4D-SFM PHOTOGRAMMETRY FOR MONITORING SEDIMENT DYNAMICS IN A DEBRIS-FLOW CATCHMENT: SOFTWARE TESTING AND RESULTS COMPARISON

The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-2, 2018 ISPRS TC II Mid-term Symposium “Towards Photogrammetry 2020”, 4–7 June 2018, Riva del Garda, Italy 4D-SFM PHOTOGRAMMETRY FOR MONITORING SEDIMENT DYNAMICS IN A DEBRIS-FLOW CATCHMENT: SOFTWARE TESTING AND RESULTS COMPARISON S. Cucchiaroa,b , E. Masetc,∗, A. Fusielloc and F. Cazorzia a DI4A – University of Udine, Via delle Scienze, 206 – Udine, Italy (sara.cucchiaro, federico.cazorzi)@uniud.it b Department of Life Sciences – University of Trieste, Via E. Weiss, 2 – Trieste, Italy c DPIA – University of Udine, Via delle Scienze, 206 – Udine, Italy , Commission II, WG II/10 KEY WORDS: 4D-SfM Photogrammetry, Sediment dynamics monitoring, Software comparison, Debris flow hazard ABSTRACT: In recent years, the combination of Structure-from-Motion (SfM) algorithms and UAV-based aerial images has revolutionised 3D topographic surveys for natural environment monitoring, offering low-cost, fast and high quality data acquisition and processing. A continuous monitoring of the morphological changes through multi-temporal (4D) SfM surveys allows, e.g., to analyse the torrent dynamic also in complex topography environment like debris-flow catchments, provided that appropriate tools and procedures are employed in the data processing steps. In this work we test two different software packages (3DF Zephyr Aerial and Agisoft Photoscan) on a dataset composed of both UAV and terrestrial images acquired on a debris-flow reach (Moscardo torrent - North-eastern Italian Alps). Unlike other papers in the literature, we evaluate the results not only on the raw point clouds generated by the Structure-fromMotion and Multi-View Stereo algorithms, but also on the Digital Terrain Models (DTMs) created after post-processing. Outcomes show differences between the DTMs that can be considered irrelevant for the geomorphological phenomena under analysis. This study confirms that SfM photogrammetry can be a valuable tool for monitoring sediment dynamics, but accurate point cloud post-processing is required to reliably localize geomorphological changes. 1. INTRODUCTION Geomorphic processes such as debris flows are one of the main sources of risk for human lives and infrastructures in mountain catchments. Several countermeasures can be taken to mitigate their effects and, among all hydraulic engineering structures, check dams are the most common technique to manage debris flow hazard (Hübl et al., 2005, Piton et al., 2017). These control works significantly affect sediment dynamics and a continuous monitoring of the morphological evolution is therefore required to improve management strategies and torrent control planning (Victoriano et al., 2018). The acquisition of repeated topographic surveys lets not only to characterize debris flows in terms of their geomorphic activity, but also to infer the sediment dynamics at multiple temporal and spatial scales (4D) and their link to torrent control works. Being low-cost, automatic and easy to use, Structure-from-Motion (SfM) photogrammetry represents a powerful alternative to laser scanning technique for acquiring high-resolution topography in a variety of environments (Westoby et al., 2012, James and Robson, 2012, Smith and Vericat, 2015, Carrivick et al., 2016), allowing also to increase the frequency of the surveys. Moreover, the possibility of integrating images acquired from the ground and by an Unmanned Aerial Vehicle (UAV), makes SfM and Multi-View Stereo (MVS) methodologies ideal tools to obtain a complete reconstruction of the topographic surface, even in the presence of steep slopes and areas characterized by limited accessibility as in debris flow catchments (Bemis et al., 2014). ∗ Corresponding author In the last years, many software packages have been developed to combine computer vision algorithms and photogrammetry principles in order to obtain accurate 3D reconstructions in an automatic way, without user interaction (Carrivick et al., 2016). Since this technique requires relatively little training and has a high level of automation, the majority of end-users consider the software as a black-box and they are often unaware of the accuracy and reliability of the obtained 3D model (Micheletti et al., 2015b, Smith et al., 2016). Many papers that provide an evaluation of the most popular commercial software solutions can be found in the literature (Nikolov and Madsen, 2016, Remondino et al., 2017) but they usually assess the accuracy on the point cloud generated by the SfM-MVS process (Aicardi et al., 2016). Instead, when monitoring geomorphological processes such as sediment dynamics, the point cloud represents only the initial step of the workflow that leads to create the Digital Terrain Model (DTM), a fundamental tool to study topography evolution. Indeed, DTMs derived from different surveys can be subsequently compared, i.e., the old DTM is subtracted to the new one (DTM of Difference, DoD) to identify morphological changes over time (Wheaton et al., 2010, Cavalli et al., 2017, Vericat et al., 2017). For these reasons, in this work we test two different software tools (3DF Zephyr Aerial v. 3.503 and Agisoft Photoscan v. 1.2.0) and evaluate the results not only on the raw point cloud, but also on the DTM obtained after post-processing. The paper is organized as follows. Section 2 describes in detail the image processing steps and the subsequent post-processing of the point clouds, which leads to the creation of the DTMs. In Sec. 3 the comparison between the results obtained by the two software packages is reported, focusing on the derived DoDs. Fi- This contribution has been peer-reviewed. https://doi.org/10.5194/isprs-archives-XLII-2-281-2018 | © Authors 2018. CC BY 4.0 License. 281 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-2, 2018 ISPRS TC II Mid-term Symposium “Towards Photogrammetry 2020”, 4–7 June 2018, Riva del Garda, Italy nally, Sec. 4 draws the conclusions. 222 images Terrestrial + UAV Sony Alpha 5000 20 Mpx, f: 16mm 2. TESTS AND ANALYSES 222 images Terrestrial + UAV Sony Alpha 5000 20 Mpx, f: 16mm I. Data acquisition PS The following paragraphs describe in detail the image processing procedure and the dense point cloud post-processing steps carried out to compare 3DF Zephyr and Photoscan products. Figure 1 shows the complete workflow applied in this study (an exhaustive description of the post-processing phases can be found in (Cucchiaro et al., 2018b)). 2.1 Agisoft Lens II. Camera calibration 3DF Lapyx # oriented images: 222 computing time: 3h 48’ # 3D pts: 1,581,701 III. Sparse point cloud # oriented images: 222 computing time: 3h 45’ # 3D pts 241,917 RMSE CP (cm) X= 2.4 Y= 2.9 Z= 4.7 IV. Alignment error analysis RMSE CP (cm) X= 2.0 Y= 1.9 Z= 5.9 pts/m2: 14219 computing time: 4h V. Dense point cloud pts/m2: 12634 computing time: (...truncated)