Lidar-Imagery Fusion Reveals Rapid Coastal Forest Loss in Delaware Bay Consistent with Marsh Migration

Abstract

Tidal wetland ecosystems and their vegetation communities are broadly controlled by tidal range and inundation frequency. Sea-level rise combined with episodic flooding events are causing shifts in thresholds of vegetation species which reconstructs the plant zonation of the coastal landscape. More frequent inundation events in the upland forest are causing the forest to convert into tidal marshes, and what is left behind are swaths of dead-standing trees along the marsh–forest boundary. Upland forest dieback has been well documented in the mid-Atlantic; however, reliable methods to accurately identify this dieback over large scales are still being developed. Here, we use multitemporal Lidar and imagery from the National Agricultural Imagery Program to classify areas of forest loss in the coastal regions of Delaware. We found that 1197 ± 405 hectares of forest transitioned to non-forest over nine years, and these losses were likely driven by major coastal storms and severe drought during the study period. In addition, we report decreases in Lidar-derived canopy height in forest loss areas, suggesting forest structure changes associated with the conversion from forest to marsh. Our results highlight the potential value of integrating Lidar-derived metrics to determine specific forest characteristics that may help predict future marsh migration pathways.

Keywords

• remote sensing
• marsh migration
• ghost forest
• change detection
• Lidar
• forest structure