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.
Monitoring and analysing data on environmental pollution during forest fires and their health impacts in different geographical areas will help to improve the quality of the risks of determining adverse effects on health and identifying vulnerable groups of the population. The algorithm for assessing the potential and realised health risk in a dangerous period includes several consecutive stages. For the algorithmic implementation of methods for assessing the impact of air quality on public health, a service-oriented geoportal system is being created. Specialised original services have been developed. They allow users to perform all the basic operations with the file system on the server through the user's browser only. For web-services effective applying, Jupiter Notebook is used. In addition to standard libraries, it is possible to use WPS services that increase data processing capabilities.
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