Spatial Analysis of Biophysical Variables

Map 1: Distribution of mud samples in northern Australian Economic Exclusion Zone (EEZ) area.

Figure 1: Distribution of mud
samples in northern Australia.

Spatially continuous data of biophysical variables are increasingly required in marine environmental sciences and management. They are usually not readily available, especially for deep marine regions. Spatial distribution data of biophysical variables are often derived from point sources. Spatial interpolation methods are then employed for estimating biophysical variables at the unsampled locations using point samples within a certain distance of a point to be estimated based on their spatial relationship.

Spatial and statistical modelling methods being employed by Geoscience Australia include:

  • spatial statistics and geostatistics
  • geographical information systems (GIS) approaches
  • modern statistics
  • multivariate statistical analysis and
  • machine learning techniques.
Map 2: Predicted spatially continuous seabed mud content using inverse distance squared.

Figure 2: Predicted spatially
continuous seafloor mud content
using the inverse distance
squared method.

Geoscience Australia is investigating a range of spatial interpolation methods to identify those that best model seafloor physical properties for resource management applications.

Geoscience Australia is employing spatial and statistical data modelling techniques to derive seascapes which describe areas of the seafloor that are characterised by different biophysical properties, and to better capture the physical heterogeneity of Australia's seafloor for linking with marine biodiversity. Both of these applications are based on the premise of physical surrogacy (See Surrogacy).

Map 3: Predicted spatially continuous data of mud in the northern region of Australia's EEZ

Figure 3: Predicted spatially
continuous data of seafloor
mud content in northern Australia.