Determination of land surface temperature using Landsat images (Case study: Bushehr coastal lands)
Subject Areas : Natural resources and environmental management
Fazel Amiri
1
(
Associate Professor, Department of Natural Resources and Environment, Bushehr Branch, Islamic Azad University, Bushehr, Iran
)
Tayebeh Tabatabaie
2
(
Associate Professor, Department of Environment, Bushehr Branch, Islamic Azad University, Bushehr, Iran
)
Keywords: Coastal land, Landsat Satellite, Stefan-Boltzmann method, Land surface temperature (LST),
Abstract :
Background and Objective Land surface temperature (LST) has become an important issue in the world today, as it affects the climate and environment at the local, regional and global levels, and these changes in land surface temperature are mainly caused by it arises from urbanization, and human activities and extreme Landuse and Land-cover (LULC) changes. Due to the limitations of meteorological stations, remote sensing can be used as the basis of many meteorological data. One of the most important practical aspects of remote sensing in climate studies is the estimation of surface temperature. In this research, the temperature of the earth's surface between 1990 and 2018 was extracted from the images of TM and OLI sensors of the coastal lands of Bushehr, using the Stefan-Boltzmann method.Materials and Methods The land study area of Bushehr city, which is on the northern coast of the Persian Gulf, with dimensions of 20×8 km2 an area of 1011.5 km2 and with an average minimum temperature of 18.1oC and an average maximum temperature of 33 oC, relative humidity between 58-75% and the average annual rainfall is 272 mm, it’s located in the geographical location of 50°50' to 10°51 E longitude and 28°40' to 29°00' N latitude. The data used in this research include; Landsat 8 (OLI) data in 2018 and TM data in 1990, which were downloaded from the United States Geological Survey (USGS) data center (https://earth explorer.usgs.gov). In order to calculate the parameters related to temperature extraction, the meteorological data of the synoptic stations located in the studied area were used. After taking the images, due to the larger range of the images, the images were cut (Resized) and then the geometric correction of the images was done using topographic maps on a scale of 1/25000 and all the images were adjusted to the UTM coordinate system of the 39 N were adapted. In geometric correction, the RMS error of all images was less than 0.5 pixels. In order to compare the results of Stefan-Boltzmann method for extracting LST with ground data, thermal map data obtained was compared with soil temperature data (obtained from meteorological stations in the selected area). In order to evaluate the Stefan-Boltzmann method from ground data, the Mean Absolute Error (MAE) index statistical method was used.Results and Discussion The average minimum and maximum Land surface temperature (LST) extracted from the 1990 TM image was 26.5 and 45 °C, respectively, and for the 2018 OLI image, it was 30.1 and 48.6 °C, respectively. The results showed that the Mean Absolute Error (MAE) index values for TM and OLI sensors are to 7.1 and 5.6, respectively. The results of the research showed that the Stefan-Boltzmann method provided a reliable result in estimating the Land surface temperature.Conclusion This research aims to extract LST by Stefan-Boltzmann method. The results of this method were estimated using the Mean Absolute Error (MAE) statistical index for the study period (1990-2018). Applying the MAE on the produced thermal maps, it was found that the Stefan-Boltzmann method is suitable for future research in the fields of thermal remote sensing by observing the results of using the MAE index on thermal maps. Therefore, we conclude that the Stefan-Boltzmann method is suitable for estimating the surface temperature of the land in coastal areas. Finally, it is suggested that for quantitatively describing LST patterns a GIS/RS-based method, and methods such as spatial autocorrelation and semivariance are used.
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Mia MB, Bromley CJ, Fujimitsu Y. 2013. Monitoring heat losses using Landsat ETM+ thermal infrared data: a Case study in Unzen Geothermal field, Kyushu, Japan. Pure and Applied Geophysics, 170(12): 2263-2271. doi:https://doi.org/10.1007/s00024-013-0662-1.
Mia MB, Nishijima J, Fujimitsu Y. 2014. Exploration and monitoring geothermal activity using Landsat ETM+images: A case study at Aso volcanic area in Japan. Journal of Volcanology and Geothermal Research, 275: 14-21. doi:https://doi.org/10.1016/j.jvolgeores.2014.02.008.
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Stathopoulou M, Cartalis C. 2007. Daytime urban heat islands from Landsat ETM+ and Corine land cover data: An application to major cities in Greece. Solar Energy, 81(3): 358-368. doi:https://doi.org/10.1016/j.solener.2006.06.014.
Sun Q, Wu Z, Tan J. 2012. The relationship between land surface temperature and land use/land cover in Guangzhou, China. Environmental Earth Sciences, 65(6): 1687-1694. doi:https://doi.org/10.1007/s12665-011-1145-2.
Voogt JA, Oke TR. 2003. Thermal remote sensing of urban climates. Remote Sensing of Environment, 86(3): 370-384. doi:https://doi.org/10.1016/S0034-4257(03)00079-8.
Wang J, Wang G, Liu Y, Qi J. 2021. Temporal normalization of land surface temperature retrieved from Landsat-8 data. In: 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS. IEEE, pp 6921-6924. doi:https://doi.org/6910.1109/IGARSS47720.42021.9553163.
Wang W, Liu K, Tang R, Wang S. 2019. Remote sensing image-based analysis of the urban heat island effect in Shenzhen, China. Physics and Chemistry of the Earth, Parts A/B/C, 110: 168-175. doi:https://doi.org/10.1016/j.pce.2019.01.002.
_||_Cai G, Du M, Xue Y. 2011. Monitoring of urban heat island effect in Beijing combining ASTER and TM data. International Journal of Remote Sensing, 32(5): 1213-1232. doi:https://doi.org/10.1080/01431160903469079.
Chen X, Zhang Y. 2017. Impacts of urban surface characteristics on spatiotemporal pattern of land surface temperature in Kunming of China. Sustainable Cities and Society, 32: 87-99. doi:https://doi.org/10.1016/j.scs.2017.03.013.
Chen X-L, Zhao H-M, Li P-X, Yin Z-Y. 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. doi:https://doi.org/10.1016/j.rse.2005.11.016.
Clinton N, Gong P. 2013. MODIS detected surface urban heat islands and sinks: Global locations and controls. Remote Sensing of Environment, 134: 294-304. doi:https://doi.org/10.1016/j.rse.2013.03.008.
Dashtakian K, Dehghani M. 2008. Land surface temperature analysis of desert area in relation with vegetation and urban development using RS and GIS (Case study: Yazd-Ashkezar area). Pajouhesh-va-Sazandegi, 20(4): 169-179. (In Persian).
Ebrahimi Heravi B, Rangzan K, Riahi Bakhtiari H, Taghizadeh A. 2015. Determination of urban surface temperature using landSat images (Case study: Karaj). Journal of RS and GIS for Natural Resources, 6(2): 19-32. https://girs.bushehr.iau.ir/article_516786.html?lang=en. (In Persian).
Gluch R, Quattrochi DA, Luvall JC. 2006. A multi-scale approach to urban thermal analysis. Remote Sensing of Environment, 104(2): 123-132. doi:https://doi.org/10.1016/j.rse.2006.01.025.
Imhoff ML, Zhang P, Wolfe RE, Bounoua L. 2010. Remote sensing of the urban heat island effect across biomes in the continental USA. Remote Sensing of Environment, 114(3): 504-513. doi:https://doi.org/10.1016/j.rse.2009.10.008.
Kumar KS, Bhaskar PU, Padmakumari K. 2012. Estimation of land surface temperature to study urban heat island effect using Landsat ETM+ image. International Journal of Engineering Science and Technology, 4(2): 771-778.
Mia MB, Bromley CJ, Fujimitsu Y. 2013. Monitoring heat losses using Landsat ETM+ thermal infrared data: a Case study in Unzen Geothermal field, Kyushu, Japan. Pure and Applied Geophysics, 170(12): 2263-2271. doi:https://doi.org/10.1007/s00024-013-0662-1.
Mia MB, Nishijima J, Fujimitsu Y. 2014. Exploration and monitoring geothermal activity using Landsat ETM+images: A case study at Aso volcanic area in Japan. Journal of Volcanology and Geothermal Research, 275: 14-21. doi:https://doi.org/10.1016/j.jvolgeores.2014.02.008.
Nichol JE, Fung WY, Lam K-s, Wong MS. 2009. Urban heat island diagnosis using ASTER satellite images and ‘in situ’ air temperature. Atmospheric Research, 94(2): 276-284. doi:https://doi.org/10.1016/j.atmosres.2009.06.011.
Pu R, Gong P, Michishita R, Sasagawa T. 2006. Assessment of multi-resolution and multi-sensor data for urban surface temperature retrieval. Remote Sensing of Environment, 104(2): 211-225. doi:https://doi.org/10.1016/j.rse.2005.09.022.
Ranagalage M, Dissanayake D, Murayama Y, Zhang X, Estoque RC, Perera E, Morimoto T. 2018. Quantifying surface urban heat island formation in the world heritage tropical mountain city of Sri Lanka. ISPRS International Journal of Geo-Information, 7(9): 341. doi:https://doi.org/10.3390/ijgi7090341.
Roberts DA, Quattrochi DA, Hulley GC, Hook SJ, Green RO. 2012. Synergies between VSWIR and TIR data for the urban environment: An evaluation of the potential for the Hyperspectral Infrared Imager (HyspIRI) Decadal Survey mission. Remote Sensing of Environment, 117: 83-101. doi:https://doi.org/10.1016/j.rse.2011.07.021.
Rousta I, Sarif MO, Gupta RD, Olafsson H, Ranagalage M, Murayama Y, Zhang H, Mushore TD. 2018. Spatiotemporal analysis of land use/land cover and its effects on surface urban heat island using Landsat data: A case study of Metropolitan City Tehran (1988–2018). Sustainability, 10(12): 4433. doi:https://doi.org/10.3390/su10124433.
Sahana M, Dutta S, Sajjad H. 2019. Assessing land transformation and its relation with land surface temperature in Mumbai city, India using geospatial techniques. International Journal of Urban Sciences, 23(2): 205-225. doi:https://doi.org/10.1080/12265934.2018.1488604.
Schwarz N, Lautenbach S, Seppelt R. 2011. Exploring indicators for quantifying surface urban heat islands of European cities with MODIS land surface temperatures. Remote Sensing of Environment, 115(12): 3175-3186. doi:https://doi.org/10.1016/j.rse.2011.07.003.
Simwanda M, Murayama Y. 2018. Spatiotemporal patterns of urban land use change in the rapidly growing city of Lusaka, Zambia: Implications for sustainable urban development. Sustainable Cities and Society, 39: 262-274. doi:https://doi.org/10.1016/j.scs.2018.01.039.
Sobrino JA, Oltra-Carrió R, Sòria G, Jiménez-Muñoz JC, Franch B, Hidalgo V, Mattar C, Julien Y, Cuenca J, Romaguera M. 2013. Evaluation of the surface urban heat island effect in the city of Madrid by thermal remote sensing. International Journal of Remote Sensing, 34(9-10): 3177-3192. doi:https://doi.org/10.1080/01431161.2012.716548.
Song J, Du S, Feng X, Guo L. 2014. The relationships between landscape compositions and land surface temperature: Quantifying their resolution sensitivity with spatial regression models. Landscape and Urban Planning, 123: 145-157. doi:https://doi.org/10.1016/j.landurbplan.2013.11.014.
Stathopoulou M, Cartalis C. 2007. Daytime urban heat islands from Landsat ETM+ and Corine land cover data: An application to major cities in Greece. Solar Energy, 81(3): 358-368. doi:https://doi.org/10.1016/j.solener.2006.06.014.
Sun Q, Wu Z, Tan J. 2012. The relationship between land surface temperature and land use/land cover in Guangzhou, China. Environmental Earth Sciences, 65(6): 1687-1694. doi:https://doi.org/10.1007/s12665-011-1145-2.
Voogt JA, Oke TR. 2003. Thermal remote sensing of urban climates. Remote Sensing of Environment, 86(3): 370-384. doi:https://doi.org/10.1016/S0034-4257(03)00079-8.
Wang J, Wang G, Liu Y, Qi J. 2021. Temporal normalization of land surface temperature retrieved from Landsat-8 data. In: 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS. IEEE, pp 6921-6924. doi:https://doi.org/6910.1109/IGARSS47720.42021.9553163.
Wang W, Liu K, Tang R, Wang S. 2019. Remote sensing image-based analysis of the urban heat island effect in Shenzhen, China. Physics and Chemistry of the Earth, Parts A/B/C, 110: 168-175. doi:https://doi.org/10.1016/j.pce.2019.01.002.
