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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, 2026, V. 23, No. 5, pp. 50-68

Designing a methodology for constructing a digital land cover model of a large territory based on high-resolution multispectral satellite imagery (case study of Loukhsky District, Republic of Karelia, Russia)

V.V. Tarasenko 1 , B.V. Raevsky 1 
1 Karelian Research Centre RAS, Petrozavodsk, Russia
Accepted: 08.07.2026
DOI: 10.21046/2070-7401-2026-23-5-50-68
The paper presents a hybrid methodology of creating a digital land cover model (DLCM) of Loukhsky District of the Republic of Karelia spanning over 20 000 km2, based on interpretation of remote sensing data from Sentinel-2 and application of Google Earth Engine cloud service tools as of 2024. High-resolution (10 m) multispectral satellite imagery was interpreted by a combination of supervised (random forest) and unsupervised (k-means clustering) classification. Considering extensiveness of the study area and impossibility to obtain an acceptable-quality cloudless coverage of the entire area during the growing season (June–September 2024), a grid of 18 raster tiles was formed for subsequent step-by-step decoding of multispectral “median” images. At the first stage of creating the DLCM, we used data from the forest management inventory of Paanajärvi National Park and assumed that the overlap areas (15–20 %) of the neighboring tiles are occupied on the ground by consistent land cover. The result of the research is an up-to-date and reliable DLCM of a large area within the Arctic zone of the Russian Federation, with scale accuracy of M 1:50,000. The DLCM can be used during integrated monitoring of this Arctic territory and as a basis for regular monitoring of the state of terrestrial ecosystems and for decision-making at all government levels.
Keywords: Arctic zone of Russia, land cover, digital land cover model, Sentinel-2, multispectral satellite images, Google Earth Engine cloud service, machine learning, geographic information system technologies, GIS
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