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Application of Remote Sensing for Temperature Monitoring: the Technique for Land Surface Temperature Analysis

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Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
This research aimed to present the technique for land surface temperature analysis with the data from Landsat-8 Operational Land Imager (OLI) /Thermal Infrared Sensors (TIR) in Meuang Maha Sarakham District, Maha Sarakham Province, Northeast Thailand. The research was conducted as following three steps: 1) Collecting the satellite data in thermal infrared band from Landsat-8 TIR satellite to adjust the value of Top of Atmosphere (ToA) Reflectance and then analyzing the land Surface temperature 2) Collecting multi-band data from Landsat-8 OLI satellite to adjust the value of Top of Atmosphere (ToA) Reflectance and then analyzing values of Normalized Difference Vegetation Index (NDVI), Fractional Vegetation Cover (FVC) and Land surface Emissivity (LSE) 3) Bringing the results of 1) and 2) to analyze the land surface temperature with split window algorithm. The research results indicated that the analysis of the data from Landsat-8 OLI/TIR satellites in 18 March 2015 indicated a mean temperature of 33.57 °C.
Rocznik
Strony
53--60
Opis fizyczny
Bibliogr. 28 poz., rys., tab.
Twórcy
autor
  • Faculty of Science, Mahasarakham University, Katarawichai, Mahasarakham, 44150, Thailand
  • Space Technology and Geoinformatics Research Unit, Faculty of Science, Mahasarakham University, Mahasarakham, 44150, Thailand
  • Faculty of Science, Mahasarakham University, Katarawichai, Mahasarakham, 44150, Thailand
  • Space Technology and Geoinformatics Research Unit, Faculty of Science, Mahasarakham University, Mahasarakham, 44150, Thailand
  • Faculty of Science, Mahasarakham University, Katarawichai, Mahasarakham, 44150, Thailand
  • Space Technology and Geoinformatics Research Unit, Faculty of Science, Mahasarakham University, Mahasarakham, 44150, Thailand
Bibliografia
  • 1. Asaeda, T., Ca, V.T., and Wake, A. 1996. Heat Storage of Pavement and its Effect on the Lower Atmosphere. Atmospheric Environment, 30 (3), 413–427.
  • 2. Barsi, J.A., Schott, J.R., Hook, S.J., Raqueno, N.G., Markham, B.L., and Radocinski, R.G. 2014. Landsat-8 thermal infrared sensor (TIRS) vicarious radiometric calibration. Remote Sensing, 6 (11), 11607–11626.
  • 3. Campbell, J.B. 1996. Introduction to Remote Sensing. Taylor & Francis, London.
  • 4. David Cole, Nikolaas Dietsch, Gary Gero, David Hitchcock, Megan Lewis, Eliasson, I. 1996. Urban noctural temperatures, street geometry and land use. Atmospheric Environment, 30 (3), 379–392.
  • 5. François Becker and Zhao-Liang Li. 1990. Towards a local split window method over and surfaces. International Journal of Remote Sensing, 11 (3), 369–393.
  • 6. Fei Wang, Zhihao Qin, Caiying Song, Lili Tu, Arnon Karnieli and Shuhe Zhao. 2015. An improved mono-window algorithm for land surface temperature retrieval from landsat 8 thermal infrared sensor data. Remote Sensing, 7 (4), 4268–4289.
  • 7. Julie Magee, Misha Sarkovich, Jim Yarbrough, and Barry Zalph. 2015. Reducing Urban Heat Islands: Compendium of Strategies Heat Island Reduction Activities. https://www.epa.gov/sites/production/files/2014–06/documents/activitiescompendium.pdf [access 02/02/2015].
  • 8. Kimura, F., Takahashi, S. 1991. The effects of landuse and anthropogenic heating on the surface temperature in the Tokyo metropolitan area: a numerical experiment. Atmospheric Environment, 25 (2), 155–164.
  • 9. Liang, S. 2004. Quantitative remote sensing of land surface, New Jersey, Wiley: Interscience.
  • 10. Laosuwan, T., Sangpradid, S., Gomasathit, T. and Rotjanakusol, T. 2016. Application of Remote Sensing Technology for Drought Monitoring in Mahasarakham Province, Thailand. International Journal of Geoinformatics, 12 (3), 17–25.
  • 11. Rajeshwari, A. and Mani, N.D. 2014. Estimation of Land Surface Temperature of Dindigul District using Landsat 8 Data. International Journal of Research in Engineering and Technology, 3 (5), 122–126.
  • 12. Shahid Latif. 2014. Land Surface Temperature Retrival of Landsat-8 Data Using Split Window Algorithm- A Case Study of Ranchi District. International Journal of Engineering Development and Research, 2 (4), 2840–3849.
  • 13. Shaohua Zhao, Qiming Qin, Yonghui Yang, Yujiu Xiong and Guoyu Qiu. 2009. Comparison of two split-window methods for retrieving land Surface temperature from MODIS data. Journal of Earth Syst. Science, 118 (4), 345–353.
  • 14. Svensson, M.K. & Eliasson, I.E. 2002. Diurnal air temperatures in built-up areas in relation to urban planning. Landscape and Urban Planning, 61 (1): 37–54.
  • 15. Szymon Szewrański, Jan Kazak, Marta Szkaradkiewicz, Józef Sasik. 2015. Flood risk factors in suburban area in the context of climate change adaptation policies – Case study of Wroclaw, Poland. Journal of Ecological Engineering, 16 (2), 13–18.
  • 16. Taha, H. 1997. Urban climates and heat islands: Albedo, evapotranspiration, and anthropogenic heat. Energy and Buildings, 25 (2), 99–103.
  • 17. Teerawong Laosuwan and Pornchai Uttaruk. 2014. Estimating Tree Biomass via Remote Sensing, MSAVI 2, and Fractional Cover Model. IETE Technical Review, 31(5), 362–368.
  • 18. Teerawong Laosuwan and Yannawut Uttaruk. 2016. Estimating Above Ground Carbon Capture using Remote Sensing Technology in Small Scale Agroforestry Areas, Agriculture and Forestry, 62 (2), 253–262.
  • 19. Thai Meteorological Department. 2015. Weather Warning. https://www.tmd.go.th/en/index.php [access 07/03/2015].
  • 20. Ugur Avdan and Gordana Jovanovska. 2016. Algorithm for Automated Mapping of Land Surface Temperature Using LANDSAT 8 Satellite Data. Journal of Sensors. Article ID 1480307, 1–8.
  • 21. USGS. 2013. Using the USGS Landsat 8 Product http://landsat.usgs.gov/Landsat8UsingProduct.php [access 01/05/2013].
  • 22. Wanpen Charoentrakulpeeti. 2012. Impact of Land Cover on Atmospheric Temperature in Bangkok. NIDA Journal of Environmental Management, 8(1), 1–18.
  • 23. Watkins, R. 1999. The impact of the urban environment on the energy demand for cooling buildings. Unpublished report, Brunel University and the Building Research Establishment Ltd.
  • 24. Wong, N.H. and Yu, C. 2005. Study of green areas and urban heat island in a tropical city. Habitat International, 29 (3), 547–558.
  • 25. Xu, H.Q. and Chen, B.Q. 2004. Remote sensing of the urban heat island and its changes in Xiamen City of SE China. Journal of Environmental Sciences. 16 (2), 276–281.
  • 26. Yannawut Uttaruk and Teerawong Laosuwan. 2016. Remote sensing based vegetation indices for estimating above ground carbon sequestration in orchards. Agriculture and Forestry, 62 (4), 193–201.
  • 27. Yannawut Uttaruk and Teerawong Laosuwan. 2017. Carbon Sequestration Assessment of the Orchards Using Satellite Data. Journal of Ecological Engineering, 18 (1), 11–17.
  • 28. Zhou, J., Chen, Y.H., Wang, J.F., Zhan, W.F. 2011. Maximum nighttime urban heat island (UHI) intensity simulation by integrating remotely sensed data and meteorological observations. IEEE J. Sel. Top. Appl. Earth Observ, 4, 138–146.
Uwagi
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-0f5d07f4-d396-4e0f-aca5-aa12d0edabc6
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