Identification of Logged and Windthrow Areas from Sentinel-2 Satellite Images Using the U-Net Convolutional Neural Network and Factors Affecting Its Accuracy

Author:

Kanev A. I.,Tarasov A. V.,Shikhov A. N.,Podoprigorova N. S.,Safonov F. A.

Publisher

Pleiades Publishing Ltd

Reference28 articles.

1. Bartalev, S.A., Egorov, V.A., Krylov, A.M., Stytsenko, F.V., and Khovratovich, T.S., The evaluation of possibilities to assess forest burnt severity using multi-spectral satellite data, Sovrem. Probl. Distantsionnogo Zondirovaniya Zemli Kosmosa, 2010, vol. 7, no. 3, pp. 215–225.

2. Bartalev, S.A., Egorov, V.A., Zharko, V.O., Loupian, E.A., Plotnikov, D.E., Khvostikov, S.A., and Shabanov, N.V., Sputnikovoe kartografirovanie rastitel’nogo pokrova Rossii (Land Cover Mapping over Russia using Earth Observation Data), Moscow: IKI RAS, 2016 [in Russian].

3. Koroleva, N.V. and Ershov, D.V., Assessment of an error of determination of areas of windfalls using space images of high spatial resolution of Landsat-TM, Sovrem. Probl. Distantsionnogo Zondirovaniya Zemli Kosmosa, 2012, vol. 9. no. 1, pp. 80–86.

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5. Tarasov, A.V., Operational mapping of forest cover disturbances based on satellite images with high spatial and temporal resolution, Cand. Sci. (Eng.) Dissertation, Perm, 2021.

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