Coastal Wetland Mapping Using Time Series Sar Imagery and Lidar: Alligator River National Wildlife Refuge, North Carolina

Mapping and monitoring of vast coastal wetlands vulnerable to dynamic coastal erosion, sea-level rise, fire, and marsh succession require remote sensing approaches that capitalize on newly available sensors, advanced classification techniques, and combinations of multi-sensor and multi-date data. This pilot study assesses the feasibility and accuracy potential for mapping specific coastal wetlands of high priority for the National Wetland Inventory (NWI) in the Alligator River National Wildlife Refuge, North Carolina. Wetland classes of high mapping value owing to their ecological dynamics and extent include palustrine forests (swamp forests and pocosins), emergent estuarine marshes, irregularly-flooded shrub-scrub transition, and invasive Phragmites australis patches occurring along shores throughout the region. These classes selected to test input data and classification methodology using an array of multidate SAR imagery (ALOS PALSAR) and LiDAR-derived rasters (minimum elevation, vegetation canopy height, slope, and curvature) in combinations. Initial results illustrate strong potential for multidate SAR imagery and enhanced accuracy achievable by integration of vegetation canopy LiDAR for broad-scale mapping of coastal wetland vegetation change. Canopy structural changes over spring, early and late summer seasons were captured by PALSAR HH and HV polarization bands, yielding the highest overall accuracies multidate combination and with inclusion of LiDAR canopy and minimum elevation data. Field observations corroborated the remote sensing and offer useful calibration data for sea-level rise simulation models and invasive species monitoring. In concert with the historical continuity of Landsat for broader coastal land cover dynamics, these data and techniques offer significant enhancements for future monitoring of coastal change.