– Remote Sensing Susceptibility Analysis of Landslide in Chittagong City Corporation Area

In Chittagong city, landslide phenomena is the most burning issue which causes great problems to the life and properties and it is increasing day by day and becoming one of the main problems of city life. On 11 June 2007, a massive landslide happened in Chittagong City Corporation (CCC) area, a large number of foothill settlements and slums were demolished; more than 90 people died and huge resource destruction took place. It is therefore essential to analyze the landslide susceptibility for CCC area to prepare mitigation strategies as well as assessing the impacts of climate change. To assess community susceptibility of landslide hazard, a landslide susceptibility index map has been prepared using analytical hierarchy process (AHP) model based on geographic information system (GIS) and remote sensing (RS) and its susceptibility is analyzed through community vulnerability assessment tool (CVAT). The major findings of the research are 27% of total CCC area which is susceptible to landslide hazard and whereas 6.5 sq.km areas are found very highly susceptible. The landslide susceptible areas of CCC have also been analyzed in respect of physical, social, economic, environmental and critical facilities and it is found that the overall CCC area is highly susceptible to landslide hazard. So the findings of the research can be utilized to prioritize risk mitigation investments, measures to strengthen the emergency preparedness and response mechanisms for reducing the losses and damages due to future landslide events.


Introduction
Due to its geographical location, Chittagong city suffers from numerous natural disasters like landslide, water logging, cyclone, flood etc.But at present landslides are the most burning issues in respect of Chittagong City Corporation (CCC) area.Because Chittagong hills are degrading by different anthropogenic stress such as hill cutting for construction, sand and clay mining purpose, increasing settlement in foothills, deforestation [1] which are very much responsible for landslide occurrences.The city, under the jurisdiction of City Corporation (Figure 1), has a population of about 2.5 million, is constantly growing with an area approximately 155 square kilometers [2].A north-south hill range crosses the city and many settlements and slums have been developed in the foothills and lower income people are living in these areas in a risky situation.Almost every year CCC has experienced several devastated landslide incidences that brought vast damage to properties and natural environment, and some loss of human life, as shown in Table 1.The landslides in Chittagong are classified as 'Earth Slides' since those consist of 80% sand and finer particles [3].It has been stated that the rainfall intensity and duration play very important role in producing these shallow landslides in Chittagong because of climate change [3].Heavy monsoon rainfall intensified by strong storm from the Bay of Bengal (BOB) can cause abnormal precipitation in the area which mostly triggered landslides in Chittagong [4].It is therefore essential to analyze the susceptibility of landslide for CCC area so that appropriate mitigation strategies can be developed to help combat impacts of climate change.To prepare community susceptibility map of landslide hazard, geographic information system (GIS) and remote sensing (RS) based analytical hierarchy process (AHP) model is used in this research.Community vulnerability assessment tool (CVAT) is used to assess susceptible areas according to physical, social, economic, environmental and critical facilities of CCC area.

Literature review
Landslide hazard models are the most powerful analytical and diagnostic tool for geomorphologists and decision makers to predict the spatial and temporal occurrence of mass movement.Most landslide hazard analysis take into account an up to date landslide inventory that represents the fundamental tool for identifying the hill slope instability factors that triggers landslide [5].Reliability of the susceptibility maps depends mostly on the amount and quality of available data, the working scale and the selection of the appropriate methodology of analysis and modeling.The process of creating the maps involves several qualitative or quantitative approaches [6,7].Various geo-structural as well as causative factors based approaches have been proposed for landslide susceptibility zoning [8].
Expert opinions are used in the process of qualitative methods.Most common types simply examine landside inventory maps to identify sites of similar geological and geomorphological properties that are likely susceptible to failure.Some qualitative approaches, however, incorporate the idea of ranking and weighting, and may evolve to be semi-quantitative in nature.However, more sophisticated assessments involved techniques such as AHP, bivariate, multivariate, logistic regression, fuzzy logic, or artificial neural network (ANN) have been reported in recent years [9,10,11].The application of the analytical hierarchy process (AHP) method [12] was widely used in landslide susceptibility mapping.Weighted linear combination (WLC) technique was reported in the study conducted by Ayalew et al.2004 [13].Quantitative methods are based on numerical expressions of the relationship between controlling factors and landslide activity.There are two types of quantitative methods: deterministic and statistical [14].Within these techniques the probabilistic and statistical methods have been most commonly used in recent years.These method become much more popular using GIS and RS techniques [5].Remote sensing and digital elevation models (DEMs) can be constituted as a feasible option for natural disaster control [6].Several researchers have also used statistical techniques such as logistic regression [15].All the statistical methods, despite the methodological and operational differences, are based on the common assumption that slope failure in the future will be more likely to occur under those conditions which led to past and present instability [16].They mentioned some advantages and disadvantages of using different methods in different scales.So the quantitative techniques such as analytical hierarchy process (AHP) [17] can be utilized in this study.
Community vulnerability assessment tool (CVAT) was developed by NOAA coastal services center to support state and local governments to conduct community wide susceptibility assessments, in their efforts to reduce hazard susceptibility.The foundation for the methodology was established by the Heinz Center Panel on Risk, Susceptibility [18].The Community vulnerability assessment Tool (CVAT) can be designed as a toolkit comprised of various climate changes adaptation tools, which, when used in a facilitated process, helps guide communities in looking at specific climate change issues [19].CVAT differs from conventional susceptibility assessments in two important ways: (1) it addresses social as well as physical susceptibility and (2) it provides guidance on engaging people from the community who typically are not involved in disaster planning, but who often suffer the most as a result of disasters [20].The assessment is best carried out through a process of meaningful and sustained community engagement to help ensure that the needs, capabilities and concerns of all groups, particularly those who are often under represented.
Landslide becomes a problem when they interfere with human lives, activities and properties.Though landslide have become a severe problem in hilly areas, significant number of poor and vulnerable people often resides in such environments, adopting typical socio-economic activities [21].So the main objective of this study to assess the susceptibility of landslide occurrences in Chittagong city corporation area.

Methodologies and Data Processing
The instability factors that can introduce severe landslides in some particular areas include surface and bedrock, lithology and structure, seismicity, slope, steepness, morphology, stream evolution, groundwater conditions, climate, vegetation cover, land use, and human activity [22].In this study, ten major factors are considered according to the importance of the location and data availability such as elevation, slope aspect, slope angle, land cover, available vegetation (NDVI), distance to road, distance to water body, drainage density, geology and geomorphology.
Landslide hazard incident data are collected from field survey's to prepare the landslide hazard inventory map.Informal interview and open discussion has also been conducted with the authorities of different concerned organizations, experts and people living in susceptible areas of Chittagong city.The secondary data such as rainfall data, demographic data, satellite image (character of data shown in Table 2), Geology and Geomorphology data and different types of GIS data base on CCC are also collected from archives of different organization.Data procession and its sources have been described in the following part: All the collected data are converted to raster grid with (30 m × 30 m) cells and the raster grids are projected to Transverse Mercator (TM) using DWGS 1984 datum.The details of the data collection procedure and ways of preparing the thematic layers are described as follows:

Landslide inventory map
Landslide inventory is an essential part and basic information for any landslide zoning such as susceptibility, risk and hazard zonings [23].A total of 20 landslide locations are identified in the study area through field survey and the latitude and longitude values are collected using a Hand GPS device.The 20 observed landslide locations in CCC are shown in Figure 2a.

Land cover map
Land cover data are generated from Landsat 8 (2013), collected from the Global visualization viewer (GLOVIS) of United States Geological survey (USGS) website.It has been prepared using the supervised classification techniques followed by knowledge-based expert classification systems depending on reference maps to improve the accuracy of the classification process [24,25].Five (5) major classes are taken such as sandy land, vegetation, water bodies, built-up area, paddy fields and shrubs (Figure 2b) and reference pixels are compared with the base map (2010) collected from the Chittagong development authority (CDA).The overall accuracy is found as 85.25% and overall Kappa Statistics is 0.8160 [26].

Normalized Difference Vegetation Index (NDVI) map
The normalized difference vegetation index (NDVI) is developed from the reflective bands of Landsat 8 satellite data for estimating available type of vegetation cover shown in Figure 2c.It has been prepared using the following Equation ( 1

Slope
Slope has been generated from the raster DEM 30-meter contour interval obtained from USGS archive.The analyst tool identifies the slope (e.g., gradient, or rate of maximum change in z-value) from each cell of a raster surface.The output slope angle raster can be calculated as percent slope angle or degree of slope angle [27].Generally steeper slope is more susceptible to landslide but most of the observed landslide occurrences in Chittagong were found within a slope range from 15-45 degrees [29], as shown in Figure 3a.

Aspect map
The aspect represents the down slope direction of the maximum rate of change in value from each cell to its neighbors [27].Slope facing south, southwest, west receive maximum rainfall in Chittagong region [29].Any slope faces maximum rainfall is more susceptible to landslide than others.Final results are reported in terms of the eight (8) basic compass directions shown in Figure 3b.

Elevation map
The elevation map has been prepared from the DEM layer where the relative height of the layer is considered.Higher elevation is characterized by compacted sandstone in Chittagong city which is also resistance to sliding activity but moderate elevation of 8-12 m has high susceptibility for landslide occurrences [29], as shown in Figure 4a.

Distance to road
Landslides may occur on the road and on the side of the slopes affected by roads [30].The distance from road classes closer to the road covered the higher percentage of landslide area while the classes far from the road had lowest percentage of landslide [31].The distance from road network layer is prepared using 'Euclidean distance' technique which gives the distance from each cell in the raster to the closest source, shown in Figure 4b.Euclidean distance is calculated from the center of the source cell to the center of each of the surrounding cells, true Euclidean distance is calculated in each of the distance [27].

Distance to water body
The area closer to the river and stream has high level of landslide susceptibility [32].Euclidean distance [27] is also used to prepare distance to water body layer map at 200 meter intervals (Figure 5a).

Drainage density
DEM data is used to prepare Drainage density map.The presence of stream line influences stability by toe erosion or by saturating the toe material or both [33].The distances from stream line of 100m intervals is produced using Euclidean distance [27] technique, as shown in Figure 5b.

Geology and Geomorphology
Geology and Geomorphology data are collected from Geological Survey of Bangladesh (GSB).The highest preference are given to the geologic formations of slope and valley deposit where majority of landslides had previously occurred (Figure 6a).
The geomorphologic data are also classified into four classes and according to expert opinions, hilly landforms are most susceptible to landslide occurrences shown in Figure 6b.

Precipitation
Due to climate change, CCC is experiencing high intensity of rainfall in recent years which is making the landslide situation worse [34].Previous ten years (2003-2013) of precipitation data has been collected from Bangladesh Metrological Department [35] and the average daily precipitation of the whole CCC area is more of less same.Besides the study areas are too small and there is only one weather station installed in this area.So the triggering effect of the precipitation factor is assumed to be uniform.
The demographic data such as population distribution, gender, age group on CCC area collected from Bangladesh bureau of statistics (BBS) [36] and the GIS data base (physical facilities, critical facilities, economic facilities, environmental risk sites etc.) for CCC area have also been collected from Chittagong development authority (CDA).

Analytical hierarchy process (AHP)
Analytical hierarchy process (AHP) method is a multi-factor decision-making process which is used to derive the weights associated with suitability/attribute map layers [17].AHP involves building a hierarchy of decision elements (factors) and then making comparisons between possible pairs in a matrix to give a weight for each element and also a consistency ratio [37].Factor weights for each criterion are determined by a pair wise comparison matrix [17,38].In the construction of a pair wise comparison matrix, each factor is rated against every other factor by assigning a relative dominant value between 1 and 9 to the intersecting cell, shown in Table 3.When the factor on the vertical axis is more important than the factor on the horizontal axis, this value varies between 1 and 9.Conversely, the value varies between the reciprocals 1/2 and 1/9.Since we have used ten parameters, the comparison matrix has 100 boxes.The diagonal boxes of a pair-wise comparison matrix always take ascertain value of 1.The boxes in the upper and lower halves are symmetrical with one another and the corresponding values are, therefore, reciprocal with each other.The CR is a ratio between the matrix's consistency index and random index and in general ranges from 0 to 1.The consistency ratio (CR) is obtained by comparing the consistency index (CI) with average random consistency index (RI).The consistency ratio (CR) is defined in Equation ( 2): (2) A CR close to 0 indicates the high probability that the weights were generated randomly [17].The consistency index (CI) of a matrix of comparisons can be calculated through Equation ( 3): (3) Table 3. Scale of preference between two parameters in AHP [17].

Degree of preferences Explanation 1
Equally Two activities contribute equally to the objective 3 Moderately Experience and judgment slightly to moderately favor one activity over another.

Strongly
Experience and judgment strongly or essentially favor one activity over another.

7
Very strongly An activity is strongly favored over another and its dominance is showed in practice.

Extremely
The evidence of favoring one activity over another is of the highest degree possible of an affirmation.

Reciprocals Opposites
Used for inverse comparison.
In this study, AHP considers weighting and rating system developed by collecting questionnaires from expert opinions and secondary data sources.The class weightage and the factor weightage are multiplied each other to produce a combined weightage map of landslide susceptibility as Equation ( 4): Where, SI is the required susceptibility index of the given pixel.
Ri and Wi are class weight (or rating value) and factor weight for factor i respectively.
The weightage maps are classified into five (5) classes using Natural breaks (Jenks) classification method characterized by very high, high, medium, low and very low susceptibility.Natural breaks classes are based on natural groupings inherent in the data.Class breaks are identified that best group similar values and that maximize the differences between classes.Natural breaks are data-specific classifications which is used for this study purpose [27].Validity of the map was examined using 20 known landslide locations within the area obtained from the field surveys and from official records of the responsible authorities.
Landslide susceptibility assessment for CCC area has been carried out through Community susceptibility assessment Tool (CVAT).Landslide susceptibility analysis for CCC area has also been divided into five segments naming physical susceptibility analysis, critical facilities susceptibility analysis, social susceptibility analysis, economic susceptibility analysis and environmental susceptibility analysis.The first step involved with physical susceptibility analysis of all types of road and residential structures which are located in landslide hazard prone areas.The physical structures location is overlaid over the map of land slide susceptible areas and the physical susceptibility analysis map is prepared.Then critical facilities (community services, education and research, service activity) and economic facilities (commercial activities, manufacturing and processing) that are within close proximity to susceptible areas are identified by overlying the critical facilities from GIS data location over the map of landslide susceptible areas.Social susceptibility can be analyzed through special consideration areas which are identified by population distribution, gender, age group of people and literacy rate etc.For environmental susceptibility analysis include dense forest, shrubs and water body's areas, which are considered to determine the potential threat to environment for landslide hazard.

Landslide susceptibility mapping and discussion
Landslide susceptibility mapping consists of the derived factor weights and class weights, and a calculated CR, as seen in Table 4.In this research, the resulting CR for all the cases is found less than 0.10 (Table 4 and Table 5).From Table 6, it is found that the LSI had a minimum value of 0.053, and a maximum value of 0.457, with an average value of 0.162 and a standard deviation of 0.061.The LSI represents the relative susceptibility of a landslide occurrence.These LSI values are then divided into five classes based the natural breaks range [27], which represent five different zones in the landslide susceptibility map showing in Figure 7, Only 11% of the total areas are classified as being in the VHS (4%) or HS (7%) landslide susceptibility zones but they have accommodated about 80% of the landslide reference points.Other areas are located in the MS (18%), LS (39%), and VLS (33%) susceptibility zones and only 4 landslide incidences (out of 20) are being observed in the MS zones.The frequency ratio (FR) values are computed from ratio of the percentage landslide occurrences and the percentage area coverage (for each individual class to the whole study area).The possible values begin from 0 onwards where relatively high ones (much greater than 1) indicate high chance of having landslides while low values (close to 0) indicate lower chance of having landslide over the area.The FR values of 11.832 for the VHS zone and 5.289 for the HS zone indicate the higher chance of having landslide activities in these areas when compared to those of the MS (1.142) and LS (0), as shown in Table 6.6. Allocation of the reference landslide points within the defined landslide susceptibility class and the associated frequency ratio (FR) of each class.
The very high susceptible landslide locations in CCC area are identified as (e.g., Lebubagan area, kusumbag residential area, Batali hill area, Motijharna area, Foy's Lake area, Khulshi area, Nasirabad area, Goalpara slum, kanandhara abasik prokolpo).Among those locations, (e.g.,Motijorana area and Batali Hill) are considered as the most susceptible locations in CCC.These areas are also heavily populated and occupied by lower income groups.Most of the inhabitants are poor factory workers.Any large scale landslide can cause massive destruction to these areas and cause death of many people.Since landslide occurrences only recorded in the very high and highly susceptible areas in CCC, the very high, high and moderate susceptible areas are taken in this research for susceptibility analysis process.

Susceptibility analysis and results
The susceptibility analysis is conducted based on the area located from landslide susceptibility mapping process.Involving the community in the preparation of the susceptibility assessment can improve its effectiveness and ensure that the assessment is relevant to those who are the most at risk.

Physical susceptibility analysis
Physical susceptibility to landslide hazard is divided into two separate segments naming infrastructural susceptibility and road susceptibility for CCC area.

Infrastructural susceptibility
The total numbers of residential structures in the CCC area are 186006, among which 71991 structures are found susceptible due to landslide hazard.Among all the susceptible structures in CCC, 7% are found very high susceptible, 21% high susceptible and 72% are also found medium susceptible, shown in Figure 8.The formation of informal settlements (generally termed "slums") on hill slopes with unplanned hill-cutting are the main cause of susceptibility to landslides [39].Motijorna Tankir par and kusumbag, Badsha mia road area are the most susceptible locations for landslide occurrences which are mostly constructed on illegally occupied lands, mostly in hilly regions of CCC area.

Road susceptibility
The entire CCC area is covered by network of roads such as pucca, semi-pucca and kacha road.The total area also is served by 2888 km of road (using Bangladesh Transverse Mercator projection), among them approximately 806 km of road have been found susceptible.About 5% (43.36 km) of susceptible roads are very highly and more than 95% (800 km) of roads are highly to medium susceptible to landslide hazard shown in Figure 9. Approximately more than 13 km of katcha road is identified as severely susceptible.The medium susceptible road occupies the larger portion of the road network.The roads located in very highly to highly susceptible areas such as Motijorna Tankir par I-II and Batali hill road are very much threatened by landslide hazard, as shown in Figure 10.The road locations that are susceptible to landslide hazard are given in Table 7.

Critical facilities susceptibility analysis
The critical facilities susceptibility analysis includes community service, education and research institute, service activity, transport and communication facilities of CCC area.The total numbers of very high susceptible critical facilities are 144 (Table 8), which is 4% of total critical facilities in CCC area.Accordingly, the high susceptible critical facilities are found as 636 (19%) and medium susceptible critical facilities are 2648 (77%), among which the education and research and service activity facilities possesses higher susceptibility to landslide occurrences.The overall number of educational institutions in CCC area are 1437, among those, 51 educational institutions are very highly and 737 are highly affected and 30 service oriented structures are very highly susceptible to landslide hazard, also shown in Table 8.

Total medium susceptible critical facilities 2648
Important critical facilities found in (e.g., Ward no 2, 9, 14, 15, 16) are very highly susceptible to landslide, shown in Figure 11.The critical facilities that are found very highly susceptible to landslide hazard are given in Table 9.

Social susceptibility analysis
Social susceptibility is analyzed through the identification of Population distribution, Gender, age group of people and literacy rate of the study area.The population density is not included because of not having density data of individual wards of CCC area.The susceptible population such as affected population, affected female population, affected population below 10 years and affected population above 60 years can be calculated in respect of total population residing in the landslide susceptible area.The susceptible areas in CCC are divided into three phases such as very high susceptible, high susceptible and medium susceptible area.The very high susceptible population is identified as 56777 which is 7 % of total susceptible population in CCC area.The high susceptible and medium susceptible population comprises 20 % and 73 % of total susceptible population shown in Figure 12.Women are also more vulnerable to disasters because of their roles as mothers and caregivers: when disaster is about to strike, their ability to seek safety is restricted by their responsibilities to the very young and the very old, both of whom require help and supervision [40].The percentage of female population susceptible to landslide hazard can be described as very high susceptible 7 % (26668), high susceptible 20 % (76713) and medium susceptible 73 % (273511) of total affected female population.The selected age groups for this analysis are taken as "below 10 years" and "above 60 years".Then the population within the susceptible age groups is distributed among the affected community.The population below 10 years susceptible to landslide in CCC area are found to be 11312 (very high susceptible), 34214 (high susceptible) and 123123 (medium susceptible).The total population above 60 years in CCC area are 44016, among them 6 % (2618) are found in very high susceptible, 20 % (8625) high susceptible and 74 % (32772) medium susceptible to several landslide occurrences also shown in Figure 12.
Literacy increases disaster awareness among people and has an influential effect on successful disaster management process.Literacy rate of almost all the wards of the study area is around 70% [36].Literacy rate has not been considered because it will not impose much difference in the susceptibility assessment process.

Economic susceptibility analysis
Economic susceptibility depends on how many important economic activities are located in close proximity to susceptible areas which comprises of commercial and manufacturing industries.Among all the economic activities, 19 of manufacturing and processing industries and 299 commercial activities are threatened very highly to landslide hazard.There are also 1242 commercial centers and 208 manufacturing and processing industries that are highly susceptible to landslide hazard.The scarcity of land requirement forces to establish new commercial or manufacturing industries in the landslide prone hills and become highly susceptible to landslide hazard.It is found from Figure 13 that, 4% structures are very highly and 16% of total susceptible structures are highly susceptible, since most of the economic activities are located in the plain land, the economic activities susceptibility is lower in the hilly regions.Very important economic activities located in very high susceptible zone are shown in Figure 14.The economic facilities that are found very highly susceptible to landslide hazard are given in Table 10.

Environmental susceptibility analysis
A landslide phenomenon in CCC area poses a serious effect on environment.Since most of the landslide occurrences are recorded on hilly areas composed with dense forest, there lies severe risk to landslide hazard.Among all the forest areas in CCC area, 24 % (4.99 km²) lies in very low, 16 % (3.34 km²) in low, 34 % (7.29 km²) in medium, 21 % (4.47 km²) in high and 5 % (1.14 km²) in very high susceptible areas, as shown in Figure 15.Though the dense forest in very high susceptible areas are lower but the overall susceptibility of forest areas are significantly higher because of having higher percentage of forest areas in very high susceptibility class.The medium susceptibility class (Figure 16c) has covered highest percentage of forest areas but the overall susceptibility to landslide occurrences are lower in this class (Figure 16).The very high susceptible areas in Figure 15, occupies 63 % (1.14 km²) forest land, 27 % (.49 km²) shrubs or agriculture land, 8% (.14 km²) barren land, 2 % (.04 km²) urban area and 0 % (0 km²) water which also shows forest areas in this class are very highly susceptible to landslide hazard.

Conclusions
Hill cutting and heavy rainfall are prime factors for landslides in Chittagong that causes death to hundreds people with a great property loss.The study is an attempt to see the efficacy of AHP and CVAT tools for analyzing landslide susceptibility of the CCC area.As susceptible areas of the landslide are found in the study, it would be rational to provide supportive actions for preparing disaster management plan for these susceptible areas.Besides, several steps can be taken to reduce the effect of landslide such as to understand the processes and mechanisms of landslide, Sensitive hill areas needs special environmental impact assessment (EIA) before undertaking any development activity, density restriction should be established, Relocation of the foothill slums, comprehensive awareness is to be administered to enhance public awareness, harmonization of institutional mandates should be developed through an inter-organizational coordination mechanism etc. Finally on the basis of the study it can be concluded that if the government and other concerned authorities take necessary steps, susceptibility of landslide hazards can be reduced to an extent tolerable to the city people.

Table 1 .
Summary of the crucial landslide incidences in the last 10 years (2003-2013.Source: Comprehensive Disaster Management Programme-II 2012 and Field survey, 2014.

Figure 1 .
Figure 1.Location map of the study area (CCC).

( 1 )Figure 2 :
Figure 2: Landslide instability factors; (a) Observed landslide locations in CCC; (b) Land Cover map of CCC area and; (c) NDVI map of CCC area.

Figure 3 :
Figure 3: Landslide instability factors; (a) Slope map of CCC area; (b) Aspect map of CCC area.

Figure 4 :
Figure 4: Landslide instability factors; (a) Elevation map of CCC area; (b) Distance to road map of CCC area.

Figure 5 :
Figure 5: Landslide instability factors; (a) Distance to water body map of CCC area; (b) Drainage density map of CCC area.

Figure 6 :
Figure 6: Landslide instability factors; (a) Geology map of CCC area; (b) Geomorphology map of CCC area.

Figure 7 .
Figure 7. Landslide susceptibility map of CCC area.

Figure 8 .
Figure 8. Percentage of total residential structure susceptible to landslide.

Figure 10 .
Figure 10.Road susceptibility map of CCC area.

Figure 11 .
Figure 11.Critical facilities susceptibility map of CCC area.

Figure 12 .
Figure 12.Social susceptibility according to population criteria and susceptible area in CCC.

Figure 14 .
Figure 14.Economic facilities susceptibility map of CCC area.

Figure 15 .
Figure 15.Environmental susceptibility to landslide hazard

Table 2 .
Detailing of Landsat 8 and ASTER scene of CCC area.Source: US Geological Survey, 2013

Table 5 .
Pair wise comparison matrix, factor weights and consistency ration of the data layers.

Table 7 :
Road locations very highly susceptible to landslide in CCC area.

Table 8 .
Number of susceptible critical facilities.

Table 9 :
Critical facilities very highly susceptible to landslide in CCC area.

Table 10 :
Economic facilities very highly susceptible to landslide in CCC area.