Photogrammetric UAV sees a surge in use for high-resolution mapping, but its use to map terrain under dense vegetation cover remains challenging due to a lack of exposed ground surfaces. This paper presents a novel object-oriented classification ensemble algorithm to leverage height, texture and contextual information of UAV data to improve landscape classification and terrain estimation. Its implementation incorporates multiple heuristics, such as multi-input machine learning-based classification, object-oriented ensemble, and integration of UAV and GPS surveys for terrain correction. Experiments based on a densely vegetated wetland restoration site showed classification improvement from 83.98% to 96.12% in overall accuracy and from 0.7806 to 0.947 in kappa value. Use of standard and existing UAV terrain mapping algorithms and software produced reliable digital terrain model only over exposed bare grounds (mean error = −0.019 m and RMSE = 0.035 m) but severely overestimated the terrain by ~80% of mean vegetation height in vegetated areas. The terrain correction method successfully reduced the mean error from 0.302 m to −0.002 m (RMSE from 0.342 m to 0.177 m) in low vegetation and from 1.305 m to 0.057 m (RMSE from 1.399 m to 0.550 m) in tall vegetation. Overall, this research validated a feasible solution to integrate UAV and RTK GPS for terrain mapping in densely vegetated environments.

This research applied terrestrial LiDAR for laboratory beach evolution experiments to quantify the impact of resolution on topographic mapping and change analyses. The multi-site registration and multi-temporal scanning processes produced high accuracy (−0.002 ± 0.003 m) topographic models in a wave tank environment. Morphological analyses based on surface change and profiles showed that models of all resolutions were capable of capturing major sediment changes in relatively smooth areas. However, higher resolution models were necessary in areas with rough surfaces and sudden elevation changes, while coarser resolution models smoothed the roughness and underestimated feature height (e.g., peaks and troughs). Decreasing resolutions from 1 to 10 cm resulted in a 2% underestimation of erosional volumes with a linear regression of y = −0.0964x + 0.4185 (R² = 0.9651) and 3.5% overestimation of depositional volumes with a linear regression of y = 0.0664x + 0.3308 (R² = 0.3645). However, its impact on erosion and deposition volume assessment based on object-oriented beach evolution analysis is less significant, except when fragment objects dominate the sediment changes. For Coastal Morphology Analyst (CMA), the impact of resolution is more observable through 2D object mapping in terms of object size, number, and spatial distribution. Finally, wave modeling experiments proved that resolutions caused significant changes on the behavior of the maximum wave height, the shape of the wave fronts and magnitudes of the currents.