Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2022, Vol. 19, No. 3, pp. 47-61
Inventory of the Kostomukshskiy Strict Nature Reserve vegetation using Landsat images
B.V. Raevsky
1 , V.V. Tarasenko
1 , N.V. Petrov
1 1 Karelian Research Centre RAS, Petrozavodsk, Russia
Accepted: 03.06.2022
DOI: 10.21046/2070-7401-2022-19-3-47-61
Digital mapping of boreal vegetation based on remote sensing data interpretation is of great importance for monitoring natural and anthropogenic dynamics of North Russian forest ecosystems. We comparatively assessed the effectiveness of three supervised classification methods, viz. “minimal distance”, “Mahalanobis distance” and “maximal likelihood”, applied to the Kostomukshskiy Strict Nature Reserve (SNR) territory. All these classifications produced results with a high level of reliability (Cohen’s kappa). The final results of space image interpretation were verified using forest survey data and the outcome was that the “minimal distance” procedure enabled the most realistic spatial modeling of the nature reserve’s vegetation cover. Automatic classification of medium spatial resolution multispectral remote sensing data followed by post-classification treatment made it possible to develop an updatable digital map of the nature reserve’s ecosystems roughly equivalent to the map of forest stands. But in contrast to this traditional specialized thematic map, remote data interpretation techniques permit visualizing the latent process of spruce canopy formation, which commonly takes place when natural disturbances (e.g. fires) in the area are rare. The resultant data show that at least in the period since nature reserve foundation its landscapes have luckily avoided large-scale catastrophic events, and now they are in a dynamic balance condition.
Keywords: multispectral space images, supervised classification, Landsat program, vegetation cover, forests, remote sensing data, interpretation
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