Intensity and Pattern of Land Surface Temperature in Hat Yai City, Thailand

Poonyanuch RUTHIRAKO; Rotchanatch DARNSAWASDI; Wichien CHATUPOTE

Authors

Poonyanuch RUTHIRAKO Faculty of Environmental Management, Prince of Songkla University, Songkhla 90110
Rotchanatch DARNSAWASDI Faculty of Environmental Management, Prince of Songkla University, Songkhla 90110
Wichien CHATUPOTE Faculty of Natural Resources, Prince of Songkla University, Songkhla 90110

Keywords:

LST, NDVI, NDBI

Abstract

Land Surface Temperature (LST) is an important factor in global climate. LST is governed by surface heat fluxes, which are affected by urbanization. In order to understand urban climate, LST needs to be examined. This study aimed to investigate the intensity and pattern of LST and examine the relationships between LST and the characteristics of urban land use, indices, and population density in Hat Yai City. Landsat 5TM images were used for interpretation of land use characteristics and derivation of LST, normalized difference built-up index (NDBI) and normalized vegetation index (NDVI). The characteristics of land use were classified into 4 types: commercial/high density residential, medium density residential, minimum density residential and vegetation cover/park. The average maximum and minimum LST derived from Landsat 5TM were 25.9, 33.7 and 15.8 °C, respectively. The areas with high LST were located principally in central built-up areas, slightly northwest-southeast of the study area, including the commercial center and the newly expanded residential areas. The LST pattern was well related to land use types and population density. The relationship between LST and NDVI however portrayed negative correlation, while that between LST and NDBI highlighted a positive correlation. It is concluded that NDVI and NDBI can be used to evaluate the risk of Urban Heat Island (UHI) and may help city managers better prepare for possible impacts of climate change.

doi:10.14456/WJST.2015.7

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Author Biographies

Poonyanuch RUTHIRAKO, Faculty of Environmental Management, Prince of Songkla University, Songkhla 90110

Faculty of Environmental Management

Wichien CHATUPOTE, Faculty of Natural Resources, Prince of Songkla University, Songkhla 90110

Faculty of Natural Resources

References

I Douglas. The case for urban ecology. Urban Nat. Mag. 1992; 1, 15-7.

HA Choi, WK Lee and WH Byun. Determining the effect of green spaces on urban heat distribution using satellite imagery. Asian J. Atmos. Environ. 2012; 6, 127-35.

OR García-Cueto, AT Martínez and GB Morales. Urbanization effects upon the air temperature in Mexicali, B.C., México. Atmósfera 2009; 22, 349-65.

KS Kumar, PU Bhaskar and K Padmakumari. Estimation of land surface temperature to study urban heat island effect using landsat ETM+image. Int. J. Eng. Sci. Tech. 2012; 4, 771-8.

AM Shabeer and D Mehesha. Estimation of land surface temperature in urbanized area and microclimate change using spatial technique - A MODIS BASEDA approach for Cochin City. Papeles de Geografia 2011; 53-54, 241-5.

JA Voogt and TR Oke. Thermal remote sensing of urban climates. Rem. Sens. Environ. 2003; 86, 370-84.

M Bobrinskaya. 2012, Remote Sensing for Analysis of Relationships between Land cover and Land Surface Temperature in Ten Megacities. Master of Science thesis in Geoinformatics. Royal Institute of Technology (KTH), Stockholm, Sweden.

Q Weng. Thermal infrared remote sensing for urban climate and environmental studies: Methods, applications, and trends. J. Photogram. Rem. Sens. 2009; 64, 335-44.

KC Tan, HS Lim, MZ MatJafri and K Abdullah. Landsat data to evaluate urban expansion and determine land use/land cover changes in Penang Island, Malaysia. Environ. Earth Sci. 2010; 60, 1509-21.

KP Gallo and JD Tarpley. The comparison of vegetation index and surface temperature composites for urban heat island analysis. Int. J. Rem. Sens. 1996; 17, 3071-6.

H Kato. A statistical method for separating urban effect trends from observed temperature data and it’s an application to Japanese temperature records. J. Meteorol. Soc. Jpn. 1996; 74, 639-53.

BC Mitchell. 2011, Urbanization and Land Surface Temperature in Pinellas County, Florida, Master of Arts Dissertation. University of South Florida, Florida, United State of America.

Z Shuzhen and S Tong. Urban Climatology. Meteorological Press, Beijing, 1994. p. 1-618.

OR García-Cueto, E Jáuregui-Ostos, D Toudert and A Tejeda-Martinez. Detection of the urban heat island in Mexicali, B.C., Mexico and its relationship with land use. Atmósfera 2007; 20, 111-31.

Q Sun, J Tan and Y Xu. An ERDAS image processing method for retrieving LST and describing urban heat evolution: a case study in the Pearl River Delta Region in South China. Environ. Earth Sci. 2010; 59, 1047-55.

JA Voogt. Urban Heat Islands: Hotter Cities. Available at: http://www.actionbioscience.org/ environment/voogt.html, accessed November 2013.

RL Wilby. Constructing climate change scenarios of urban heat island intensity and air quality. Environ. Plann. B 2008; 35, 902-19.

H Shudo, J Sugiyama, H Yokoo and J Oka. A study on temperature distribution influenced by various land uses. Energ. Build. 1997; 26, 199-205.

WH Carnahanand and RC Larson. An analysis of an urban heat sink. Rem. Sens. Environ. 1990; 33, 65-71.

JA Sobrino, JC Jiménez-Munoz and L Paolini. Land surface temperature retrieval from Landsat TM5. Rem. Sens. Environ. 2004; 90, 434-40.

KP Gallo, TW Owen, DR Easterling and PF Jameson. Temperature trends of the U.S. Historical Climatology Network based on satellite-designated land use/land cover. J. Climate 1999; 12, 1344-8.

WS Lee, KT Kim, HK Park, SG Jung, JH You, JH Oh and YC Yoon. Effects of spatial characteristics on the Urban thermal environment in Changwon, Korea. In: Proceeding of the 7th International Conference on Urban Climate, Yokohama, Japan, 2009.

Y Zha, J Gao and S Ni. Use of normalized difference built-up index in automatically mapping urban areas from TM imagery. Int. J. Rem. Sens. 2003; 24, 583-659.

Y Chen, J Wang and X Li. A study on urban thermal field in summer based on satellite remote sensing. Rem. Sens. Land Resour. 2002; 4, 55-9.

Q Weng. A remote sensing-GIS evaluation of urban expansion and its impact on surface temperature in Zhujiang Delta, China. Int. J. Rem. Sens. 2001; 22, 1999-2014.

M Srivanit, K Hokao and V Phonekeo. Assessing the impact of urbanization thermal environment: A case study of Bangkok Metropolitan. Int. J. Appl. Sci. Tech. 2012; 2: 243-56.

CY Sun, HT Lin and WS Ou. The relationship between urban greening and thermal environment. In: Proceeding of Urban Remote Sensing Joint Event, Paris, France. 2007, p. 1-6.

L Liu and Y Zhang. Urban heat island analysis using the landsat TM data and ASTER data: A case study in Hong Kong. Rem. Sens. 2011; 3, 1535-52.

TA Schroeder, WB Cohen, C Song, MJ Canty and Z Yang. Radiometric correction of multi-temporal Landsat data for characterization of early successional forest patterns in western Oregon. Rem. Sens. Environ. 2006; 103, 16-26.

G Chanderand and B Markham. Revised Landsat-5 TM radiometric calibration procedures and post calibration dynamic ranges. IEEE Trans. Geosci. Rem. Sens. 2003; 41, 2674-7.

J Cristóbal, JC Jiménez-Muñoz, JA Sobrino, M Ninyerola and X Pons. Improvements in land surface temperature retrieval from the Landsat series thermal band using water vapor and air temperature. J. Geophys. Res. 2009; 114, 1-16.

TS Purevdorj, R Tateishi, T Ishiyama and Y Honda. Relationships between percent vegetation cover and vegetation indices. Int. J. Rem. Sens. 1998; 19, 3519-35.

Z Phithakkumpol. Migration and the Violence in the Far South. In: C Satha-Anand (ed.). Imagined and Bangkok, Matichon, 2009.

Q Weng, D Lu and J Schubring. Estimation of Land surface temperature - vegetation abundances relationship for urban heat island studies. Rem. Sens. Environ. 2004; 89, 467-83.

ME Baris, S Sahin and ME Yazgan. The contribution of trees and green spaces to the urban climate: The case of Ankara. Afr. J. Agr. Res. 2009; 4, 791-800.