Automatic identification of forest species using machine learning methods based on satellite image processing

Monitoring of the condition and species diversity of tree species plays a significant role in the forest resource management. The emergence of high-quality multispectral satellite images opens up opportunities for using information about vegetation in a certain area over a long period of time extracted from images in addition to data obtained as a result of field research. The paper considers the use of satellite images from Sentinel 2 for automated classification using the example of the territory of the natural monument ”Kumysnaya Polyana Nature Park”located in the vicinity of Saratov. Machine learning methods with a teacher were used as automation tools. The paper describes three approaches, differing in the algorithms used, as well as ways of structuring the information obtained from satellite images. As a result of the work, the test part of the study area was classified into seven types of trees, including regions dominated by the most common species. In the future, a similar approach to automating the marking of forest maps will potentially increase the data update interval in field research and increase their effectiveness.