FOREST COVER DIGITAL MAPPING OF THE KARELIAN PART OF THE WHITE SEA COASTAL ZONE BASED ON IMPROVED INTERPRETATION METHOD OF REMOTE SENSING DATA

• Published in: Proceedings of the Karelian Research Centre of the Russian Academy of Sciences
• Main Authors: Viktor Tarasenko, Boris Raevsky
• Format: Article in Journal/Newspaper
• Language: English; Russian
• Published: Karelian Research Centre of the Russian Academy of Sciences 2019
• Subjects: remote sensing data; unsupervised image classification; supervised image classification; digital thematic map; forest management inventory database; Science; Q; White Sea; karelian; Republic of Karelia
• Online Access: https://doi.org/10.17076/bg1067; https://doaj.org/article/e4217e74347f4f629b35f7073dcf014b

A modified interpretation method based on a combination of unsupervised and supervised image classification has been applied to space medium-resolution images taken in wintertime (data of OLI device of the LandSat 8 satellite) to create a digital thematic map of coniferous vegetation (for the Karelian part of the White Sea coastal zone). The template for the classification was the digital forest management inventory database (DB) for part (7.6 %) of the study area. Since the forest management inventory DB does not cover the entire study area, it is particularly interesting to determine the feasibility of producing digital vector layers of coniferous stands through supervised classification of medium-resolution remotely sensed data based on small amounts of template inventory information. To create training sets/supervised classification signatures, bitmap layers were formed from a color RGB composite of the source multi-spectral satellite image for sets of inventory units of each coniferous species. Unsupervised classification by the K-means method was performed for each prevalent species with a division into 5/8/10 clusters. Analysis of the findings revealed that the optimal number of clusters corresponds to 5 groups. Weighted average inventory parameters of template units were calculated to identify correlations with training sets. As a result of refining the technique for DB data classification, a set of digital thematic vector layers in GIS format, each containing coniferous stands as polygonal objects reliably identified by the main species and the growing stock, was produced. The set of digital layers of coniferous stands created using medium-resolution remotely sensed data can be used for the purposes of environmental monitoring and forecasting of human impact on the natural environment in the north-eastern part of the Republic of Karelia.