Identyfikatory
Warianty tytułu
Języki publikacji
Abstrakty
At present, the climate has constantly been changing, especially the increase in global average temperature that results in the risk of severe climatic conditions such as heat wave, drought and flood. The objective of this study is to estimate land surface temperature (LST) by applying Landsat satellite data in Mueang Maha Sarakham District, Maha Sarakham Province, Thailand. The study focuses on investigating the temperature changes for the years 2006 and 2015. The research was conducted by analyzing the satellite data in the thermal infrared band with a geo-informatics package software mutually with mathematical models. The operation results indicated that the average LST was at 26.28°C in 2006 and 27.15°C in 2015. In order to verify the accuracy of the data in this study, the results of the annual satellite data analysis were brought to find out a statistical correlation with the LST data from the Meteorological Station of Thai Meteorological Department (TMD). The results indicated that there was a correlation of the data at a high level in 2006 and 2015. The results of this study indicated that the satellite data analysis method is reliable and can be used to analyze, track, and verify data to predict surface temperatures effectively.
Słowa kluczowe
Rocznik
Tom
Strony
401--409
Opis fizyczny
Bibliogr. 31 poz., rys., tab., wykr.
Twórcy
autor
- Mahasarakham University, Faculty Science, Department of Physics Khamriang, Kantarawichai Maha Sarakham 44150, Thailand
autor
- Mahasarakham University, Faculty Science, Department of Physics Khamriang, Kantarawichai Maha Sarakham 44150, Thailand
Bibliografia
- Asaeda, T., Ca, V.T. & Wake, A. (1996). Heat Storage of Pavement and its Effect on the Lower Atmosphere. Atmospheric Environment, 30(3), 413-427.
- Barsi, J.A., Schott, J.R., Hook, S.J., Raqueno, N.G., Markham, B.L. & Radocinski, R.G. (2014). Landsat-8 thermal infrared sensor (TIRS) vicari ous radiometric calibration. Remote Sensing, 6(11), 11607-11626.
- Blackett, M. (2014). Early analysis of Landsat-8 thermal infrared sensor imagery of volcanic activity. Remote Sensing, 6(3), 2282-2295.
- Chander, G. & Markham, B.L. (2003). Revised Landsat-5 TM Radiometric Calibration procedures, and post-calibration dynamic ranges. IEEE Transactions on Geoscience and Remote Sensing, 41, 2674-2677.
- Charoentrakulpeeti, W. (2012). Impact of Land Cover on Atmospheric Temperature in Bangkok. NIDA Journal of Environmental Management, 8(1), 1-18.
- Chen, F., Yang, S., Yin, K. & Chan, P. (2017). Challenges to quantitative applications of Landsat observations for the urban thermal environment. Journal of Environmental Sciences, 59, 80-88.
- Cole, D., Dietsch, N., Gero, G., Hitchcock, D., Lewis, M. & Eliasson, I. (1996). Urban noctural temperatures, street geometry and land use. Atmospheric Environment, 30(3), 379-392.
- Kataoka, K., Matsumoto, F., Ichinose, T. & Taniguchi, M. (2009). Urban warming trends in several large Asian cities over the last 100 years. Science of Total Environment, 407(9), 3112-3119.
- Lagouarde, J.P., Bach, M., Sobrino, J.A., Boulet, G., Briottet, X., Cherchali, S. & Hagolle, O. (2013). The MISTIGRI thermal infrared project: scientific objectives and mission specifications. International Journal of Remote Sensing, 34(9-10), 3437-3466.
- Laosuwan, T., Gomasathit, T. & Rotjanakusol, T. (2017). Application of remote sensing for temperature monitoring: the technique for land surface temperature analysis. Journal of Ecological Engineering, 18(3), 53-60.
- Laosuwan, T., Sangpradid, S., Gomasathit, T. & Rotjanakusol, T. (2016). Application of Remote Sensing Technology for Drought Monitoring in Mahasarakham Province, Thailand. International Journal of Geoinformatics, 12(3), 17-25.
- Li, Z., Wu, H., Wang, N., Qiu, S., Sobrino, J.A., Wan, Z., Tang, B. & Yan, G. (2013). Land Surface Emissivity Retrieval from Satellite Data. Internarnational Journal of Remote Sensing, 34, 3084-3127.
- Liang, S. (2004). Quantitative remote sensing of land surface. Hoboken NJ, Wiley: Interscience.
- Mallick, J., Kant, Y. & Bharath, B. (2008). Estimation of land surface temperature over Delhi using Landsat-7 ETM+. Journal of Indian Geophysical Union, 12(3), 131-140.
- Oke, T.R. (1997). Urban Climates and Global Environmental Change. In R.D. Thompson & A. Perry (eds.), Applied Climatology: Principles & Practices (pp. 273-287). New York NY: Routledge.
- Mirzaei, P.A. & Haghighat, F. (2010). Approaches to study Urban Heat Island – Abilities and limitations. Building and Environment, 45(10), 2192-2201.
- Peebkhunthod, U., Chunpang, P. & Laosuwan, T. (2018). Application of Landsat Data for Detecting Land Surface Temperature in Mueang Maha Sarakham District, Maha Sarakham Province. Journal of Science and Technology MSU, 37(1), 130-135.
- Qureshi, S., Breuste, J.H. & Lindley, S.J. (2010). Green Space Functionality Along an Urban Gradient in Karachi, Pakistan: A Socio-Ecological Study. Human Ecology, 38(2), 283-294.
- Rajeshwari, A. & 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.
- Reuter, D.C., Richardson, C.M., Pellerano, F.A., Irons, J.R., Allen, R.G., Anderson, M. & Tesfaye, Z. (2015). The Thermal Infrared Sensor (TIRS) on Landsat 8: Design overview and pre-launch characterization. Remote Sensing, 7(1), 1135-1153.
- Rozenstein, O., Qin, Z., Derimian, Y. & Karnieli, A. (2014). Derivation of Land Surface Temperature for Landsat-8 TIRS Using a Split Window Algorithm. Sensors, 14(4), 5768-5780.
- Schott, J., Gerace, A., Brown, S., Gartley, M., Montanaro, M. & Reuter, D.C. (2012). Simulation of image performance characteristics of the Landsat data continuity mission (LDCM) thermal infrared sensor (TIRS). Remote Sensing, 4(8), 2477-2491.
- Sobrino, J.A. & Raissouni, N. (2000). Toward remote sensing methods for land cover dynamic monitoring: Application to Morocco. International Journal of Remote Sensing, 21(2), 353-366.
- 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.
- United States Geological Survey – USGS (2018). Landsat 7 Data Users Handbook – Appendix B. Retrieved from: https://landsat.usgs.gov/landsat-7-data-users-handbook-appendix-b.
- Uttaruk, Y. & Laosuwan, T. (2017). Drought Detection by Application of Remote Sensing Technology and Vegetation Phenology. Journal of Ecological Engineering, 18(6), 115-121.
- Uttaruk, Y., Rotjanakusol, T. & Laosuwan, T. (2018). Above ground carbon biomass assessment using satellite remote sensing reflection values. Research & Knowledge, 41(1), 41-46.
- Vlassova, L., Perez-Cabello, F., Nieto, H., Martín, P., Riańo, D. & de la Riva, J. (2014). Assessment of Methods for Land Surface Temperature Retrieval from Landsat-5 TM Images Applicable to Multiscale Tree-Grass Ecosystem Modeling. Remote Sensing, 6(5), 4345-4368.
- Wang, S., He, L. & Hu, W. (2015). A Temperature and Emissivity Separation Algorithm for Landsat-8 Thermal Infrared Sensor Data. Remote Sensing, 7(8), 9904-9927.
- Wong, N.H. & Yu, C. (2005). Study of green areas and urban heat island in a tropical city. Habitat In ternational, 29(3), 547-558.
- Zhou, J., Chen, Y.H., Wang, J.F. & Zhan, W.F. (2011). Maximum nighttime urban heat island (UHI) in tensity simulation by integrating remotely sensed data and meteorological observations. IEEE Journal of Selected Topics in Applied Earth Observations, 4, 138-146.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-fb692088-bed7-4cf7-9130-61cf423b0a6c