The article includes a detailed description of various methods for preventing and managing the risk of forest fires, including regulatory, organizational, economic, socio-psychological, preventive, repressive, and compensatory methods. The authors provide a classification of these methods based on the mode of influence, nature of impact, and direction of action. The aim of the study is to identify and analyze effective approaches and techniques aimed at reducing the risk of forest fires and controlling pest spread in forest ecosystems. The study utilized methods of chamber analysis and review of scientific works, complemented by field research. This allowed for the development of well-founded recommendations for reducing the risk of forest fires. Special attention was given to the analysis of forestry data, which included information on the distribution of forests by land categories, classification of plantations, and their sanitary state. Field research helped to update and refine data on the condition of plantations and identify key factors that increase the risk of fires. Modern methods of forest fire detection were used, including video surveillance cameras, satellite monitoring, territory patrolling, and the use of unmanned aerial vehicles. The article describes an early forest fire detection system that includes IoT devices interconnected in a porous topology and equipped with temperature, carbon dioxide, hydrogen, and hydrocarbon gas sensors. The article also presents methods of multifactorial data analysis and principal component techniques to reduce data dimensionality and identify key factors influencing fire occurrence. Regression analysis is used to establish relationships between different variables and predict the likelihood of fires. Identified methods for reducing the risk of forest fires and the impact of pests and diseases include physical-mechanical, chemical, biological, and forestry methods such as manual insect collection, caterpillar shaking, trap application, chemical spraying, and the creation of mixed and multi-aged plantations. Research prospects may cover the following key aspects: studying and integrating advanced technologies such as artificial intelligence, machine learning, and remote sensing for early detection of forest fires and monitoring pest populations; studying the impact of climate change on the frequency and intensity of forest fires and pest spread, as well as developing adaptation strategies.
forest fire risk, forest pests, forest vegetation zones, geoinformation system, sanitary condition, conifers
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