Coastal wetland mapping combining multi-date SAR and LiDAR

Inventorying and monitoring are imperative to management of vulnerable coastal wetlands. Multi-date and multi-sensor remote sensing offer new capabilities to wetland programmes such as the US National Wetland Inventory. This pilot study focuses on swamp forests and pocosins, marshes, shrub–scrub and invasive Phragmites australis. Combinations of spaceborne multi-date Synthetic Aperture Radar (SAR) imagery and airborne light detection and ranging (LiDAR) elevation (bare earth elevation and vegetation height) were evaluated. Multi-date SAR data (horizontal-horizontal and horizontal-vertical dual polarizations) highlighted physiognomic dynamics, with LiDAR vegetation canopy discerning selected classes. The highest overall accuracy used SAR, LiDAR canopy and digital elevation model (DEM) data (81% κ = 0.744), but not significantly different from the SAR-only classification (81% κ = 0.742). Both classifications exceeded the data combination using SAR data and DEM (66% κ = 0.521) and SAR data with vegetation canopy (80% κ = 0.725). This approach requires investigation using advanced classification algorithms to prove its potential for monitoring wetland change, sea-level rise, and invasive species.