This study demonstrates a comparative analysis of surface and satellite measurements. The average concentrations of PM2.5 and CO, SO2, measured at the Listvyanka station located on the coast of Lake Baikal, were considered. Satellite measurements data (Copernicus Sentinel-5P) were recomputed based on the SILAM model. A joint analysis of data showed that satellite measurements were suitable for a spatial description of regional air pollution. The computed maxima coincided with the surface measurements in terms of time periods and general monitoring results. However, at extreme increases in concentrations of pollutants, a significant difference in the numerical values was registered. Satellite monitoring data confirmed the relationship between the increase in PM2.5 and CO concentrations in the air basin at the Listvyanka station and the transfer of smoke plumes from intense forest fires located at a distance of 1,500 – 2,000 km.
The territory of the Baikal Natural Territory (BNT) is quite large and inaccessible in some places. Therefore, remote sensing is the only source of regular data for research of the spatial-temporal land cover dynamics of the BNT. Regular processing and classification of land cover is required to monitor BNT. Satellite image classification is a common method of information extraction related to the structure and changes in land cover. In this work we used Sentinel-2 multispectral images for classifying land cover of the BNT. There are many methods to analyze and classify remote sensing data. The article discusses algorithms for the land cover classification: the vegetation index NDVI, machine learning based on Random Forest algorithm and the convolutional neural network xResNet50. The results of all methods are tested for compliance with the verification dataset for BNT.
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