Wildfires are the most dangerous destabilizing factor for forest ecosystems. Due to the increasing climate aridity, the risk remains high for the growth of forest fires frequency. Therefore, developing science-based forest practices to mitigate the ecological, economic and social losses caused by forest fires is an urgent topic. More than 90% of the total forest fires occur in pine forests. The rapid health deterioration of damaged pine trees leads to significant economic losses due to the changes in their marketability and reduction in the wood technical quality. Therefore, timely diagnosis and accurate prediction of the tree mortality probability are vital tasks to reduce negative ecological and economic effects caused by forest fires.
Materials and Methods
The post-pyrogenic development of the stands was analysed on 11 sample plots laid out in the middle-aged pine forests within the Left-Bank Forest-Steppe during 2013–2017. The stands in the sample plots were of different ages, mensuration rates and had different damage intensity. The study was conducted within Kharkiv Region in the forests of the State Enterprises “Zhovtneve Forest Economy” and “Zmiyivske Forest Economy”. The condition of the damaged trees was determined according to the Sanitary Forests Regulations in Ukraine. Both the correlation and regression analyses, as well as ANOVA were done using generally accepted methods. Multiple regression analysis and logistic regression analysis (binary regressions) were used to construct predictive models of the individual tree mortality probability. The quality of logistic regressions was checked by the ROC analysis.
The indicators describing visual manifestations of tree damage after surface fires and middle-aged Pinus sylvestris L. trees fire resistance have been improved and refined. It was found that with an increase of average bark char height, the number of dead trees in the studied tree groups also increases (r = 0.87, tf = 5.80, t0.01= 3.17). It was determined that the fire resistance of trees increases with the natural degree of tree thickness in the pine stand (statistically significant correlation was found between the natural degree of thickness and the health condition of damaged trees: r = -0.54; p = 0.05). A prognostic mortality model to determine the probability of the mortality of individual trees damaged by surface summer fires has been developed. It includes the average height of bark char and natural degree of tree thickness (the forecast model accuracy is over 78%) as predictors.
The proposed tree mortality model should be used to classify the trees damaged by surface fires, in the shortest possible time after a forest fire, as trees that will die within one year after they would have been damaged. Using this approach in forestry management will minimize the losses caused by changes in the marketability of tree stands damaged by fires. It is advisable to use the proposed critical damage values when planning forest measures (sanitation felling planning) in pine forests damaged by surface fires.
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