GEOSPATIAL MODEL FOR LARGE SCALE SEA CLIFF ROCKFALL SUSCEPTIBILITY MAPPING
- 1Department of Geographic Engineering, Geophysics and Energy, Instituto Dom Luiz, Faculty of Sciences, University of Lisbon, Campo Grande, 1749-016 Lisbon, Portugal
- 2Centre of History, School of Arts and Humanities, University of Lisbon, Alameda da Universidade, 1600-214 Lisbon, Portugal
- 3Department of Geology, Instituto Dom Luiz, Faculty of Sciences, University of Lisbon, Campo Grande, 1749-016 Lisbon, Portugal
Keywords: photogrammetry, cliff erosion, logistic regression, predisposing factors
Abstract. Due to their relevance to the environment and economy, coastal areas are considered national strategic segments that should be preserved. Since erosion phenomena occur intensively in those areas, it is capital to monitor them in order to identify risk zones. In addition to national and regional studies, it is also necessary to conduct local monitoring of erosion prone areas, especially those which are often frequented by people, such as beaches limited by high cliffs. Large scale vertical mapping is necessary to model their susceptibility to mass movements, in order to provide adequate prevention, protection and assistance.
Recent techniques like laser scanning or aerial photogrammetry using UAVs allow the definition of the status quo of a cliff wall and its situation a few years back. But to assess the susceptibility to rock mass movements in such cliff segments, inventories of past events are of primordial importance. These inventories allow applying several statistic models to better understand susceptibility together with a set of variables of internal and external nature regarding the cliff site.
We present a case study focused on the beach of Ribeira d’Ilhas (Mafra, Portugal), where a workflow of terrestrial photogrammetry for present day situation and recovery of old analogue stereoscopic pairs (1999, 2000, 2003) was implemented. A mass movement inventory (1999-2014) was compiled by multitemporal comparison followed by a detailed characterization of the cliff using a GIS software. Finally, the application of the logistic regression method allowed the definition of a susceptibility map of the cliff wall towards the occurrence of mass movements.