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Journal of Automation and Information Sciences
SJR: 0.232 SNIP: 0.464 CiteScore™: 0.27

ISSN Imprimer: 1064-2315
ISSN En ligne: 2163-9337

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Journal of Automation and Information Sciences

DOI: 10.1615/JAutomatInfScien.v51.i6.30
pages 25-40

Estimation of the Thermodynamic Temperature of the Earth's Surface Using Satellite Data Based on the Land Cover Classification in the Optical Radiation Range

Yarema I. Zyelyk
Institute of Space Research of National Academy of Sciences of Ukraine and National Space Agency of Ukraine, Kyiv, Ukraine
Lyudmila V. Podgorodetskaya
Institute of Space Research of National Academy of Sciences of Ukraine and State Space Agency of Ukraine, Kiev
Sergey V. Chornyy
Institute of Space Research of National Academy of Sciences of Ukraine and State Space Agency of Ukraine, Kiev

RÉSUMÉ

The method for estimation of the thermodynamic temperature field of the Earth's surface using satellite data of the long-wave infrared range is studied and implemented in the environment of Quantum GIS using the Semi-Automatic Classification Plugin. This method is based on the land cover classification in the optical radiation range using the machine learning. The supervised land cover classification into four main macroclasses was carried out using the maximum likelihood method according to the reflectivity for the formed data set of spectral channels in the optical radiation range. When classifying, for each macroclass several training areas are created, each of which defines the certain child class. Training regions are formed by region growing method by attaching adjacent pixels to some selected pixel-seed based on the proximity of their spectral signature vectors. The reclassification of the resulting classification raster was performed, and for each macroclass the characteristic known value of the thermal emissivity was assigned. The research results are illustrated by the example of the estimation of the surface thermodynamic temperature of the wetland mineral and peat soils in the lowlands of the Kyiv region using satellite images of Landsat-8 (OLI, TIRS). It has been established that the contours of heated terrain areas, obtained from the conditions of exceeding of the experimentally selected threshold values of the thermodynamic temperature, based on the constructed temperature raster of the land surface, are consistent with the information of the State Emergency Service of Ukraine about dates and places of the peatland fires.

RÉFÉRENCES

  1. Alipour T., Sarajian M.R, Esmaseily A., Land surface temperature estimation from thermal band of LANDSAT sensor, case study: Alashtar city. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XXXVIII-4/C7, 2004, URL: https://www.researchgate.net/publication/215444122_Land_surface_temperature_estimation_from_thermal_ band_of_landsat_sensor_case_study_Alashtar_City (01.02.2019). .

  2. Stankevich S.A., Filippovich V.E., Lubsky N.S., at al., Intercalibration of methods for restoring the thermodynamic temperature of the surface of an urbanized territory based on thermal space shooting materials, Ukrainskiy zhurnal distantsionnogo zondirovaniya Zemli, 2015, No. 7, 12-21, URL: http://ujrs.org.ua/ujrs/article/view/ 59/77 (01.02.2019). .

  3. YuX., Guo X., WuZ., Land surface temperature retrieval from Landsat 8 TIRS - comparison between radiative transfer equation based method, split window algorithm and single channel method, Remote Sensing, 2014, 6, No. 10, 9829-9852. .

  4. Sobrino J., Jimenez-Munoz J.C., PaoliniL., Land surface temperature retrieval from LANDSAT TM 5, Remote Sensing of Environment, Elsevier, 2004, 90, 434-440. .

  5. WengQ., LuD., SchubringJ., Estimation of land surface temperature-vegetation abundance relationship for urban heat island studies, Remote Sensing of Environment, Elsevier, 2004, 89, 467.

  6. Using the USGS Landsat Level-1 Data Product, URL: https://www.usgs.gov/land-resources/nli/ landsat/using-usgs-landsat-level-1-data-product (01.02.2019). .

  7. Artis D.A., Carnahan W.H., Survey of emissivity variability in thermography of urban areas, Remote Sensing of Environment, 1982, 12, 313-329. .

  8. Snyder W.C., WanZ., Zhang Y., FengY.-Z., Classification based emissivity for land surface temperature measurement from space, International Journal of Remote Sensing, 1998, 19, 2753.

  9. Perez Hoyos I.C., Comparison between land surface temperature retrieval using classification based emissivity and NDVI based emissivity, International Journal of Recent Development in Engineering and Technology, 2014, 2, No. 2, 26-30. .

  10. Mallick J., Kant Y., Bharath B.D., Estimation of land surface temperature over Delhi using Landsat-7 ETM+, J. Ind. Geophys. Union., 2008, 12, No. 3, 131-140. .

  11. Mallick J., Singh C.K., Shashtri S., Rahman A., Mukhcrjee S., Land surface emissivity retrieval based on moisture index from LANDSAT TM satellite data over heterogeneous surfaces of Delhi city, International Journal of Applied Earth Observation and Geo information, 2012, 19, 348-358. .

  12. Lishchenko L.P., PazinichN.V., Monitoring the state of peatlands to identify fire hazardous areas using remote methods, Ukrainskiy zhurnal distantsionnogo zondirovaniya Zemli, 2016, No. 8, 29-39, URL: https://ujrs.org.ua/ujrs/article/view/72/89 (02/01/2017). .

  13. PazinichN.V., Lishchenko L.P., KrylovaG.B., Filipovich V.S., LubskyM.S., Investigation and monitoring of fire hazardous peatlands on the basis of Earth remote sensing data, Nauchnyi vestnik: Grazhdanskaya zashchita i pozharnaya bezopasnost, 2016, No. 1, 88-94. .

  14. Semi-automatic classification plugin documentation, URL: https://semiautomaticclassification manual-v5.readthedocs.io/en/latest/# (01.11.2018). .

  15. QG1S. A free and open source geographic information system, URL: https://www.qgis.org/en/site/ (01.02.2019)). .

  16. Zyelyk Ya.L., PidgorodetskaL.V., Chornyy S.V., Techniques of land surface temperature estimation in semi-automatic classification plugin and its application for monitoring of firehazardous peatlands, Aerospace observations for sustainable development and security. Proceedings of the sixth Ukrainian conference "GEO-UA" (Ukraine, Kiev, September 18-19, 2018), FOP Verotsky S.V., Kiev, 2018, 53-54. .

  17. Chavez P.S., Image-based atmospheric correction - revisited and improved, Photogrammetric Engineering and Remote Sensing, 1996, 62(9), 1025-1036. .

  18. MoranM., Jackson R., Slater P., Teillet P., Evaluation of simplified procedures for retrieval of land surface reflectance factors from satellite sensor output, Remote Sensing of Environment, 1992, 41, 169-184. .

  19. Richards J.A., JiaX., Remote sensing digital image analysis: An introduction, Springer, Berlin, Germany, 2006. .

  20. Elvidge Christopher D., Zhizhin Mikhail, Hsu Feng-Chi, Baugh Kimberly, Khomarudin M Rokhis, Vetrita Yenni, Sofan Parwati, Suwarsono and Dadang Hilman, Long-wave infrared identification of smoldering peat fires in Indonesia with nighttime Landsat data, Environ. Res. Lett., 2015, 10, DOI: 10.1088/1748-9326/10/6/06502. .

  21. Zyelyk Ya., Chornyy S., Pidgorodetska L., Mathematical models of the joint calibration process and optimal filtration of integrated multispectral data products of space Earth observations in visible, thermal and radio spectral bands, Abstracts of the 17th Ukrainian Conference on Space Research, Odesa, August 21-25, 2017, SRI, NAS and SSA of Ukraine, Kiev, 2017. .

  22. Chornyy S.V., Zyelyk Ya.I., Pidgoretska L.V., A method for improving the spatial resolution of images of thermal fields on the basis of a combination of multispectral data, Aerospace observations for the sake of sustainable development and safety, Materials of the sixth Ukrainian conference "GEO-UA", Kiev, September 18-19, 2018, 49-50. .


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