Assessment of earthquake-induced landslide inventories and susceptibility maps using slope unit-based logistic regression and geospatial statistics

Abstract

Inventories of seismically induced landslides provide essential information about the extent and severity of ground effects after an earthquake. Rigorous assessment of the completeness of a landslide inventory and the quality of a landslide susceptibility map derived from the inventory is of paramount importance for disaster management applications. Methods and materials applied while preparing inventories influence their quality, but the criteria for generating an inventory are not standardized. This study considered five landslide inventories prepared by different authors after the 2015 Gorkha earthquake, to assess their differences, understand the implications of their use in producing landslide susceptibility maps in conjunction with standard landslide predisposing factors and logistic regression. We adopted three assessment criteria: (1) an error index to identify the mutual mismatches between the inventories; (2) statistical analysis, to study the inconsistency in predisposing factors and performance of susceptibility maps; and (3) geospatial analysis, to assess differences between the inventories and the corresponding susceptibility maps. Results show that substantial discrepancies exist among the mapped landslides. Although there is no distinct variation in the significance of landslide causative factors and the performance of susceptibility maps, a hot spot analysis and cluster/outlier analysis of the maps revealed notable differences in spatial patterns. The percentages of landslide-prone hot spots and clustered areas are directly proportional to the size of the landslide inventory. The proposed geospatial approaches provide a new perspective to the investigators for the quantitative analysis of earthquake-triggered landslide inventories and susceptibility maps.

Using Sentinel-1 radar amplitude time series to constrain the timings of individual landslides: a step towards understanding the controls on monsoon-triggered landsliding

“…In comparisons of multiple inventories of the same event prepared by different people or groups, there are often small discrepancies in the exact size, shape and location of each landslide (Milledge et al, 2022;Pokharel et al, 2021). Spatial mismatches between landslide polygon locations could lead to pixels on the edges of landslides being excluded from the analysis.…”

A new index representative of seismic cracks to assess post‐seismic landslide susceptibility

“…Since the DCI is used to represent fine geomorphic features, model performance could be more appropriately evaluated using a measure that can capture the difference of landslide bodies, such as the error index proposed by Carrara (1993), rather than using the AUC as an overall measure. For example, Pokharel, Alvioli, and Lim (2021) used the error index to demonstrate morphological differences of individual landslides between inventories.…”

Distinct Susceptibility Patterns of Active and Relict Landslides Reveal Distinct Triggers: A Case in Northwestern Turkey

“…We used the r.slopeunits software [28]) to generate SUs. Specifically, we subdivided the study area into SUs [28][29][30]45]. We computed 50,104 SUs, covering ~24,000 km 2 out of ~29,400 km 2 of the study area as we excluded flat areas that are not prone to landslides (Figure 2) [31,46,47].…”