Darwin Harbour Habitat Mapping Program: Predicted seabed mud content

This resource contains a predicted seabed mud content grid for the greater Darwin Harbour region as part of a baseline seabed mapping program of Darwin Harbour and Bynoe Harbour. This project was funded through offset funds provided by an INPEX-led Ichthys LNG Project to the Northern Territory Government's Department of Environment and Natural Resources (NTG-DENR) with co-investment from Geoscience Australia (GA) and the Australian Institute of Marine Science (AIMS). The intent of this program is to improve knowledge of the marine environments in the Darwin and Bynoe Harbour regions by collating and collecting baseline data that enable the creation of thematic habitat maps and information to underpin marine resource management decisions. The predicted seabed mud content grid was derived from a compilation of multiple surveys undertaken by GA, AIMS and NTG-DENR between 2011 and 2017, including GA0333 (Siwabessy et al., 2015), GA0341 (Siwabessy et al., 2015), GA0351/SOL6187 (Siwabessy et al., 2016), GA4452/SOL6432 (Siwabessy et al., 2017), GA0356 (Radke et al., 2017), and GA0358 and GA0359 (Radke et al., 2018), adding to those from a previous survey GA0333 collected by GA, AIMS and NTG-DENR. This dataset provides spatially continuous predictions of seabed %mud (< 63 µm) content for the Darwin and Bynoe harbour region, northern Australian marine margin. Data are presented in 10 m resolution raster grids format and ascii text file. Predictions are based on 395 samples and nine environmental variables derived from high resolution multibeam sonar bathymetry and backscatter data. Accuracy of predictions is very high, with a VEcv = 62% for mud; and the predictive accuracy has been increased by 198.3% for mud in comparison with the commonly used method (i.e., IDW). Absences in predictions occur in this dataset as a result of non-availability associated with predictive variables. This dataset supersedes previous predictions of mud content for the Darwin and Bynoe harbour region with demonstrated improvements in predictive accuracy.