Photogrammetric time-lapse workflow for automated rockfall monitoring

<p>In recent years, photogrammetric models have gained widespread use in geosciences due to their ability to reproduce natural surfaces. These models offer a cost-effective and user-friendly alternative to other systems, such as LiDAR, for creating 3D point clouds. On the other hand, rockfalls pose a significant risk to society, as they are the most common natural hazard in mountainous areas and can occur with great speed, resulting in high levels of danger. The aim of this communication is to show results on the development of new algorithms and time-lapse photogrammetric systems for automatic rockfall monitoring (Blanch, 2022).</p> <p>To acquire the data, a photogrammetric system consisting of different photographic modules and a data transmission module has been developed. This system uses conventional cameras (24Mpx-48Mpx) powered by solar panels and it is controlled by a Raspberry Pi. The system captures time-lapse images, can be programmed, configured flexibly, and it can send images remotely for near real-time processing. The system has been installed at two sites with rockfall activity. One in the Puigcerc&#243;s cliff, located in the Origens UNESCO Gobal Geopark (Spain), and the other in the Tajo de San Pedro cliff located in the Alhambra de Granada - UNESCO World Heritage Site (Spain).</p> <p>Data processing comprises two main steps. The first step involves the automatic photogrammetric process using SfM-MVS algorithms. Thereby, the MEMI workflow is applied to improve the level of detection in the change-detection comparison (Blanch et al., 2021). Afterwards, a workflow based on M3C2 (Lague et al., 2013) comparison and DBSCAN clustering is applied to identify possible rockfall clusters. The resulting clusters are processed via a machine learning approach to automatically discriminate the true rockfall events from the candidate clusters . To perform this task, various metric parameters, i.e. features, of the candidate clusters are calculated, and a Random Forest machine learning model is used to perform the classification.</p> <p>The presented approach facilitates the automated monitoring of rockfalls in near-real time, while improving the detection threshold in the 3D change-detection models, resulting in a more detailed characterisation of active zones and defining the framework that allows for automated 4D rockfall monitoring in high temporal frequency.</p> <p>Blanch, X., 2022.&#160; Developing Advanced Photogrammetric Methods for Automated Rockfall Monitoring. Doctoral dissertation. http://hdl.handle.net/10803/675397</p> <p>Blanch, X.; Eltner, A.; Guinau, M.; Abellan, A., 2021. Multi-Epoch and Multi-Imagery (MEMI) Photogrammetric Workflow for Enhanced Change Detection Using Time-Lapse Cameras.&#160;<em>Remote Sens.</em>&#160;,&#160;<em>13</em>, 1460. https://doi.org/10.3390/rs13081460</p> <p>Lague, D., Brodu, N., Leroux, J., 2013. Accurate 3D comparison of complex topography with terrestrial laser scanner: Application to the Rangitikei canyon (N-Z). ISPRS Journal of Photogrammetry and Remote Sensing 82, 10&#8211;26. https://doi.org/10.1016/j.isprsjprs.2013.04.009</p>