GIS-based multicriteria evaluation with multiscale analysis to characterize urban landslide susceptibility in data-scarce environments

• Landslide susceptibilities were determined at multiple scales using multicriteria evaluation.
• Susceptibility factors are dependent on the study site, spatial scale and spatial resolution.
• Calculated landslide susceptibilities were reliable when validated with real inventory data.
• The susceptibility maps can be compared within and between various spatial resolutions.
• Multiscale analysis provides critical input for effective decision-making at various levels.

Landslides can have a severe negative impact on the socio-economic and environmental state of individuals and their communities. Minimizing these impacts is dependent on the effective identification of risk areas using a susceptibility analysis process. In such a process, output maps are generated to determine various levels of threat to human populations. However, the reliability of the process is controlled by critical factors such as data availability and data quality. In data-scarce environments, susceptibility analysis done at multiple interlocking geographic scales can provide a convergence of evidence to reliably identify risk areas. In this study, multiscale analysis and fuzzy sets are combined with GIS-based multicriteria evaluation (MCE) to determine landslide susceptibility levels for areas of the Metro Vancouver region, British Columbia, Canada. Landslide-conditioning parameters are chosen based on their relevance and effect on a particular scale of analysis. These parameters are derived for three geographic scales using digital elevation models, drainage networks and road networks. An analytical hierarchy process (AHP) analysis provides relative weights of importance to combine variables. The landslide susceptibility analysis is done for regional, municipal and local scales at resolutions of 50 m, 10 m, and 1 m respectively. At each scale, susceptibility models are validated against real inventory data using the seed cell area index (SCAI) method. The strong inverse correlation between the map classes and the SCAI adds to confidence in the results. The developed approach can enable analysts in data-scarce environments to reliably identify susceptible areas thereby improving hazard mitigation, emergency services targeting, and overall community planning.