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Landslides are global hazards that contribute significantly to worldwide catastrophes. Since landslides cause fatalities and property damage, understanding movement patterns is crucial to mitigate risks or potential reactivations. Slope kinematic modeling can utilize geodetic surveying or direct observations, though Unmanned Aerial Vehicles with photogrammetric or laser scanning technologies have become prevalent in recent decades. This study introduces a new approach for monitoring landslides through UAV photogrammetry and LiDAR data. LiDAR and photogrammetry raster images were integrated by merging them according to landscape features, encompassing areas with vegetation and human-made structures. This fusion method leverages the complementary strengths of photogrammetry and LiDAR, optimizing their capabilities for distinct terrain features: photogrammetry excels in capturing detailed textures, while LiDAR penetrates vegetation to provide accurate ground-level data. By integrating these technologies, the Digital Feature Model (DFM) offers superior accuracy compared to conventional elevation models, making it particularly effective for monitoring complex terrains. To assess the robustness of the proposed methodology, 496 points were measured with topo-geodetic instruments within an active landslide, each point representing different terrain features. This analysis demonstrated the effective merging of data from both sensors, leading to thorough results that accurately represented the ground surface with the most suitable technology. The RMSE values for the vertical differences between the ground truth and the proposed DFM were 0.060 m, notably lower than the 0.206 m from photogrammetry and 0.441 m from LiDAR. This novel method can enhance the evaluation of landslide dynamics and movements, as follow-up surveys using this approach yield greater accuracy regarding differences between epochs. • Topic of high interest for the Geoscience and Soil Science academic community. • State of the art instrumentation used for hazard mapping and monitoring. • Innovative method enhances the accuracy of established methods. • Novel approach to data fusion between photogrammetry and LiDAR datasets. • Possible methodological improvements and future use in other research studies.
Sestraș et al. (Wed,) studied this question.