Introduction. One of the main sources of errors in the recognition of forest cover classes from satellite data is the differences in the spectral brightness coefficients of identical objects in different parts of the image. To recognize the same classes of vegetation, it is proposed to process the scene, taking into account information about the diversity of forest growth conditions. The aim of the work is to develop a methodology for mapping of forest cover, taking into account the seasonal dynamics of the spectral-reflective characteristics of vegetation in different growing conditions.
Objects and methods. The object is the vegetation cover of the Sayano-Shushensky biosphere reserve (51 °46' - 52 °37' N, 91 °04' - 92 °26' E, area about 400 000 ha)is located in the mountainous part of southern Siberia. Landsat 8 (OLI) for 2016, DEM SRTM (90 m), forest inventory data for 2016, ground truth data and thematic maps.were used for forest mapping.
Data processing was carried out using software packages ArcGIS 10, ERDAS Imagine 2014, Trimble eCognition 8. For automated classification of spectral features of satellite data and terrain characteristics determined by DEM, both pixel approaches (ISODATA, MAXLIKE) and object-oriented segmentation method (Multiresolution) were used.
Results and Discussion. A conjugate classification of forest growth conditions and vegetation has been developed on the base of DEM-based topographic profiles crossing the territory of the reserve. Uncontrolled classification of DEM features (elevation above sea level and slope) by ISODATA method was carried out to identify land cover classes according to the conjugate classification. To generalize the obtained the classes, segmentation of the DEM (elevation above sea level) was performed. As a result of applying both methods, segments relatively uniform in elevation and texture of the relief were identified. The resulting classes were interpreted as geomorphological complexes (GMC) of forest growth conditions. These areas are homogeneous in terms of the ratio of mesorelief forms, underlying rocks, the range of elevation above sea level, dissection surface degree (they are similar in climatic and ecological regimes) and the predominant type of vegetation: tundra, subalpine and subalpine woodlands and sparse forests, mountain taiga forests, subtaiga forest-steppe complex.
The assessment of vegetation cover diversity of the reserve was carried out using the classification of the composite of satellite images Landsat-OLI 8 (June, September) for 2016 and layer of GMC of forest growth conditions. To classify the 16-channel composite within each GMC of forest growth conditions, training samples were created for forest vegetation classes and non-forest lands. The classification was performed using the MAXLIKE method.
The set of forest cover classes obtained in different GMC of forest growth conditions was combined into 9 classes according to conjugate classification: Siberian pine forests; Siberian pine forests with Fir/Spruce; Siberian pine forests with Larch; Larch forests with Siberian pine; Larch forests with Fir/Spruce; Larch forests; Larch forests with Scots pine (mixed with Siberian pine/Spruce); Scots pine forests with Larch; Spruce forests with Fir (mixed with Larch/ Siberian pine). Forest cover map was created at a scale of 1:200 000.
