Evaluating the Influence of Spatial Resolution on Landslide Detection: A Case Study in the Carlyon Beach Peninsula, Washington

Abstract

Abstract. Landslides are geological events in which masses of rock and soil slide down the slope of a mountain or hillside. They are influenced by topography, geology, weather, and human activity, and can cause extensive damage to the environment and infrastructure, as well as delay transportation networks. Therefore, it is imperative to detect early-warning signs of landslide hazards as a means of prevention. Traditional landslide surveillance consists of field mapping, but the process is costly and time consuming. Modern landslide mapping uses Light Detection and Ranging (LiDAR) derived Digital Elevation Models (DEMs) and sophisticated algorithms to analyze surface roughness and extract spatial features and patterns of landslide and landslide-prone areas. This study follows a previous study performed that demonstrated that it is possible to detect unstable terrain using algorithmic mapping techniques. The focus of this study is to show how spatial resolution can influence the accuracy of the classification results. The DEM data was resampled from 6 to 12, 24, 48 and 96 ft spatial resolution. The surface feature extractors employed (local topographic range, local topographic variability, slope, and roughness) are fused and analyzed simultaneously by applying k-means and Gaussian Mixture Model (GMM) clustering methods. When compared with the detailed, independently compiled landslide reference map, our data shows a decrease in performance as spatial resolution decreases. These results suggest that spatial resolution does impact the performance of landslide classification.