Using multivariate statistics to solve risk assessment problems for forest ecosystems

Taseiko O.; Ivanova U.; Rihter E.; Pitt A.

Дополнительные ссылки: Scopus · eLibrary · Источник статьи

База данных: Каталог библиотеки СФУ

Библиографическое описание: Using multivariate statistics to solve risk assessment problems for forest ecosystems / O. Taseiko [et al.]. - Текст : непосредственный // Int. Multidisciplinary Sci. Geoconf. Surveying Geology Mining Ecology Manage., SGEM : International Multidisciplinary Scientific Geoconference, 2020. - Vol. 2020-August, Is. 3.1. - P. 777-784. - This research project No 18-47-240006: “Methods and information technologies for risk assessment of the development of socio-natural-technogenic systems in an industrial region” was funded by the Russian Foundation for Basic Research, Government of Krasnoyarsk Territory, Krasnoyarsk Regional Fund of Science. - ISBN ISSN 13142704, DOI 10.5593/sgem2020/3.1/s14.100.

Аннотация: 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. © 2020 International Multidisciplinary Scientific Geoconference. All rights reserved.

Год издания: 2020

Авторы: Taseiko O. , Ivanova U. , Rihter E. , Pitt A.

Издательство: International Multidisciplinary Scientific Geoconference

Источник: International Multidisciplinary Scientific GeoConference Surveying Geology and Mining Ecology Management, SGEM

Выпуск: Vol. 2020-August, Is. 3.1

Мероприятие: International Multidisciplinary Scientific Geoconference: Water Resources, Forest, Marine and Ocean Ecosystems, SGEM 2020 (18 August 2020 through 24 August 2020)

Номера страниц: 777-784

Держатель оригинала документа: Reshetnev Siberian State University of Science and Technology, Krasnoyarsk, Russian Federation, Krasnoyarsk Branch of the Federal Research Center for Information and Computational Technologies, Krasnoyarsk, Russian Federation, Siberian Federal University, Krasnoyarsk, Russian Federation, Federal Research Center, Krasnoyarsk Science Center of the Siberian Branch of the Russian Academy of Sciences, Krasnoyarsk, Russian Federation

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Using multivariate statistics to solve risk assessment problems for forest ecosystems
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