Mapping Stony Rise Landforms with Remote Sensing and Geophysical Data Using a Machine Learning Approach

Elevated areas on basalt lava flows, locally known as stony rises, are volcanic landforms located in the Victorian Volcanic Plain (VVP) in southeastern Australia. Stony rises are recognized as having high geological, ecological, and cultural values. Currently, the mapping of stony rises is carried out on a study-by-study basis, via traditional field surveys. Mapping of such features could be performed more rapidly and at a landscape level by utilizing remotely sensed datasets such as LiDAR, satellite and aerial imagery, and geophysical data, and applying detection techniques such as machine learning. This study incorporates an ensemble of remotely sensed data and predictor variables to characterize stony rise morphology through a machine learning approach that uses an object based Random Forest. We were able to successfully detect stony rises across various lava flows in the VVP. Future geomorphological studies should aim to incorporate the use of spectral and geophysical predictor variables for large area landform detection and analysis.