Abstract
In Turkey, landslides are the second most common natural disasters that cause damages in Turkey that follow the earthquakes. Thus, landslide risk assessment is of crucial importance in this area. Therefore in this study a quantitative approach for mapping landslide risk is developed for property and life at local scale. The approach is first based on the identification of existing elements at risk in the area by the developed algorithm. Then the vulnerability approach focuses on determination of quantitative vulnerability values for each element at risk by considering temporal and spatial impacts by adopting a “damage probability matrix“ approach. The loss estimation was combined with the hazard values which are based on former work done in Bartın Kumluca area where a detailed study of landslide occurrence and hazard in the recent past (last 30 years) was carried out. The final result risk maps for property ($/pixel/year) and life (life/pixel/year) shows all losses per pixel annually for each element at risk in Hepler village.
TopBackground
Quanitative Risk Assesment (QRA) requires quantification of losses due to a given landslide hazard. When landslide hazards are investigated in a local scale, it is necessary to predict the run-out area to identify the potential damages to elements at risk. In any QRA it is of fundamental importance to identify and characterize elements at risk (e.g. buildings, roads, utilities, lifelines) for predicting losses. If insufficient or no digital data exist on a local scale, remote sensing products can be used to extract the element at risks, such as road networks and buildings. Most of the works in the literature concentrates only on the extraction of a single object such as only buildings or only roads. Typical examples of early works in building detection are Lin and Nevatia (1998), Kim and Nevatia (1999), Peng and Jin (2007), Vakalopoulou et al. (2015). On the other hand, Klang (1998), Laptev et al. (2000), Amini et al. (2002), Mena and Malpica (2003)Christophe and Inglada (2007), Yang and Wang (2007), Aytekin et al. (2012) used the most common algorithms for the detection of road.
Key Terms in this Chapter
RS: Remote sensing is the acquisition of information from earth without making physical contact with the object by using satellite- or aircraft-based sensor technologies.
Landslide Risk Assessment: Establishing the likelihood to which future slides could adversely impact society.
Element at Risk: Features under risk including buildings, roads, land use, and infrastructure.
Vulnerablity: Defined as degree of loss, suffer harm for a given element or set of elements within the area of hazard event.
GIS: Geographic information systems is a technology that is used to capture, store, manipulate, analyze, manage, and present spatial data.
Landslide Hazard: Information about probability of occurrence of a landslide in a given area over a specified period of time.