METHOD OF IDENTIFICATION OF TREE SPECIES COMPOSITION OF FORESTS ON THE BASIS OF GEOGRAPHIC INFORMATION DATABASE


 
 
Monitoring of the forests with the help of remote sensing of the Earth systems is actual on the present stage of people development. Existing systems of research do not allow to use geographic information databases and don't include the spectrum of exploration that are presented in our work. The aim of this article is to develop the method of identification of tree species composition of forests on the basis of geographic information database in the integration with correlation analysis. It will give the possibility to find the most negative things that influence on the condition of forest ecosystems and neutralize them at the first stage of their influence. Satellite images that are received from the satellites Landsat 5 and 8 are used to make the database. Tree species composition of Mozhariv forest during 20 years is analyzed as an example in this article. Classification of tree species composition of forests using space images is made on the basis of Bayes classifier. There is a special geographic information database for further data processing and identification of tree species composition is developed. Forest is analyzed on the number of trees in each species during continuous time measures (in 2000, 2010 and 2020) in this article. Results of the work are presented with the help of graphics. Correlation analysis, which helps to make the analysis of tightness of connections between changes trees in number and different forest criteria, is made to identify tree species composition of forest. Developed method together with well-known methods of monitoring of forests help us to make effective application for analysis of tree species composition of forest changes and to make better existing information systems including different quality demands. Peculiarity of developed information system is in using of the data processing module as a part of geographic information system and implementing correlation analysis with identification of factors that influence each other. It helps to increase quality in management and monitoring of forest resources. Geographic information becomes available for users. 
 
 
