| Summary: | Storms are considered one of the rapid climatic events that have a dramatic impact on coastal morphology, hence they require further investigation and quantifying of coastal changes and responses. Light detection and ranging (LiDAR) is the most advanced technology to be widely used by researchers for coastal geomorphological studies. The purpose of this study is to apply an object-based approach using repeated LiDAR surveys to understand the short-term morphological changes that occurred on Santa Rosa Island, Florida after category 3 hurricanes Ivan (2004) and Dennis (2005), making it the first study to apply this method, as opposed to previous studies commonly used field-based approaches. The first analysis was conducted using a coastal morphology analysis (CMA) tool. In the second analysis, the extracted mean elevation change values were linked to three factorsmean vegetation, mean slope, and mean elevationto demonstrate their contribution to the change using ordinary least square (OLS) analysis. The third analysis was carried out using the classification and regression tree (CART) analysis. Of the study area, 18.64% encountered erosional processes and 11.35% with depositional processes during Hurricane Ivan, whereas during Hurricane Dennis, 5.91% faced erosional processes and 8.18% was affected by depositional processes. Both hurricanes resulted in a net sediment loss; 283,167 m3 during Hurricane Ivan and 52,440 m3 during Hurricane Dennis. Generally, objects tended to be irregular, asymmetrical, and shaped with smooth boundaries. Along the coast, most objects tended to have an elongated shape, but inland the shapes were more irregular. The overall OLS model during Hurricane Ivan yielded statistically significant results for the three factors, with a confidence level of 0.00 and an adjusted r-square of 0.40; and during Hurricane Dennis, the mean vegetation and mean elevation results yielded significant statistical results (p-value 0.00), while slope did not show significance and had an adjusted r-square of 0.47. CART analysis of both hurricanes ranked the mean elevation as the most important factor in predicting the mean elevation change, followed by the mean slope and finally the mean vegetation variable.
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