Research article

DOI: 10.15287/afr.2022.2328

Monitoring tree mortality in Ukrainian Pinus sylvestris L. forests using remote sensing data from earth observing satellites

Oleh V. Skydan, Tetiana P. Fedoniuk , Оleksandr S. Mozharovskii, Оleksandr V. Zhukov, Anastasiia A. Zymaroieva, Viktor М. Pazych, Vitaliy V. Hurelia, Taras V. Melnychuk

Oleh V. Skydan
Polissia National University
Tetiana P. Fedoniuk
Polissia National University. Email: tanyavasiluk2015@gmail.com
Оleksandr S. Mozharovskii
National Space Facilities Control and Test Center
Оleksandr V. Zhukov
Bogdan Khmelnitsky Melitopol State Pedagogical University
Anastasiia A. Zymaroieva
Polissia National University
Viktor М. Pazych
Polissia National University
Vitaliy V. Hurelia
Polissia National University
Taras V. Melnychuk
Chornobyl Radiation-Ecological Biosphere Reserve

Online First: December 30, 2022
Skydan, O., Fedoniuk, T., Mozharovskii, О., Zhukov, О., Zymaroieva, A., Pazych, V., Hurelia, V., Melnychuk, T. 2022. Monitoring tree mortality in Ukrainian Pinus sylvestris L. forests using remote sensing data from earth observing satellites. Annals of Forest Research DOI:10.15287/afr.2022.2328


This article considers the application of remote sensing data to solve the problems of forestry in the Polissia zone (Ukraine). The satellite remote sensing was shown to be applicable to monitoring the damage caused by diseases and pests to forest resources and to assessing the effects of fires. During the research, a detailed analysis and optimization of the information content of Sentinel-2 long-term data sets was performed to detect changes in the forest cover of Polissia, affected by pests and damaged by fires. The following classification algorithms were used for automated decryption: the maximum likelihood method; cluster classification without training; Principal Component Analysis (PCA); Random Forest classification. The results of this study indicate the high potential of Sentinel-2 data for application in applied problems of forestry and vegetation analysis, despite the decametric spatial resolution. Our proposed workflow has achieved an overall classification accuracy of 90 % for the Polissia region, indicating its reliability and potential for scaling to a higher level, and the proposed forecast model is stationary and does not depend on time parameters. To improve the classification results, testing of different combinations of bands emphasized the importance of Band 8 in combination with red edge bands, as well as other bands with a resolution of 10 m for summer scenes. The red margin shows clearly visible differences in the spectral profiles, but bands with a higher resolution of 10 m were crucial for good results.


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