N.Yu. Gushan1

1 Financial University under the Government of the Russian Federation (Moscow, Russia)
1 249351@edu.fa.ru

Modern forestry faces the need to process huge amounts of spatio-temporal data coming from various sources: satellites, sensors, video surveillance. This creates challenges related to scalability, response speed, and information reliability when monitoring forest fires, logging, and other emergencies.

The objective of the study is to analyze the principles of operation of situation centers in forestry from the perspective of big data processing, as well as to conduct a comparative assessment of decision support methods.

The article examines the architecture and tasks of forest situation centers, provides examples of Russian and international implementations, and describes a hierarchical decision support model that takes computational constraints into account. A comparative analysis of multi-criteria evaluation methods (AHP and TOPSIS) in terms of computational efficiency under different conditions was conducted.

The conclusions and recommendations presented in this work can be used in the design and modernization of forestry situation centers, the selection of analytical methods in conditions of limited resources, and the integration of AI into environmental digital platforms.

Gushan N.Yu. Forestry situation centers: data processing and big data challenges. Neurocomputers. 2025. V. 27. № 6. P. 88−95. DOI: 10.18127/j19997493-202506-09 (in Russian).

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