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Research was carried out improve efficiency of thematic mapping based on the recognition of plant communities in the subzone of dark coniferous forests for South of Sakhalin on multi-time satellite images of average resolution Landsat 8. We used reference samples of sites where geobotanical studies were conducted, for improve the quality of recognition during automated decryption. Experiments were conducted decode vegetation on singlechannel, synthesized multi-zone images obtained in different seasons of year. Spectral characteristics allow us identify plant communities in images based on morphological and physiological properties of various plants, which were quantified by reflection of vegetation in the spring image, and an integral indicator of photosynthetic activity of vegetation, which was evaluated by NDVI index calculated from spring and autumn images. Conceptual and methodological aspects of direct expert interpretation of vegetation from Landsat images by classification methods using ESRI ArcGIS raster algebra tools are considered. On example of study of vegetation communities of subzone of dark-coniferous forests of the South of Sakhalin with sufficient level of reliability, dark-coniferous forests, stone birch forest, cedar elfin formation, valley forests, thickets of Kuril bamboo, as well as residential zones, agricultural lands, areas devoid of vegetation as result of gravitational slope processes, wetlands, windfalls and man-made wasteland were identified. Decoding of vegetation cover from Landsat images showed that use of seasonal time series can significantly increase the reliability of the interpretation of most species of plant communities for the South of island. The research area is characterized by significant difference in altitude from 0 to 1100 m, as a result presence of high-altitude zone in the vegetation cover, which must be taken into account when decoding. Mapping is completed by performing automatic vectorization of raster layers and further generalization of vector polygons in accordance with selected map scale.

Mapping of vegetation communities of the subzone of dark coniferous forests of the South Sakhalin based on space surveys