detailed inventory mapping and spatial analyses to...

Journal of Earth Science, Vol. 27, No. 6, p. 1016–1026, December 2016 ISSN 1674-487X Printed in China DOI: 10.1007/s12583-016-0905-z

Tian, Y. Y., Xu, C., Xu, X. W., et al., 2016. Detailed Inventory Mapping and Spatial Analyses to Landslides Induced by the 2013 Ms 6.6 Minxian Earthquake of China. Journal of Earth Science, 27(6): 1016–1026. doi:10.1007/s12583-016-0905-z. http://en.earth-science.net

Detailed Inventory Mapping and Spatial Analyses to Landslides Induced by the 2013 Ms 6.6

Minxian Earthquake of China

Yingying Tian1, 2, Chong Xu*1, Xiwei Xu1, Jian Chen2 1. Key Laboratory of Active Tectonics and Volcano, Institute of Geology, China Earthquake Administration, Beijing 100029, China

2. School of Engineering and Technology, China University of Geosciences, Beijing 100083, China

ABSTRACT: 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. KEY WORDS: Minxian Earthquake, landslide inventory, spatial analyses, landslide number density (LND), landslide area percentage (LAP).

0 INTRODUCTION

Landslide, as one of the most common and serious natural hazards, can be triggered under many conditions, such as intense rainfall, earthquake shaking, variation of water level, snowmelt, typhoon and so on (Jiang et al., 2016; Yang et al., 2016; Xie et al., 2015; Luo et al., 2014; Wu et al., 2014; Dai et al., 2002; Keefer, 1999). Due to the serious hazard and impact, earthquake-induced landslide has become a focus issue in engineering, geology, envi-ronment and other related research fields (Ren et al., 2014; Xu et al., 2014c; Yin Z Q et al., 2014; Huang and Li, 2009; Yin Y P et al., 2009). The objective and detailed coseismic landslide inventory is the foundation of spatial distribution analyses, hazard assessment and landscape evolution research in the earthquake-struck areas (Xu et al., 2012b; Yang et al., 2015; Parker et al., 2011; Keefer, 2002). With the development of remote sensing and geographic information system (GIS) technology, lots of landslide databases based on single earthquake have been realized, such as the 1994 Northridge Earthquake of American (Harp and Jibson, 1996), 1999 Chi-Chi Earthquake of Taiwan (Lee, 2014), 2004 Niigate Earthquake of Japan (Yamagishi and Iwahashi, 2007), 2008 Wenchuan Earthquake of China (Xu et al., 2014c), 2010 Haiti *Corresponding author: [email protected] © China University of Geosciences and Springer-Verlag Berlin Heidelberg 2016 Manuscript received May 15, 2016. Manuscript accepted September 7, 2016.

Earthquake (Xu et al., 2014a), 2013 Lushan Earthquake of China (Xu and Xu, 2014a) and so on. Based on these databases, many research results have been published about spatial distribution analyses of earthquake-induced landslides and hazard assessment (Li et al., 2013; Xu, 2013; Xu et al., 2013, 2012a), which would aid disaster prevention and mitigation in affected areas.

On July 22, 2013, 07:45:56 am (Beijing time), an earth-quake (Ms 6.6) struck the area between Minxian and Zhangxian counties, Gansu Province (hereafter called Minxian Earthquake for short). Its epicenter is located at 34.5ºN, 104.2ºE, and its hypocenter is at 20 km depth. Shortly afterwards, it was followed by a lot of aftershocks, among which the largest one is the Ms 5.6 event at 09:12:34 am on the same day, with focal depth of 14 km. This shock happened in the western Qinling Mountainous region with active geologic structure, steep terrain and thick loess cov-ered. Plus longtime rainfall before and after the main shock and aftershocks, a large number of secondary geological hazards have been induced, such as collapses, landslides and liquefac-tions. In addition, the anti-seismic performance of buildings in this area is poor. Consequently, the damage caused by this earthquake is unusually serious, affecting 780 000 people in 33 counties of Gansu Province, with direct economic losses up to 2.6 billion Yuan (RMB).

Immediately after the event, Xu et al. (2014b) have carried out a preliminary inventory mapping of these earthquake- triggered landslides by field investigations. They outlined a re-gion of 873.95 km2 that encompasses almost all suspected landslides. From it, they chose a rectangular study area about 330

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1018 Yingying Tian, Chong Xu, Xiwei Xu and Chen Jian

THEOS satellite (15 m), Pleiades (0.5 m) and ZY-3 (2.1 m). The pre-earthquake images were acquired on 28 Jun., 2013 and 29 May, 2013, and post-earthquake images on 2 Aug., 2013, 11 Oct., 2013 and 24 Oct., 2013 (Table 1). Due to the high resolution and larger coverage, the Google Earth images were chosen as the preferred images for landslide inventory mapping. Firstly, the images close to the earthquake origin time were preprocessed in some ways, including geometric correction and fuse for pre-earthquake SPOT images, post-earthquake Pleiades images and ZY-3 images. 2.1.2 DEM data

This work employed the ALOS DEM of 5 m resolution to extract topographic information, rather than 30 m DEM which is commonly applied. This is because that certain landscape fea-tures are less discernible with a relatively low resolution DEM, such as terrain relief, gullies, and surface curvatures (Tang et al., 2001; Thompson et al., 2001). As shown in Figs. 2b and 2c, the 5 m high-resolution DEM can better reflect subtle changes of terrain than 30 m resolution DEM. Compared to the Pleiades image (Fig. 2a), the terrace scarps can be recognized on 5 m DEM. However, only their rough outlines can be identified from 30 m DEM.

The local terrain data, including elevation, slope angle, slope aspect, curvature, slope position and distance to drainages, were extracted based on ArcGIS platform. Geologic data (li-

thology) were derived from 1 : 200 000 geologic maps, including Lintan sheet (I-48-(08)), Longxi sheet (I-48-(09)), Zhuoni sheet (I-48-(14)) and Minxian sheet (I-48-(15)). These geologic maps were provided by the National Geological Data Center. In terms of strata and lithology in the study area, the lithology was rec-lassified into 8 categories. The descriptions of lithology and strata are shown in Table 2.

The seismic data includes earthquake intensity, PGA and distance to the seismogenic fault. By digitalizing the intensity map published by China Earthquake Administration (CEA), the intensity data of the study area were achieved. The regional peak ground acceleration (PGA) counters were downloaded from USGS website. The regional distribution of faults was obtained according to He et al. (2013). Landslides triggered by earth-quakes are linearly distributed along the seismogenic fault (Xu, 2015a; Gorum et al., 2014). Although the 2013 Minxian quake did not have obvious surface ruptures, the fault F2-2 (Fig. 1) is just consistent with the northwest strike direction of the long axis of the landslide-distribution area. In combination with the structural model built by Zheng et al. (2013a), Xu et al. (2014b) suggested that the NE branch of the Lintan-Dangchang fault may be the seismogenic fault for the 2013 event, which is also con-sistent with the isoseismal map, surface damage distribution, focal mechanism solution and the distribution of aftershocks (He et al., 2013). In this work, we continued to hold that F2-2 is the seismogenic fault for the 2013 Minxian event.

Table 1 Remote sensing images used for this inventory

No. Category Date Satellite platform Image type/resolution

1 Pre-earthquake 2012-02-21 SPOT-4 PAN/10 m; MS/20 m

2 2012-05-29 SPOT-4 PAN/10m; MS/20 m

3 2012-05-29 Google Earth –

4 2012-11-18 SPOT-5 PAN/2.5 m; MS/10 m

5 2012-11-18 THEOS MS/15 m


7 Post-earthquake 2013-07-23 Pleiades PAN/0.5 m; MS/2 m


9 2013-08-05 ZY-3 PAN/2.1 m; MS/5.8 m

10 2013-10-11 Google Earth –

11 2013-10-24 Google Earth –

Note: PAN indicates panchromatic image; MS indicates multispectral images.

Figure 2. Pleiades image and hill shades extracted from different DEMs for partial study area. (a) Pleiades image (2.1 m); (b) ALOS DEM (5 m); (c) ASTER DEM

(30 m).

Detailed Inventory Mapping and Spatial Analyses to Landslides Induced by the 2013 Ms 6.6 Minxian Earthquake of China 1019

Table 2 Descriptions of lithology and strata in the study area

No. Stratum Lithology

1 Holocene (Qh) Sand and gravel

2 Late Pleistocene (Qp) Sand and gravel beds

3 Neogene (N) Sandstone, conglomerate, claystone and sandy mudstone

4 Eogene (E) Sandstone, conglomerate

5 Jurassic (J) Conglomerate, carbonaceous shale clip coal or oil shale

6 Triassic (T) Thick sandstone, slate and a small amount of limestone

7 Permian (P) Carbon-containing slate, slate, sandstone, conglomerate

8 Devonian (D) Silty slate, powder sandstone, slate

2.2 Methods 2.2.1 Inventory mapping of landslides triggered by Minxian Earthquake

As the 2013 Minxian Earthquake happened in a rugged mountainous area and the landslides are distributed in a large scope, it is impossible to perform detailed field investigations in the whole study area. Therefore, based on the typical samples achieved from field observations, this work was focused on inventory mapping of landslides using high-resolution remote sensing images. From the Google Earth platform, we obtained multi-temporal phases of high resolution images that can be displayed in 3D view, providing information of colors, textures and terrains to facilitate landslide interpretation. Through the comparison of typical landslides obtained from field survey and high-resolution remote sensing images (e.g., Fig. 3), the inter-pretation criteria were established. Since there are a lot of small landslides, and considering the low precision of automatic ex-

traction method, the visual interpretation method has been em-ployed for landslide inventory mapping.

Following the inventory principles of earthquake-induced landslides proposed by Xu (2015b), the images close to the origin time obtained from Google Earth were chosen as the primary basic data. By analyzing the differences of colors, tex-tures and terrains between pre- and post-earthquake images, the landslides triggered by the earthquake were identified and deli-neated as polygons. For the places without Google Earth images, the SPOT and Pleiades images were used as supplementary data. At last, the landslide inventory maps were validated by results of filed investigations, landslide photos and satellite images.

2.2.2 Spatial distribution analyses of landslides

With the help of the surface tool on ArcGIS 10.2 platform, the data of elevation, slope gradient, slope aspect and curvature of the study area were obtained from 5 m DEM. The slope

Figure 3. Photos and different images of Yongguangcun #1, #2 landslides. (a) Overview photo of Yongguangcun #1, #2 landslides; (b) Yongguangcun #1 landslide

photo; (c) Yongguangcun #2 landslide photo; (d) pre-earthquake image of Google Earth; (e) post-earthquake image of Google Earth; (f) post-earthquake image of

ZY-3 (2.1 m); (g) post-earthquake image of Pleiades (0.5 m).


position was extracted based on slope gradients obtained from 5 m DEM by using the topographic position index (TPI) tools (Weiss, 2001). Using hydrology tool of ArcGIS, the data on major drainages for the study area were achieved. Buffers were

built by 2 km-wide bands for the extracted drainages and fault F2-2. The classification results and distribution maps of these factors are shown in Table 3 and Fig. 4, respectively. The seismic intensity distribution in the research area is displayed in Fig. 1.

Table 3 Classification of conditioning factors of earthquake-induced landslides

No. Factor Classification

1 Elevation (m) (1) <2 300; (2) 2 300–2 400; (3) 2 400–2 500; (4) 2 500–2 600; (5) 2 600–2 700; (6) 2 700–2 800;

(7) 2 800–2 900; (8) 2 900–3 000; (9) 3 000–3 100; (10) 3 100–3 200; (11) >3 200

2 Slope (º) (1) <10; (2) 10–20; (3) 20–30; (4) 30–40; (5) 40–50; (6) 50–60; (7) 60–70; (8) >70

3 Aspect (1) FLAT; (2) N; (3) NE; (4) E; (5) SE; (6) S; (7) SW; (8) W; (9) NW

4 Curvature (1) -20; (2) -20– -10; (3) -10– -4; (4) -4–0; (5) 0–4; (6) 4–10; (7) 10–20; (8) >20

5 Slope Position (1) Valleys; (2) lower slopes; (3) gentle slopes; (4) steep slopes; (5) upper slopes; (6) ridges

6 Distance to drainages (km) (1) 0–2; (2) 2–4; (3) 4–6; (4) 6–8; (5) >8

7 Lithology (1) Qh; (2) Qp; (3) N; (4) E; (5) J; (6) T; (7) P; (8) D

8 Intensity (1) VII; (2) VIII

9 PGA (g) (1) 0.04; (2) 0.08; (3) 0.12; (4) 0.16; (5) 0.20; (6) 0.24; (7) 0.28; (8) 0.32; (9) 0.36

10 Distance to fault (km) (1) 0–2; (2) 2–4; (3) 4–6; (4) 6–8; (5) 8–10; (6) 10–12; (7) 12–14; (8) >14

Figure 4. Distribution maps of factors in the study area. (a) Elevation; (b) slope angle; (c) slope aspect; (d) curvature; (e) slope position; (f) distance to drainages;

(g) lithology; (h) PGA; (i) distance to fault.


3 RESULTS 3.1 Inventory of Earthquake-Triggered Landslides

The previous landslide inventory (Xu et al., 2014b) is shown in the dotted rectangle of Fig. 5a, which includes 2 330 landslides distributed in an area about 330 km2. Although this rectangle contains the most concentrating landslides, there still lots of landslides have been omitted. As a further effort, based on the high-resolution remote sensing images updated on Google Earth platform later, this work made an inventory mapping on a larger scope, and recognized 6 478 landslides induced by the 2013 Minxian Earthquake (Fig. 5b). These landslides occupy an area of 1.71 km2, with the largest one about 46 280.38 m2, and the smallest 3.68 m2. Of them, 234 landslides are larger than 1 000 m2, 401 landslides are between 500 and 1 000 m2, and rest

3 156 landslides are smaller than 100 m2. In number statistics, these landslides are mostly small-scale shallow disrupted ones in this region, expressed as loess falls, slides or topples on ter-race scarps (Fig. 6). Majority of these landslides occurred on the right hand side of the Tao River, in agreement with the field investigation (Xu et al., 2014b). In addition, there are 1 980 landslides on the left hand side of the Tao River.

3.2 Spatial Distribution Analyses of Landslides

To analyze the spatial distribution, this work selected 3 322 landslides with areas larger than 100 m2. The conditioning factors, namely elevation, slope angle, slope aspect, curvature, slope position, distance to drainages, lithology, seismic intensity, PGA, and the distance to the fault F2-2, were taken into

Figure 5. Inventory maps of landslides triggered by 2013 Minxian Earthquake. (a) Previous work (Xu et al., 2014b); (b) this work.


Figure 6. Number versus area of Minxian Earthquake-triggered landslides.

consideration for this distribution analysis. The evaluating indi-cators applied were landslide number density (LND) and landslide area percentage (LAP). Of them, LND describes the concentration of landslides, defined as landslide number per km2; LAP means the scale of landslides, which is defined as the percentage of landslide area in each factor category. On average, the resultant LND and LAP in the study area are 7.41 per km2 and 0.2%, re-spectively. The spatial analyses results are shown in Fig. 7.

The results show that with the growing elevation, the LND and LAP values increase firstly and then decrease. The largest LND and LAP values both are in the elevation range of 2 300– 2 500 m, meaning that the places within such elevation range are prone to large-scale landslides. The slope angle and LND and LAP values show a positive correlation. This is to say that the larger the slope angle is, the much easier for the slope to slide. The LND value reaches the summit when slope angle is up to 60º–70º and drops when gradients are larger than 70º. The LAP values increase with the gradient increasing, and meets the maximum when gradients are larger than 70º. It means that the localities with gradients larger than 70º have a few large-scale landslides. The relationships between slope aspect versus LND and LAP values are the same. As shown in Fig. 7c, slopes fac-ing E, SE, S and SW directions are easily to slide. The larger the absolute curvature value is, the much more susceptible for-slopes to failure. However, the concave slopes with negative curvature values are much sensitive when the earthquake hap-pened. The slope is relatively stable in the nearly flat terrain. From Fig. 7c, the LND and LAP values of gentle slopes with slope angle less than 5º are lowest of all. It indicates that the gentle slopes are much more stable. The LND and LAP values of the ridges both are largest, this is because the steep loess scarps are presented by using 5 m resolution DEM, and a large number of loess falls, slides and topples occurred on the scarps of ter-races. Slopes become much easier to slide when being closer to drainages. The correlations between drainages and LND and LAP values are shown in Fig. 7f. With increasing distance to the drainages, the LND and LAP curves both generally reveal that the landslide susceptibility at these places tends to reduce.

The strength of rock and soil varies with different lithology. Fig. 7g shows the relation between lithology and values of LND and LAP. Because of the smallest coverage, there is only one landslide greater than 100 m2 in the area covered by Jurassic (J) of conglomerate, carbonaceous shale with coal. So the region of

this lithology has the minimum LND and LAP values. The clas-sified area of Permian (P) with carbon-containing slate, slate, sandstone, and conglomerate is the largest in size for the study area, but its LND and LAP values are relatively low, likely due to their high strength. In contrast, the gravel beds of Late Pleis-tocene (Qp) and conglomerate of Neogene (N) and Eogene (E) have higher LND and LAP values, probably because they are poorly cemented, thus easily to generate slope failure.

Earthquake intensity is an index to express the influence and damage to human, buildings or environments. The study area is registered with seismic intensity of VII and VIII degrees (Fig. 1). The spatial analyses show that LND and LAP values increase with the seismic intensity. As determination of earth-quake intensity is partly dependent on feelings of people in the affected area, it may be somewhat subjective (Wang et al., 2010). Therefore, it is better to use the peak ground acceleration (PGA) to quantify the acting force of an earthquake. However, the correlation between PGA and LND and LAP values seem poor. The summits of LND and LAP values lie in the regions of 0.08 and 0.24 g, respectively. As shown in Fig. 7i, the area of 0.08 g has a lot of small scale landslides, while the region of 0.24 g has some relatively large scale landslides. The LND and LAP values drop with increasing distance to the fault F2-2, which means that landslides are mainly concentrated along the seismogenic fault. 4 DISCUSSION 4.1 Seismogenic Fault and Spatial Distribution of Landslides

Many case studies have demonstrated that earthquake- triggered landslides are often distributed linearly along the causative faults. The examples include the 1999 Chi-Chi Taiwan Earthquake-induced landslides linearly distributed along the Chelungpu fault (Lee, 2013), landslides triggered by 2005 Kashmir Earthquake mainly distributed along the Kashmir boundary fault (Kamp et al., 2008), landslides distributed along the Yinxiu-Beichuan fault triggered by 2008 Wenchuan great temblor (Xu et al., 2014c; Gorum et al., 2011), and the 2010 Yushu Earthquake-induced landslides concentrated along the Garze-Yushu fault (Xu and Xu, 2014b). It means that it is poss-ible to infer the surface trace of the seismogenic fault in light of distribution of coseismic landslides. To do so for the 2013 Minxian Ms 6.6 event which did not rupture the ground surface, we drew a series of 2 km-width bands along the fault F2-2 (Fig. 8a), and numbered them from southwest to northeast, yielding 15 bands. Consequently, the fault branch F2-1 lies in the bands of 4, 5 and 6, F2-2 locates in bands of 8 and 9, and F2-3 crosses sev-eral bands on the right side of F2-2. The LND and LAP values of each band are shown in Fig. 8b. It is clear that the places close to F2-2 have the highest LND and LAP values of bands 8 and 9. Such values reach small peaks in the influence scopes of F2-1 (bands 4, 5 and 6). However, these values are generally low around F2-3. This phenomenon may imply that there exist cer-tain differences in activity among the branches of the Lintan- Dangchang fault. And the fault branch F2-2 is most likely to be the seismogenic fault of the 2013 Minxian Ms 6.6 Earthquake, and F2-1 might also be active and also has contributed to trigger coseismic landslides.


Figure 7. Relationships between each factor and LND and LAP values. CA is the area of each category (km2, shaded column). LND is the landslide number

density (km-2, red polylines). LAP is the landslide area percentage (%, blue polylines).

4.2 Drainages and Spatial Distribution of Landslides

Landslides are usually concentrated along steep banks of major rivers. The Tao River is the largest stream in the study region. When this river is considered as the factor of drainage that influenced coseismic landslides, the buffers of Tao River were reclassified into nine classes with intervals of 2 km: (1) 0–2; (2) 2–4; (3) 4–6; (4) 6–8; (5) 8–10; (6) 10–12; (7) 12–14; (8) 14–16; (9) >16 km.

Both LND and LAP values largely descend with the in-

creasing distance to Tao River (Fig. 9b), consistent with case considering other major rivers (Fig. 7f). However, when only the Tao River is taken into account, statistical result shows 50% of all landslides are distributed in the range of 4 km to the river, and 73% in the range of 6 km. If other major drainages are also considered, 57% of all landslides appear in the range of 2 km, and 85.5% in the range of 4 km to drainages. It indicates that when more major rivers are taken into consideration, the ana-lyses can yield a better correlation between the coseismic


Figure 8. Relationships between distance to F2-2 with LND and LAP values. (a) Branches of the Lintan-Dangchang fault (F2-1, F2-2, F2-3) and distribution of the

2 km-wide bands within the study area. (b) CA (km2), LND (km-2) and LAP (%) of each band. Horizontal axis is the number of each band. Other explanations are

same as Fig. 7.

Figure 9. Relationships between distance to Tao River and LND and LAP values. (a) Classifications for the buffers of Tao River; (b) LND (km-2) and LAP (%)

values within each distance classification. Horizontal axis is the numbers for buffers of Tao River. Other explanations are the same as Fig. 7.

landslides and drainages. In other words, it may be more rea-sonable, since other major drainages can also pose controls on distribution of landslides.

5 CONCLUSIONS

The 2013 Minxian, Gansu Ms 6.6 Earthquake occurred at the northeastern margin of the Tibetan Plateau. The NW trending Lintan-Dangchang fault might be responsible to this event. As the affected area is in the mountainous region with widespread loess coverage, this earthquake triggered a large number of landslides. In this work, a landslide distribution area outlined by previous research was selected as the target for more detailed landslide inventory mapping and spatial analyses, which is about 873.95 km2. Based on high resolution remote sensing images, the visual interpretation of landslides in this scope has been carried out, realizing a detailed inventory of the landslides caused by this earthquake. In total, 6 478 landslides were registered, which occupy an area of 1.71 km2. Consistent with the field investiga-tion, landslides of area less than 100 m2 accounts for 50% of the total, which are small loess falls, slides and topples distributed along the scarps of terraces.

Considering conditioning factors of terrain, geology and seismology, statistical analyses were carried out to 3 322 landslides with area larger than 100 m2. Furthermore, DEM of 5 m resolution was used to extract the related topographic infor-mation. In this study, LND (landslide number density) and LAP (landslide area percentage) were used as the evaluating indexes. The results show that the largest LND and LAP values are in the elevation range of 2 300–2 500 m. The slope angle has a positive correlation with LND and LAP values. The slopes facing E, SE, S and SW direction are more prone to sliding when the earth-quake happened. The concave and convex slopes both have larger LND and LAP values. Ridges show high landslide sus-ceptibility, while gentle slopes have a contrary trend. Landslides are mostly distributed near drainages. The LND and LAP values approximately descend with the increasing distance to drainages. Taking more major rivers into account can make the spatial analyses much objective. Areas underlain by gravel beds of Late Pleistocene (Qp) have the highest LND and LAP values, fol-lowed by easily weathered sandstone and conglomerate of Eo-gene (E) and Neogene (N). As for seismic intensity, the LND and LAP values in the region of VIII-degree are twice that of


the VII-degree region. However, the relations between PGA and LND and LAP values are not clear. The largest LND and LAP values are in the scopes of 0.08 and 0.24 g, respectively. The LND and LAP values decrease with distance being far away from the branch F2-2 of the Lintan-Dangchang fault. According to LND and LAP values of each 2 km-wide band on either side of this fault, F2-2 is thought to be the seismogenic fault. And the branch F2-1 might also be involved in generation of the earthquake and induced some coseismic landslides. ACKNOWLEDGMENTS

This research was supported by the National Natural Science Foundation of China (No. 41472202) and Key Laboratory for Geo-hazards in Loess area, MLR (No. KLGLAMLR2014003). We give our sincere thanks to reviewers and editor for improving the manuscript. The final publication is available at Springer via http://dx.doi.org/10.1007/s12583-016-0905-z.

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