HCAS Optimised SLGA Products

HCAS Optimised SLGA Products

Ross Searle

With the development of the second version of the Habitat Condition Assessment System for Australia (HCAS) there was a requirement to develop some new products based on the Soil and Landscape Grid of Australia (SLGA) that are optimised for use within the HCAS system. This page describes the processing performed on the SLGA to generate HCAS optimised inputs.

Both the SLGA soil attribute products and the environmental covariate stack used to produce the SLGA soil attribute products were processed.

The HCAS optimised products formatted as Cloud Optimised GeoTIFFS (COGS) can be found at - https://esoil.io/TERNLandscapes/Public/Products/TERN/NonAnthropogenic/

There is also a QGIS project which allows for quick display of the 3 ArcSecond COG products - https://esoil.io/TERNLandscapes/Public/Products/TERN/NonAnthropogenic/Ecology%20Modelling%20Optimised%20SLGA%20Products.qgz

The R code used to perform the processing can be found at - https://github.com/AusSoilsDSM/SLGADevelopment/tree/main/Ross/NonAthropogenic . Contact ross.searle@csiro.au to get access to this

The SLGA Soil Attribute Surfaces

The main processing steps to generate HCAS optimised inputs from the SLGA soil attribute surfaces included remodelling the surfaces from the original observed point data using a revised set of covariates that minimised the effects of anthropogenic artefacts in the modelled surfaces, then filling any mainland NA values to produce a continuous raster eg no lakes or rivers, expanding the existing SLGA coastline slightly to allow for users to provide their own coastline, splining the SLGA standard depths at every pixel to generate a topsoil and a subsoil soil attribute product.

The original SLGA attribute surfaces that have been remodelled for use in HCAS include -  Total nitrogen, Total phosphorous, Bulk density, Soil organic carbon, Clay, pH - Water, Depth of Soil, Depth of Regolith, Available phosphorus and Available Water Capacity.

All processing was performed using R on the CSIRO Petrichor High Performance Compute Cluster.

The processing steps are :

Figure 1 shows a comparison of the 0-5 cm clay percent soil attribute from the original SLGA and the new HCAS optimised version.

Notes - Whilst the new HCAS optimised soil attribute surfaces generally have validation statistics reasonably similar the original SLGA soil attribute surfaces the SLGA surfaces have better validation statistics. This is due to a range of reasons, including a smaller number of covariates being used in the modelling, more sophisticated modelling approaches being used in the original SLGA products and expert knowledge being used to determine the best final product. Also while the spatial patterns are sometimes similar, the HCAS optimised products are always different to the original SLGA products.

Fig 1 (a) : SLGA 0-5cm Clay percent

Fig 1(b) : HCAS Optimised 0-5cm Clay percent

Table 1 : Validation statistics for HCAS optimised soil attribute surfaces


Fig 2 : Model fits for the HCAS optimised soil attributes

Topsoil & Subsoil Aggregated Soil Attribute Surfaces

The SLGA soil attribute surfaces represent soil attribute values at 6 standard depths. Sometimes this is too much detail for users who just want general estimates of soil attribute value at the surface and in the subsoil. This project generated Topsoil and Subsoil aggregated products from the new HCAS Optimised soil attribute surfaces. At each pixel for each soil attribute a mass preserving spline (Bishop, et., al.) was applied across the 6 standard depths to produce a soil attribute value estimate for 0-30cm and 30cm to maximum soil depth. Fig 

Figure 3 : Topsoil (left) and Subsoil (right) depth aggregated soil water pH maps

SLGA Covariates

The SLGA raster environmental covariate stack is potentially suitable for use in the HCAS. To enable this they have been processed in a similar fashion to the soil attributes surface to make them suitable for use in HCAS. 

The processing steps include :