Scholarly record
USING MULTIVARIATE STATISTICS TO SOLVE RISK ASSESSMENT PROBLEMS FOR FOREST ECOSYSTEMS
Abstract
Different natural and man-made hazards lead to the loss of millions of hectares of forest every year; sustainable forest management, which will decrease or prevent loss, is vitally important for our planet?s health. Sustainable forest management in the Russian Federation implies the effective multi-purpose use of forest resources, which involves implementing preventive and reforestation measures. However, the forest extends over vast territories, which complicates the work of determining the state of the system as a whole and, specifically, for individual areas. To best manage forest ecosystems, we should use a risk-based approach to correctly evaluate many factors and their influence probability, but the necessary methodology is currently lacking. This work uses multidimensional statistics methods that allow us to divide Krasnoyarsk Territory into groups of clusters with similar characteristics. For each region within this Territory, the risk of permissible impacts is calculated according to the proposed formula, and interval groups of risks are determined for cluster groups. The proposed method allows us to determine the intervals of risk values for each group of clusters. For a more detailed study of each cluster, it is necessary to expand and clarify the list of factors considered, as well as determine the most significant indicators that affect the forest ecosystem. Risk assessment was carried out according to two indicators: forest fires and forest pathology; however, this formula can be modified to take into account the introduction of new variables, for example, anthropogenic impact (deforestation area) or climate change.
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References5
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Decree of the Governor of Krasnoyarsk Territory 01.11.2019 №300-ug «On approving the forestry plan of Krasnoyarsk Territory »
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Bityukova V. R. Economic and geographical assessment of the environmental consequences of the transformation of the territorial and sectoral structure of the Russian economy in 1990-2012. Abstract of dissertation for the degree of doctor geographical sciences. 2014. 46 p.
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Number of times cited according to Crossref: 1
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