A recent 3D digitization technology has advanced the development of 3D digital outcrop models (3D-DOMs). Yet, few studies report the effort in extracting the physical properties of rocks from 3D-DOMs. We aim to investigate the relationship between the surface morphology and the physical properties of the rocks at outcrops. First, we created high-resolution Digital Elevation Model (DEM) images with different spatial resolutions from high-resolution 3D-DOMs, and calculated Terrain Ruggedness Index (TRI), Roughness, and Topographic Position Index (TPI). The 3D-DOMs were generated by the drone flyover in the Itoshima Peninsula Coast and the Oga Peninsula Coast. Second, we produced orthoimages using the 3D-DOMs and extracted them to the data in the Hue, Saturation, and Value (HSV) color space. We also newly defined a ruggedness index GTRI by normalizing TRI using the maximum value of TRI. We examined the correlation between HSV color spaces and the ruggedness index GTRI. Third, we examined the use of a machine learning model to predict the ruggedness of the orthoimage in the Oga Coast, with the Itoshima Coast data, HSV color space data, and GTRI data as the supervisory data, explanatory variables, and the objective function, respectively.
We found TRI is the most effective index representing the small-spatial scale roughness. Our ruggedness parameter GTRI showed good correlations with those in the HSV color space. Among the good correlations, GTRI showed the best correlation with V, suggesting a correlation between the outcrop ruggedness and V in the orthoimage. We further found that our machine learning model generally reconstructed well the GTRI image of the Oga Coast, with the successful prediction of the presence of relatively large-scale ruggedness such as cracks. The results suggest that our method by integrating the TRI parameters and HSV color spaces acquired from UAV photography can be a powerful method to estimate rock properties.