Abstract On February 6, 2023, two earthquakes shook the southern and central Türkiye causing significant loss of human life and devastating many cities. These are related to the active East Anatolian Fault (EAF). In this study, the digital image correlation (DIC) technique is applied to map the coseismic displacements. For that, a pair of scenes from the Sentinel-2 satellite acquired before and after the seismic events was used. The results showed that the methodological approach can be effectively used to map and monitor large-scale geological phenomena and can assist in seismic risk assessment.

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 homogeneous while TLS points have an uneven distribution. Small distances between point clouds were predominant in the vicinity of the landslide and greater differences only occurred on the scene edges. DEMs (Digital Elevation Model) were generated from both point clouds. The volume of material displaced from the upper part (scarp) of the landslide was 70.05 m3 for the TLS DEM and 77.15 m3 for the SfM-MVS while the volume of material accumulated in the lower part (body) of the landslide was 66.85 m3 and 62.68 m3 respectively. Slope and roughness were calculated and showed very similar results for TLS and SfM-MVS.

Abstract Landslide identification is important for understanding their conditioning factors, and for constructing susceptibility, risk, and vulnerability maps. In remote sensing this can be accomplished manually or through classifiers. This study compares three image classifiers (Maximum Likelihood, Random Forest, and Support Vector Machines (SVM)) used in identifying landslides in Itaóca (São Paulo, Brazil). Two datasets were used: a RapidEye-5 (5 m) image and a Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) (12.5 m). Seven pixel-based classifications were produced, two for each classifier and a binary class that identified only landslides and non-landslides. One classification contained five spectral bands (5B), while the other contained six bands (6B) and included the slope derived from the DEM. The results were validated using Kappa index and F1 score. The SVM 6B classification achieved the best results among the validation indices used herein. It identified a landslide area of 399,325 m². The results contribute to landslide mapping in tropical environments using pixel-based classifiers. However, although the SVM classification was successful, only landslides with larger areas were captured by the algorithms, confirming the importance of conducting further analyses using images with finer spatial resolution.

ABSTRACT: This work presents the development of a three-dimensional (3D) model of an outcrop of the Corumbataí Formation (Permian, Paraná Basin, Brazil) using Structure from Motion - Multi-View Stereo (SfM-MVS) technique in order to provide a structural analysis of clastic dikes cutting through siltstone layers. While traditional photogrammetry requires the user to input a series of parameters related to the camera orientation and its characteristics (such as focal distance), in SfM-MVS the scene geometry, camera position and orientations are automatically determined by a bundle adjustment, an iterative procedure based on a set of overlapping images. It is considered a low-cost technique in terms of hardware and software, also being able to provide point density and accuracy on par to the ones obtained withTerrestrial Laser Scanning. The results acquired on this research have good agreement with previous works, yielding a NNW main orientation for the dikes measured in the field and on the 3D model. The development of this work showed that SfM-MVS use and practice on geosciences still needs more studies on the optimization of the involved parameters (such as camera orientation, image overlap and angle of illumination), which, when accomplished, will result in less processing time and more accurate models.