INTERNATIONAL JOURNAL OF GEOMATICS AND GEOSCIENCES Volume 4, No 2, 2013

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1 INTERNATIONAL JOURNAL OF GEOMATICS AND GEOSCIENCES Volume 4, No 2, 2013 Copyright 2010 All rights reserved Integrated Publishing services Research article ISSN Landslide susceptibility zonation in Kukalthurai Halla watershed, Moyar sub-basin in Nilgiris mountains, South india using Remote Sensing and GIS Department of Geology, University of Madras, Guindy Campus, Chennai , Tamil Nadu, India. rammohan.muniv@gmail.com ABSTRACT Landslides occur frequently due to heavy rains in Nilgiri mountains in South India. Though, the landslide hazard is moderate, occurrence of landslide events lead to considerable loss of life, damage to property and disruption of communication. The death toll and damage to houses are increasing during the recent years necessitating the need for preparing a Landslide Susceptibility Zonation map in which safe zones wherein developmental activities can be taken up are identified. A Geographical Information System based study has been carried out in Kukulthurai Halla macro-watersheds in Nilgiri mountains using the Frequency Ratio (FR) method. The Zonation map prepared with 75% of the landslides was validated using the remaining 25% of the landslides. Keywords: Landslides, Frequency Ratio method, GIS, Nilgiris, Hazard Zonation 1. Introduction Landslides are one of the hazards that cause loss of life, damage to houses, roads, bridges in mountainous regions. Areas wherein urbanization and associated developmental activities are taking place suffer more as the slopes are steepened for the said activities decreasing the slope stability. Nilgiris mountains is one of the most popular hill stations in south India and during the past hundred years it has undergone tremendous development. While vast tracts of land with forests locally called as sholas with short trees were transformed into tea estates, forest plantations and horticultural farms, the pasture lands were used for growing vegetable crops. The deforestation and steepening of slope for housing and road lying resulted in the increase in the frequency of landslides in the mountains particularly in the eastern part. The triggering factor for the landslides in Nilgiris is heavy rainfall particularly during northeast monsoon. The seriousness of landslide hazard was first realized in the year 1978 Nilgiris when heavy rainfall triggered landslides in over 150 locations (Seshagiri et al. 1980, 1982). The problem became more serious in 1979 when landslides were recorded in nearly 200 locations. While the 1978 landslides were shallow landslides, induced by very heavy rainfall for two days, 4 th and 5 th November, 1978, the 1979 slides were deep landslides caused by a prolonged spell of rainfall for a week from 12 th to 19 th November, 1979 (Ganapathy, 2013). Majority of the landslides have occurred in the tea estates and areas where vegetable crops were grown and the death toll was 4 due to a 1 km long debris slide in Selas near Ketti. Settlements where less damaged as they were in safe zones. Since, , the frequency of landslides has increased and the landslide during October, 1990, buried more than 35 families in a place called Geddai and in 1993, the landslide in Marappalam killed 12 persons, 15 were reported missing and 21 persons were killed when two busses were washed away down steep slopes (Ganapathy, Hada, Submitted on October 2013 published on November

2 2012). In 2009, heavy rains resulted in the death of 42 persons. The history of landslides show that the events which were isolated events in the past have became widespread since Further, the death toll and damage to houses are also showing an increase as people are establishing their homes in unsafe slopes. Therefore, for effective management of the landslide hazard, it is necessary to avoided in areas where landslide susceptibility is high for developmental activities particularly housing. GIS is an effective tool to recognize landslide susceptibility zones and there are many studies applied including probabilistic models viz., frequency ratio method (Lee, Talib 2005) weight of evidence method (Sharma, Kumar, 2008) and logistic regression method (Atkinson, Massari, 1998), (Dai et al, 2001). Further methods like fuzzy logic and artificial neural network models have also been used (Ercanoglu, Gokceoglu, 2002), (Pistocchi et al, 2002), (Lee et al, 2004). In the present study, the frequency ratio method which is the simplest method is used to generate a landslide susceptibility map. 2. Study area The area falling under Kukulthurai macro-watersheds with a total extent of 57 km 2 is selected for study as the area is severely affected by landslides during the years 1978 and It lies between the latitudes 11 o 24'12" N and 11 o 29' 55" N and longitudes 76 o 55' 31" E and 76 o 51' 40" E, and forms parts of Survey of India Toposheet Nos 58 A/15/NW (Figure 1). Figure: 1 Map showing the location of Kukulthurai macro-watershed The minimum and maximum altitude of the area selected is 1,465 m and 2,420 m respectively above mean sea level. The Malagatti Betta peak in Honnatalai hills ranges is the highest peak located in the southern boundary of the macro-watershed. The area receives rain both during 367

3 SW and NE monsoons and out of the average annual rainfall of 1770 mm NE monsoon in the months of October to December contributes 42%. The Nilgiri Mountain is an uplifted crustal segment and is bounded by the east-west trending Moyar shear in the north, NE-SW trending Bhavani shear in the south and the N-S trending New Amarambalam Forest lineament in the west. Charnockites for the country rocks which contains linear enclaves of banded magnetite quartzites, fuchsite quartzite, meta-gabbros, websterites and magnetite quartzites. It is high grade granulite facies terrain and the rocks have been subjected to very high P-T conditions of metamorphism. These rocks are well exposed in the boundary of the Nilgiris and are restricted to few linear escarpments and ridges in the macro-watershed investigated. Lineaments are mainly in NW-SE and followed by NE-SW directions and the streams generally follow the lineaments. The geomorphic units encountered are escarpments/ridges, less dissected undulating plateau, moderately dissected plateau, valley fill. Intense weathering of the rocks has resulted in the formation of thick residual soil, overlain by thin laterite cover. 3. Methodology The method is based on the principle that the slope failures in future will most likely be in geologic, geomorphic and hydrologic situations that have lead to past and present failures (Varnes, 1984). The landslide inventory map form the basis of the study and its relationship with the landslide causing factors are evaluated. The methodology comprise the following steps: preparation of digital database of spatial data which are related to landslides, classification of the data, intersection analysis to calculate the frequency ratio, assignment of the frequency ratio to the thematic layers, weighted sum analysis and calculation of landslide susceptibility index, suitable classification of landslide susceptibility and validation. For validation 75% of the landslides were used for the analysis and the remaining landslides were overlaid on the zonation map prepared to observe the percentage of landslides falling in high and very high landslide susceptibility zones. 3.1 Data layers The basic requirement of landslide susceptibility mapping using frequency ratio method is an accurate landslide inventory map. The landslide inventory map was prepared by using the map prepared by Geotechnical Cell, Coonoor, Nilgiris district and also by field work. Every village in the macro-watershed was visited and the local people were enquired for the occurrence of landslides. The sites were inspected to verify whether landslide has taken place. Due to revegetaion, it is not possible to recognise shallow landslides but the deep landslides have their paleo-scars preserved. A total of 45 landslides occur in a total area of 57 km 2 and majority of the landslides are less than 1000 m 2 in size. Nine landslide causing factors were considered for the study. They are slope, drainage density, landuse/land cover, distance to drainage, lineament density, distance to lineament, geomorphology, aspect, and distance to road. Though the geology of the area is an important causative factor which is considered in the susceptibility analysis of landslides, in Kukulthurai halla watershed it was not considered as the charnockites and the associated rocks do not differ in their strength. The thematic layers of these factors considered were prepared by digitising the information from Survey of India Toposheets of scale 1:25,000 and 10 m contour interval and Landsat ETM+ data obtained in The elevation of the area was digitised as point data from contours in 10 m interval and DEM was created. Using DEM, slope and aspect layers were created. The drainage network in the area was digitised from SOI toposheets and was used to create distance to drainage and drainage density layers. Similarly distance to road layer was prepared from the road network extracted 368

4 from SOI toposheets. The geomorphology, landuse and lineament maps were prepared by interpreting the Landsat data. The lineaments were mapped from Landsat data and was used in the preparation of distance to lineament and lineament density layers. 3.2 Landslide inventory map Determination of the location of past landslides and their characterization are essential for landslide susceptibility analysis. The preparation of such a landslide inventory is a tedious work as landslides occur individually and have to be identified, mapped and inventoried one by one (Van Westen et al, 2006). Landslide inventories contain basic information about landslides such as location, classification, morphology, volume, run-out distance, activity and date of occurrence/activity (Wieczorek, 1984), (Fell et al, 2008). However, in the present study the landslides are represented as point data. The landslide inventory map prepared for the study shows that the landslides have occurred close to the roads and first to third order streams and at elevation ranging from 1800 to 2200 m above MSL (Figure 2). 4. Causative factors, their relationship between the landslides and frequency ratio The relation between the landslides and the thematic layers considered is quantified as frequency ratio which is the ratio between the percentage of landslides and the percentage of the class within the area. The ratio gives the probability of landslide in an area. For the calculation of frequency ratio, the raster layer of the causative factors viz., slope, geomorphology, drainage density, etc., are first using suitable scale. The landslide inventory map with 75% of the landslides selected using Hawaths Tools in ArcGIS is overlaid on each thematic layer and the percentage of landslides falling in each class is determined. This percentage is divided by the percentage of the class in the watershed gives the frequency ratio. The frequency ratios calculated for the various causative factors is given in Table Slope Slope plays a crucial role in governing the stability of a region and as slope increases, the probability of slope failure increases. In increase in slope leads to high shear stress which results in slope failure. However, landslides can occur even in very low slope as the soil wetness is more in such slopes and in the presence of a stream which undermines the banks, the terrain can be destabilised. Similarly in very high slopes, landslides are few as such slopes are occupied by massive rocks without colluvial accumulations (Lee, Pradhan, 2006), (Mathew et al., 2007). Slope ranges from 0 to 57 and is classified into 6 classes viz., 0 5, 5-10, 10-15, 15-20, and more than 25. The frequency ratio is more than 1 in the slope class 5 to 10 and with the latter more susceptible to slope failure. The lowest probability 0.33 is found for steeper slopes (>25 ). 4.2 Slope Aspect Slope aspect, which is the direction the slope faces is considered as a causative factor as it exerts a control over landslide by the variation in the wetness and vegetation cover (Mathew et al., 2007) and direction of the monsoon winds. Though the region receives more rain during SW monsoon when compared to the NE monsoon, landslides occur mainly during the months of November and December when the ground is saturated with water. Hence, NW slope aspect is characterised by high frequency ratio of 1.80 followed by SE (1.31) and NE (1.29). 369

5 4.3 Drainage Density and distance to drainage Majority of the landslides have occurred in the proximity to streams and hence has a strong influence on landslide. Firstly, drainage leads to erosion of the flanks and toe inducing landslide. It also leads to percolation of the rainwater in poorly drained region. While distance to drainage is used by many workers, drainage density is also used (Mathew et al, 2007), (Chauhan et al, 2010). Figure 2: Landslide inventory map of the area o colour coded DEM with road and streams The distance to drainage layer was prepared by using buffer operation and is divided into 5 categories with 50 m interval. Majority of the landslides 32 out of 34 have occurred within a distance of 100 m from the streams and hence areas within a distance of 50 m and 100 m are characterised by a frequency ratio of 1.02 and 1.11 respectively. The frequency ratio of the areas within a distance of 150 to 200 m is 0.66 and farther away no slides have occurred. The drainage density in the watershed ranges from 0 to 9.92 km/km 2 and was classified into 5 classes at 2 km/km 2 interval. The highest frequency ratio is obtained for the class 2 to 4 and 4 to 6 km/km 2 followed by 8 to 9.92 km Geomorphology The watershed is located in the Nilgiri plateau and the southern boundary coincides with the escarpments of the Honnathalai RF the eastern continuation of the Doddabetta peak. The area is also dissected by numerous lineaments and hence has been classified into three geomorphic units viz., 1. Less dissected deflation slope, moderately dissected plateau and 3. Valleys fill by (Seshagiri et al, 1983). A fourfold classification is adopted in the present study demarcating escarpments as a separate unit. Moderately dissected plateau landform is the dominant type followed by less dissected deflation slope, valley fill and escarpments. Most landslides 19 out 370

6 of 34 have occurred in the less dissected deflation slope and a frequency ratio of 2.70 is characteristic of this landform indicating its higher influence on slope instability. 4.5 Lineament density and distance to lineament Lineaments represent faults and fracture zones and are observed in satellite data. They are one of the landslide causative factors particularly in areas of active tectonics like Himalayas (Chauhan et al, 2010). Both lineament density and distance to lineament are used for the calculation of frequency ratio. The lineament density is classified into 5 classes at 50 m interval and lineament density is classified at 500 m/km 2 interval (Fig.3). The calculated frequency ratio shows that the areas with lineament density 0 to 0.5 km/km2 is more susceptible to landslide (1.95) and all the other classes have ratio less than 1. Similarly, the frequency ratio is highest (2.28) for the areas which are more than 250 m from lineament and the ratio is less than or very close to 1 for the areas which are 0 to 250 m from lineament. 4.6 Landuse The landuse map of the area was prepared by interpretation of Landsat ETM + data and field visits. The natural vegetation occurring in the watershed is classified as montane zone forests and Montane zone savannas (Venugopal 2004). While the forest locally called as sholas are mixed forests comprising a number of plants and trees which are stunted with deep rooting system, the savannas are vast tracts of grasslands. The indigenous vegetation was instrumental in preventing slope instability with excellent binding capacity. However, the natural vegetation has been changed ever since British arrived in Nilgiris at the beginning of 19 th century. The traditional cropping pattern was changed and the forests were cleared for establishing tea and coffee estates and for cultivation of vegetables and horticultural products. In the post independence period the pace of development and changes were accelerated. The area under crops were increased manifold, urban sprawl was extensive due to the tourist potential. For example the area under tea cultivation increased from 3,000 hectares to 9,000 hectare during the period from 1920 to 1950 (Venugopal 2004) and the present day area under tea cultivation is 45,974 hectares (nilgiris.nic.in). New roads were laid and existing roads were widened to cope up with the developments. The landuse map of the area shows that areas with vegetable crops and tea are the most common landuse which covers 53.8% of the area followed by forests and forest plantation. About 80% of the landslides have occurred in areas with vegetable crops and hence, the frequency ratio is 2.76 and though only two slides have taken place in areas with settlements, as the extent of the landuse is low, the frequency ratio is 2.0 (Figure 4). 4.7 Distance to Road The distance to road layer was prepared by using buffer operation and is divided into 6 categories with 100 m interval. The cutting of slope for road results in slope instability and 88% of the landslides have taken place within a distance of 400 m from the roads with the highest percentage of 38 in the areas which are within a distance of 100 m from road. The frequency ratio calculated progressively reduces as the distance increases. Areas within a distance of 300 m have frequency ratio more than 1 indicating high probability of landsides. Areas farther away from the road have frequency ratio less than 1 indicating low probability. 371

7 Figure 3: Layers of the causative factors prepared in GIS for the Kukulthurai macrowatershed 372

8 4.8. Landslide Susceptibility mapping Figure 4: Landuse map The landslide susceptibility map (LSM) was prepared using the frequency ratio model. The layers of all the causative factors were converted into 30 x 30 m grid. The number of pixels in the Kukulthurai watershed was The frequency ratio calculated was assigned to the causative conditions by reclassification in ArcGIS. The landslide Susceptibility Index (LSI) was calculated by summation of the frequency ratio by Weighted Sum Overlay tool using the equation: LSI = ƩFr (where Fr is the frequency ratio of each causative factor type or range). The LSI calculated for the Kukulthurai watershed had a minimum value of 2.82 and maximum value of and the mean and standard deviation of is 9.96 and 2.63 respectively. The susceptibility to landslides increase with increasing LSI and areas with high LSI are more susceptible to slope instability. The area classified into five classes viz., very low, low, moderate, high and very high landslide susceptibility classes using natural breaks (Jenks) method. The very high landslide susceptibility class is 8.72 percentage of the total area in which 17 (50%) landslides fall. Similarly, 9 (26.5%) landslides fall in high susceptibility class which is 19.35% of the watershed. Both the classes form 76.5% of the total area indicating the correctness of the LSM. 373

9 Class Table 1: Various causative factors Landslide occurrence Landslid e % Pixels in Domain Pixels in Domain % Frequen cy Ratio Slope > Drainage Density (Km/Km 2 ) > Distance to Drainage 0-50 m m m m >250 m Lineament Density (km/km 2 ) > Distance to Lineament 0 50 m m m m >250m Geomorphology Escarpment Moderately dissected plateau Less dissected undulating plateau Valley fill Landuse Dense forest Forest plantation Out crop

10 Forest Settlement Shrub Tea estate Vegetable crop Aspect Flat Northeast East Southeast South Southwest West Northwest North Distance to Road m m m m m >500 m To validate the map 11 landslide points not used in the preparation of the LSM is were overlaid on the LSM map and the number of landslides falling in each landslide susceptibility class is determined. The results of the overlay analysis are given in (Table 2). From the table it is evident that 81.8% of the landslides fall in high and very high landslide susceptibility class indicating that the LSM prepared by the frequency ratio method is reliable. Table 2: Result of validation using 11 landslide points not used for the susceptibility mapping Landslide Susceptibility Class No. of Landslide landslides % Very low low Moderate High Very high Total of High and Very High Final LSM The calculation of frequency ratio was repeated with all the 56 landslide points and the entire process of reclassification assigning the frequency ratio and summation analysis by using overlay analysis is carried out and the LSI determined. The LSM of the prepared using all the 56 points is given in (Fig. 5). The map shows that the up slopes of the watershed are more susceptible to landslides and few settlements viz., Madithurai, Kattabettu, Honnatalai, 375

11 Timanihatti, Billikombai and Kudumanai located in the southwestern part the area are located in high and very high landslide susceptibility zones. 5. Conclusion Landslides have become frequent in Nilgiris Mountains and the death toll and damage to settlements is increasing. As landslides are difficult to predict, identification of high landslide susceptibility zones where settlements and other developmental activities can be excluded will be a major step for attempting comprehensive hazard management. Frequency Ratio Method has been used for preparing the landslide susceptibility map for the study area using past landslide. Figure 5: Landslide Susceptibility map of Kukulthurai Watershed Table 3: Summary of result Landslide Susceptibility Class No. of landslides Landslide % Area in km 2 Area % Density LS/Km 2 Very low low Moderate High Very high Total of High and Very High Total area

12 Nine causative factors were used and the care was also taken to prepare a digital database very for the terrain systems and its corresponding sub variables. Based on the results of the frequency ration model, the landslide susceptibility map was prepared and classified into five zones using Natural breaks (Jenks) method. The summary of the results are given in Table 3. The results show that 8.72 and 19.35% of the area is classified as very high and high landslide susceptibility zones respectively in which 54.6% and 27.3% of the landslides fall. To assess the vulnerability, the number of pixels of different landslide susceptibility in the settlement areas was determined. The density of landslide in the high and very high susceptibility classes are 1.35/km 2 and 5.8/km 2 which is very high when compared with the density of 1.0 landslide/km 2 in the entire area. 6. References 1. Atkinson, P. M., R. Massari, (1998), Generalized linear modelling of susceptibility to landsliding in the Central Apennines, Italy. Computer and Geosciences, 24, pp Chauhan, S., (2010), Landslide susceptibility zonation of the Chamoli region, Garhwal Himalayas, using logistic regression model, Landslides, DOI /s Dai, F. C., C. F. Lee, J. Li, and Z. W. Xu., (2001), Assessment of landslide susceptibility on the natural terrain of Lantau Island, Hong Kong. Environmental Geology 40,pp Ercanoglu, M., Gokceoglu, C., (2002), Assessment of landslide susceptibility for a landslide-prone area (North of Yenice, NW Turkey), by fuzzy approach. Environ Geolgy, 41,pp Ganapathy, G. P., Hada, C. L., (2012), Landslide Hazard Mitigation in the Nilgiris District, India Environmental and Societal Issues, International Journal of Environmental Science and Development, 3, pp Ganapathy, G. P., (2013), Landslide Hazard Mitigation in the Nilgiris District of western Ghats, India. 6, pp Guzzetti, F., (2000), Landslide fatalities and the evaluation of landslide risk in Italy, Engineering Geology, 58, pp Lee, S., Talib, J.A., (2005), Probabilistic landslide susceptibility and factor effect analysis. Environ Geol, 47, pp Lee, S., Pradhan, B., (2006), Probabilistic landslide hazards and risk mapping on Penang Island, Malaysia. J Earth Syst Sci, 115, pp Mathew, J., V.K., Rawat, G.S., (2007), Weight of evidence modeling for landslide hazard zonation mapping in part of Bhagirathi valley, Uttarakhand. Current Science, 92(5), pp

13 11. Pistocchi, A., Luzi, L., Napolitano, P., (2002), The use of predictive modeling techniques for optimal exploitation of spatial databases: a case study in landslide hazard mapping with expert system-like methods. Environ Geol, 41,pp Seshagiri, D. N., Upendran, R., Lakshmikantham, C.B., (1980), Nilgiris Landslides: Report on the First Phase of geological Investigation A collaborative Study. Unpublished Report of State Geology Branch, Industries Department.48, p. 13. Sharma, M., Kumar, R., (2008), GIS- based landslide hazard zonation: a case study from the Parwanoo area, Lesser and Outer Himalaya, H.P., India. Bulletin of Engineering Geology and the Environment, DOI /s Van Westen, C.J., Van Asch, T.W.J., Soeters, R., (2006). Landslide hazard and risk 1. zonation-why is it still so difficult? Bull. Engineering Geol. Environment, 65, pp Varnes, D.J., (1984), Landslide Hazard Zonation: A Review of Principles and Practice, Natural Hazards (UNESCO) Paris. 3, pp Venugopal, D., (2004), Development-Conservation Dilemma in the Nilgiri Mountains of South India.1 (1), pp Wieczorek, G. F., (1984), preparing a Detailed Landslide Inventory Map for Hazard Evaluation and Reduction: Bulletin of the asociation of Engineering Geologists, 21, pp