750 IEEE JOURNAL OF SELECTED TOPICS IN …or.nsfc.gov.cn/bitstream/00001903-5/258211/1/...750 IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, VOL

750 IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, VOL. 8, NO. 2, FEBRUARY 2015

Estimating the Contribution of Loose Depositsto Potential Landslides Over Wenchuan

Earthquake Zone, ChinaTao Zeng, Abduwasit Ghulam, Wu-Nian Yang, Mark Grzovic, and Matthew Maimaitiyiming

Abstract—Loose deposits may lead to catastrophic landslidesand rockslides especially during or after heavy rainfall events. Inthis paper, we propose a method to estimate the volume and spatialdistribution (VSD) of loose deposits using airborne and space-borne optical remote sensing data and Interferometric SyntheticAperture Radar (InSAR) coherence. The estimated VSD of coseis-mic loose deposits in Wenchuan, China, the epicenter of theSichuan Earthquake, is then used to predict potential landslides.First, high-resolution digital elevation models (DEMs) and digi-tal orthophoto map (DOM) are developed using stereo imagerycollected by an unmanned aerial vehicle (UAV) over a core testsite. Next, the VSD of loose deposits is determined using the UAVimagery, DEM, and a landslide inventory map developed by fieldsurveys, and a power–law relationship between the area and vol-ume is established. In order to determine the loose deposits of theentire study area, the InSAR coherence of PALSAR images col-lected before and right after the earthquake, Landsat image clas-sification, and DEM combined with Analytical Hierarchy Process(AHP) model are integrated in a decision tree algorithm. Finally,we develop a quantitative method of determining the severityand likelihood of potential geohazards based on VSD of coseis-mic loose deposits and other variables including the NormalizedDifference Vegetation Index (NDVI), slope gradient, elevation,and land-cover/land-use (LCLU). A total of 42 catchments areaare identified as the hot spots with a very high or high possibil-ity of potential landslides. The VSD of coseismic loose deposits

Manuscript received February 27, 2014; revised August 02, 2014; acceptedAugust 11, 2014. Date of publication September 09, 2014; date of currentversion February 09, 2015. This work was supported in part by the National KeyTechnology R&D Program under Grant 41071265, 2013 and Grant 41372340,2011, and in part by the Key Program of Science and Technology Departmentof Sichuan Province (Geohazard information extraction and application usingUAV images, 2013). (Corresponding authors: Abduwasit Ghulam and Wu-NianYang.)

T. Zeng is with the Department of Surveying and Mapping Engineering,Chengdu University of Technology, Chengdu 610059, China; the KeyLaboratory of Geoscience Spatial Information Technology, Ministry of Landand Resources of China, Chengdu University of Technology, Chengdu 610059,China; and also with the Center for Sustainability, Saint Louis University,Saint Louis, MO 63108 USA (e-mail: [email protected]).

A. Ghulam is with the Center for Sustainability, Saint Louis University, SaintLouis, MO 63108 USA (e-mail: [email protected]).

W.-N. Yang is with the Department of Surveying and Mapping Engineering,Chengdu University of Technology, Chengdu 610059, China; and also withthe Key Laboratory of Geoscience Spatial Information Technology, Ministryof Land and Resources of China, Chengdu University of Technology, Chengdu610059, China (e-mail: [email protected]).

M. Grzovic is with the Center for Sustainability and the Department of Earthand Atmospheric Sciences, Saint Louis University, Saint Louis, MO 63108USA (e-mail: [email protected]).

M. Maimaitiyiming is with the Center for Sustainability and the Departmentof Earth and Atmospheric Sciences, Saint Louis University, Saint Louis,MO 63108 USA (e-mail: [email protected]).

Color versions of one or more of the figures in this paper are available onlineat http://ieeexplore.ieee.org.

Digital Object Identifier 10.1109/JSTARS.2014.2349953

over these catchments is about 92.34% and 77.96% of the totalpotential loose deposits area.

Index Terms—Landslide, loose deposits, remote sensing,Wenchuan earthquake.

I. INTRODUCTION

T HE GREAT Wenchuan earthquake on May 12, 2008 trig-gered a large number of landslides and slope failures.

Approximately, 56 000 landslides were produced by the earth-quake [1], [2], which created loose debris and unstable fracturedrock deposits. Numerous soil failures and debris serve as thematerial sources for subsequent debris flows [3]. Under nor-mal weather conditions, such loose solid materials are in ametastable state. During the rainy season, these deposits maylose their stability due to heavy rainfall infiltration [4]. Forinstance, 75 large-scale debris flows occurred in the Wenchuanearthquake-affected areas following heavy rainstorms onAugust 13, 2010 [5]. Such events are expected to happen in thefuture and could cause enormous casualties and huge economiclosses in the Wenchuan earthquake-affected areas [6], [7].

Loose deposits in the area affected by the Wenchuanearthquake may be traced to coseismic loose deposits andpotential loose deposits. The former is the direct source frommultiple deposits such as landslides, and trench depositstriggered by the earthquake. The latter is an indirect sourceattributed to soil erosion due to rainfall, wind, and gravity,which is formed during a long period of land surface processesfollowing an earthquake. The terms describing these forms ofland movement are often defined as types of soil erosion [8].In this contribution, we use the term “soil erosion” to representthe mass-wasting processes that are the main drivers of indirectpotential loose deposits. Since the great Wenchuan earthquake,the extent of loose solid material has increased throughout theregion affected by the earthquake. This implies that potentialloose deposits may be activated by strong soil erosion in thecase of heavy rainfall and gravity anomalies.

There have been numerous studies that focused on remotesensing and geographic information system (GIS)-based analy-sis of landslides, and the susceptibility of the earthquake zoneto geohazards [4], [9]–[16]. A number of publications analyzedthe susceptibility and risk of debris flows [6], [17]–[20]. Severalauthors also assessed the risks of loose deposits formed bythe 2008 Wenchuan earthquake at smaller regional scales [4],[8], [21]. There are significant discrepancies, however, in thecalculation of the volume of coseismic loose deposits. Also, a

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ZENG et al.: ESTIMATING THE CONTRIBUTION OF LOOSE DEPOSITS TO POTENTIAL LANDSLIDES 751

model of potential loose deposits caused by lengthy soil ero-sion developed for one area may not be sufficiently generalizedto apply other areas due to the complexity and heterogeneity oflocal topography. Very few studies, to the best of our knowl-edge, explored the relationship between the quantity (includingvolume and area) and spatial distribution of loose deposits andits potential hazard contribution (PHC) at the catchment level.

The estimated volume of materials displaced by the coseis-mic landslides ranges between 5 and 15 km3, which will have along-term influence on the stability of slopes in the earthquakedisaster area [2]. Therefore, predictive assessment of potentialdebris flows or landslides requires estimation of the volume,area, and the stability of loose deposits in the earthquake area.The stability of loose deposits is related to the rainfall intensityand tectonic activity in the region, but this is beyond the scopeof this paper. The objectives of this study are to: 1) determinethe volume and spatial distribution (VSD) of coseismic loosedeposits; 2) estimate the area of potential loose deposits basedon a developed soil erosion model; and 3) identify the causalrelationship between loose deposits and their contribution topotential geological hazards in catchments.

Following the description of the study area and data inSection II, a model that quantifies the volume of loosedeposits and the area of potential loose deposits is presentedin Section III. The VSD of loose deposits and its potential riskcontribution at each catchment are also analyzed in this section.The results and conclusion are presented in Sections IV and V,respectively.

II. STUDY AREA AND DATA

A. Study Area

The study area located in Wenchuan County, Sichuan, Chinais about 4085 km2 extending from 102◦51′E to 103◦44′E andfrom 30◦45N to 31◦43′N (Fig. 1). It is situated in the tran-sitional mountainous belt between the Sichuan Basin and theWestern Sichuan Plateau, which is characterized by ruggedmountains with elevations between 780 and 5800 m as wellas deeply incised valleys. It has a complex geological struc-ture that consists of widely distributed loose quaternary layersand intensely weathered magmatic rocks. Two main faults crossthis area are: 1) the 30◦N−45◦E trending Qingchuan–Maowen;and 2) the N35◦E trending Yingxiu–Beichuan. Annual rain-fall is 930.4 mm, and most of that occurs between June andSeptember, with an increasing trend after 2008. The yearlyaverage temperature and yearly average sunlight duration atthe low elevation region are 13.8◦C and 1368 h, respectively.The Wenchuan river network is dendritic, and it belongs to theMin Jiang river water system. There are four main rivers thatrun through Wenchuan County: 1) the Min Jiang river; 2) theZagunao river; 3) the Shou Xi river; and 4) the Erhe river.

This area was chosen because it has experienced strong seis-mic activities that led to the accumulation of large amountof loose deposits. Therefore, the region bears great potentialfor future catastrophic landslides. Additionally, there are goodquality multitemporal remote sensing images available that canbe used to investigate the changes in loose deposits followingthe Wenchuan earthquake.

B. Data Sources

The data comprised aerial images, satellite images, dig-ital elevation model (DEM), field investigations, and otherinformation about the soil type obtained from the literature.The aerial images were collected from a low-altitude aerialcampaign conducted on May 14, 2008 over a strip alongthe Min Jiang river. The coverage of the aerial image is244.14 km2 over the area from Yingxiu town to Yinxing vil-lage (Fig. 1). The aerial imagery was collected by an unmannedaerial vehicle (UAV) equipped with 50 mm focal length dig-ital camera with a spatial resolution of 0.42 m. Aerial stereoimages were processed by digital photogrammetry methodsto produce a high-resolution DEM and digital orthophotomap (DOM) of the core test site following the earthquake.Twelve ground control points were used to assess the qual-ity of the UAV generated terrain. The vertical accuracy ofthe DEM was 0.2 m at 90% confidence interval (CE90) withroot-mean-square error (RMSE) of 0.34 m. The DEM andDOM were then used to build a three-dimensional (3-D) ter-rain model to measure the landslide area and volume of loosedeposits.

The Phased Array type L-band Synthetic Aperture Radar(PALSAR), developed by the Japanese Ministry of Economy,Trade and Industry (METI) as a joint project with JapanAerospace Exploration Agency (JAXA), is an active, low-frequency (L-band) microwave sensor that provides cloud-freeand day-and-night surface observations. The earthquakeregion is a mountainous area covered by thick forest. Weused PALSAR data because: 1) of its greater penetrationability through the forest canopy since it uses low-frequencybeams; and 2) the available data coverage was excellent. FourInterferometric Synthetic Aperture Radar (InSAR) pairs onpaths 475 (frame# 610 and 620) and 476 (frame# 600 and610) cover the entire study area. Images from path 475 werecollected on June 20, 2007 and June 22, 2008, and the pairs onpath 476 were collected on April 8, 2008 and May 24, 2008,respectively.

The Landsat images used were those with minimum cloudinterference and that covered the whole study area. Landsat-7Enhanced Thematic Mapper Plus (ETM+) images collectedon December 17, 2008 and May 21, 2013 were used. 2008’ETM+ imagery was used to extract geohazard informationof the area and to develop a landslide inventory map followedthe Wenchuan earthquake. 2013’ ETM+ image was usedto estimate the potential loose deposits based on the soilerosion model.

A slope and elevation classification map of the entire studyregion was generated using The Advanced Spaceborne ThermalEmission and Reflection Radiometer (ASTER) Global DigitalElevation Model (GDEM) Version 2 obtained from the U.S.Geological Survey (USGS) (http://gdex.cr.usgs.gov/gdex/).Field survey data include surveys in the core disaster zone thatwere used to determine and rectify the information extracted byremote sensing images taken within a few days after the earth-quake. Additionally, other data such as the detailed geology andsoil type were collected. Table I shows the basic informationfrom our data used in this work.


Fig. 1. Location map of the study area. Stream network is draped over a 30-m DEM, and the rectangle indicates area examined during the airborne campaign.

III. METHODOLOGY

Since debris flows are often associated with intensive rain-fall, we analyzed the PHC of loose deposits at the catchmentscale. A total of 207 catchments were generated from a 30-mDEM along with raster layers of the flow direction and streamnetwork by using the ArcGIS platform. The workflow of cal-culating coseismic loose deposits and spatial distribution ofpotential loose deposits is shown in Fig. 2.

A. Developing a 3-D Terrain Model Using High-ResolutionStereo Aerial Images

The 3-D terrain visualization is the integrated technique ofgeographical information system (GIS), digital photogramme-

try system (DPS), remote sensing (RS), and 3-D virtual realitytechnology [22], [23]. DEMs and DOMs from UAV imageswere generated using digital photogrammetric technology anddigital aerial triangulation method [24], [25] following thesteps below:

1) inner orientation;2) relative orientation and strip-auto-collected with auto-

matic measurement of tie points;3) semiautomatic ground control points (GCPs) measure-

ment;4) adjustment and quality control;5) interactive stereo measurement to produce DEM;6) digital orthorectification combined with DEM to produce

the DOM.


TABLE IUSE OF VARIOUS DATA SOURCES

Fig. 2. Data processing flowchart.

Furthermore, the virtual global GIS platform was used tobuild the 3-D visualization of the digital terrain model com-bined with the DEMs and DOMs. The 3-D GIS software offersfunctions for data integration in the form of 3-D visualizationon the computer screen, data interactive manipulation, and dataanalyst [26].

The boundary of each landslide in the core test site calledthe “training landslide” was measured on the high-resolution3-D terrain model of the core study area (Fig. 3). The traininglandslides were photointerpreted from high-resolution UAVimagers and were verified and documented by field surveys.The volume of the training landslides was calculated as the


Fig. 3. Measuring the boundary of the landslide and the coordinate of the pointson 3-D terrain model. The main image is the DOM with a pseudo color inset of0.42-m resolution DEM (to the top) developed using stereo imagery collectedby an UAV over a core test site. The inset image to the upper-right corner showsa digitized polygon outline of a surveyed landslide.

product of the landslide area and depth [27], [28]. A key point indefining a relationship to link the area to the volume of a land-slide is the estimation of the landslide depth. This was done bycomparing the elevation estimation, using a DEM over the coretest site, from before the earthquake and after the earthquake.We surveyed about 20 points within each training landslide onthe 3-D terrain model and in the field. Those points were dis-tributed at the landslide leading edge, center, and trailing edge.A total of 38 training landslides were photointerpreted in thecore test site.

B. Establishing the Power-Law Relationship of Area andVolume

An empirical relationship between the volume of individuallandslides and the landslide area is often used to estimate thevolume of coseismic loose deposits [29]–[31]. We adopted thepower-law relationship between the volume of loose depositsand the area of the landslide as defined by [29]

Vl = ε×Aαl (1)

where Vl(in m3) is the volume of coseismic loose deposits,Al(in m2) is the landslide area, and ε and α are the parameters.

The relationship between the volume of loose deposits andthe area of the landslide is not linear, but exponential as definedby the power-law equation. In order to obtain the two param-eters ε and α, we took logarithm on both sides of the (1) aslogVl = logε+ α ∗ logAl and used a regression analysis todetermine the two parameters. The two scaling parameters εand α in (1) were then derived from 38 training landslidesusing the documented area and volume estimates from the 3-Dvisualization model and the field survey data. The two parame-ters obtained from the regression analysis were ε = 0.169 andα = 1.361 with the standard error of the scaling exponent equalto 0.034 and R2 = 0.94.

The standard error of the log-transformed volume was calcu-lated by [2], [29]

Slog, v =

√√√√√n∑

i=1

e2i

n− 2(2)

where ei (in m3) is the difference between the sample value andthe estimated function value, and n is the number of samples.We used the 38 training landslides log-transformed volume andarea to obtain Slog,v = 0.154 m3. The reason n− 2 is usedrather than n− 1 is that the two parameters (the slope andthe intercept) were estimated in order to estimate the sum ofsquares.

C. Calculating the Volume of Coseismic Loose Deposits

1) InSAR Coherence to Map Coseismic Loose Deposits:Phase coherence derived from InSAR is a measurement ofspatial local correlation between two SAR images and can beinfluenced by random changes in the land surface, e.g., destruc-tion due to an earthquake [32], disturbance from landslides[33], volcanic activities [34], and vegetation change [35]. Thestudy area is a densely vegetated terrain that was exposed tosignificant earthquake destruction. One might expect very lowcoherence values over disturbed areas due to the earthquake andhigher coherence over stable scatterers (e.g., buildings or rock)as demonstrated by several recent publications that reportedquality InSAR coherence products over the study site [36],[37]. A decision tree algorithm based on InSAR coherence wastested to separate disturbed and undisturbed bare soil in orderto overcome spectral confusions of these land cover types onLandsat images. However, we found no significant contributionof InSAR coherence to detect loose deposits, which might beattributed to the temporal and volumetric decorrelation betweenthe InSAR pairs.

2) Areal Extent of Landslide: The aireal extent of coseismiclandslide information was extracted by interpreting a LandsatETM+ image collected on December 17, 2008 using the sup-port vector machine (SVM) classification method [38]. Byexperimenting with various optional parameters, a radial basisfunction was used as the kernel type for the SVM operationwith a Gamma kernel function of 0.17 and penalty parameter of100. The pyramid levels and classification probability thresh-old were set to 0. Additional data including aerial photos andfield surveys over Wenchuan County were supplemented. Alltypes of geohazards (e.g., shallow, disrupted landslides, rockfalls, and deep-seated landslides) were considered as landslidesbecause it is difficult to distinguish the types of different ofgeohazards with pan-sharpened 15-m spatial resolution ETM+images. Five land cover categories were considered: forest,grass, landslide (all types of geohazard), water/snow, and bareland. The urban area of Wenchuan is small compared with otherland cover types. Additionally, it is difficult to distinguish theurban land cover from bare soil in Landsat imagery. For thesereasons, the urban land cover was not considered in Landsatclassification.


TABLE IIACCURACY ASSESSMENT OF THE CLASSIFICATION RESULTS USING GROUND TRUTH

Classification accuracy was satisfactory for landslideextraction; in particular, the producer’s and user’s accuracy forlandslide class was greater than 90% and 99%, respectively. Butas shown in Table II, the bare land class was confused withlandslides with a user’s accuracy of 72.4% due to its similarspectral signatures with coseismic loose deposits on a Landsatpixel. As a result, 4454 landslides with a total area of 52.37 km2

were identified in study area. The area of landslides varies fromhundreds of square meters to a few square kilometers.

3) Volume of Coseismic Loose Deposits: The volume ofcoseismic loose deposits was then calculated by the power–lawrelationship between the landslide area and volume establishedin Section III-B. The volume of the loose deposits correspond-ing to landslides varied from 966.43 m3 to 25.1841× 106 m3.The standard error of estimate (SEE) of loose deposits volumeSv is then (adapted from [29])

Sv = Vl × ln 10× Slogv. (3)

By using the spatial statistics of the catchment and landslidesdata, we summarized the number and the area of landslidesin each catchment. For each catchment, the total volume ofcoseismic loose deposits was calculated using

Vc =n∑

i=1

Vl (4)

where Vc (in m3) is the total volume of coseismic loose depositsand n is the number of landslides in the catchment.

The uncertainty of total volume ST (in m3) was estimatedusing the propagation of errors formula [29]

ST =√

S2V1

+ S2V2

+ · · ·+ S2Vn

. (5)

D. Assessing the Potential Loose Deposits

1) Soil Erosion Map: The susceptibility of soil erosion wasestimated on each pixel unit using the developed soil erosionmodel given in.

E =

n∑j=1

Ej ×Wj (6)

where E is the soil erosion susceptibility; Ej is the contributingfactor of soil erosion; and Wj is the weight of a soil erosionfactor. It is worth noting that rainfall is one of the key trig-gering factors of landslides. The soil erosion intensity wouldbe the result of susceptibility and rainfall intensity. The actualslope failure mechanism, however, is complex. Therefore, thiscontribution focuses on the soil erosion susceptibility withoutconsidering the contribution of rainfall.

Determining the factors that regulate soil erosion require acomprehensive understanding of the local geographic condi-tions. The erosion factors used in this study included eleva-tion, slope, vegetation fraction (fv), and land-cover/land-use(LCLU) [39].

LCLU maps were obtained from ETM+ image classificationand photointerpretation combined with field investigations. Thefraction of vegetation was computed using the NormalizedDifference Vegetation Index (NDVI) [40], [41]. The fv wasreclassified to the following range of values: >70%, 50%–70%,30%–50%, 10%–30%, and <10%. A slope and elevation mapwere generated from a DEM over the whole study area withslopes ranging from <10◦, 10–25◦, 25–40◦, 40–55◦, and >55◦,and elevations from <1100, 1100–1400, 1400–1700, 1700–2100, and >2100. These factors were then used to determinethe susceptibility of soil erosion as shown in Table III.

Then, we used the Analytical Hierarchy Process (AHP)method introduced by Saaty [43] to weight each factor [43]–[45]. AHP is a semiquantitative method that uses a pairwisecomparison of various soil erosion factors to determine theinput factors weight. When arranging the factors in a hierarchicorder, there should be relative importance of one factor overanother forming a pairwise comparison matrix with numericalvalues as shown in Table IV [44].

A pairwise comparison matrix was constructed to rank eachsoil erosion factor on the basis that each factor is rated againstevery other factor by assigning a relative dominant valuebetween 1 and 9 to the intersecting cell (Table V).

Finally, the soil erosion susceptibility was calculated usingthe weighted linear sum or input factors as described in (6).The pixel values corresponding to the soil intensity were thenreclassified into five classes (very low, low, moderate, severe,


TABLE IIISOIL EROSION FACTORS AND SOIL EROSION INTENSITY

TABLE IVPREFERENCE SCALE BETWEEN TWO PARAMETERS IN AHP [43]

TABLE VPAIRWISE COMPARISON MATRIX AND FACTORS WEIGHT

Fig. 4. Statistical analyses of the landslide area and loose deposits volume in each catchment.

and very severe erosion), using natural breaks, to determine theclass intervals for the soil erosion map. The snow and watersurfaces were considered as the nonerosion areas for the soilerosion map.

2) Assessing the Potential Loose Deposits: We extractedthe severe and very severe soil erosion data from the soil erosionmap and transformed these data from raster to vector formatto produce the potential loose deposits map of the entire study


Fig. 5. Spatial distribution of the volume of the coseismic loose deposits at the catchment scale.

area. Since landslide or debris flow hazards are often triggeredby intensive rainfall or abrupt changes in geodynamics, it is piv-otal to examine the potential loose deposits at the catchmentscale. Therefore, the catchment specific loose deposits weregenerated by spatially joining the potential loose deposits mapof entire region to the catchment layer.

E. Assessing the PHC at Catchment Scale

The contribution of the loose deposits to the potential geo-logical hazards was calculated for each catchment using two

factors: 1) the proportion of the area of loose deposits over thetotal area in a catchment (Ac

At) (%); and 2) proportion of vol-

ume of the loose deposits over the total volume of a catchment(Vc

Vt) (%), where Ac is the potential loose deposits area in each

catchment; At is the total potential loose deposits area; Vc isthe volume of loose deposits in each catchment; and Vt is thetotal volume of loose deposits. The PHC in each catchment wascalculated using

PHC =1

2×(Ac

At+

Vc

Vt

). (7)


TABLE VISTATISTICS OF POTENTIAL HAZARD CONTRIBUTING RANKS OF 104 CATCHMENTS

Fig. 6. Soil erosion and loose deposits maps of 2013 over Wenchuan County. (a) Soil erosion map. (b) Loose deposits map.

IV. RESULTS AND DISCUSSION

A. Distribution of the Volume of Coseismic Loose Deposits inCatchments

Fig. 4 shows that the volume of loose deposits in eachcatchment is proportional to the landslide area and the num-ber of landslides. Overall, the area and volume relationshipshows an exponentially increasing trend reflecting the power–law relationship. Spatially, the larger volume of coseismicloose deposits is distributed in the eastern part of the studyregion, specifically over the east-central regions (Fig. 5). Asshown in Table VI, the volumes of coseismic loose depositsproduced by the Wenchuan earthquake varied considerably ineach catchment. The coseismic loose deposits were nonuni-formly distributed in 104 catchments. Catchment 104 has thelargest volume of loose deposits, 61.7 Mm3 (M = 106, Mega)and accounts for 11.43% of the total loose deposits volume.The reclassified range of the volume of the coseismic loose

deposits (Figs. 5 and 6 and Table VI) indicated that thevery small (0.005−0.078 Mm3), small (0.112−0.821 Mm3),medium (1.001−9.804 Mm3), high (10.301−20.187 Mm3),and very high (30.905−61.669 Mm3) volumes of coseismicloose deposits in catchments constitute 0.1%, 2.0%, 29.8%,45.1%, and 23.1% of the total coseismic loose deposits volume,respectively.

B. Potential Loose Deposits

The soil erosion map of 2013 generated from the devel-oped soil erosion model using the AHP technique is shownin Fig. 6(a). The potential loose deposits region was devel-oped from the high and very high soil erosion susceptibilitydata [Fig. 6(b)]. Using the ArcGIS spatial analysis functioncombined with the severe and very severe soil erosion sus-ceptibility data, the result of total potential loose depositsarea in 2013 is 500.4 km2 which is 13.5% of the study area.


Fig. 7. Distribution of 104 catchments in different potential hazard contributing ranks.

Each catchment’s potential loose deposits area Ac varied from0.15 to 34.84 km2.

C. PHC Analyses

Two factors, the proportion of the area and the proportionof the volume of coseismic loose deposits and potential loosedeposits, were given the same weight and used to calculate thePHC of loose deposits in each catchment.

The values of PHC were then categorized into four classes,namely, low, medium, high, and very high to show the rank of

the PHC in each catchment (Fig. 7). For the convenience of sub-sequent analysis, the main faults, main rivers, and the recordeddebris flow events that occurred from 2010 to 2013 in this areawere draped on a DEM.

Results showed that the low, medium, high, and very highcontributing ranks for the hazard potential was about 45.2%,14.4%, 27.9%, and 12.5% of the calculated catchments, respec-tively (Table VI). Clearly, there exists a positive correlationbetween the volume and the area of the total loose deposits andthe magnitude of contribution to the potential hazard.

It was also found that the northern and southeastern parts ofthe study area with elevations ranging from 764 to 2800 m bear


TABLE VIIDEBRIS FLOWS OF FIELD INVESTIGATION IN WENCHUAN REGION FROM 2010 TO 2013

a high to very high possibility of potential landslides and/ordebris flows. Most of the high to very high contributing ranksfor the hazard zones are located along the Min Jiang river. Allthe high to very high contributing ranks for the hazard zonesare located closed to Qingchuan-Maowen fault and Yingxiu-Beichuan fault (Fig. 7).

According to some of the literatures and the investigationby the Land and Recourses Department of Sichuan Province,many strong land movements similar to debris flows and land-slides occurred in the potential hazard regions identified in thisstudy especially in the summers from 2008 to 2013. We inves-tigated 10 heavy debris flow events that happened in this areafrom 2010 to 2013 (marked in Fig. 7), among them four eventsoccurred in catchments ranked with a high hazard potentialand six events occurred in catchments ranked with very a highpotential of risk (Table VII).

Our results showed that 42 catchments with 92.3% of thetotal coseismic loose deposits volume and 78% of the totalpotential loose deposits area are in a very high or high risk ofpotential future hazards. Based on field surveys and investiga-tions in the Wenchuan earthquake zone from 2010 to 2013, ourestimations were confirmed by at least 10 heavy debris flowevents that happened in very high or high potential hazard con-tributing zones. The results indicated that the high and very highrisk catchments are subject to potential geohazards and, there-fore, need to be closely monitored for hazard mitigation andprevention purposes.

V. CONCLUSION

Estimating the spatial distribution of the total loose depositsand its contribution to a potential hazard were two majorsteps forward in geohazard (e.g., landslide and debris flow)mitigation. In this paper, two different sources of Wenchuanearthquake loose deposits—coseismic loose deposits andpotential loose deposits, were estimated in terms of their areaand volume.

A robust regression method was used to calculate two scal-ing parameters of the volume–area relationship over a core testsite. Ground truth area and volume of loose deposits were col-lected using field surveys, high-resolution stereo images, andaerial photos collected during UAV air campaign. By analyzingthe landslide area and the volume of coseismic and potentialloose deposits in each catchment, we established an exponen-tial relationship between the volume of loose deposits and thearea and number of landslides reflecting a power–law regres-sion. A total of 4454 landslides about 52.37 km2 were identifiedin study area. The massive volume of coseismic loose depositsin each catchment is the major contributing factor of potentialgeohazards.

Potential loose deposits are activated by strong soil erosionlike debris flows and slope failures. A better understandingof the soil erosion process is necessary to extend quantita-tive estimates of potential loose deposits over time and space.The potential loose deposit region map developed in this papershowed that the total potential loose deposits area in 2013 was500.41 km2, which was 13.5% of the study area. The potentialloose deposits area in each catchment is another contributingfactor of potential geohazards.

Overall, the results of this study provided detailed informa-tion about the area, volume, and spatial distribution of loosedeposits, and advanced our understanding of the potential geo-hazards in the region. It is worth noting that the soil erosionmodel developed in this paper can be applied to any otherregions as most of the input variables can be derived from freelyavailable satellite data. The coefficients of the power–law rela-tionship that are used to estimate the volume of loose deposits,however, are site specific, and they need to be determined foreach test site. Another limitation of our approach is the calcu-lation of the depth of the loose deposits using high-resolutionDEM. Assisted with field surveys, we were able to accuratelyestimate the depth of the loose deposits using the DEM; how-ever, the number of samples we collected in the field was notlarge.


Accurate predictions of future landslides and debris flows arestill a tremendous challenge as it is related to so many complexparameters that are difficult to predict with high confidence.However, alternative images or hot spots of how future hazardsmight unfold in space indicated by its contributing factors mayprovide answers to decision-makers working in hazard preven-tion and mitigation. Future efforts should focus on incorpo-rating additional factors like rainfall intensity, geology, crustaldeformations, and geodynamics of the study area.

ACKNOWLEDGMENT

The authors would like to thank the Bureau of Land andResources and the Bureau of Surveying and Mapping ofSichuan Province for providing the data for our research andalso the editor and anonymous reviewers of this paper.

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Tao Zeng received the B.S. degree in surveying andmapping from Wuhan University of Technology ofSurveying and Mapping, Wuhan, China, in 2000,the M.S. and Ph.D. degrees in geodetection andinformation technology from Chengdu University ofTechnology, Chenghua, China, in 2006 and 2010,respectively.

She was a Lecturer with the Department of SpatialInformation Engineering, Chengdu University ofTechnology, Chengdu, China, in 2000, and is cur-rently an Associate Professor with the College of

Earth Sciences, Chengdu University of Technology. Her research interestsinclude information extraction using multisensors remote sensing image, digi-tal photogrammetry techniques, GIS, monitoring of natural hazards, and remotesensing of ecology and environment.

Abduwasit Ghulam received the B.S. degree inphysical geography and the M.S. degree in cartog-raphy and GIS from Xinjiang University, Ürümqi,China, the Ph.D. degree in cartography and GISfrom Peking University, Beijing, China, and thePostdoctoral research in quantitative remote sensingfrom the Computer Sciences and Remote SensingLaboratory (LSIIT), Image Sciences, University ofStrasbourg, Strasbourg, France.

From 2007, he has been an Assistant Professorwith the Center for Sustainability, Saint Louis

University, St. Louis, MO, USA. His research interests include retrieval ofsurface bio/geophysical variables using remote sensing, InSAR/DInSAR tech-niques and polarimetry, GIS and hydrological modeling, monitoring of naturalhazards, and remote sensing of arid and tropical environments.

Wu-Nian Yang received the B.S. degree in min-eral exploration and the M.S. degree in structuralgeology from Chengdu College of Geology, Beijing,China, in 1978 and 1987, respectively, and the Doctorof Engineering degree in mathematical geologyfrom Chengdu University of Technology, Chenghua,China, in 1994.

From 1998 to 1999, he was a Visiting Professorwith the University of Paris VI, Paris, France, andCollege National des Mines de Paris, Paris, France,and is currently a Professor with the College of

EarthSciences, Chengdu University of Technology. He teaches a number ofundergraduate and graduate courses including Remote Sensing Technology,Digital Image Processing, Remote Sensing Applications in GeologicalExploration, and Remote Sensing in Geology. His research interests includegeovisualization, remote sensing, GIS, and GPS applications in land resourcemapping, water resources management, and geological hazards.

Mark Grzovic received two B.S. degrees in geol-ogy and in mathematics from Lamar University,Beaumont, TX, USA, in 2007, the M.S. degree ingeology from the University of Missouri, Columbia,MO, USA, in 2013 and is currently pursuingthe Ph.D. degree in geophysics from Saint LouisUniversity, St. Louis, MO, USA.

His research interests include microwave remotesensing applications: early detection, characteriza-tion, monitoring, and hazard evaluation of potentialgeological hazards.

Matthew Maimaitiyiming received the B.S. degreein geographic information system from XinjiangUniversity, Ürümqi, China, in 2009, and the M.S.degree in geospatial technology from UniversidadeNova de Lisboa, Lisbon, Portugal, in 2013, and is cur-rently a Ph.D. student in environmental sciences andGIS track of integrated applied sciences program atSaint Louis University, St. Louis, MO, USA.

His research interests include remote sensing appli-cations in precision agriculture, specifically, focusedon monitoring plant nutrient uptake in response to

abiotic stress associated with climate and land use changes by using field,airborne, and satellite-based hyperspectral remote sensing.

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