. 38(1–2): 1–11.
Jasinski, M. F. 1990. Sensitivity of the normalized difference vegetation index to subpixel canopy cover, soil albedo, and pixel scale. Remote Sensing of Environment. 32: 169-187.
Siddique, G. Roy, A. Mandal, M.H. Ghosh, S. Basak, A. Singh, M. Mukherjee, N. 2020. An assessment on the changing status of urban green space in Asansol city, West Bengal. Geo Journal.
Akbar, T.A. Hassan, Q.K. Ishaq, S. Batool, M. Butt, H.J. Jabbar, H. 2019. Investigative Spatial Distribution and Modelling of Existing and Future Urban Land Changes and Its Impact on Urbanization and Economy. Remote sensing.
Esbah, H. 2009. Analyzing landscape change through landscape structure indices: case of the city of Aydin, Turkey. Journal of Applied Science. 9(15): 2744-2752
Small, C. 2001. Estimation of urban vegetation abundance by spectral mixture analysis. International Journal of Remote Sensing. 22: 1305– 1334.
Yang W, Yang L, Merchnat, J. W. 1997. An analysis of AVHRR/ NDVI-ecoclimatological relations in Nebraska, USA. International Journal of Remote Sensing. 18: 2161– 2180.
Ga_sparovi, M. 2020.Urban growth pattern detection and analysis. Urban Ecology. 35-48
About Asansol Municipal Corporation. 2017. https://web.archive.org/web/20170312080951/http://asansolmunicipalcorporation.org/over_view.php
Apperley, J. 2016. Emissivity. What is it and why is it so important? https://www.processparameters.co.uk/emissivity-what-is-it-and-why-is-it-so-important/
What is Emissivity?. https://www.flukeprocessinstruments.com/en-us/service-and-support/knowledge-center/infrared-technology/what-emissivity%3F
What are the band designations for the landsat satellites? https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites.
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