ORIGINAL PAPER

Manifestation of remote sensing data and GIS on landslidehazard analysis using spatial-based statistical models

Biswajeet Pradhan & Ahmed M. Youssef

Received: 1 June 2009 /Accepted: 16 August 2009# Saudi Society for Geosciences 2009

Abstract This paper presents landslide hazard analysis atCameron area, Malaysia, using a geographic informationsystem (GIS) and remote sensing data. Landslide locationswere identified from interpretation of aerial photographsand field surveys. Topographical and geological data andsatellite images were collected, processed, and constructedinto a spatial database using GIS and image processing. Thefactors chosen that influence landslide occurrence aretopographic slope, topographic aspect, topographic curva-ture, and distance to rivers, all from the topographicdatabase; lithology and distance to faults were taken fromthe geologic database; land cover from TM satellite image;the vegetation index value was taken from Landsat images;and precipitation distribution from meteorological data.Landslide hazard area was analyzed and mapped using thelandslide occurrence factors by frequency ratio and bivar-iate logistic regression models. The results of the analysiswere verified using the landslide location data andcompared with the probabilistic models. The validationresults showed that the frequency ratio model (accuracy is89.25%) is better in prediction of landslide than bivariatelogistic regression (accuracy is 85.73%) model.

Keywords Landslide . Hazard . Frequency ratio .

Logistic regression . GIS . Remote sensing .

Cameron Highland .Malaysia

Introduction

Globally, landslides cause approximately 1,000 deaths peryear with property damage of about US$ 4 billion. Recentlyin Malaysia, landslides pose serious threats to settlementsand to structures that support transportation, naturalresource management, and tourism. They cause consider-able damage to highways, waterways, and pipelines. Mostof these landslides occurred on cut slopes or on embank-ments alongside roads and highways in mountainous areas.Few landslides occurred near high-rise apartments and inresidential areas, causing death to human beings. The recentlandslides which occurred near the North Klang ValleyExpressway is a good example of the tropical landslide inMalaysia. In tropical countries like Malaysia, most land-slides are triggered by heavy rainfall. In the literature, manyattempts have been made to predict these landslides andminimize the human and property loss if they happen.

Recently, there have been studies on landslide hazardevaluation using GIS, and many of these studies haveapplied probabilistic methods (Luzi et al. 2000; Parise andJibson 2000; Baeza and Corominas 2001; Lee and Min2001; Clerici et al. 2002; Donati and Turrini 2002; Lee etal. 2002a, b; Rece and Capolongo 2002; Lee and Choi2003; Lee et al. 2004b; Chung and Fabbri 2003; Lee andPradhan 2006, 2007; Pradhan et al. 2006; Youssef et al.2009a and b). One of the statistical methods available, thelogistic regression method, has also been applied tolandslide hazard mapping (Dai and Lee 2002; Pradhan etal. 2008; Vijith and Madhu 2008). There are other methods

B. Pradhan (*)Institute of Cartography,Faculty of Forest, Hydro and Geosciences,Dresden University of Technology,01062 Dresden, Germanye-mail: Biswajeet.Pradhan@mailbox.tu-dresden.dee-mail: biswajeet@mailcity.com

A. M. YoussefGeological Hazards and Engineering, Applied Geology Section,Saudi Geological Survey,Jeddah 21514, Kingdom of Saudi Arabia

Arab J GeosciDOI 10.1007/s12517-009-0089-2

for hazard mapping such as geotechnical method and thesafety factor method (Gokceoglu et al. 2000; Shou andWang 2003; Remondo et al. 2003). There are other newapproach to landslide hazard evaluation using GIS; datamining using fuzzy logic and artificial neural networkmethods have been applied in various case studies (Pradhanet al. 2009; Pradhan and Lee 2009a, b; Pradhan and Lee2007; Ercanoglu and Gokceoglu 2002; Pistocchi et al.2002; Lee et al. 2003a, b; Lee et al. 2004a).

In this paper, the use of remote sensing data along withother tabular and metadata were used to delineate the landslidehazard zones for the Cameron Highland. Terrain informationsuch as slope, aspect, curvature, distance to rivers, lithology,distance to faults, soil, land cover, normalized differencevegetation index (NDVI), and precipitation information havebeen updated to enable the quantification of landslidecausative parameters. Landslide hazard mapping has beenapplied and verified using both frequency ratio and bivariatelogistic regression models. The qualitative landslide hazardanalysis has been carried out using the map overlyingtechniques in GIS environment.

A key assumption using the frequency ratio approach isthat the potential (occurrence possibility) of landslides willbe comparable to the actual frequency of landslides (Leeand Pradhan 2006). A landslide inventory map wasprepared in the study area by interpretation of aerialphotographs and field surveys in combination with theGIS to evaluate the frequency and distribution of shallowlandslides. Topography and lithology databases wereprepared including fault, land cover, vegetation index valueextracted from Landsat TM satellite image, and precipita-tion distribution from the meteorological data for theanalysis. Then, the calculated and extracted factors wereconverted to a 1010 m grid (ARC/INFO GRID type).Statistical-based probabilistic model such as frequency ratioand bivariate logistic regression were applied using thedatabase, and the spatial relationships between the landslide

location and each landslide-related factor were analyzed.Using the frequency ratio models, the relationship was usedas each factors rating in the overlay analysis. Using logisticregression, a formula of landslide occurrence possibilitywas extracted using the relationships. This formula wasused to calculate the landslide hazard index, and the indexwas mapped to represent landslide hazard. Finally, the mapswere verified and compared using known landslide loca-tions, and success rates and ratio areas were calculated forquantitative validation. In the study, geographic informationsystem (GIS) software, ArcView 3.3, and ARC/INFO 9.0version software packages and SPSS 12.0 statisticalprogram were used as the basic analysis tools for spatialmanagement and data manipulation.

The study area (Fig. 1), which is part of the districts ofCameron Highland, seeing a rapid development with landclearing for housing estate and hotel/apartment, has beenselected as pilot study area. The study area covers an areaof 660 km2 and is located near the northern central part ofpeninsular Malaysia. It is bounded to the north by Kelantan,west by Perak. Annual rainfall is very high averagingbetween 2,500 mm to 3,000 mm per year (Lee and Pradhan2007). There are two pronounced wet seasons, which arefrom September to December and February to May whilerainfall peaks are between November to December andMarch to May. The geomorphology of the area consists ofundulating plateau stretching about 12 km. The geology ofthe Cameron Highland consists of mostly quaternary andDevonian granite. Many landslides have been recordedalong stream scouring the sides of the streams.

Database construction using GIS and remote sensing

Accurate detection of the location of landslides is veryimportant for probabilistic landslide hazard analysis. Theapplication of remote sensing methods, such as aerial photo-

Study Area

Fig. 1 Study area

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graphs and satellite images, is used to obtain significant andcost-effective information on landslides. In this study,1:25,0001:50,000-scale aerial photographs were used todetect the landslide locations. These photographs were takenwithin the period of 19812003, and the landslide locationswere detected by photo interpretation, and the locations wereverified by fieldwork. Recent landslides were recognized inaerial photographs from breaks in the forest canopy, bare soil,or other geomorphic characteristics typical of landslide scars,head and side scarps, flow tracks, and soil and debris depositsbelow a scar. To assemble a database to assess the surface areaand number of landslides in the study area, a total of 324landslides were mapped.

To apply the probabilistic method, a spatial database thatconsiders landslide-related factors was designed and con-structed. These data are available in Malaysia either aspaper or as digital maps. The list of spatial database isshown in Table 1. Ten factors that were considered incalculating the probability, and the factors were extractedfrom the constructed spatial database. The factors weretransformed into a grid spatial database using the GIS, andlandslide-related factors were extracted using the database.A digital elevation model (DEM) was created first from thetopographic database. Contour and survey base points thathad elevation values from the 1:25,000-scale topographicmaps were extracted, and a DEM was constructed with aresolution of 10 m. Using this DEM, the slope angle, slopeaspect, and slope curvature were calculated. In the case ofthe curvature, negative curvatures represent concave, zerocurvatures represent flat, and positive curvatures representsconvex. The curvature map was produced using the ESRIroutine in Arc View 3.2. In addition, the distance to riverswas calculated using the topographic database. The riverbuffer was calculated and classified in ten equal areaclasses. Using the geological database, the lithology wasextracted, and the distance to faults were calculated. Thelithological map was obtained from a 1:63,300-scalegeological map. The fault line buffer was calculated in a50-m interval. The soil map is obtained from a 1:100,000-scale soil map. Land cover data was classified using a

Landsat TM image employing a supervised classificationmethod supported with topographic map and field data. Theland cover map has been classified into six classes such asdense forest area, barren land, agriculture, rubber, residen-tial area (concrete), sparse forest area, and residential area(nonconcrete) were extracted for land cover mapping.Finally, the NDVI map was obtained from Landsat TMsatellite images. The NDVI value was calculated using theformula NDVI=(IRR)/(IR+R), where IR value is theinfrared portion of the electromagnetic spectrum, and Rvalue is the red portion of the electromagnetic spectrum.The NDVI value denotes areas of vegetation in an image.Precipitation data was interpolated using the meteorologicalstation data for the study area over last 20 years.

The factors were converted to a raster grid with 1010 m cells for application of the bivariate logistic regressionand frequency ratio model. The area grid was 2,418 rowsby 1,490 columns (i.e., total number is 3,602,820) and 324cells had landslide occurrences.

Methodology

Frequency ratio model and its application

Frequency ratio approaches are based on the observedrelationships between distribution of landslides and eachlandslide-related factor to reveal the correlation betweenlandslide locations and the factors in the study area. Usingthe frequency ratio model, the spatial relationships betweenlandslide occurrence location and each factors contributinglandslide occurrence were derived. The frequency is calculat-ed from analysis of the relation between landslides and theattributing factors. Therefore, the frequency ratios of eachfactors type or range were calculated from their relationshipwith landslide events as shown in Fig. 2. In the relationanalysis, the ratio is that of the area where landslides occurredto the total area so that a value of 1 is an average value. If thevalue is greater than 1, it means a higher correlation, while avalue lower than 1 means a lower correlation.

Table 1 Data layer of study area

Classification Subclassification GIS data type Scale

Geological hazard Land slide Point coverage 1:25,0001:50,000

Basic map Topographic map Line and point coverage 1:25,000

Geological map Polygon coverage 1:63,300

Soil map Polygon coverage 1:100,000

Land cover GRID 3030 m

Normalized difference vegetation index (NDVI) GRID 3030 m

Precipitation GRID 1010 m

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To calculate the landslide hazard index (LSH), each factorsfrequency ratio values were summed to the training area as inEq. 1. The landslide hazard value represents the relativehazard to landslide occurrence. So, the greater the value, thehigher the hazard to landslide occurrence, and the lower thevalue, the lower the hazard to landslide occurrence.

LSH Fr1 Fr2 . . . . . . Frn 1

(where LSH is landslide hazard index and Fr is rating of eachfactors type or range. The landslide hazard map was madeusing the LSH values, and interpretation is shown in Fig. 3

Bivariate logistic regression model and its application

Logistic regression allows one to form a multivariateregression relation between a dependent variable and

0

10

20

30

40

50

0 - 15 16 - 25 26 - 35 > 36(a) Slope in degrees

Freq

uenc

y (%

)

0,00

1,00

2,00

3,00

4,00

5,00

FR

0

5

10

15

20

25

Flat N NE E SE S SW W NW(b) Slope aspect

Freq

uenc

y (%

)

0,00

0,50

1,00

1,50

2,00

FR

0102030405060708090

Concave Flat Convex

(c) Slope curvature

Freq

uenc

y (%

)

0,00

0,50

1,00

1,50

2,00

2,50

FR

0

5

10

15

20

[0-91) [92 ~183) [184 ~275)

[276 ~367)

[368 ~458)

[459 ~550)

[551 ~642)

[643 ~734)

[735 ~826)

[> 826]

(d) Distance to rivers (m)

Freq

uenc

y (%

)

0,00

0,40

0,80

1,20

1,60

FR

0

20

40

60

80

100

Acid intrusives (undifferentiated) Schist, phyllite, slate and limestone. Minorintercalations of sandstone and volcanics

(e) Lithology

Freq

uenc

y (%

)

0,00

0,40

0,80

1,20

1,60

FR

0

4

8

12

16

20

[0 ~ 78) [80 ~160)

[161 ~246)

[247 ~342)

[343 ~451)

[452 ~590)

[591 ~776)

[777 ~1045)

[1046 ~1551)

[> 1551]

(f) Distance to faults (m)

Freq

uenc

y ra

tio (%

)0,00

0,40

0,80

1,20

1,60

2,00

FR

0

20

40

60

80

100

ST ULD(g) Soil series

Freq

uenc

y (%

)

0,00

1,00

2,00

3,00

4,00

5,00

FR

0

20

40

60

80

PRI_FOREST CUTTING SEC_FOREST SETTLEMENT RUBBER WATERBODY(h) Landcover

Freq

uenc

y (%

)

0,00

1,00

2,00

3,00

4,00

FR0

5

10

15

20

25

[-0.783 ~0.605)

[-0.605 ~-0.428)

[-0.428 ~-0.251)

[-0.251 ~-0.073)

[-0.073 ~0.104)

[0.104 ~0.282)

[0.282 ~0.459)

[0.459 ~0.636)

[0.636 ~0.814)

[> 0.814]

(i) ndvi

Freq

uenc

y (%

)

0,00

0,50

1,00

1,50

2,00

2,50

FR

05

1015202530

[2613-2651)

[2652~1676)

[2677 ~2695)

[2696 ~2707)

[2708 ~2718)

[2719 ~2730)

[2731 ~2742)

[2743 ~2753)

[2754 ~2763)

[2764~2772]

(j) Precipitation amount (mm)

Freq

uenc

y (%

)

0,000,501,001,502,002,503,003,50

FR

Study area Landslide areas Frequency ratio values (FR)

GRASS

Fig. 2 Distribution of landslide causative parameters for the studyarea from a representative sample of 2,654,698 grid cells throughoutthe study area. Parameters are classified using a priori information and

their frequency ratio (FR) values to landslide occurrences, which isalso shown in the histograms obtained with likelihood frequency ratiomodel

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several independent variables. Logistic regression, which isone of the multivariate analysis models, is useful forpredicting the presence or absence of a characteristic oroutcome based on values of a set of predictor variables. Theadvantage of logistic regression is that through the additionof an appropriate link function to the usual linear regressionmodel, the variables may be either continuous or discrete orany combination of both types, and they do not necessarilyhave normal distributions. In the case of multiregressionanalysis, the factors must be numerical, and in the case of asimilar statistical model, discriminant analysis, the variablesmust have a normal distribution. In the present situation, thedependent variable is a binary variable representing

presence or absence of landslide. Where the dependentvariable is binary, the logistic link function is applicable(Atkinson and Massari 1998). For this study, the dependentvariable must be input either as 0 or 1, so the model applieswell to landslide possibility analysis. Logistic regressioncoefficients can be used to estimate ratios for each of theindependent variables in the model.

Quantitatively, the relationship between the occurrence andits dependency on several variables can be expressed as:

p 1= 1 ez 2where p is the probability of an event occurring. In thepresent situation, the value p is the estimated probability of

Fig. 3 Landslide hazard mapbased on frequencyratio model

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landslide occurrence. The probability varies from 0 to 1 onan S-shaped curve, and z is the linear combination. It followsthat logistic regression involves fitting an equation of thefollowing form to the data:

zb0 b1x1 b2x2 . . . bnxn 3where b0 is the intercept of the model, the bi (i=0, 1, 2, ,n) are the slope coefficients of the logistic regression model,and the xi (i=0, 1, 2, , n) are the independent variables.The linear model formed is then a logistic regression ofpresence or absence of landslides (present conditions) on theindependent variables (prefailure conditions).

Using the bivariate logistic regression model, the spatialrelationship between landslide occurrence, and factorsinfluencing landslides was assessed. The spatial databasesof each factor were converted to ASCII format files for usein the statistical package, and the correlations betweenlandslide and each factor were calculated. There are twocases. In the first case, only one factor was used. In thiscase, logistic regression mathematical equations wereformulated for each case. Finally, the probability thatpredicts the possibility of landslide occurrence was calcu-lated using the spatial database using Eqs. 2 and 3. In thesecond case, all factors were used. In this case, logistic

Fig. 4 Landslide hazard mapbased on bivariate logisticregression model

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regression mathematical equations were formulated asshown in Eqs. 2 and 4 for each case.

zn 0:0655 SLOPE ASPECTc 0:0494 CURVATURE 0:0007 DRAINAGE LITHOLOGYc 0:0004 LINEAMENT SOILc LANDCOVEREc 0:7563 NDVI 0:0155 PRECIPITATION 64:1220 4

(where SLOPE is slope value; CURVATURE is curvaturevalue; DRAINAGE is distance from drainage value;LINEAMENT is distance from lineament value; NDVI isNDVI value; ASPECTc, LITHOLOGYc, SOILc, LAND-COVEREc, and PRECIPITATION is precipitation value;and zn is a parameter). Using formula (2) and (3), thelandslide hazard map was made.

Model validation and comparison

For validation of landslide hazard calculation models, twobasic assumptions are needed. One is that landslides arerelated to spatial information such as topography, soil, forest,and land cover, and the other is that future landslides will betriggered by a specific factor such as rainfall or earthquake. Inthis study, the two assumptions are satisfied because thelandslides were related to the spatial information, and thelandslides were triggered by heavy rainfall in the study area.

The landslide hazard analysis result was validated usingknown landslide locations. Validation was performed bycomparing the known landslide location data with thelandslide hazard map (Fig. 4). Each factor used andfrequency ratio were compared. The rate curves werecreated, and its areas of the under curve were calculated forall cases. The rate explains how well the model and factorpredict the landslide. So, the area under the curve can assessthe prediction accuracy qualitatively. To obtain the relativeranks for each prediction pattern, the calculated index valuesof all cells in the study area were sorted in descending order.Then the ordered cell values were divided into 100 classeswith accumulated 1% intervals. The rate verification resultsappear as a line in Fig. 5. For example, in the case offrequency model used, 90% to 100% (10%) class of thestudy area where the landslide hazard index had a higherrank could explain 61% of all the landslides. In addition, the80% to 100% (20%) class of the study area where thelandslide hazard index had a higher rank could explain 82%of the landslides. In the case of logistic regression modelused, 90% to 100% (10%) class of the study area where the

landslide hazard index had a higher rank could explain 51%of all the landslides. In addition, the 80% to 100% (20%)class of the study area where the landslide hazard index hada higher rank could explain 76% of the landslides. Tocompare the result quantitatively, the areas under the curvewere recalculated as the total area is 1, which means perfectprediction accuracy. So, the area under a curve can be usedto assess the prediction accuracy qualitatively. In the case offrequency ratio model used, the area ratio was 0.8925, andwe could say that the prediction accuracy is 89.25%. In thecase of logistic regression model used, the area ratio was0.8573, and we could say the prediction accuracy is 85.73%.Overall, the case of frequency ratio model used showed ahigher accuracy than logistic regression model.

Conclusions and discussion

In the present study, both frequency analysis and logisticregression methods were applied for the landslide hazardmapping for Cameron highland. The validation results showthat the frequency ratio model has predication accuracy of3.52% (89.2585.73%), which is better than the logisticregression model. Here, the authors can conclude that theresults of frequency ratio model had shown the best predictionaccuracy in landslide hazard mapping.

The frequency ratio model is simple. The process of input,calculation, and output can be readily understood. The largeamount of data can be processed in the GIS environmentquickly and easily. The logistic regression model requiresconversion of the data to ASCII or other formats for use in thestatistical package and later, reconversion to incorporate it intothe GIS database. Moreover, it is hard to process the largeamount of data in the statistical package. In the case of a

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0 10 20 30 40 50 7060 80 90 100

Frequency Ratio

Logistic Regression

Fig. 5 Cumulative frequency diagram showing landslide hazardindex rank occurring in cumulative percent of landslide occurrence

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similar statistical model (discriminant analysis), the factorsmust have a normal distribution and in the case of multi-regression analysis, the factors must be numerical. However,for logistical regression, the dependent variable must be inputas 0 or 1, therefore, the model applies well to landslideoccurrence analysis.

Recently, landslide hazard mapping has shown a greatdeal of importance suitable to urban developments. Theresults shown in this study can help the developers,planners, and engineers for slope management and landuse planning. However, one must be careful while using themodels for specific site development. This is because of thescale of the analysis where other slope factors need to beconsidered. Therefore, the models used in this study arevalid of generalized planning and assessment purposes.

Acknowledgment Authors would like to thank to the MalaysianRemote Sensing Agency and Department of Surveying, Malaysia forproviding various datasets in this research. Thanks are also due toMalaysian Meteorological Service Department for providing rainfalldata for the research.

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Arab J Geosci

Manifestation of remote sensing data and GIS on landslide hazard analysis using spatial-based statistical modelsAbstractAbstractIntroductionDatabase construction using GIS and remote sensingMethodologyFrequency ratio model and its applicationBivariate logistic regression model and its application

Model validation and comparisonConclusions and discussionReferences

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