Abstract:Gravitational mass movements are natural destructive processes that can cause enormous losses. New technologies such as laser scanning and remotely piloted aircrafts (RPA) along with Structure-from-Motion-Multi-View Stereo (SfM-MVS) photogrammetry technique provides an alternative to conventional mapping methods. A hill with a landslide located in the city of Cunha -SP was surveyed by terrestrial laser scanner (TLS) and RPA-based SfM-MVS. SfM-MVS point cloud covers a larger area and point distribution is more … Show more
“…Similarly, it was mentioned that UAV campaigns at low altitudes and SfM photogrammetry can prevail over the visibility limitations that are present in land-based methodologies, such as TLS [57][58][59]. The TLS and UAV data combination for landslide monitoring in Brazil is presented in [60]. It is mentioned that the combination of those two methods presents great advantages over conventional, costly and time-consuming methods.…”
Landslides are among the most dangerous and catastrophic events in the world. The increasing progress in remote sensing technology made landslide observations timely, systematic and less costly. In this context, we collected multi-dated data obtained by Unmanned Aerial Vehicle (UAV) campaigns and Terrestrial Laser Scanning (TLS) surveys for the accurate and immediate monitoring of a landslide located in a steep and v-shaped valley, in order to provide operational information concerning the stability of the area to the local authorities. The derived data were processed appropriately, and UAV-based as well as TLS point clouds were generated. The monitoring and assessment of the evolution of the landslide were based on the identification of instability phenomena between the multi-dated UAV and TLS point clouds using the direct cloud-to-cloud comparison and the estimation of the deviation between surface sections. The overall evaluation of the results revealed that the landslide remains active for three years but is progressing particularly slowly. Moreover, point clouds arising from a UAV or a TLS sensor can be effectively utilized for landslide monitoring with comparable accuracies. Nevertheless, TLS point clouds proved to be denser and more appropriate in terms of enhancing the accuracy of the monitoring process. The outcomes were validated using measurements, acquired by the Global Navigation Satellite System (GNSS).
“…Similarly, it was mentioned that UAV campaigns at low altitudes and SfM photogrammetry can prevail over the visibility limitations that are present in land-based methodologies, such as TLS [57][58][59]. The TLS and UAV data combination for landslide monitoring in Brazil is presented in [60]. It is mentioned that the combination of those two methods presents great advantages over conventional, costly and time-consuming methods.…”
Landslides are among the most dangerous and catastrophic events in the world. The increasing progress in remote sensing technology made landslide observations timely, systematic and less costly. In this context, we collected multi-dated data obtained by Unmanned Aerial Vehicle (UAV) campaigns and Terrestrial Laser Scanning (TLS) surveys for the accurate and immediate monitoring of a landslide located in a steep and v-shaped valley, in order to provide operational information concerning the stability of the area to the local authorities. The derived data were processed appropriately, and UAV-based as well as TLS point clouds were generated. The monitoring and assessment of the evolution of the landslide were based on the identification of instability phenomena between the multi-dated UAV and TLS point clouds using the direct cloud-to-cloud comparison and the estimation of the deviation between surface sections. The overall evaluation of the results revealed that the landslide remains active for three years but is progressing particularly slowly. Moreover, point clouds arising from a UAV or a TLS sensor can be effectively utilized for landslide monitoring with comparable accuracies. Nevertheless, TLS point clouds proved to be denser and more appropriate in terms of enhancing the accuracy of the monitoring process. The outcomes were validated using measurements, acquired by the Global Navigation Satellite System (GNSS).
“…Similar to stereo vision, the Structure from Motion (SfM) algorithm identifies corresponding points in successive images, reconstructing their position in three-dimensional space as a function of camera displacement, resulting in a sparse (or coarse) point cloud; the Multi View Stereo step is tasked with refining the model and densifying the point cloud [ 11 , 12 ]. In geosciences, this technique has proven useful for generating digital elevation models with high spatial resolution for the “virtual” analysis of rocky outcrops, mining monitoring, and geotechnical studies, among others [ 10 , 11 , [13] , [14] , [15] ]. Fig.…”
“…TLS and UAV photogrammetry is widely used in examining 3D forest structure [22], and both have advantages and disadvantages. TLS produces denser and more detailed point clouds than UAVs [23][24][25]. However, aerial images can be acquired with consumergrade UAVs, digital cameras, and open-source software [16,20], which reduces costs.…”
Structural complexity of trees is related to various ecological processes and ecosystem services. To support management for complexity, there is a need to assess the level of structural complexity objectively. The fractal-based box dimension (Db) provides a holistic measure of the structural complexity of individual trees. This study aimed to compare the structural complexity of Scots pine (Pinus sylvestris L.) trees assessed with Db that was generated with point cloud data from terrestrial laser scanning (TLS) and aerial imagery acquired with an unmanned aerial vehicle (UAV). UAV imagery was converted into point clouds with structure from motion (SfM) and dense matching techniques. TLS and UAV measured Db-values were found to differ from each other significantly (TLS: 1.51 ± 0.11, UAV: 1.59 ± 0.15). UAV measured Db-values were 5% higher, and the range was wider (TLS: 0.81–1.81, UAV: 0.23–1.88). The divergence between TLS and UAV measurements was found to be explained by the differences in the number and distribution of the points and the differences in the estimated tree heights and number of boxes in the Db-method. The average point density was 15 times higher with TLS than with UAV (TLS: 494,000, UAV 32,000 points/tree), and TLS received more points below the midpoint of tree heights (65% below, 35% above), while UAV did the opposite (22% below, 78% above). Compared to the field measurements, UAV underestimated tree heights more than TLS (TLS: 34 cm, UAV: 54 cm), resulting in more boxes of Db-method being needed (4–64%, depending on the box size). Forest structure (two thinning intensities, three thinning types, and a control group) significantly affected the variation of both TLS and UAV measured Db-values. Still, the divergence between the two approaches remained in all treatments. However, TLS and UAV measured Db-values were consistent, and the correlation between them was 75%.
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