UAV photogrammetry development during the last decade has allowed to catch information at a very high spatial and temporal resolution from terrains with very difficult or impossible human access. This paper deals with the application of these techniques to study and produce information of very extreme topography which is useful to plan works on this terrain or monitoring it over the time to study its evolution. The methodology stars with the execution of UAV flights on the cut slope studied, one with the cam vertically oriented and other at 45º respect that orientation. Ground control points (GCPs) and check points (CPs) were measured for georeference and accuracy measurement purposes. Orthophoto was obtained projecting on a fitted plane to a studied surface. Moreover, since a digital surface model (DSM) is not able to represent faithfully that extreme morphology, information to project works or monitoring it has been derived from the point cloud generated during the photogrammetric process. An informatics program was developed to generate contour lines and cross sections derived from the point cloud, which was able to represent all terrain geometric characteristics, like several Z coordinates for a given planimetric (X, Y) point. Results yield a root mean square error (RMSE) in X, Y and Z directions of 0.053 m, 0.070 m and 0.061 m respectively. Furthermore, comparison between contour lines and cross sections generated from point cloud with the developed program on one hand and those generated from DSM on other hand, shown that the former are capable of representing terrain geometric characteristics that the latter cannot. The methodology proposed in this work has been shown as an adequate alternative to generate manageable information, as orthophoto, contour lines and cross sections, useful for the elaboration, for example, of projects for repairing or maintenance works of cut slopes with extreme topography.

The objective of this study is to evaluate the effects of the 3D point cloud density derived from unmanned aerial vehicle (UAV) photogrammetry and structure from motion (SfM) and multi-view stereopsis (MVS) techniques, the interpolation method for generating a digital terrain model (DTM), and the resolution (grid size) of the derived DTM on the accuracy of estimated heights in small areas, where a very accurate high spatial resolution is required. A UAV-photogrammetry project was carried out on a bare soil of 13 × 13 m with a rotatory wing UAV at 10 m flight altitude (equivalent ground sample distance = 0.4 cm). The 3D point cloud was derived, and five sample replications representing 1, 2, 3, 4, 5, 10, 15, 20, 30, 40, 50, 60, 70, 80 and 90% of the original cloud were extracted to analyze the effect of cloud density on DTM accuracy. For each of these samples, DTMs were derived using four different interpolation methods (Inverse Distance Weighted (IDW), Multiquadric Radial Basis Function (MRBF), Kriging (KR), and Triangulation with Linear Interpolation (TLI)) and 15 DTM grid size (GS) values (20, 15, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0.67, 0.5, and 0.4 cm). Then, 675 DTMs were analyzed. The results showed, for each interpolation method and each density, an optimal GS value (most of the cases equal to 1 cm) for which the Root Mean Square Error (RMSE) is minimum. IDW was the interpolator which yielded best accuracies for all combination of densities and GS. Its RMSE, considering the raw cloud, was 1.054 cm. The RMSE increased 3% when a point cloud with 80% extracted from the raw cloud was used to generate the DTM. When the point cloud included the 40% of the raw cloud, RMSE increased 5%. For densities lower than 15%, RMSE increased exponentially (45% for 1% of raw cloud). The grid size minimizing RMSE for densities of 20% or higher was 1 cm, which represents 2.5 times the ground sample distance of the pictures used for developing the photogrammetry project.