ISSN 0474-8662. Information Extraction and Processing. 2020. Issue 48 (124)
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Thermal remote sensing data analysis in monitoring of natural objects

Alokhina O.V.
Karpenko Physico-Mechanical Institute of the NAS of Ukraine, Lviv
Ivchenko D.V.
Karpenko Physico-Mechanical Institute of the NAS of Ukraine, Lviv
Pits N.A.
Karpenko Physico-Mechanical Institute of the NAS of Ukraine, Lviv

https://doi.org/10.15407/vidbir2020.48.061

Keywords: thermal radiation, thermal field, unsupervised classification, intensity of thermal radiation, quantization, satellite image classification.

Cite as: Alokhina O.V., Ivchenko D.V., Pits N.A. Thermal remote sensing data analysis in monitoring of natural objects. Information Extraction and Processing. 2020, 48(124), 61-71. DOI:https://doi.org/10.15407/vidbir2020.48.061


Abstract

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. Itís 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. Itís 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.


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