Comparison of leaf-off and leaf-on combined UAV imagery and airborne LiDAR for assessment of a post-mining site terrain and vegetation structure: Prospects for monitoring hazards and restoration success

Abstract

Mining is an important human activity that significantly affects the landscape character, particularly through excavation of spoil material and its deposition on spoil banks. The information on terrain or vegetation cover of spoil banks is often required for two different reasons: (i) to monitor and prevent adverse effect of hazards associated with unstable terrain; and (ii) to assess restoration success. Traditionally used in situ methods for monitoring surface displacement or restoration success are restricted in terms of spatial and temporal coverage. Therefore, in this study, we assessed the value of photogrammetrically and Light Detection and Ranging (LiDAR) derived point clouds for characterizing a post-mining site. We acquired images under leaf-off and leaf-on conditions and showed that point densities of point clouds acquired photogrammetrically under leaf-off conditions exceeded densities of those acquired under leaf-on conditions and uniformly covered ground of the entire study area (an average density of 288 points per m 2 ). In addition, the accuracy of the digital terrain model (DTM; 1 m resolution) derived from images acquired under leaf-off conditions was comparable to the LiDAR-derived DTM (RMSE of 0.19 m and 0.12 m, respectively). While LiDAR-derived DTM accuracies were consistent across vegetation categories (RMSE 0.12–0.14 m), accuracy of image-based DTMs declined in the following order: forest (RMSE 0.15 m), steppes (RMSE 0.21 m), and aquatic vegetation (RMSE 0.36 m). We suggest the leaf-off UAV imagery as a viable alternative for building DTMs that can be utilized for assessment of risks associated with instability of spoil banks terrain. In addition, we also suggest that a combination of acquisitions under leaf-off and leaf-on conditions have a potential to replace expensive airborne LiDAR surveys for applications requiring information on vegetation cover or vegetation height.