Today, the geographical interpretation of thermal satellite images, by the number of processing methods and applications, remains one of the least deeply studied areas. Geographic objects are characterized by different thermal and radiation properties. Therefore, they react differently to changes in the intensity of solar radiation, which is recorded in thermal images by differences in image brightness. What this article deals with is the usage of thermal satellite images from TIRS system of Landsat 8 in the monitoring of natural objects. Thermal images are a special source of geographical information that reflects the actual thermal radiation of objects on the earth's surface. It’s been defined that the thermal field of natural territories characterizes by high seasonal spatial-temporal variability. So, seasonal dynamics of the intensity of thermal radiation of natural have characteristic differences. It’s defined that winter characterizes by weak contrasts in the intensity of thermal radiation. Water bodies are best identified during this period. For spring, the increased intensity is observed for open woodless areas, in summer for agricultural lands, and in autumn the highest level of thermal radiation intensity is observed within open ground areas. Also, it was determined that the seasonal variability of thermal radiation intensity of different objects shows regularities related to the features of these objects. In other words, it can be their interpretation feature. The structure of the thermal field of protected areas was defined according to the unsupervised classification of a multitemporal thermal image using the IsoCluster algorithm. The accuracy of the performed classification was proved by the full compatibility of classified elements of thermal structure with natural objects.

1. Polyakov, A. V.; Timofeyev, Yu. M.; Uspensky, A. B. Retrieving the Atmospheric Temperature/Humidity Profiles Using the High Spectral Resolution Data from IR Satellite Sensor IRFS-2. Issledovanie Zemli iz Kosmosa. 2009, 5, 3-10. [in Russian]

2. Sobrino, J.A.; Jimenez-Munoz, J.C.; Paolini, L. Land surface temperature retrieval from Landsat-5/TM. Remote Sensing of Environment. 2004, 90, 434-440. https://doi.org/10.1016/j.rse.2004.02.003

3. Yang, J.S.; Wang, Y.Q.; August, P.V. Estimation of Land Surface Temperature Using Spatial Interpolation and Satellite-Derived Surface Emissivity. Journal of Environmental Informatics. 2004, 4(1), 37-44. https://doi.org/10.3808/jei.200400035

4. Tan, K.C.; Lim, H.S.; Mat Jafri, M.Z.; Abdullah, K. Land Surface Temperature Retrieval by Using ATCOR3_T and Normalized Difference Vegetation Index Methods in Penang Island. American Journal of Applied Sciences. 2010, 7(5), 717-723. https://doi.org/10.3844/ajassp.2010.717.723

5. Voogt, J.A.; Oke, T.R. Thermal remote sensing of urban climates. Remote Sensing of Environment. 2003, 86, 370-384. https://doi.org/10.1016/S0034-4257(03)00079-8

6. Weng, Q. Thermal infrared remote sensing for urban climate and environmental studies: Methods, applications, and trends. ISPRS Journal of Photogrammetry and Remote Sensing. 2009, 64, 335-344. https://doi.org/10.1016/j.isprsjprs.2009.03.007

7. Abushenko, N.A.; Altyntsev, D.A.; Mazurov, A.A.; Minko, N.P. The Estimation of Areas of Large Forest Fires Using AVHRR/NOAA. Issledovanie Zemli iz Kosmosa. 2000, 2, 87-93. [in Russian]

8. Alokhina, O.; Ivchenko, D.; Koshovy, V.; Rusyn, B. Landscape metrics for changes detection in land cover of the West Polesie Transboundary Biosphere Reserve. Environmental problems. 2017, 2(4), 227-240.

9. Ahmad, A.; Sufahani, S. F. Analysis of Landsat 5 TM data of Malaysian land covers using ISODATA clustering technique, 2012 IEEE Asia-Pacific Conference on Applied Electromagnetics (APACE), Melaka, Malaysia, December 11-13, 2012, 92-97. https://doi.org/10.1109/APACE.2012.6457639