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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. 6, pp. 171-187

Land cover mapping of Kivach Strict Nature Reserve and its surroundings based on remotely sensed data

B.V. Raevsky 1 , V.V. Tarasenko 2 
1 Forest Research Institute of Karelian Research Centre RAS, Petrozavodsk, Russia
2 Department of Multidisciplinary Scientific Research of Karelian Research Centre RAS, Petrozavodsk, РоссияRussia
Accepted: 30.09.2024
DOI: 10.21046/2070-7401-2024-21-6-171-187
Digital vector mapping of land cover based on interpretation of remotely sensed data is of great importance for monitoring natural and anthropogenic dynamics in forest ecosystems. A comparative analysis of the efficiency of using supervised classifiers, such as Minimal Distance (MD), Classification and Regression Trees (CART), and Random Forest (RF), built into the cloud-based planetary scale platform GEE (Google Earth Engine), was performed in application to Kivach Strict Nature Reserve (SNR) and its surroundings. It is shown that in the case of Sentinel-2 images, the CART classifier provided the best assessment accuracy. This has enabled creating a renewable digital map of SNR ecosystems, which is roughly equivalent to a 1:50 000 forest stand map. The final results of Landsat space images interpretation were verified using forest survey data, revealing that MD procedure was capable of producing the most adequate spatial model of the investigated vegetation cover. Interpretation of a time series of remote sensing data revealed a latent process of spruce augmenting its share in the canopy, which commonly takes place in secondary forests of this kind. The data obtained provide evidence that in the current age stage, succession processes in these forests trend towards a decrease in the spatial share of deciduous stands in the forest structure.
Keywords: multispectral space images, Landsat, Sentinel-2, supervised classification, land cover, secondary forests, remote sensing data, interpretation
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