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ISSN 2070-7401 (Print), ISSN 2411-0280 (Online)
Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa
CURRENT PROBLEMS IN REMOTE SENSING OF THE EARTH FROM SPACE

  

Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2024, Vol. 21, No. 1, pp. 31-50

Determining mixed forest species composition based on joint processing of public satellite maps and multi-temporal Sentinel-2 images

E.V. Dmitriev 1, 2 , T.V. Kondranin 2 , P.G. Melnik 3 , S.A. Donskoi 4 
1 Marchuk Institute of Numerical Mathematics RAS, Moscow, Russia
2 Moscow Institute of Physics and Technology, Dolgoprudny, Moscow Region, Russia
3 Mytischi Branch of Bauman Moscow State Technical University, Moscow region, Mytischi, Russia
4 Roslesinforg, Moscow, Russia
Accepted: 21.11.2023
DOI: 10.21046/2070-7401-2024-21-1-31-50
The problem of determining species composition of mixed forests for the European part of Russia is considered. The method proposed is based on joint processing of multi-temporal multispectral images of medium spatial resolution (Sentinel-2) and very high spatial resolution satellite images obtained from open mapping services such as Bing Maps, Google Maps, etc. The main stages of thematic processing are segmentation of forest stands using textural features and pixel-by-pixel classification of tree species using spectral-temporal features. The textural segmentation method is based on a combined use of statistical and spectral methods for extracting texture features that allows reducing the negative impact of noise characteristic of satellite maps. The results of forest stand segmentation in a test area (the territory of Bronnitsky forestry, Moscow region) revealed that the total probability error does not exceed 3.5 % with a natural error level due to boundary pixels of 0.6 %. Accuracies of determining species composition using both vegetation indices and directly data from satellite spectral channels are analyzed. The processing results in the second case demonstrate significantly higher reliability. Classification errors for individual species obtained by using the cross-validation method vary from 1 to 8 %. Comparison with terrestrial forest inventory data shows the coincidence of the dominant species for 87 % of the total area of forest inventory plots in the test area.
Keywords: remote sensing, pattern recognition, thematic processing, texture features, multitemporal multispectral satellite images, species composition of forest stands
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