Landslide Hazards

Landslides are major hazards, causing thousands of deaths around the world each year as well as damaging homes and infrastructure. Air- and space-borne remote sensing data—mainly optical imagery, InSAR, and lidar—have enabled advances in landslide identification and monitoring that can improve quantitative assessments of hazard, especially when paired with on-the-ground measurements. The main advantage of remote sensing is the combined high spatial resolution (~decimeters-decameters), good temporal resolution (~days-years), and high accuracy (~centimeters-decimeters). This chapter focuses on how recent remote sensing techniques have improved understanding of landslide hazards in three main areas. First, for landslide mapping, an essential first step in hazard analysis, lidar and optical imagery have been important for improving the objectivity and accuracy of landslide inventory maps. Second, for slow-moving landslide monitoring, InSAR and repeat optical imagery have been especially useful for developing multiyear deformation time series that can be used to inform both empirical and physically based models for predicting their motion. Third, those time series are also beginning to document some landslides’ movement patterns leading up to catastrophic failure, and those data can similarly inform our mechanistic understanding of that process. Areas of promising future work include building on quantitative topographic analyses to better understand the geomorphic record of landslides, continuing to generate longer time series of landslide motion to refine and test geomechanical models, and capitalizing on high spatial resolution, three-dimensional surface measurements to infer landslide subsurface properties. Since subsurface fluids play an important role in landslide behavior, coupling remote sensing observations with data from dense networks of increasingly inexpensive in situ sensors and satellite-based soil moisture data is an especially promising area of future research.