Preparation of earthquake-triggered landslide inventory maps using remote sensing and GIS technologies:Principles and case studies

Inventory maps of earthquake-triggered landslides can be constructed using several methods,which are often subject to obvious differences due to lack of commonly accepted criteria or principles,To solve this problem,the author describes the principles for preparing inventory maps of earthquake-triggered landslides,focusing on varied methods and their criteria,The principles include the following key points：all landslides should be mapped as long as they can be recognized from images;both the boundary and source area position of landslides should be mapped;spatial distribution pattern of earthquake-triggered landslides should be continuous;complex landslides should be divided into distinct groups;three types of errors such as precision of the location and boundary of landslides,false positive errors,and false negative errors of earthquake-triggered landslide inventories should be controlled and reduced;and inventories of co-seismic landslides should be constructed by the visual interpretation method rather than automatic extraction of satellite images or/and aerial photographs,In addition,selection of remote sensing images and creation of landslides attribute database are also discussed in this paper,Then the author applies these principles to produce inventory maps of four events：the 12 May 2008 Wenchuan,China M_w 7.9,14 April 2010 Yushu,China M_w 6,9,12 January 2010 Haiti M_w7.0,and 2007 Aysen Fjord,Chile M_w 6.2,The results show obvious differences in comparison with previous studies by other researchers,which again attest to the necessity of establishment of unified principles for preparation of inventory maps of earthquake-triggered landslides.

An open-accessed inventory of landslides triggered by the M_(S)6.8 Luding earthquake,China on September 5,2022

This study constructs a preliminary inventory of landslides triggered by the M_(S) 6.8 Luding earthquake based on field investigation and human-computer interaction visual interpretation on optical satellite images.The results show that this earthquake triggered at least 5007 landslides,with a total landslide area of 17.36 km^(2),of which the smallest landslide area is 65 m^(2)and the largest landslide area reaches 120747 m^(2),with an average landslide area of about 3500 m^(2).The obtained landslides are concentrated in the IX intensity zone and the northeast side of the seismogenic fault,and the area density and point density of landslides are 13.8%,and 35.73 km^(-2) peaks with 2 km as the search radius.It should be noted that the number of landslides obtained in this paper will be lower than the actual situation because some areas are covered by clouds and there are no available post-earthquake remote sensing images.Based on the available post-earthquake remote sensing images,the number of landslides triggered by this earthquake is roughly estimated to be up to 10000.This study can be used to support further research on the distribution pattern and risk evaluation of the coseismic landslides in the region,and the prevention and control of landslide hazards in the seismic area.

Landslides triggered by the 1970 Ms 7.7 Tonghai earthquake in Yunnan,China: an inventory, distribution characteristics, and tectonic significance

The 1970 Tonghai earthquake, which occurred on January 5, 1970, in Tonghai County, Yunnan Province, China, triggered a large number of landslides. Since the occurrence of the earthquake, there have been a huge number of research reports on the seismogenic structure and earthquake mechanism, but rare reports on the seismic landslides. As the largest earthquake recorded in the Qujiang fault zone, the study on the coseismic landslides triggered by this earthquake are of great significance to the prevention and mitigation of earthquake-induced landslides in this region. In this study, we established a coseismic landslide inventory for the VⅢ–X seismic intensity areas of the Ms 7.7 Tonghai earthquake, and conducted spatial analysis on the coseismic landslides, mainly having analyzed the effect of the topographic factors, geological factors, and seismic factors on the development of the coseismic landslides. To enhance the understanding of this earthquake, we converted the earthquake epicenter and magnitude with empirical formulas based on the distributions and areas of the coseismic landslides. Comparing with coseismic landslides in other earthquake-hit areas, we found the capability that this earthquake could induce landslides is low. This study provides a useful supplement to the global coseismic landslide inventories and could be the basic data for seismic landslide assessment in this earthquake-prone region.

Landslide inventory validation and susceptibility mapping in the Gerecse Hills, Hungary

Landslides pose a threat to property both in the populated and cultivated areas of the Gerecse Hills (Hungary). The currently available landslide inventory database holds the records from many sites in the area, but the database is out-of-date. Here we address the problem of revising the National Landslides Cadastre landslide inventory database by creating a landslide suscept-ibility map with a multivariate model based on likelihood ratio functions. The model is applied to the TanDEM-X DEM (0.4″ res.), the current landslide inventory of the area, and data acquired from geological maps. By comparing the distributions of four variables in the landslide and non-landslide area with grid computation methods, the model yields landslide susceptibility estimates for the study area. The estimations show to what extent a certain area is similar to the sample areas, therefore, its likelihood to be affected by landslides in the future. The accuracy of the model predictions was checked in the field and compared to the results of our previous study using the SRTM-1 DEM for a similar analysis. The model gave accurate estimates when certain correction measures were applied to the input datasets. The limitations of the model, the input datasets, and the suggested correction measures are also discussed.

Distribution patterns of landslides triggered by the 2022 Ms 6.8 Luding earthquake,Sichuan,China

At 12:52 pm on September 5,2022,an Ms 6.8 earthquake occurred in Luding County,Sichuan Province,China.Based on high-resolution aerial photographs and satellite imageries obtained after the earthquake,as well as field investigation,a total of 8685 earthquake-triggered landslides(EQTLs)were interpreted.The landslides covered an area of 30.7km^(2),with a source area of 9.4 km^(2).These EQTLs were mainly distributed in areas with a seismic intensity of VIII and IX.Most of the landslides were small and medium in size,and their types included landslide,rockfall,and rock slump.Characteristic landslide distributions were found,EQTLs were distributed along the Xianshuihe fault,landslide area decreased gradually with an increased distance to the fault;EQTLs were distributed along the Daduhe River and roads;besides,landslide distribution was associated with ground deformation caused by the earthquake.EQTLs’characteristics indicated that,a large number of EQTLs were located near the foot of the slope;the full area of EQTLs and their source area followed a power function.This study concluded that Luding EQTLs were greater in number and area but relatively smaller in terms of affected area.Investigations on geo-hazards post-earthquake and risk assessment were proposed in the earthquake-stricken area to support the rehabilitation and reconstruction.

Detailed Inventory Mapping and Spatial Analyses to Landslides Induced by the 2013 Ms 6.6 Minxian Earthquake of China

On July 22, 2013, an earthquake （Ms 6.6） occurred in Minxian, Gansu Province of China, causing a large number of landslides. Based on high resolution remote sensing images before and after this event, we made the visual interpretation to these coseismic landslides, and prepared a detailed inventory. The inventory registers totally 6 478 landslides in the study area. Of them, 3 322 landslides are larger than 100 m2. Based on 5 m resolution DEM, these landslides were used to perform spatial analyses using landslide number density （LND） and landslide area percentage （LAP）. The results show that the highest LND and LAP values are in the elevation range of 2 300-2 500 m and steeper slopes. Slopes facing E, SE, S and SW directions, slopes with larger absolute curvature values, ridges, scopes of gravel beds of Late Pleistocene （Qp） and the VIII-degree seismic intensity are more prone to sliding with high LND and LAP values. The largest LND and LAP values are in the scopes of 0.08 and 0.24 g, respectively. According to landslide distribution, we infer that F2-2 branch of Lintan-Dangchang fault is the seismogenic fault. With the increasing distances to this branch fault and drainages, LND and LAP values tend to decrease.

Landslides Triggered by the 3 August 2014 Ludian(China) Mw 6.2 Earthquake: An Updated Inventory and Analysis of Their Spatial Distribution

The 3 August 2014 Ludian, Yunnan, China Mw 6.2(Ms 6.5) earthquake triggered a large number of coseismic landslides. Based on pre-and post-quake high-resolution optical satellite images, this study established a new, complete and objective database of these landslides with field investigations. The updated inventory shows that this earthquake triggered at least 12 817 landslides with a total occupation area of 16.33 km2, covering a nearly circular area about 600 km2, which all exceed those in our previous work and other relevant studies. In addition, we used this database to examine the correlations of the landslides with topographic, geologic, and seismic factors. Results show that the landslides occurred mostly at places with slope gradients 10o–40o, showing an increase tendency with steeper slopes. Affected by the propagation direction of the earthquake rupture, the eastward-facing slopes are more prone to landsliding. The differences between the landslide susceptibility in different strata indicate that lithology is also an important controlling factor. The landslide density of the places with peak ground acceleration(PGA) greater than 0.16 g is obviously larger than those with PGA less than 0.16 g. Meanwhile, the greater the distance from the epicenter, the lower the susceptibility of landslides is. This study suggests that when using satellite images to create coseismic landslide inventories, it should meet certain conditions, including high resolution, whole coverage, and timely data collection. The correct criteria of coseismic landslide inventorying also should be followed. Such inventories can provide a reliable basis for hazard assessment of earthquake-triggered landslides and other quantitative studies.

Landslides Triggered by the 2020 Qiaojia M_(w)5.1 Earthquake, Yunnan, China: Distribution, Influence Factors and Tectonic Significance

On May 18, 2020, an M_(w)5.1 earthquake occurred in Qiaojia County, Yunnan Province, China. This moderate-sized event triggered massive coseismic landslides, resulting in some damage. In this work, through visual interpretation of high-resolution(0.8–2 m) Gaofen satellite images before and after the earthquake, 167 landslides were delineated, 18 of which were inspected in the field. Using the landslide number density(LND) and landslide area percentage(LAP), we characterized the spatial distribution of these landslides, and analyzed their possible influence factors and tectonic significance. The results show that these landslides are distributed mostly in the NW-SE direction, roughly parallel to the long axis of seismic intensity zones and the strike of the Xiaohe-Baogunao fault(XBF). The LND and LAP decrease with increasing distances to the fault and from the epicenter to fault ends of the XBF. These permit to suggest that the seismogenic fault of the Qiaojia earthquake is likely a hidden branch of the XBF. All of the landslides induced by this event occurred in the region with the seismic intensity of six degrees or greater of the 2014 M_(w)6.2 Ludian earthquake. Therefore, it was inferred that the 2020 Qiaojia earthquake was probably the subsequent release of accumulated elastic strain after the 2014 Ludian earthquake in a same tectonic stress regime.