Statistical Analysis of Tree-Forest Damage by Snow and Wind:Logistic Regression Model for Tree damage and Cox Regression for Tree Survival

Abstract

Forest stands and individual trees are often devastated by natural disasters such as typhoons and heavy snowfall in Japan, resulting in significant economic losses to the forestry sector. Our objective is to identify key risk factors that affect the degree of damage. We apply two types of statistical approach: one is, a logistic regression model to snow damage data to investigate if there is any geographical element affecting the degree of damage at the stand level, and the other is, a survival analysis on tree failure data for factors affecting the degree of damage at the individual tree level. A logistic regression analysis revealed that the risk probability of snow damage is higher on older and thin stands. The analysis also indicates taking advantage of certain geographic conditions to reduce wind burden could decrease the degree of damage. A Cox regression analysis showed that tree age, diameter at breast height, and species were key factors that influenced the degree of tree failure. Specifying risk factors throughout statistical modeling helps to provide a comprehensive, systematic, and objective method to assess risk in forest management.