PUBLICATION on Comparison of leaf-off and leaf-on combined UAV imagery and airborne LiDAR for assessment of a post-mining site terrain and vegetation structure

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

Vítězslav Moudrý1, Kateřina Gdulová1, Michal Fogl1, Petr Klápště1, Rudolf Urban12, Jan Komárek1, Lucie Moudrá1, Martin Štroner2, Vojtěch Barták1, Milič Solský1

 1Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Kamýcká 129, Praha – Suchdol, 165 00, Czech Republic

2 Czech Technical University in Prague, Faculty of Civil Engineering, Department of Special Geodesy, Thákurova 7, Prague 6, 166 29, Czech Republic

 Corresponding author: Vítězslav Moudrý

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 m2). 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.


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