Knowledge of the spatial and temporal changes caused by episodic disturbances and seasonal variability is essential for understanding the dynamics of mangrove forests at the landscape scale, and for building a baseline that allows detection of the effects of future environmental change. In combination with LiDAR data, we calculated four vegetation indices from 150 Landsat TM images from 1985 to 2011 in order to detect seasonal changes and distinguish them from disturbances due to hurricanes and chilling events in a mangrove‐dominated coastal landscape. We found that normalized difference moisture index (NDMI) performed best in identifying both seasonal and event‐driven episodic changes. Mangrove responses to chilling and hurricane events exhibited distinct spatial patterns. Severe damage from intense chilling events was concentrated in the interior dwarf and transition mangrove forests with tree heights less than 4 m, while severe damage from intense hurricanes was limited to the mangrove forest near the coast, where tree heights were more than 4 m. It took 4–7 months for damage from intense chilling events and hurricanes to reach their full extent, and took 2–6 yr for the mangrove forest to recover from these disturbances. There was no significant trend in the vegetation changes represented by NDMI over the 27‐yr period, but seasonal signals from both dwarf and fringe mangrove forests were discernible. Only severe damage from hurricanes and intense chilling events could be detected in Landsat images, while damage from weak chilling events could not be separated from the background seasonal change.

This paper examines the feasibility of using LIDAR surveys to update existing historical shoreline data sets by comparing contour shorelines and the high water line (HWL) at eight study locations in North and South Carolina. The analysis was based on airborne LIDAR topography and orthoimagery collected simultaneously during June 2000. The popular method of digitizing the wet-dry line from orthoimagery was used to measure the HWL. Contour shorelines were derived by using the previous high tide (HW), the mean high water datum (MHW), and the mean higher high water datum (MHHW) of nearby tide gauges. A method was developed to quantitatively compare the positions of the HWL and the contour shorelines in a GIS. The mean high water and mean higher high water contour shoreline positions were the best match to the high water line at 7 of 8 locations, and differed by less than 5.4 meters from the digitized high water line positions. This difference is well within the errors associated with past methods for...