The Hydrogeological Landscape (HGL) framework divides geographic space into regions with similar landscape characteristics. HGL regions or units are used to facilitate appropriate management actions tailored to individual HGL units for specific applications such as dryland salinity and climate-change hazard assessment. HGL units are typically constructed by integrating data including geology, regolith, soils, rainfall, vegetation and landscape morphology, and manually defining boundaries in a GIS environment. In this study, we automatically construct spatially contiguous regions from standard HGL data using Self-Organising Maps (SOM), an unsupervised statistical learning algorithm. We compare the resulting SOM-HGL units with manually interpreted HGL units in terms of their spatial distributions and attribute characteristics. Our results show that multiple SOM-HGL units successfully emulate the spatial distributions of individual HGL units. SOM-HGL units are shown to define subregions of larger HGL units, indicating subtle variations in attribute characteristics and representing landscape complexities not mapped during manual interpretation. We also show that SOM-HGL units with similar attributes can be selected using Boolean logic. Selected SOM-HGL units form regions that closely conform to multiple HGL units not necessarily connected in geographic space. These SOM-HGL units can be used to establish generalised land management strategies for areas with common physical characteristics. The use of SOM for the construction of HGL units reduces the subjectivity with which these units are defined and will be especially useful over large and/or inaccessible regions, where conducting field-based validation is either logistically or economically impractical. The methodology presented here has the potential to contribute significantly to land-management decision-support systems based on the HGL framework.
Cracknell, M. J., & Cowood, A. L. (2016). Construction and analysis of Hydrogeological Landscape units using Self-Organising Maps. Soil Research, 54(3), 328–345. https://doi.org/10.1071/SR15016