Abstract

Hurricanes can cause catastrophic damage to forestlands, prompting forest owners to engage in salvage logging to mitigate losses and resulting in a sudden and pronounced impact on the timber supply. At the same time, salvaging efforts have profound ecological repercussions, reshaping forest dynamics and affecting vital ecosystem processes. Given the relevant impact of salvage logging to the timber market and its potential ecological effects, evaluating the main factors motivating or limiting its occurrence and the common spatial patterns of salvage sites could offer valuable support for post-storm management efforts following future hurricane events.This study evaluates the land cover change (LCC) of forested areas following hurricanes, recognizing that the change to land cover classification could be derived from storm damage or additional modifications, such as salvage logging activities. We used a set of predictive machine learning models to investigate the primary factors influencing these modifications and determining a possible connection to salvage operations. To serve this purpose, we selected variables that relate to both forest’s vulnerability to hurricane disturbance and salvage site selection criteria. This research was centered primarily on the category-five Hurricane Michael, using three additional hurricanes of different intensities to compare results. Random Forest was the bestperforming model for predicting LCC in all cases, having wind speed and distance to nearest wood-consuming mills as the most important features when trained on Hurricane Michael data, emphasizing the relevance of market- and operation-related variables alongside weather disturbance aspects. The findings of our study lead us to infer that the observed modifications in land cover following hurricanes likely represent an additional transformation induced by the removal of damaged timber material, potentially serving as a proxy for salvage logging activities.

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