Spatio-Temporal Urban Heat Island Phenomena Assessment using Landsat Imagery: A Case Study of Bangkok Metropolitan and its Vicinity, Thailand
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Published:
Apr 3, 2018
Keywords:
Land surface temperature (LST) Urban heat island (UHI) WAI URI TGCI Bangkok metropolitan and its vicinity (BMV)
Main Article Content
Abstract
Applications of LST data to advanced research on UHI phenomena and its intensity are still relatively low in Thailand. The main objectives of this study are (1) to extract and predict LST data associated with urban and non-urban areas from Landsat imageries and (2) to quantify the intensity of UHI phenomena and its changes over BMV between 2006 and 2026. The research methodology was conducted systematically to extract and predict the LST associated with the urban and non-urban areas in order to assess the intensity of UHI phenomena. The results show that WAI as UHI intensity is extremely critical between 2006 and 2022 and becomes critically severe during 2024 and 2026. The result also show that URI as a degree of UHI development has increased from 2010 to 2016, however, it will suddenly decrease in 2018 and continuously increase between 2020 and 2026. In addition, TGCI analysis indicates that a decreasing temperature trend is dominant in the existing urban areas while an increasing temperature trend shows remarkably in urban expansion areas. These findings confirm the impacts of urbanization and urban development state on UHI intensity. In conclusion, the approaches and results of this study can be applied to master the urban planning properly, especially the mitigation of UHI phenomena in the future.
Article Details
How to Cite
Ongsomwang, S., Dasananda, S., & Prasomsup, W. (2018). Spatio-Temporal Urban Heat Island Phenomena Assessment using Landsat Imagery: A Case Study of Bangkok Metropolitan and its Vicinity, Thailand. Environment and Natural Resources Journal, 16(2), 29–44. Retrieved from https://ph02.tci-thaijo.org/index.php/ennrj/article/view/117655
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Original Research Articles
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References
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Weng Q, Yang S. Urban air pollution patterns, land use, and thermal landscape: an examination of the linkage using GIS. Environmental Monitoring and Assessment 2006;117:463-89.
Xu H, Chen Y, Dan S, Qiu W. Dynamical monitoring and evaluation methods to urban heat island effects based on RS&GIS. Procedia Environmental Sciences 2011;10:1228-37.
Xu HQ, Chen, BQ. Remote sensing of the urban heat island and its changes in Xiamen City of SE China. Journal of Environmental Science 2004;16:276-81.
Zha Y, Gao J, Ni S. Use of normalized difference built-up index in automatically mapping urban areas from TM imagery. International Journal of Remote Sensing 2003;24(3):583-94.
Zheng S, Zhou X, Singh RP, Wu Y, Ye Y, Wu C. The spatiotemporal distribution of air pollutants and their relationship with land-use patterns in Hangzhou city, China. Atmosphere 2017;8(6):110.
Artis DA, Carnahan WH. Survey of emissivity variability in thermography of urban areas. Remote Sensing of Environment 1982;12(4):313-29.
Chang HT. A temporal and spatial analysis of urban heat island in basin city utilizing remote sensing techniques. Proceeding of the International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences; 2016 July 12-19; Prague, Czech Republic; 2016.
Chowdhury EH, Hassan QK. Operational perspective of remote sensing-based forest fire danger forecasting systems. ISPRS Journal of Photogrammetry and Remote Sensing 2015;104:224-36.
Dan S, Xu H, Xue W, He J, Dan B. Comparison and analysis of research methods for urban heat island effects based on Landsat TM6. Proceedings of 2010 Second IITA International Conference on Geoscience and Remote Sensing; 2010 Aug 28-31; Qingdao: China; 2010.
Dontree S. Relation of land surface temperature (LST) and land use/land cover (LULC) from remotely sensed data in Chiang Mai-Lamphun Basin. Proceeding of the SEAGA Conference; 2010 Nov 23-26; Hanoi, Vietnam; 2010.
Estoque RC, Murayama Y, Myint SW. Effects of landscape composition and pattern on land surface temperature: an urban heat island study in the megacities of Southeast Asia. Science of the Total Environment 2017;577:349-59.
Fang G. Prediction and analysis of urban heat island effect in Dangshan by remote sensing. International Journal on Smart Sensing and Intelligent Systems 2015;8(4): 2195-211.
Fitzpatrick-Lins K. The Accuracy of Selected Land Use and Land Cover Maps at Scales of 1:250,000 and 1:100,000. U.S. Govt. Print. Off.; 1980.
He C, Shi P, Xie D, Zhao Y. Improving the normalized difference built-up index to map urban built-up areas using a semiautomatic segmentation approach. Remote Sensing Letters 2010;1(4):213-21.
Jeevalakshmi D, Narayana Reddy S, Manikiam B. Land surface temperature retrieval from LANDSAT data using emissivity estimation. International Journal of Applied Engineering Research 2017;12(20):9679-87.
Jensen JR. Remote Sensing of the Environment: An Earth Resource Perspective. 2nd ed. New Jersey: United States: Pearson Prentice Hall; 2017.
Kamusoko C, Aniya M, Adi B, Manjoro M. Rural sustainability under threat in Zimbabwe - Simulation of future land use/cover changes in the Bindura district based on the Markov-cellular automata model. Applied Geography 2009;29:435-47.
Kikon N, Singh P, Singh SK, Vyas A. Assessment of urban heat islands (UHI) of Noida city, India using multi-temporal satellite data. Sustainable Cities and Society 2016;22:19-28.
Land Development Department. Land Use Data in Thailand: Land Use in the Central Region. [Internet]. 2016 [cited 2018 Mar 5]. Available from: http://www1.ldd.go.th/WEB_OLP/report_research_C.html
Lim HS, MatJafri MZ, Abdullah K, Wong CJ. Air pollution determination using remote sensing technique. In: Jedlovec G, editor. Advances in Geoscience and Remote Sensing. InTech: Croatia; 2009. p. 71-92.
Mendelsohn R, Kurukulasuriya P, Basist A, Kogan F, Williams C. Climate analysis with satellite versus weather station data. Climatic Change 2007;81:71-83.
Office of the National Economic and Social Development Board. [Internet]. 2016 [cited 2018 Mar 5]. Available from:http://www.nesdb.go.th/nesdb_en/more_news.php?cid= 156&filename=index.
Ongsomwang S, Ruthamnong S. Burned area assessment using BAMS: a case study of upper northern region, Thailand. Suranaree Journal of Science and Technology 2017;24(3):327-42.
Ongsomwang S, Saravisutra A. Optimum predictive model for urban growth prediction. Suranaree Journal of Science and Technology 2011;18(2):141-52.
Paegelow M, Olmedo MTC. Possibilities and limits of prospective GIS land cover modelling-a compared case study: Garrotxes (France) and Alta Alpujarra Granadina (Spain). International Journal of Geographical Information Science 2005;19(6):697-722.
Potapov P, Hansen MC, Stehman SV, Loveland TR, Pittman K. Combining MODIS and landsat imagery to estimate and map boreal forest cover loss. Remote Sensing of Environment 2008;112:3708-19.
Qiao Z, Tian T, Zhang L, Xu X. Influences of urban expansion on urban heat island in Beijing during 1989-2010. Advances in Meteorology 2014;2014:11.
Roth M. Urban heat islands. In: Fernando HJS, editor. Handbook of environmental fluid dynamics Volume 2: Systems, Pollution, Modeling, and Measurements. New York: Taylor & Francis Group; 2013. p. 143-59.
Sang L, Zhang C, Yang J, Zhu D, Yun W. Simulation of land use spatial pattern of towns and villages based on CA-Markov model. Mathematical and Computer Modelling 2011;54:938-43.
Singh P, Kikon N, Verma P. Impact of land use change and urbanization on urban heat islands in Lucknow city, central India: a remote sensing based estimate. Sustainable Cities and Society 2017;32:100-14.
Sobrino JA, Jiménez-Mun῀oz JC, Paolini L. Land surface temperature retrieval from LANDSAT TM 5. Remote Sensing of Environment 2004;90:434-40.
Srivanit M, Kazunori H, Vivarad P. Assessing the impact of urbanization on urban thermal environment: a case study of Bangkok metropolitan. International. Journal of Applied Science and Technology 2012;2(7):243-56.
Sukthong P. Thermal Remote Sensing Application on Urban Heat Island Case Study: Pathumthani Urban Areas. [Dissertation]. Thammasat University; 2008.
Suwanprasit C. Pattern of land-use change and urban heat island during 20 years in Chiang Mai, Thailand. Proceedings of 17th the International Multidisciplinary Scientific Geo Conference; 2017 June 29 - July 5; Albena: Bulgaria; 2017.
Thai Meteorological Department (TMD). The Climate of Thailand. [Internet]. 2015 [cited 2016 Dec 15]. Available from: https://www.tmd.go.th/en/archive/ thailand_climate.pdf
Tran H, Uchihama D, Ochi S, Yasuoka Y. Assessment with satellite data of the urban heat island effects in Asian mega cities. International Journal of Applied Earth Observation and Geoinformation 2006;8(1):34-48.
United States Geological Survey (USGS). Landsat 8 (L8) data user handbook [Internet]. 2016 [cited 2016 Jan 14]. Available from: http://landsat.usgs.gov/ documents/Landsat8 DataUsers Handbook.pdf.
Wang M, Zhang Z, He G, Wang G, Long T, Peng Y. An enhanced single-channel algorithm for retrieving land surface temperature from Landsat series data. Journal of Geophysical Research: Atmospheres 2016;121(19): 11,712-22.
Weng Q, Yang S. Urban air pollution patterns, land use, and thermal landscape: an examination of the linkage using GIS. Environmental Monitoring and Assessment 2006;117:463-89.
Xu H, Chen Y, Dan S, Qiu W. Dynamical monitoring and evaluation methods to urban heat island effects based on RS&GIS. Procedia Environmental Sciences 2011;10:1228-37.
Xu HQ, Chen, BQ. Remote sensing of the urban heat island and its changes in Xiamen City of SE China. Journal of Environmental Science 2004;16:276-81.
Zha Y, Gao J, Ni S. Use of normalized difference built-up index in automatically mapping urban areas from TM imagery. International Journal of Remote Sensing 2003;24(3):583-94.
Zheng S, Zhou X, Singh RP, Wu Y, Ye Y, Wu C. The spatiotemporal distribution of air pollutants and their relationship with land-use patterns in Hangzhou city, China. Atmosphere 2017;8(6):110.