Analyzing the Effects of Spatial Resolution for Small Landslide Susceptibility and Hazard Mapping

Abstract

Commission I, WG I/2 KEY WORDS: LiDAR, landslide, feature, extraction, spatial, resolution, DEM ABSTRACT:Spatial resolution plays an important role in remote sensing technology as it defines the smallest scale at which surface features may be extracted, identified, and mapped. Remote sensing technology has become a vital component in recent developments for landslide susceptibility mapping. The spatial resolution is essential, especially when landslides are small and the dimensions of slope failures vary. If the spatial resolution is relevant to the surface features found in the landslide morphology, it will help improve the extraction, identification and mapping of landslide surface features. Although, the spatial resolution is a well-known issue, few studies have demonstrated the potential effects it may have on small landslide susceptibility mapping. For these reasons, an evaluation to assess the impact of spatial resolution was performed using data acquired along a transportation corridor in Zanesville, Ohio. Using a landslide susceptibility mapping algorithm, landslide surface features were extracted and identified on a cell-by-cell basis from Digital Elevation Models (DEM) generated at 50, 100, 200 and 400 cm spatial resolution. The performance of the landslide surface feature extraction algorithm was then evaluated using an inventory map and a confusion matrix to assess the effects of spatial resolution. In addition to assessing the performance of the algorithm, we statistically analyzed the surface features and their relevant patterns. The results from this evaluation reveal patterns caused by the varying spatial resolution. From this study we can conclude that the spatial resolution has an effect on the accuracy and surface features extracted for small landslide susceptibility mapping, as the performance is dependent on the scale of the landslide morphology.

Towards the Optimal Pixel Size of Dem for Automatic Mapping of Landslide Areas

“…Similar results were demonstrated by Hengl (2006), Paudel et al (2016), Tarolli and Tarboton (2006), Penna et al (2014) and Mora et al (2014). Based on achieved results, it can be determined that selected spatial resolution of DEM depends mostly on objective of the study and used methodology.…”

“…On the contrary landslide susceptibility mapping does not require such high resolution of DEM (Pawłuszek and Borkowski, 2016, Pawłuszek and Borkowski, 2017, Mora et al, 2014. Some studies demonstrated that choosing the finest DEM resolution is not always the best choice (Mora et al, 2014, Tarolli and Tarboton, 2006, Penna et al, 2014. Thus, it is an open question, if small divergences in micro-topography scale, which are represented in DEM of a fine resolution increase the performance of landslide mapping process.…”

“…For instance, high resolution DEM allows to examine landslide morphology and recognize landslide features. On the contrary landslide susceptibility mapping does not require such high resolution of DEM (Pawłuszek and Borkowski, 2016, Pawłuszek and Borkowski, 2017, Mora et al, 2014. Some studies demonstrated that choosing the finest DEM resolution is not always the best choice (Mora et al, 2014, Tarolli and Tarboton, 2006, Penna et al, 2014.…”

“…Spatial resolution of DEM reveals the surface features, patterns and morphology. It is known that inappropriate DEM resolution may entail misjudgment of landslide identification or misinterpretation of landslide features or morphology (Mora et al, 2014). Moreover, different landslide investigations can require various DEM resolutions.…”

“…However, information provided by DEM depends on its spatial resolution (Mora et al, 2014, Tarolli, 2014. Spatial resolution of DEM reveals the surface features, patterns and morphology.…”