Methods

Windthrow risk in many parts of the world has been modeled and assessed. Empirical models are best suited for areas with complex, heterogeneous stand structure and composition (Lanquaye-Opoku and Mitchell 2005, Mitchell and others 2001) like the forests of Maine. This empirical approach typically utilizes regression models relating wind damage to physical stand components. Generally, the models produce a probability value rating or index of damage potential based on the stand’s suite of environmental conditions. Index modeling of spatial phenomena is enhanced with GIS, which allows for the integration of spatially explicit model parameters.

Flow Chart

Logistic regression is a commonly used tool for evaluating these models and isolating highly correlated component variables (Lanquaye-Opoku and Mitchell 2005, Mitchell and others 2001). Rather than using logistic regression, this project produced a generalized model, retaining variables that would not be statistically significant in a logistic regression analysis. This approach is unique because it attempts to create a model that may be applicable regionally and not be limited to the landscape where it was developed.

Eight environmental parameters, (elevation, soil rooting depth, topographic exposure, stand species type, stand height, stand density, stand history, and exposed edges) were used to generate a spatially explicit vulnerability index value. Mitchell (1995, 1998) advocates grouping the factors into three broad categories—exposure, soils, and stand characteristics—to form a windthrow triangle, a conceptual model of the relationships among these interacting factors. For this model, factors were broken down further into site and stand parameters and combined to generate the cumulative windthrow risk index (Figure on the right). Data for model variables, including a spatially referenced database of windthrow history, was acquired from various sources and covered an area of private landholdings in the northern portion of the State.