Initially, our map will depict the distribution of vegetated saltmarsh ecosystems. This map is designed to integrate with the Blue Carbon Method, which defines saltmarsh as an ecosystem that: (a) is comprised of salt tolerant plants that are herbaceous as well as some woody shrubs; and (b) occurs on floodplains and in estuaries and can be flushed with water from a combination of water sources.

Additionally, we are collecting training data for sparsely vegetated saltpans for future analysis.

This is the first Blue Carbon method under the Emissions Reduction Fund (ERF). Blue Carbon is a term commonly used for coastal wetland and marine ecosystems that can sequester and store high amounts of organic carbon, and release very low amounts of greenhouse gases. This method enables Australian Carbon Credit Units (carbon credits) to be earned by projects that remove or modify tidal restriction mechanisms and allow tidal flow to be introduced to coastal wetland ecosystems (that may be vegetated or unvegetated) through permanent or seasonal inundation with saline or brackish water.

Read more about the Blue Carbon Method here.

We use satellite image composite metrics and stacked machine learning classifiers to assess whether 30-m pixels across Australia's entire coastal zone are likely to be saltmarsh. Pixels with high likelihood of being saltmarsh are classified as saltmarsh pixels and are included in the map.

For more information on our modelling approaches please refer to some of our recent papers:

• Murray et al., 2018 (Remap)

• Murray et al, 2019 (Global tidal flats)

• Lyons et al., 2020 (Allen Coral Atlas)

• Navarro et al., 2021 (south-eastern Australia saltmarsh)

• Murray et al., 2021 (Global intertidal change)

The first map will be delivered at 30-m pixel resolution. However, our training data and models are sufficiently flexible to support predictions using other sensors, such as ESA Sentinel data. Future versions of the map may be produced at resolutions up to 10-m.

This project is funded by the Clean Energy Regulator, with the modelling workflow developed at JCU's Global Ecology Lab (via an ARC Discovery Early Career Fellowship to Murray) in collaboration with UNSW Centre for Ecosystem Science.

The majority of the team is based at JCU Townsville (Nick Murray and Alejandro Navarro) and UNSW (Mitchell Lyons). The team at JCU's focus is to develop the training dataset and the classification models workflows and testing in Google Earth Engine, while supporting the team at UNSW to test the ability for these models to be ported to Digital Earth Australia.

We also have a technical advisory group, which includes scientists from Geoscience Australia, DAWE and the Clean Energy Regulator, and an ecology advisor group, which includes scientists at UQ, JCU and UNSW.

We will be systematically reaching out the saltmarsh research community in search of training data, expertise, and for periodic map reviews. You can contact us here (alejandro.navarrootero@jcu.edu.au) or join our google group to receive updates on the project.

This project is running from mid-2021 to mid-2023. During this period we will circulate draft maps to the expert advisory groups and other end users. If you are interested in reviewing draft maps, please contact us directly or join our Google Group.

At the end of the project we expect the map will be made freely available for a range of end-users.

Data freshness relates to how up-to-date the dataset is. This project will be implemented on Digital Earth Australia and, at the completion of the project, the remote sensing pipeline and training data will be inherited by Geoscience Australia and an annual update schedule will be established.

The Global Intertidal Change models have the ability to simultaneously map and monitor other coastal ecosystem types, such as tidal flats, mangroves, and supratidal forests. We are collaborating widely to develop the training sets needed for this type of application in Australia. In addition, we are also developing a seagrass training set to support next-generation seagrass remote sensing models.