Capturing exposed bedrock in the upland regions of Great Britain: A geomorphometric focused random forest approach

• Published in: Computers & geosciences 2025-02, Vol.196, p.105814, Article 105814
• Main Authors: Williams, Chris, Whitbread, Katie, Hall, Alex, Roberson, Sam, Finlayson, Andrew, Palamakumbura, Romesh N., Hulbert, Andrew, Paice, Matthew
• Format: Article
• Language: English
• Subjects: Bedrock exposure; Digital mapping; Postglacial landscape; Random forest
• ISSN: 0098-3004
• DOI: 10.1016/j.cageo.2024.105814

Rock exposure distribution maps provide invaluable information for a range of applications from geohazard assessment through to aggregate resource potential assessments. Despite the usefulness of such information, it is only available to a limited extent across Great Britain (GB). Recent developments in the application of machine learning approaches to map exposed rock distribution rely on existing geological and land cover maps as the key input data for model training. We present a catchment-scale approach for delivering high-resolution rock exposure maps for GB mountain terrains. Our application has two objectives: establish a consistent and cross-applicable approach enabling feature identification from elevation datasets; use the results and diagnostics of the application to assist in further environmental process understanding. We utilize manual aerial image interpretation, and a suite of geomorphic terrain variables generated from a 5 m Digital Terrain Model as inputs to a distributed random forest model. Eight separate catchment models were derived from the training datasets using a leave-one-out approach. Aggregated results indicate a model accuracy of 79%, with a relatively high model sensitivity (78%) at the cost of relatively low precision (20%). Variable importance assessment highlighted patterns consistent with expected geomorphic controls on rock exposure related to gravity-driven slope processes in mountain landscapes. These results highlight the potential of multi-variant approaches for high-resolution rock exposure mapping, and lay a foundation for further development, particularly in relation to opportunities for further training data capture to ensure model accuracy. The ability to associate features based on geomorphological variables - indicative of landscape processes including erosion and deposition - presents opportunities that go beyond rock exposure such as for critical mineral and resource assessment. This approach will be applied for initial site characterisation as part of future onshore and offshore geological survey activities where high-resolution terrain and bathymetric data are available. •Bedrock exposure predictions in upland areas of Great Britain achieved with accuracy of 79%.•Terrain information enables rapid classification of bedrock exposures.•Derivatives associated with gravity-driven slope processes act as key predictors.•Resource-effective classification approach compared to past manual equivalents.•Approach can b