Typification of Forest Areas by Natural-Production Conditions Based on Cluster Analysis

Abstract

The effectiveness of harvesting machines, their reliability, and the level of negative environmental impact depends on the degree of adaptation of the equipment to natural-production conditions (NPC). To choose the equipment it is necessary to allocate groups of areas with close NPC. The purpose of the study is to form methodological tools for forest industry typification of forest areas by NPC. It is proposed to carry out the typification of forest areas based on cluster analysis. For this purpose, a methodology has been developed, including: setting the goal of typing areas by NPC; data collection on NPC; conducting cluster analysis; decision making on typification of areas by NPC. The task of cluster analysis is to divide, on the basis of a certain set of data, the set of forest areas into groups with similar NPCs. It is proposed to use Euclidean distances as a measure of belonging to one of the groups, and to determine the data set by indicators describing the NPC. The proposed methodology has been tested on the example of the European North of Russia (ENR). The study showed that three zones can be distinguished in ENR: zone A, including the Murmansk region; zone B, including the Republic of Karelia, the Republic of Komi and the Arkhangelsk region; zone C, including the Vologda region. Additionally, two subzones are distinguished in zone B: the West Karelian Upland and the territories belonging to the Northern, Subpolar and Polar Urals. The proposed methodology allows to increase the degree of formalization and convenience of the typification process of forest areas by NPC, to take into account a wide range of various aspects of natural-production conditions, their probabilistic nature, as well as to flexibly carry out the typification of areas for specific purposes. The research results may be applicable in solving problems of searching for effective technologies and rational parameters of logging machine systems.