Assessment of Coastal Saltmarsh TEC on NSW Crown Forest Estate

The operational map for Coastal Saltmarsh was constructed to resolve long-standing issues surrounding its identification, location and extent within the NSW State Forest estate covered by the eastern Regional Forest Agreements. The project’s Threatened Ecological Community Reference Panel (the Panel) reviewed the determination for Coastal Saltmarsh and agreed upon a set of diagnostic parameters for its identification. We identified that any treeless saline and sub saline native vegetation found in the intertidal zone had the potential to be Coastal Saltmarsh. We estimated the extent of the intertidal zone by using a fine scale digital elevation model to determine the highest astronomical tideline (HAT). We then mapped potential Coastal Saltmarsh by analysing recent fine scale three dimensional aerial imagery to identify any native vegetation that comprised of low-growing treeless communities and was located within the HAT and on the landward side of mangroves. Mapping criteria used a tree cover tolerance of up to 30% to include areas that had a mixed cover of mangrove, paperbark or casuarina species with a saltmarsh understorey. Exposed mudflats and banks were also mapped when they were visible. Our mapping covered 1.4 million hectares of State Forest within the south, central and north coast regions of NSW. We identified a total of 111.9 hectares of Coastal Saltmarsh within 14 State Forests along the east coast. The most extensive areas are located in Bermagui and Mogo State Forests on the south coast and in Wallaroo State Forests on the north coast. We validated our map of coastal saltmarsh using an existing independent map of estuarine habitats (Creese et al 2009). Our mapping consistently identified almost twice as much coastal saltmarsh as Creese et al (2009), but this was attributable to differences in the mapping criteria rather than any error.

Operational TEC Mapping have been derived by API at a viewing scale between 1-4000 using ADS40 50 cm pixel imagery and 1 m derived LIDAR DEM grids for floodplain EECs.