REMOTE METHODS IN STUDYING THE TEMPERATURE OF THE EARTH’S SURFACE IN CITIES (ON THE EXAMPLE OF THE CITY OF KRASNOYARSK, RUSSIA)

DOI: 10.24057/2414-9179-2018-2-24-195-205

View or download the article (Rus)

About the Authors

Anna A. Gosteva

Siberian Federal University,
Kirenskogo str., 26, ULK building, 660074, Krasnoyarsk, Russia,
E-mail: AGosteva@sfu-kras.ru

Aleksandra K. Matuzko

Institute of Computational Modelling of the Siberian Branch of the Russian Academy of Sciences (ICM SB RAS),
Akademgorodok 50/44, 660036, Krasnoyarsk, Russia,
E-mail: akmatuzko@icm.krasn.ru

Oleg E. Yakubailik

Institute of Computational Modelling of the Siberian Branch of the Russian Academy of Sciences (ICM SB RAS),
Akademgorodok 50/44, 660036, Krasnoyarsk, Russia,
E-mail: oleg@icm.krasn.ru

Abstract

There are two methods for obtaining data about the Earth surface temperature. The direct method includes measurements obtained directly by ground-based methods, like weather station data, the another is a remote method, which includes satellite remote sensing data. The satellite remote sensing is the only instrument to obtain long-term homogenous series of data about the ground temperature. The paper reviews methods of determining the surface emission coefficient based on the satellite remote sensing data. Thermal emission is an attribute of objects, phenomena, and processes, which are hidden from direct observation.

The ground surface temperature can be defined by using the values of the thermal bands. Due to its heterogeneity, the ground surface has a different emissivity, which is determined by the emissivity coefficient. The most common methods for the determining the emissivity coefficient for a satellite image are based on a normalized vegetation index or on applying an image classification. In our study the calculation of the ground surface temperature is defined in two steps. At first, in order to determine the emissivity coefficient, the classification of the image is realized by with the identification of the main types of surface: soil, water, vegetation, and buildings. Then using the values of the 10th band of the TIRS scanner of the Landsat-8 satellite and the emissivity coefficient, the ground surface temperature in Celsius degrees was calculated. The radiometric and atmospheric corrections were applied to the satellite data. 10 cloudless scenes from 2013 to 2016 has been considered. Based on the results of the study thermal anomalies were identified in Krasnoyarsk city.

The problem of the thermal anomalies is typical for all major cities of the world. The satellite thermal images are a valuable source of the information for analyzing the thermal anomalies on a selected territory. The determination of nature and boundaries of the thermal anomalies will help to understand the causes of the unfavorable ecological situation in the considered city.

On the territory of Krasnoyarsk city, the two types of the thermal anomalies can be distinguished: natural and anthropogenic. The anthropogenic objects with intense thermal emission has been outlined (or defined). In our study, plant facilities and shopping malls are such objects. These objects consist of dense materials with high heat capacities, such as asphalt, concrete, and steel. Most of the anthropogenic objects are made of such materials.

Keywords

urban heat island, thermal infrared imagery, Landsat, temperature anomalies, land surface temperature

References

  1. Asmus V.V., Dyadychenko V.N., Zagrebaev V.A. et al. Surface complex for receiving, processing, archiving and distribution of satellite data. SIC “Planeta” works. 2005. V. 1(46). P. 3–21 (in Russian).
  2. Brivio P.A., Lechi G., Zilioli E. Principi e metodi di telerilevamento, CittàStudi Edizioni. Torino, 2006. P. 449, 479.
  3. Congedo L. Semi-Automatic Classification Plugin Documentation. web resource: www.fromgistors.blogspot.com. 2017. 278 p.
  4. Hrebtov M., Hanjalić K. Numerical study of winter diurnal convection over the city of Krasnoyarsk: Effects of non-freezing river, undulating fog and steam devils. Boundary Layer Meteorology. 2017. V. 163, issue 3. P. 469–495.
  5. Khlebopros R.G., Taseiko O.V., Ivanova Yu.D., Mykhaylyut S.V. Krasnoyarsk. Ecological essays. Siberian Federal University, 2012. 130 p. (in Russian).
  6. Le Hung Trinh, Terekhin E.A., Danh Tuyen Vu. Application of remote sensing in determining land surface emissivity using LANDSAT multispectral imagery (case study: Bac Binh district, Binh Thuan province, Vietnam). Current problems in remote sensing of the Earth from space. 2015. V. 12, No 6. P. 59–68 (in Russian).
  7. Matuzko A.K. Determining water surface temperature by Earth remote sensing data. Regional problems of Earth remote sensing, Materials of IV International Scientific Сonference, SFU ISIT. 2017. P. 271–274 (in Russian).
  8. Giannini M.B., Belfiore O.R., Parente C., Santamaria R. Land Surface Temperature from Landsat 5 TM images: comparison of different methods using airborne thermal data. Journal of Engineering Science and Technology Review. 2015. V. 8, No 3. P. 83–90.
  9. Stone Jr., Rodgers M.O. Urban Form and Thermal Efficiency: How the Design of Cities Influences the Urban Heat Island Effect. APA Journal. 2001. V. 67, No 2. P. 186–198.
  10. Weng Q., Lu D., Schubring J. Estimation of land surface temperature-vegetation abundance relationship for urban heat island studies. Remote Sensing of Environment, Elsevier Science Inc. 2004. V. 89. P. 467–483.

For citation: Gosteva A.A., Matuzko A.K., Yakubailik O.E. REMOTE METHODS IN STUDYING THE TEMPERATURE OF THE EARTH’S SURFACE IN CITIES (ON THE EXAMPLE OF THE CITY OF KRASNOYARSK, RUSSIA). Proceedings of the International conference “InterCarto. InterGIS”. 2018;24(2):195–205 DOI: 10.24057/2414-9179-2018-2-24-195-205 (in Russian)