Patterns of Forest Damage in a Southern Mississippi Landscape Caused by Hurricane Katrina

Abstract

Understanding and predicting the ways in which large and intense hurricanes affect ecosystem structure, composition and function is important for the successful management of coastal forest ecosystems. In this research, we categorized forest damage resulting from Hurricane Katrina into four classes (none, low, moderate, heavy) for nearly 450 plots in a 153,000 ha landscape in southern Mississippi, USA, using a combination of air photo interpretation and field sampling. We then developed predictive damage models using single tree classification tree analysis (CTA) and stochastic gradient boosting (SGB) and examined the importance of variables addressing storm meteorology, stand conditions, and site characteristics in predicting forest damage. Overall damage classification accuracies for a training dataset (n = 337 plots) were 72 and 81% for the single tree and SGB models, respectively, with Cohen’s weighted linear κ values of 0.71 and 0.86. For an independent validation dataset (n = 112 plots), classification accuracy dropped to 57% (κ = 0.65) and 56% (κ = 0.63) for the single tree and SGB models. Proportions of agreement between observed and predicted damage were significantly greater (P < 0.05) than would be expected by chance alone for all damage classes with the training data and all but the moderate class for the validation data. Stand age was clearly the best predictor of damage for both models, with forest type, stand condition, site aspect, and distance to the nearest perennial stream also explaining much of the variation in forest damage. Measures of storm meteorology (duration and steadiness of hurricane-force winds; maximum sustained winds) were of secondary importance. The forest-wide application of our CTA model provided a realistic, spatially detailed map of predicted damage while also maintaining a relatively high degree of accuracy. The study also provides a first step toward the development of models identifying the susceptibility of forest stands to future events that could be used as an aid to incorporating the effects of large infrequent disturbances into forest management activities.