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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, 2025, V. 22, No. 6, pp. 337-349

Monitoring of flooded vegetation of rewetted peatlands based on remote sensing data

E.R. Agapova 1, 2 , M.A. Medvedeva 2 
1 Lomonosov Moscow State University, Moscow, Russia
2 Institute of Forest Science RAS, Moscow Oblast, Uspenskoye, Russia
Accepted: 04.12.2025
DOI: 10.21046/2070-7401-2025-22-6-337-349
Remote sensing data is actively used for vegetation dynamics monitoring in hard-to-reach areas where field research is difficult. These are rewetted peatlands monitoring of which is necessary to assess the quality of rewetting measures. The most difficult classes of wetland vegetation to distinguish are forested and scrub-shrub wetlands whose spectral signatures are similar to both wet and dry communities. In this paper, a method is proposed for identifying a class of flooded forested and shrub peatlands based on the use of multi-season images, as well as pre-processing of satellite data. The classification results were compared with the training ones obtained using multi-temporal images from the Landsat-8 and Sentinel-2 satellites. Two algorithms for preprocessing satellite images were tested: the principal component analysis method and the tasseled cap transformation, as well as the option of using a composite of multi-seasonal NDVI (Normalized Difference Vegetation Index) and NDMI (Normalized Difference Moisture Index) spectral indices. Three different classification methods, namely Support Vector Machine, Random Trees and K-Nearest Neighbor, were also compared. The results were evaluated on the basis of ground-truth data. The use of the Support Vector Machine classification algorithm for Landsat-8 images and the principal component method for preprocessing was recognized as the option that showed the best accuracy. In this case, the accuracy of decoding vegetation of the combined class of forested and shrub peatlands was 95 %.
Keywords: remote sensing, rewetted peatlands, Landsat-8, Sentinel-2, vegetation monitoring, multispectral images, peat bogs, peatlands, peat mining, rewetting, flooded vegetation
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