Date of Degree
Graduate Thesis - Open Access
An estimated 39 million m3 of timber was damaged across the Southeast Forest District of Mississippi due to Hurricane Katrina. Aggregated forest plot-level biometrics was coupled with wind, topographical, and soil attributes using a GIS. Data mining through Regression Tree Analysis (RTA) was used to determine factors contributing to shear damage of pines and wind-throw damage of hardwoods. Results depict Lorey’s Mean Height (LMH) and Quadratic Mean Diameter (QMD) are important variables in determining the percentage of trees and basal area damaged for both forest classes with sustained wind speed important for wind-throw and peak wind gusts for shear. Logistic regression based on stand damage classification compared to RTA revealed LMH, stand height to diameter ratio, and sustained wind variable concurrence. Reclassification of MIFI plot damage calls based on percentage of trees damaged increased predictability of wind-throw and shear classification. This research can potentially aid emergency and forest managers for better mitigation and recovery decisions following a hurricane.
Allen, Jared Seth, "Determining South Mississippi forest susceptibility to windthrow and shear damage in a hurricane environment through data mining of meteorological, physiographical, pedological, and tree level data" (2009). Theses and Dissertations MSU. 1358.