Landslides are natural disasters that cause environmental and infrastructure damage worldwide. In order to reduce future risk posed by them, effective detection and monitoring methods are needed. Landslide susceptibility and hazard mapping is a method for identifying areas suspect to landslide activity. This task is typically performed in a manual, semi-automatic or automatic form, or a combination of these, and can be accomplished using different sensors and techniques. As landslide hazards continue to impact our environment and impede the lives of many, it is imperative to improve the tools and methods of effective and reliable detecting of such events.
Recent developments in remote sensing have significantly improved topographic mapping capabilities, resulting in higher spatial resolution and more accurate surface representations. Dense 3D point clouds can be directly obtained by airborne Light Detection and Ranging (LiDAR) or created photogrammetrically, allowing for better exploitation of surface morphology. The potential of extracting spatial features typical to landslides, especially small scale failures, provides a unique opportunity to advance landslide detection, modeling, and prediction process.
This dissertation topic selection was motivated by three primary reasons. First, 3D data structures, including data representation, surface morphology, feature extraction, spatial indexing, and classification, in particular, shape-based grouping, based on LiDAR data
offer a unique opportunity for many 3D modeling applications. Second, massive 3D data, such as point clouds or surfaces obtained by the state-of-the-art remote sensing technologies, have not been fully exploited for landslide detection and monitoring. Third, unprecedented advances in LiDAR technology and availability to the broader mapping community should be explored at the appropriate level to assess the current and future advantages and limitations of LiDAR-based detection and modeling of landslide features.
This dissertation is focused on developing robust landslide detection mapping techniques using precise and accurate surface models generated from airborne LiDAR data, as well as demonstrating potential capabilities of airborne LiDAR data for small landslide detection, monitoring, vulnerability and hazard mapping. Airborne data have been used for landslide detection, mostly for landslides with large spatial extents. With continuously improving hardware capability of airborne LiDAR systems, combined with the growing availability of low-altitude platforms, such as Unmanned Aerial Systems (UAS), the possibility of effective use of airborne LiDAR to detect and map small landslides is quickly becoming a reality.
Reviewing and testing commonly used surface feature extraction techniques, such as point-based, profile-based, shape-based, and change detection techniques, such as nearest neighbor and Digital Elevation Model (DEM) of Difference (DoD), led to a conclusion that no single technique could solve the landslide predisposition for all environments and circumstances. Alternatively, to develop a unified approach, two robust techniques for landslide detection are proposed that are based on a stepwise strategy that focuses on surface geometry. The first method is based on fusing a shape-based surface feature extraction technique and change detection method using multi-temporal surface models, while the second method implements a technique to extract, identify, and map surface features found in landslide morphology using a single surface model. In addition, the impact of spatial resolution on small landslide mapping is demonstrated. Using experimental datasets available at the time of this research, the proposed methods showed that 66% and 84% of the landslides from the reference inventory could be detected by the first and the second method, respectively.