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Modeling landslide susceptibility over large regions with fuzzy overlay

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Abstract

Landslide susceptibility mapping is most effective if detailed surface and subsurface information can be combined with authoritative landslide catalogs or a deep understanding of local conditions. However, these types of homogeneous input data and catalogs are frequently not available over large areas. In this study, we model landslide susceptibility in Central America and the Caribbean islands by combining three globally available datasets and one regional dataset with fuzzy overlay. This primarily heuristic model provides the flexibility to test a range of different contributing variables and the capability to compare landslide inventories within the model framework that vary greatly in their size, spatiotemporal scope, and collection methods. We create a regional susceptibility map and evaluate its performance using receiver operating characteristics for both continuous and binned susceptibility values. This susceptibility map forms the basis for a near-real-time landslide hazard assessment system that couples susceptibility with rainfall and soil moisture triggers to estimate potential landslide activity at a regional scale. The application of this susceptibility model at the regional scale provides a foundation for transferring the methodology to other geographic areas.

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Acknowledgments

This work gratefully acknowledges José Cepeda’s (Norwegian Geotechnical Institute) guidance on the expert survey and review of this manuscript. Many thanks go to our insightful colleagues, including Carlos Aguilar (El Salvador Geological Survey), Rex Baum (USGS), Graziella Devoli (Norwegian Water Resources and Energy Directorate), Manuel Diaz (Medio Ambiente y Recursos Naturales, El Salvador), Bruce Harrison (New Mexico Tech), Pavel Havlicek (Czech Geological Survey), Eunjung Lim (University of Maryland), Shlomo Neuman (The University of Arizona), and Jonathan Resop (University of Maryland). The pioneering work of Sergio Mora and Wilhelm-Gunther Vahrson inspired this project. This work was funded by the NASA SERVIR program, NNH11ZDA001N-SERVIR.

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Authors and Affiliations

  1. Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, 20771, USA

    Dalia Kirschbaum, Thomas Stanley & Soni Yatheendradas

  2. Universities Space Research Association, 7178 Columbia Gateway Drive, Columbia, MD, 21046, USA

    Thomas Stanley

  3. Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, 20742, USA

    Soni Yatheendradas

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  1. Dalia Kirschbaum
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Correspondence to Dalia Kirschbaum.

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Kirschbaum, D., Stanley, T. & Yatheendradas, S. Modeling landslide susceptibility over large regions with fuzzy overlay. Landslides 13, 485–496 (2016). https://doi.org/10.1007/s10346-015-0577-2

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