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An approach for landslide risk assesment by using geographic information systems (gis) and remote sensing (rs)

Erener, Arzu
This study aims to develop a Geographic Information Systems (GIS) and Remote Sensing (RS) Based systematic quantitative landslide risk assessment methodology for regional and local scales. Each component of risk, i.e., hazard assessment, vulnerability, and consequence analysis, is quantitatively assessed for both scales. The developed landslide risk assessment methodology is tested at Kumluca watershed, which covers an area of 330 km2, in Bartın province of the Western Black Sea Region, Turkey. GIS and RS techniques are used to create landslide factor maps, to obtain susceptibility maps, hazard maps, elements at risk and risk maps, and additionally to compare the obtained maps. In this study, the effect of mapping unit and mapping method upon susceptibility mapping method, and as a result the effect upon risk map, is evaluated. Susceptibility maps are obtained by using two different mapping units, namely slope unit-based and grid-based mapping units. When analyzing the effect of susceptibility mapping method, this study attempts to extend Logistic Regression (LR) and Artificial Neural Network (ANN) by implementing Geographically-Weighted Logistic Regression (GWR) and spatial regression (SR) techniques for landslide susceptibility assessment. In addition to spatial probability of occurrence of damaging events, landslide hazard calculation requires the determination of the temporal probability. Precipitation triggers the majority of landslides in the study region. The critical rainfall thresholds were estimated by using daily and antecedent rainfalls and landslide occurrence dates based on three different approaches: Time Series, Gumble Distribution and Intensity Duration Curves. Different procedures are adopted to obtain the element at risk values and vulnerability values for local and regional scale analyses. For regional scale analysis, the elements at risk were obtained from existing digital cadastral databases and vulnerabilities are obtained by adopting some generalization approaches. On the other hand, on local scale the elements at risk are obtained by high resolution remote sensing images by the developed algorithms in an automatic way. It is found that risk maps are more similar for slope unit-based mapping unit than grid–based mapping unit.