Tree State Category Identification for Boreal Area Conifers Using Global Features Estimation by Fuzzy Logic Approach

Abstract

Tree state category identification allows forecasting forest development in the surveyed area. Tree state category determination process based on global features is subjective and uses concepts such as the degree of density of the crown, the degree of drying of branches, the fall of the bark, the color of the needles, etc. For global features estimation, fuzzy logic is used. To formalize these subjective concepts, linguistic variables and their terms were extracted. The characteristic functions describing the terms were piecewise linear and in this work were approximated by Gaussian functions. Such an approach in conjunction with image processing algorithms that allows to search objects on images or correct images obtained for example from unmanned aerial vehicles could be the basis of a system for automatically determining the forest plantations health state and improve the inspection quality. The study was conducted for coniferous species of the boreal zone. The mathematical model built in this work allows reducing the cost of automation of calculations related to the processing of the data obtained by forest pathological surveys, despite the fact that the accuracy value of fuzzy classification after the approximation of the membership functions remained at the same level.