A Multistage Technique to Minimize Overestimations of Slope Susceptibility at Large Spatial Scales

Abstract

Rainfall induced landslides are one of the most frequent natural hazards on slanted terrains. They lead to significant economic losses and fatalities worldwide. Most factors inducing shallow landslides are local and can only be mapped with high levels of uncertainty at larger scales. This work presents an attempt to determine slope instability using buffer and threshold techniques to downscale large areas and minimize slope uncertainties at local scales, then in a second stage, logistic regression is used to determine susceptibility at large scales. ASTER GDEM V2 is used for topographical characterization of slope and buffer analysis. Four static parameters (slope angle, soil type, land cover and elevation) for 230 shallow rainfall-induced landslides listed in a comprehensive landslide inventory for the continental United States are examined. A delimiting buffer equivalent to 5, 25 or 50 km is created around each landslide event facilitating the statistical analysis of slope thresholds. Slope angle thresholds at the pixel points 50, 75, 95, 99 and maximum percentiles are compared to one another and tested for best fit in a logistic regression environment. It is determined that values lower than the 75-percentile threshold misrepresents susceptible slope angles by not including slopes higher than 35°. Best range of slope angles and regression fit can be achieved when utilizing the 99 percentile slope angle threshold. The resulting logistic regression model predicts the highest number of cases correctly with 97.2% accuracy. The logistic regression model is carried over to ArcGIS where all variables are processed based on their corresponding coefficients. A regional landslide probability map for the continental United States is created and analyzed against the available landslide records and their spatial distributions. It is expected that future inclusion of dynamic parameters like precipitation and other proxies like soil moisture into the model will further improve accuracy. Keywords: Shallow landslides; Slope instability; Threshold analysis; Logistic regression; Regional analysis; GIS; Remote sensing Introduction Rainfall induced landslides are one of the most frequent natural hazards on slanted terrains. They usually result in great economic losses and fatalities globally. Worldwide at least 32,322 deaths between 2004 and 2010 have been reported [1] and in the United States alone, landslides cause $1-2 billion in damages and more than 25 fatalities in average each year [2]. Understanding, mapping, modeling and preventing the aftermath of these devastating events represents an important scientific and operational endeavor [3]. The term “Landslide” describes the downward and outward movement of slope-forming materials that include rock, earth, and debris or a combination of these [4]. Although landslides are considered to be dependent on the complex interaction of several static and dynamic factors [5-7] slope angle has great influence on the susceptibility of a slope to sliding. Increased slope angle usually correlates to increased likelihood of failure even if the material distribution on the slope is uniform and isotropic [5]. Undeniably, many other parameters are essential to the analysis of landslide risk. For example, changes in land use and land cover such as deforestation, forest logging, road construction, cultivation and fire on steep slopes can have a significant effect on landslide activity [8]. In addition, forest vegetation