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

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1 DOI 1.17/s ORIGINAL PAPER Manifestation of remote sensing data and GIS on landslide hazard analysis using spatial-based statistical models Biswajeet Pradhan & Ahmed M. Youssef Received: 1 June 29 /Accepted: 16 August 29 # Saudi Society for Geosciences 29 Abstract This paper presents landslide hazard analysis at Cameron area, Malaysia, using a geographic information system (GIS) and remote sensing data. Landslide locations were identified from interpretation of aerial photographs and field surveys. Topographical and geological data and satellite images were collected, processed, and constructed into a spatial database using GIS and image processing. The factors chosen that influence landslide occurrence are topographic slope, topographic aspect, topographic curvature, and distance to rivers, all from the topographic database; lithology and distance to faults were taken from the 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 the landslide occurrence factors by frequency ratio and bivariate logistic regression models. The results of the analysis were verified using the landslide location data and compared with the probabilistic models. The validation results showed that the frequency ratio model (accuracy is 89.25%) is better in prediction of landslide than bivariate logistic regression (accuracy is 85.73%) model. B. Pradhan (*) Institute of Cartography, Faculty of Forest, Hydro and Geosciences, Dresden University of Technology, 162 Dresden, Germany Biswajeet.Pradhan@mailbox.tu-dresden.de biswajeet@mailcity.com A. M. Youssef Geological Hazards and Engineering, Applied Geology Section, Saudi Geological Survey, Jeddah 21514, Kingdom of Saudi Arabia Keywords Landslide. Hazard. Frequency ratio. Logistic regression. GIS. Remote sensing. Cameron Highland. Malaysia Introduction Globally, landslides cause approximately 1, deaths per year with property damage of about US$ 4 billion. Recently in Malaysia, landslides pose serious threats to settlements and to structures that support transportation, natural resource management, and tourism. They cause considerable damage to highways, waterways, and pipelines. Most of these landslides occurred on cut slopes or on embankments alongside roads and highways in mountainous areas. Few landslides occurred near high-rise apartments and in residential areas, causing death to human beings. The recent landslides which occurred near the North Klang Valley Expressway is a good example of the tropical landslide in Malaysia. In tropical countries like Malaysia, most landslides are triggered by heavy rainfall. In the literature, many attempts have been made to predict these landslides and minimize the human and property loss if they happen. Recently, there have been studies on landslide hazard evaluation using GIS, and many of these studies have applied probabilistic methods (Luzi et al. 2; Parise and Jibson 2; Baeza and Corominas 21; Lee and Min 21; Clerici et al. 22; Donati and Turrini 22; Lee et al. 22a, b; Rece and Capolongo 22; Lee and Choi 23; Lee et al. 24b; Chung and Fabbri 23; Lee and Pradhan 26, 27; Pradhan et al. 26; Youssef et al. 29a and b). One of the statistical methods available, the logistic regression method, has also been applied to landslide hazard mapping (Dai and Lee 22; Pradhan et al. 28; Vijith and Madhu 28). There are other methods

2 for hazard mapping such as geotechnical method and the safety factor method (Gokceoglu et al. 2; Shou and Wang 23; Remondo et al. 23). There are other new approach to landslide hazard evaluation using GIS; data mining using fuzzy logic and artificial neural network methods have been applied in various case studies (Pradhan et al. 29; Pradhan and Lee 29a, b; Pradhan and Lee 27; Ercanoglu and Gokceoglu 22; Pistocchi et al. 22; Lee et al. 23a, b; Lee et al. 24a). In this paper, the use of remote sensing data along with other tabular and metadata were used to delineate the landslide hazard zones for the Cameron Highland. Terrain information such as slope, aspect, curvature, distance to rivers, lithology, distance to faults, soil, land cover, normalized difference vegetation index (NDVI), and precipitation information have been updated to enable the quantification of landslide causative parameters. Landslide hazard mapping has been applied and verified using both frequency ratio and bivariate logistic regression models. The qualitative landslide hazard analysis has been carried out using the map overlying techniques in GIS environment. A key assumption using the frequency ratio approach is that the potential (occurrence possibility) of landslides will be comparable to the actual frequency of landslides (Lee and Pradhan 26). A landslide inventory map was prepared in the study area by interpretation of aerial photographs and field surveys in combination with the GIS to evaluate the frequency and distribution of shallow landslides. Topography and lithology databases were prepared including fault, land cover, vegetation index value extracted from Landsat TM satellite image, and precipitation distribution from the meteorological data for the analysis. Then, the calculated and extracted factors were converted to a 1 1 m grid (ARC/INFO GRID type). Statistical-based probabilistic model such as frequency ratio and bivariate logistic regression were applied using the database, and the spatial relationships between the landslide location and each landslide-related factor were analyzed. Using the frequency ratio models, the relationship was used as each factor s rating in the overlay analysis. Using logistic regression, a formula of landslide occurrence possibility was extracted using the relationships. This formula was used to calculate the landslide hazard index, and the index was mapped to represent landslide hazard. Finally, the maps were verified and compared using known landslide locations, and success rates and ratio areas were calculated for quantitative validation. In the study, geographic information system (GIS) software, ArcView 3.3, and ARC/INFO 9. version software packages and SPSS 12. statistical program were used as the basic analysis tools for spatial management and data manipulation. The study area (Fig. 1), which is part of the districts of Cameron Highland, seeing a rapid development with land clearing for housing estate and hotel/apartment, has been selected as pilot study area. The study area covers an area of 66 km 2 and is located near the northern central part of peninsular Malaysia. It is bounded to the north by Kelantan, west by Perak. Annual rainfall is very high averaging between 2,5 mm to 3, mm per year (Lee and Pradhan 27). There are two pronounced wet seasons, which are from September to December and February to May while rainfall peaks are between November to December and March to May. The geomorphology of the area consists of undulating plateau stretching about 12 km. The geology of the Cameron Highland consists of mostly quaternary and Devonian granite. Many landslides have been recorded along stream scouring the sides of the streams. Database construction using GIS and remote sensing Accurate detection of the location of landslides is very important for probabilistic landslide hazard analysis. The application of remote sensing methods, such as aerial photo- Study Area Fig. 1 Study area

3 graphs and satellite images, is used to obtain significant and cost-effective information on landslides. In this study, 1:25, 1:5,-scale aerial photographs were used to detect the landslide locations. These photographs were taken within the period of , and the landslide locations were detected by photo interpretation, and the locations were verified by fieldwork. Recent landslides were recognized in aerial 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 deposits below a scar. To assemble a database to assess the surface area and number of landslides in the study area, a total of 324 landslides were mapped. To apply the probabilistic method, a spatial database that considers landslide-related factors was designed and constructed. These data are available in Malaysia either as paper or as digital maps. The list of spatial database is shown in Table 1. Ten factors that were considered in calculating the probability, and the factors were extracted from the constructed spatial database. The factors were transformed into a grid spatial database using the GIS, and landslide-related factors were extracted using the database. A digital elevation model (DEM) was created first from the topographic database. Contour and survey base points that had elevation values from the 1:25,-scale topographic maps were extracted, and a DEM was constructed with a resolution of 1 m. Using this DEM, the slope angle, slope aspect, and slope curvature were calculated. In the case of the curvature, negative curvatures represent concave, zero curvatures represent flat, and positive curvatures represents convex. The curvature map was produced using the ESRI routine in Arc View 3.2. In addition, the distance to rivers was calculated using the topographic database. The river buffer was calculated and classified in ten equal area classes. Using the geological database, the lithology was extracted, and the distance to faults were calculated. The lithological map was obtained from a 1:63,3-scale geological map. The fault line buffer was calculated in a 5-m interval. The soil map is obtained from a 1:1,- scale soil map. Land cover data was classified using a Landsat TM image employing a supervised classification method supported with topographic map and field data. The land cover map has been classified into six classes such as dense forest area, barren land, agriculture, rubber, residential area (concrete), sparse forest area, and residential area (nonconcrete) were extracted for land cover mapping. Finally, the NDVI map was obtained from Landsat TM satellite images. The NDVI value was calculated using the formula NDVI=(IR R)/(IR+R), where IR value is the infrared portion of the electromagnetic spectrum, and R value is the red portion of the electromagnetic spectrum. The NDVI value denotes areas of vegetation in an image. Precipitation data was interpolated using the meteorological station data for the study area over last 2 years. The factors were converted to a raster grid with 1 1 m cells for application of the bivariate logistic regression and frequency ratio model. The area grid was 2,418 rows by 1,49 columns (i.e., total number is 3,62,82) and 324 cells had landslide occurrences. Methodology Frequency ratio model and its application Frequency ratio approaches are based on the observed relationships between distribution of landslides and each landslide-related factor to reveal the correlation between landslide locations and the factors in the study area. Using the frequency ratio model, the spatial relationships between landslide occurrence location and each factors contributing landslide occurrence were derived. The frequency is calculated from analysis of the relation between landslides and the attributing factors. Therefore, the frequency ratios of each factor s type or range were calculated from their relationship with landslide events as shown in Fig. 2. In the relation analysis, the ratio is that of the area where landslides occurred to the total area so that a value of 1 is an average value. If the value is greater than 1, it means a higher correlation, while a value 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, 1:5, Basic map Topographic map Line and point coverage 1:25, Geological map Polygon coverage 1:63,3 Soil map Polygon coverage 1:1, Land cover GRID 3 3 m Normalized difference vegetation index (NDVI) GRID 3 3 m Precipitation GRID 1 1 m

4 5 5, 25 2, , 3, 2, 1, ,5 1,, > 36 (a) Slope in degrees, Flat N NE E SE S SW W NW (b) Slope aspect, Concave Flat Convex (c) Slope curvature 2,5 2, 1,5 1,,5, [-91) [92 ~183) [184 ~ 275) [276 ~ 367) [368 ~ 458) [459 ~ 55) [551 ~ 642) [643 ~ 734) (d) Distance to rivers (m) [735 ~ 826) [> 826] 1,6 1,2,8,4, Acid intrusives (undifferentiated) Schist, phyllite, slate and limestone. Minor intercalations of sandstone and volcanics (e) Lithology 1,6 1,2,8,4, Frequency ratio (%) [ ~ 78) [8 ~ 16) [161 ~ 246) [247 ~ 342) [343 ~ 451) [452 ~ 59) [591 ~ 776) [777 ~ 145) (f) Distance to faults (m) [146 ~ 1551) 2, 1,6 1,2,8,4, [> 1551] 1 5, 8 4, ST (g) Soil series ULD 4, 3, 2, 1,, PRI_FOREST CUTTING GRASS SEC_FOREST SETTLEMENT RUBBER WATER BODY (h) Landcover 3, 2, 1,, [-.783 ~ [-.65 ~ [-.428 ~ [-.251 ~ [-.73 ~ [.14 ~ [.282 ~ [.459 ~ [.636 ~ [>.814].65) -.428) -.251) -.73).14).282).459).636).814) (i) ndvi 2,5 2, 1,5 1,,5, [ ) [2652 [2677 ~ [2696 ~ [278 ~ [2719 ~ [2731 ~ [2743 ~ [2754 ~ [2764~ ~1676) 2695) 277) 2718) 273) 2742) 2753) 2763) 2772] (j) Precipitation amount (mm) 3,5 3, 2,5 2, 1,5 1,,5, Study area Landslide areas Frequency ratio values () Fig. 2 Distribution of landslide causative parameters for the study area from a representative sample of 2,654,698 grid cells throughout the study area. Parameters are classified using a priori information and their frequency ratio () values to landslide occurrences, which is also shown in the histograms obtained with likelihood frequency ratio model To calculate the landslide hazard index (LSH), each factor s frequency ratio values were summed to the training area as in Eq. 1. The landslide hazard value represents the relative hazard to landslide occurrence. So, the greater the value, the higher the hazard to landslide occurrence, and the lower the value, the lower the hazard to landslide occurrence. LSH ¼ Fr 1 þ Fr 2 þ...þ Fr n ð1þ (where LSH is landslide hazard index and Fr is rating of each factors type or range. The landslide hazard map was made using the LSH values, and interpretation is shown in Fig. 3 Bivariate logistic regression model and its application Logistic regression allows one to form a multivariate regression relation between a dependent variable and

5 Fig. 3 Landslide hazard map based on frequency ratio model several independent variables. Logistic regression, which is one of the multivariate analysis models, is useful for predicting the presence or absence of a characteristic or outcome based on values of a set of predictor variables. The advantage of logistic regression is that through the addition of an appropriate link function to the usual linear regression model, the variables may be either continuous or discrete or any combination of both types, and they do not necessarily have normal distributions. In the case of multiregression analysis, the factors must be numerical, and in the case of a similar statistical model, discriminant analysis, the variables must have a normal distribution. In the present situation, the dependent variable is a binary variable representing presence or absence of landslide. Where the dependent variable is binary, the logistic link function is applicable (Atkinson and Massari 1998). For this study, the dependent variable must be input either as or 1, so the model applies well to landslide possibility analysis. Logistic regression coefficients can be used to estimate ratios for each of the independent variables in the model. Quantitatively, the relationship between the occurrence and its dependency on several variables can be expressed as: p¼ 1= ð1 þ e z Þ ð2þ where p is the probability of an event occurring. In the present situation, the value p is the estimated probability of

6 landslide occurrence. The probability varies from to 1 on an S-shaped curve, and z is the linear combination. It follows that logistic regression involves fitting an equation of the following form to the data: z¼b þ b 1 x 1 þ b 2 x 2 þ...þ b n x n ð3þ where b is the intercept of the model, the b i (i=,1,2,, n) are the slope coefficients of the logistic regression model, and the x i (i=, 1, 2,, n) are the independent variables. The linear model formed is then a logistic regression of presence or absence of landslides (present conditions) on the independent variables (prefailure conditions). Using the bivariate logistic regression model, the spatial relationship between landslide occurrence, and factors influencing landslides was assessed. The spatial databases of each factor were converted to ASCII format files for use in the statistical package, and the correlations between landslide and each factor were calculated. There are two cases. In the first case, only one factor was used. In this case, logistic regression mathematical equations were formulated for each case. Finally, the probability that predicts the possibility of landslide occurrence was calculated using the spatial database using Eqs. 2 and 3. In the second case, all factors were used. In this case, logistic Fig. 4 Landslide hazard map based on bivariate logistic regression model

7 regression mathematical equations were formulated as shown in Eqs. 2 and 4 for each case. z n ¼ ð:655 SLOPEÞþASPECT c þ ð:494 CURVATUREÞ þ ð:7 DRAINAGEÞþLITHOLOGY c þð :4 LINEAMENTÞþSOIL c þ LANDCOVERE c þð :7563 NDVIÞ þ ð:155 PRECIPITATIONÞ 64:122 ð4þ (where SLOPE is slope value; CURVATURE is curvature value; DRAINAGE is distance from drainage value; LINEAMENT is distance from lineament value; NDVI is NDVI value; ASPECT c, LITHOLOGY c, SOIL c, LAND- COVERE c, and PRECIPITATION is precipitation value; and z n is a parameter). Using formula (2) and (3), the landslide hazard map was made. Model validation and comparison For validation of landslide hazard calculation models, two basic assumptions are needed. One is that landslides are related to spatial information such as topography, soil, forest, and land cover, and the other is that future landslides will be triggered by a specific factor such as rainfall or earthquake. In this study, the two assumptions are satisfied because the landslides were related to the spatial information, and the landslides were triggered by heavy rainfall in the study area. The landslide hazard analysis result was validated using known landslide locations. Validation was performed by comparing the known landslide location data with the landslide hazard map (Fig. 4). Each factor used and frequency ratio were compared. The rate curves were created, and its areas of the under curve were calculated for all cases. The rate explains how well the model and factor predict the landslide. So, the area under the curve can assess the prediction accuracy qualitatively. To obtain the relative ranks for each prediction pattern, the calculated index values of all cells in the study area were sorted in descending order. Then the ordered cell values were divided into 1 classes with accumulated 1% intervals. The rate verification results appear as a line in Fig. 5. For example, in the case of frequency model used, 9% to 1% (1%) class of the study area where the landslide hazard index had a higher rank could explain 61% of all the landslides. In addition, the 8% to 1% (2%) class of the study area where the landslide hazard index had a higher rank could explain 82% of the landslides. In the case of logistic regression model used, 9% to 1% (1%) class of the study area where the landslide hazard index had a higher rank could explain 51% of all the landslides. In addition, the 8% to 1% (2%) class of the study area where the landslide hazard index had a higher rank could explain 76% of the landslides. To compare the result quantitatively, the areas under the curve were recalculated as the total area is 1, which means perfect prediction accuracy. So, the area under a curve can be used to assess the prediction accuracy qualitatively. In the case of frequency ratio model used, the area ratio was.8925, and we could say that the prediction accuracy is 89.25%. In the case of logistic regression model used, the area ratio was.8573, and we could say the prediction accuracy is 85.73%. Overall, the case of frequency ratio model used showed a higher accuracy than logistic regression model. Conclusions and discussion In the present study, both frequency analysis and logistic regression methods were applied for the landslide hazard mapping for Cameron highland. The validation results show that the frequency ratio model has predication accuracy of 3.52% ( %), which is better than the logistic regression model. Here, the authors can conclude that the results of frequency ratio model had shown the best prediction accuracy in landslide hazard mapping. The frequency ratio model is simple. The process of input, calculation, and output can be readily understood. The large amount of data can be processed in the GIS environment quickly and easily. The logistic regression model requires conversion of the data to ASCII or other formats for use in the statistical package and later, reconversion to incorporate it into the GIS database. Moreover, it is hard to process the large amount of data in the statistical package. In the case of a Frequency Ratio Logistic Regression Fig. 5 Cumulative frequency diagram showing landslide hazard index rank occurring in cumulative percent of landslide occurrence

8 similar statistical model (discriminant analysis), the factors must have a normal distribution and in the case of multiregression analysis, the factors must be numerical. However, for logistical regression, the dependent variable must be input as or 1, therefore, the model applies well to landslide occurrence analysis. Recently, landslide hazard mapping has shown a great deal of importance suitable to urban developments. The results shown in this study can help the developers, planners, and engineers for slope management and land use planning. However, one must be careful while using the models for specific site development. This is because of the scale of the analysis where other slope factors need to be considered. Therefore, the models used in this study are valid of generalized planning and assessment purposes. Acknowledgment Authors would like to thank to the Malaysian Remote Sensing Agency and Department of Surveying, Malaysia for providing various datasets in this research. 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