The rapid growth of geo-information technology capabilities in the field of spatial data processing and analysis has led to a significant growth of the role of geo-information systems in different areas of human activity. Application of approaches to spatial information processing from satellites new for more effective and efficient assessment of the state of plant cover is caused by growing tendency of availability to data of Earth remote sensing. The article offers an information system that allows to quickly and conveniently track changes in the vegetation. The analysis was carried out on the example of the Chornobyl Area between 2000 and 2020. The Chornobyl Disaster coincides with the period of intensive vegetative plant development. During that period, they are most sensitive to radiation. It has been established that for defining the quantitative state of biomass the NDVI index at different time intervals is most often used. But this index becomes ineffective during periods of weakening of active phase of vegetation. This is therefore of practical interest to assess the possibility of using the K-means clustering for the analysis of space images of vegetation cover at different phases of vegetation. As a result of the research, water surface, land with and without vegetation has been correctly interpreted, thus determining the land with a sparse vegetation and dense vegetation cover. The maps of the vegetation cover according to the normalized vegetative index using the K-medium method were constructed, the method by which changes in vegetation over 20 years can be clearly observed. The accuracy results were verified with the Common Method Bias. According to the calculations, despite all natural cataclysms (temperature increase, drought, winter anomalies of precipitations and temperatures, storms, forest fires), as well as human activity (sanitary clear cuttings, illegal logging), vegetation in the Chornobyl zone continues to grow and its areas will increase, although not so quickly.

Today, a variety of information about forest ecosystems can be obtained using remote sensing methods. The use of space data for forest monitoring is cost-effective because it allows you to quickly obtain the objective information needed by foresters to solve practical problems. Satellite data provide wide coverage of forest lands, high accuracy of results, as well as high frequency of data obtained. Space images of the Ovruch district of the Zhytomyr region of Ukraine in the summer of 2020 were selected for the study. Determination of breed composition was carried out by the methods of controlled classification, namely the Bayesian classifier. It was found that 70 % of forests are pine, less aspen, hornbeam, birch, alder and ash tree species. According to statistics, during 2000-2020, 51.4 thousand hectares of forest plantations in Ukraine were damaged and destroyed by forest fires. Therefore, objective and timely information on the consequences of fires is needed to solve a wide range of applied problems of forestry. An important task in assessing the environmental and economic damage caused to forestry as a result of forest fires is to determine the area of damaged forests. The paper considers technologies for determining the area of the forest where the fire took place, using space images of the Landsat 8 satellite. The normalized NBR fire index before and after the fire and the DNBR index are used to identify areas burned by fire and impression levels. To predict forest fires, a mathematical model based on Bayes' theorem was created and a thematic map with fire hazard classes on a quarterly basis was created. To check the accuracy of the results of the created forecast model, the thematic map was combined with a layer of defined fire areas. This software product is quite flexible and versatile, it can be easily adapted for use not only to identify burned forest lands, but also for other areas.

This study considers the issue of assessing the time changes in forest plantations and constructing an algorithmic and software system for monitoring these changes. Modern systems that study vegetation changes do not have the necessary functionality and do not cover the range of observations discussed in this paper. Existing research methods are intended only to record changes that occur in forest ecosystems and take into consideration the peculiarities of a certain natural zone, which limits their use. At the same time, it should be understood that the requirements for modern systems should include additional components that could make the system universal and mobile. A comparative analysis of satellite images acquired from remote sensing by the Landsat 8 satellite system has been carried out to determine the areas affected by forest fires. During the classification, spectral analysis was used, and an index of fires was determined to indicate the burned areas. To analyze the changes that occur in forests due to fires, correlation-regression analysis is used. It has been proven that the area of sanitary felling after fires and the area of forest land traversed by fires demonstrated the greatest interconnection. The extrapolation and forecasting were carried out using a regression data model, the effectiveness of which is confirmed by a coefficient of determination of 0.87. The dependences built make it possible to conclude that by 2030 the number of forest fires will increase while the area of burned forests will not decrease. The developed mobile application could be popular among a significant group of users to monitor fire events. The practical result is the introduction of the built system, which makes it possible to quickly monitor forest plantations after fires and assess the areas that were affected.

The paper presents the methods for fire identification using low-resolution space images obtained from Terra Modis and NOAA satellites. There are lots of algorithms to identify potentially "fire pixels" (PF). They are based on the assessment of temperature in spectral ranges from 3.5–4 to 10.5–11.5 microns. One of the problematic aspects in the Fire Detection Method using low-resolution space images is "Cloud and Water Masking". To identify "fire pixels", it is important to exclude from the analysis fragments of images that are covered with clouds and occupied by water objects. Identification of pixels in which one or more fires are actively burning at the time of passing over the Earth is the basis of the algorithm for detecting potentially "fire pixels". The algorithm requires a significant increase in radiation in the range of 4 micrometers, as well as on the observed radiation in the range of 11 micrometers. The algorithm investigates each pixel in a scene that is assigned one of the following classes as a result: lack of data, cloud, water, potentially fire or uncertain. The pixels that lack actual data are immediately classified as "missing data (NULL)" and excluded from further consideration. Cloud and water pixels, defined by the cloud masking technique and water objects, belong to cloud and water classes, respectively. The fire detection algorithm investigates only those pixels of the Earth's surface that are classified as potentially fire or uncertain. The method was implemented using the Visual Programming Tool PowerBuilder in the data processing system of Erdas Imaging. As a result of the use of the identification method, fires in the Chornobyl exclusion zone, steppe fires and fires at gas wells were detected. Using the method of satellite fire identification is essential for the prompt detection of fires for remote forests or steppes that are poorly controlled by ground monitoring methods.

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.