Landslide inventory maps are critical to understand the factors governing landslide occurrence and estimate hazards or sediment delivery to channels. Numerous semi-automated approaches for landslide inventory mapping have been proposed to improve the efficiency and objectivity of the process, but these methods have not been widely adopted by practitioners because of the use of input parameters without physical meaning, a lack of transparency in machine-learning based mapping techniques, and limitations in resulting products, which are not ordinarily designed or tested on a large-scale or in diverse geologic units. To this end, this work presents a new semi-automated method, called the Scarp Identification and Contour Connection Method (SICCM), which adapts to diverse geologic settings automatically or semi-automatically using interventions driven by simple inputs and interpretation from an expert mapper. The applicability of SICCM for use in landslide inventory mapping is demonstrated for three diverse study areas in western Oregon, USA by assessing the utility of the results as a landslide inventory, evaluating the sensitivity of the algorithm to changes in input parameters, and exploring how geology influences the resulting landslide inventory results. In these case studies, accuracies exceed 70%, with reliability and precision of nearly 80%. Conclusions of this work are that (1) SICCM efficiently produces meaningful landslide inventories for large areas as evidenced by mapping 216 km2 of landslide deposits with individual deposits ranging in size from 58 to 1.1 million m2; (2) results are predictable with changes to input parameters, resulting in an intuitive approach; (3) geology does not appear to significantly affect SICCM performance; and (4) the process involves simplifications compared with more complex alternatives from the literature.

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Landslides occur in a variety of forms that are a function of climactic setting, tectonic setting, geomorphic and geologic setting, and the shear strength of soil and rock. While major advances in characterizing the spatial influence of these settings on landslide activity have occurred in recent years, there has been limited progress on understanding spatial trends in shear strength properties and their influence on landslide activity. Herein, we propose a regional-scale forensic methodology to establish first-order estimates of landslide shear strength. This approach is performed using (1) interpolation of rupture surface geometry and (2) three-dimensional slope stability analysis. Thereafter, distributions of back-analyzed shear strengths of landslide inventories are used to demonstrate that distinctly different trends are observed when comparing geologic units and landslide type. It is shown that there is generally an inverse correlation between landslide volume and friction angle, suggesting that larger earthflows tend to be in a residual state of shear, while smaller deep-seated failures tend to occur in stronger, possibly cemented materials. Significant uncertainty lies in characterization of hydrological conditions; nonetheless, upper and lower bounds of groundwater conditions still reflect significant differences in inferred regional trends in shear strength properties between geologic settings. A comparison of three-dimensional forensic back analyses to conventional, infinite slope conditions often used in susceptibility analyses suggests that conventional approaches may significantly overestimate landslide shear strengths at a regional scale.