Degradation Processes in Steppe Communities and Data of Earth Remote Sensing
Existing methods for assessing natural grass stands are complex and subjective. Therefore, the goal of our research was to establish the possibility of using data from Earth remote sensing to characterize natural grass stands and a degree of their degradation. The objects of the study were natural communities located in the territory of Stavropol Krai. On the basis of the carried out ground geobotanical survey, the degree of degradation of the selected plots was established: Sengileevskoe - 0 points (non-degraded); Kiankiz - 3 points (highly degraded). The dynamics of the NDVI vegetation index of the selected plots during the growing season on average over 2016-2017 (MODIS data) were determined. Grass stands, the most contrasting in the degree of degradation, had ambiguous differences in the NDVI value over the entire vegetation period. The difference in the values of the vegetation index during the period from the end of April to the end of October was 45.3% in favour of the Sengileevskoe plot, and from the beginning of the vegetation to the end of April it was 43.5% in favour of the Kiankiz plot. Therefore, NDVI calculated from satellite images of low spatial resolution (250 m) cannot fully be a criterion for evaluating natural grass stands. We assumed that for these purposes an indicator of the proportion of the area occupied by either well developed or poorly developed vegetation can be used. Therefore, in the summer period, the area of plots with NDVI values of a certain range was determined in increments of 0.1 (data of high spatial resolution of Sentinel 2). The maximum differences were observed when the areas were compared with a vegetative index in the range of 0.1-0.4. Their share was 1.13% at the Sengileevskoe plot and 98.85% at the Kiankiz plot. These results of the analysis of high-resolution satellite data confirmed the results of ground-based geobotanical surveys, indicating large differences in the state of grass stands and the degree of degradation of the selected plots.