Remote Sensing and GIS Based Landslide Susceptibility Assessment using Binary Logistic Regression Model: A Case Study in the Ganeshganga Watershed, Himalayas

A comprehensive Landslide Susceptibility Zonation (LSZ) map is sought for adopting any landslide preventive and mitigation measures. In the present study, LSZ map of landslide prone Ganeshganga watershed (known for Patalganga Landslide) has been generated using a binary logistic regression (BLR) model. Relevant thematic layers pertaining to the causative factors for landslide occurrences, such as slope, aspect, relative relief, lithology, tectonic structures, lineaments, land use and land cover, distance to drainage, drainage density and anthropogenic factors like distance to road, have been generated using remote sensing images, field survey, ancillary data and GIS techniques. The coefficients of the causative factors retained by the BLR model along with the constant have been used to construct the landslide susceptibility map of the study area, which has further been categorized into four landslide susceptibility zones from high to very low. The resultant landslide susceptibility map was validated using receiver operator characteristic (ROC) curve analysis showing an accuracy of 95.2 % for an independent set of test samples. The result also showed a strong agreement between distribution of existing landslides and predicted landslide susceptibility zones.     

Application of frequency ratio,weights of evidence and evidential belief function models in landslide susceptibility mapping

The landslide hazard occurred in Taibai County has the characteristics of the typical landslides in mountain hinterland. The slopes mainly consist of residual sediments and locate along the highway. Most of them are in the less stable state and in high risk during rainfall in flood season especially. The main purpose of this paper is to produce landslide susceptibility maps for Taibai County (China). In the first stage, a landslide inventory map and the input layers of the landslide conditioning factors were prepared in the geographic information system supported by field investigations and remote sensing data. The landslides conditioning factors considered for the study area were slope angle, altitude, slope aspect, plan curvature, profile curvature, distance to faults, distance to rivers, distance to roads, normalized difference vegetation index, lithological unit, rainfall and land use. Subsequently, the thematic data layers of conditioning factors were integrated by frequency ratio (FR), weights of evidence (WOE) and evidential belief function (EBF) models. As a result, landslide susceptibility maps were obtained. In order to compare the predictive ability of these three models, a validation procedure was conducted. The curves of cumulative area percentage of ordered index values vs. the cumulative percentage of landslide numbers were plotted and the values of area under the curve (AUC) were calculated. The predictive ability was characterized by the AUC values and it indicates that all these models considered have relatively similar and high accuracies. The success rate of FR, WOE and EBF models was 0.9161, 0.9132 and 0.9129, while the prediction rate of the three models was 0.9061, 0.9052 and 0.9007, respectively. Considering the accuracy and simplicity comprehensively, the FR model is the optimum method. These landslide susceptibility maps can be used for preliminary land use planning and hazard mitigation purpose.     

Delineation and parameterization of banikdih watershed using remote sensing and avswat model

Delineation of Banikdih Agricultural watershed in Eastern India was carried out and various watershed parameters were extracted using Geographic Information System (GIS) and Remote Sensing. Digital Elevation Model (DEM) was developed with a contour interval of 10 m in the scale of 1:25000 using ARC/INFO modules. Sub watershed, drainage, slope, aspect, flow direction, soil series, soil texture, and soil class maps were independently generated and they were properly registered and integrated for analysis. The watershed was digitally delineated using AVSWAT model that couples hydrological model and GIS with appropriate threshold value of cell size. Subsequently, stream characteristics through the interface were generated. Indian Remote Sensing Satellite IRS-1D LISS-III data pertaining to the period of October 29, 1998 and October 23, 2000 was used to develop land use/land cover thematic map using ERDAS- 8.4 version image processing software. Eight major land use/land cover classes namely water body, lowland paddy, upland paddy, fallow land, upland crop (non-paddy crops), settlement, open mixed forest, and wasteland were segregated through digital image processing techniques using maximum likelihood algorithm. The information generated would be of immense help in hydrological modeling of watershed for prediction of runoff and sediment yield, thereby providing necessary inputs for developing suitable developmental management plans with sound scientific basis.     

Landslide susceptibility mapping of a catchment area using frequency ratio,fuzzy logic and multivariate logistic regression approaches

Geospatial database creation for landslide susceptibility mapping is often an almost inhibitive activity. This has been the reason that for quite some time landslide susceptibility analysis was modelled on the basis of spatially related factors. This paper presents the use of frequency ratio, fuzzy logic and multivariate regression models for landslide susceptibility mapping on Cameron catchment area, Malaysia, using a Geographic Information System (GIS) and remote sensing data. Landslide locations were identified in the study area from the interpretation of aerial photographs, high resolution satellite images, inventory reports and field surveys. Topographical, geological data and satellite images were collected, processed, and constructed into a spatial database using GIS and image processing tools. There were nine factors considered for landslide susceptibility mapping and the frequency ratio coefficient for each factor was computed. The factors chosen that influence landslide occurrence were: topographic slope, topographic aspect, topographic curvature and distance from drainage, all from the topographic database; lithology and distance from lineament, taken from the geologic database; land cover from TM satellite image; the vegetation index value from Landsat satellite images; and precipitation distribution from meteorological data. Using these factors the fuzzy membership values were calculated. Then fuzzy operators were applied to the fuzzy membership values for landslide susceptibility mapping. Further, multivariate logistic regression model was applied for the landslide susceptibility. Finally, the results of the analyses were verified using the landslide location data and compared with the frequency ratio, fuzzy logic and multivariate logistic regression models. The validation results showed that the frequency ratio model (accuracy is 89%) is better in prediction than fuzzy logic (accuracy is 84%) and logistic regression (accuracy is 85%) models. Results show that, among the fuzzy operators, in the case with “gamma” operator (λ = 0.9) showed the best accuracy (84%) while the case with “or” operator showed the worst accuracy (69%).     

Regional Groundwater Assessment of Krishna River Basin Using Integrated GIS Approach

The groundwater occurrence and movement within the flow systems are governed by many natural factors like topography, geology, geomorphology, lineament structures, soil, drainage network and land use land cover (LULC). Due to complex natural geological/hydro-geological regime a systematic planning is needed for groundwater exploitation. It is even more important to characterize the aquifer system and delineate groundwater potential zones in different geological terrain. The study employed integration of weighted index overlay analysis (WIOA) and geographical information system (GIS) techniques to assess the groundwater potential zones in Krishna river basin, India and the validation of the result with existing groundwater levels. Different thematic layers such as geology, geomorphology, soil, slope, LULC, drainage density, lineament density and annual rainfall distribution were integrated with WIOA using spatial analyst tools in Arc-GIS 10.1. These thematic layers were prepared using Geological survey of India maps, European Digital Archive of Soil Maps, Bhuvan (Indian-Geo platform of ISRO, NRSC) and 30 m global land cover data. Drainage, watershed delineation and slope were prepared from the Shuttle Radar Topography Mission digital elevation model of 30 m resolution data. WIOA is being carried out for deriving the normalized score for the suitability classification. Weight factor is assigned for every thematic layer and their individual feature classes considering their significant importance in groundwater occurrence. The final map of the study area is categorized into five classes very good, good, moderate, poor and very poor groundwater potential zones. The result describes the groundwater potential zones at regional scale which are in good agreement with observed ground water condition at field level. Thus, the results derived can be very much useful in planning and management of groundwater resources in a regional scale.     

Landslide Mapping and Assessment by Integrating Landsat-8, PALSAR-2 and GIS Techniques: A Case Study from Kelantan State,Peninsular Malaysia

Integration of satellite remote sensing data and GIS techniques is an applicable approach for landslide mapping and assessment in highly vegetated regions with a tropical climate. In recent years, there have been many severe flooding and landslide events with significant damage to livestock, agricultural crop, homes, and businesses in the Kelantan river basin, Peninsular Malaysia. In this investigation, Landsat-8 and phased array type L-band synthetic aperture radar-2 (PALSAR-2) datasets and analytical hierarchy process (AHP) approach were used to map landslide in Kelantan river basin, Peninsular Malaysia. Landslides were determined by tracking changes in vegetation pixel data using Landsat-8 images that acquired before and after flooding. The PALSAR-2 data were used for comprehensive analysis of major geological structures and detailed characterizations of lineaments in the state of Kelantan. AHP approach was used for landslide susceptibility mapping. Several factors such as slope, aspect, soil, lithology, normalized difference vegetation index, land cover, distance to drainage, precipitation, distance to fault, and distance to the road were extracted from remotely sensed data and fieldwork to apply AHP approach. The excessive rainfall during the flood episode is a paramount factor for numerous landslide occurrences at various magnitudes, therefore, rainfall analysis was carried out based on daily precipitation before and during flood episode in the Kelantan state. The main triggering factors for landslides are mainly due to the extreme precipitation rate during the flooding period, apart from the favorable environmental factors such as removal of vegetation within slope areas, and also landscape development near slopes. Two main outputs of this study were landslide inventory occurrences map during 2014 flooding episode and landslide susceptibility map for entire Kelantan state. Modeled/predicted landslides with a susceptible map generated prior and post-flood episode, confirmed that intense rainfall throughout Kelantan has contributed to produce numerous landslides with various sizes. It is concluded that precipitation is the most influential factor for landslide event. According to the landslide susceptibility map, 65% of the river basin of Kelantan is found to be under the category of low landslide susceptibility zone, while 35% class in a high-altitude segment of the south and south-western part of the Kelantan state located within high susceptibility zone. Further actions and caution need to be remarked by the local related authority of the Kelantan state in very high susceptibility zone to avoid further wealth and people loss in the future. Geo-hazard mitigation programs must be conducted in the landslide recurrence regions for reducing natural catastrophes leading to loss of financial investments and death in the Kelantan river basin. This investigation indicates that integration of Landsat-8 and PALSAR-2 remotely sensed data and GIS techniques is an applicable tool for Landslide mapping and assessment in tropical environments.     

Site Suitability Analysis for Urban Development Using GIS Based Multicriteria Evaluation Technique

Identification of suitable site for urban development in hilly areas is one of the critical issues of planning. Site suitability analysis has become inevitable for delineating appropriate site for various developmental initiatives, especially in the undulating terrain of the hills. The study illustrates the use of geographic information system (GIS) and multicriteria evaluation (MCE) technique for selection of suitable sites for urban development in Mussoorie municipal area, Dehradun district, Uttarakhand. For this purpose Toposheet and IKONOS satellite data were used to generate various thematic layers using ArcGIS software. Criteria using five parameters, i.e. slope, road proximity, land use/land cover, land values and geological formation were used for site suitability analysis following land evaluation. The generated thematic maps of these criteria were standardized using pairwise comparison matrix known as analytical hierarchy process (AHP). A weight for each criterion was generated by comparing them with each other according to their importance. With the help of these weights and criteria, final site suitability map was prepared.     

Approaches for delineating landslide hazard areas using different training sites in an advanced artificial neural network model

The current paper presents landslide hazard analysis around the Cameron area, Malaysia, using advanced artificial neural networks with the help of Geographic Information System (GIS) and remote sensing techniques. Landslide locations were determined in the study area by interpretation of aerial photographs and from field investigations. Topographical and geological data as well as satellite images were collected, processed, and constructed into a spatial database using GIS and image processing. Ten factors were selected for landslide hazard including: 1) factors related to topography as slope, aspect, and curvature; 2) factors related to geology as lithology and distance from lineament; 3) factors related to drainage as distance from drainage; and 4) factors extracted from TM satellite images as land cover and the vegetation index value. An advanced artificial neural network model has been used to analyze these factors in order to establish the landslide hazard map. The back-propagation training method has been used for the selection of the five different random training sites in order to calculate the factor’s weight and then the landslide hazard indices were computed for each of the five hazard maps. Finally, the landslide hazard maps (five cases) were prepared using GIS tools. Results of the landslides hazard maps have been verified using landslide test locations that were not used during the training phase of the neural network. Our findings of verification results show an accuracy of 69%, 75%, 70%, 83% and 86% for training sites 1, 2, 3, 4 and 5 respectively. GIS data was used to efficiently analyze the large volume of data, and the artificial neural network proved to be an effective tool for landslide hazard analysis. The verification results showed sufficient agreement between the presumptive hazard map and the existing data on landslide areas.     

Approaches for delineating landslide hazard areas using different training sites in an advanced artificial neural network model

The current paper presents landslide hazard analysis around the Cameron area, Malaysia, using advanced artificial neural networks with the help of Geographic Information System (GIS) and remote sensing techniques. Landslide locations were determined in the study area by interpretation of aerial photographs and from field investigations. Topographical and geological data as well as satellite images were collected, processed, and constructed into a spatial database using GIS and image processing. Ten factors were selected for landslide hazard including: 1) factors related to topography as slope, aspect, and curvature; 2) factors related to geology as lithology and distance from lineament; 3) factors related to drainage as distance from drainage; and 4) factors extracted from TM satellite images as land cover and the vegetation index value. An advanced artificial neural network model has been used to analyze these factors in order to establish the landslide hazard map. The back-propagation training method has been used for the selection of the five different random training sites in order to calculate the factor’s weight and then the landslide hazard indices were computed for each of the five hazard maps. Finally, the landslide hazard maps (five cases) were prepared using GIS tools. Results of the landslides hazard maps have been verified using landslide test locations that were not used during the training phase of the neural network. Our findings of verification results show an accuracy of 69%, 75%, 70%, 83% and 86% for training sites 1, 2, 3, 4 and 5 respectively. GIS data was used to efficiently analyze the large volume of data, and the artificial neural network proved to be an effective tool for landslide hazard analysis. The verification results showed sufficient agreement between the presumptive hazard map and the existing data on landslide areas.     

GIS-based landslide susceptibility model considering effective contributing area for drainage time

This study employed GIS modelling to ascertain landslide susceptibility on Mt. Umyeon, south of Seoul, South Korea. In this study, an effective contributing area (ECA) for certain drainage time was purposed as a temporal causative factor and then used for modelling in combination with spatial causative factors such as elevation, slope, plan curvature, drainage proximity, forest type, soil type and geology. Landslide inventory map of 163 landslide locations was prepared using aerial photographic interpretation and field verifications after that digitized using GIS environment in 1:5000 scale. A presence-only-based maximum entropy model was used to establish and analyse the relationship between landslides and causative factors. Before final modelling, a jackknife test was performed to measure the variable contributions, which showed that the slope was the most significant spatial causative factor, and ECA with a drainage time of 12 h was the most significant temporal causative factor. The performances of the final models, with and without significant ECA, were assessed by plotting a receiver operating characteristic curve to be 75.5 and 81.2%, respectively.     

Macro Landslide Hazard Zonation Mapping - Case Study from Bodi – Bodimettu Ghats Section,Theni District,Tamil Nadu - India

Landslide is a common natural hazard that usually occurs in mountainous areas. Rapid urban development and high traffic intensity movements have been hampered to a great extent by phenomenon of landslides. In Ghat section, vertical cuttings and steep slopes are induced slope failures. An assessment of landslide hazards is therefore a prerequisite for sustainable development of the hilly region. In the present study, Macro Landslide Hazard Zonation was carried out in the Bodi – Bodimettu ghats section, Western Ghats, Theni district. The slope spreads over an area of about 10.09 sq km encompassing Puliuttu Ar. sub-watershed. The study was made with help of different types of data including Survey of India topographic map, geology map, important inherent factors like lithology, structure, slope morphometry, relative relief, land use/land cover and hydrogeological conditions using Bureau of Indian Standard (BSI 14496 (Part 2):1998) and related thematic maps. Based on the thematic layers, landslide hazard evaluation factor (LHEF) and total estimated hazard (TEHD) were calculated and the macro hazard zonation map was prepared. Landslide Hazard Zonation (LHZ) of the terrain shows that out of 17 facets, facets 1 to 5 and 8 falls under Moderate Hazard zone category and facets 6, 7 and 9 to 17 under the High Hazard zone category. The field study with further analysis for hazard concluded that about 68% of the total area falls in the high hazard zone.     

GIS-based ordered weighted averaging and Dempster–Shafer methods for landslide susceptibility mapping in the Urmia Lake Basin,Iran

In this paper, GIS-based ordered weighted averaging (OWA) is applied to landslide susceptibility mapping (LSM) for the Urmia Lake Basin in northwest Iran. Nine landslide causal factors were used, whereby the respective parameters were extracted from an associated spatial database. These factors were evaluated, and then the respective factor weight and class weight were assigned to each of the associated factors using analytic hierarchy process (AHP). A landslide susceptibility map was produced based on OWA multicriteria decision analysis. In order to validate the result, the outcome of the OWA method was qualitatively evaluated based on an existing inventory of known landslides. Correspondingly, an uncertainty analysis was carried out using the Dempster–Shafer theory. Based on the results, very strong support was determined for the high susceptibility category of the landslide susceptibility map, while strong support was received for the areas with moderate susceptibility. In this paper, we discuss in which respect these results are useful for an improved understanding of the effectiveness of OWA in LSM, and how the landslide prediction map can be used for spatial planning tasks and for the mitigation of future hazards in the study area.     

Identification of Water Conservation Sites in a Watershed (WRJ-2) of Nagpur District, Maharashtra using Geographical Information System (GIS) Technique

The paper deals with the application of Remote Sensing and Geographical Information System (GIS) technique for a watershed development program. For this study, the WRJ-2 watershed falling under Narkhed and Katol Tahsils of Nagpur district, Maharashtra, India is investigated. Various thematic maps (i.e. drainage, geology, soil, geomorphology and land use/ land cover) have been prepared using the remote sensing and GIS techniques. Initially, differential weightage values are assigned to all the thematic maps as per their runoff characteristics. Subsequently, the maps are integrated in GIS environment to identify potential sites for water conservation measures like gully plugs, earthen check dams, continuous contour trenches, percolation tanks, cement bandhara, afforestration and farm ponds, etc. The study depicts that the GIS technique facilitates integration of thematic maps and thereby helps in an identification of micro-zones each with unique characters in-terms of hydrogeology, thus amenable to specific water conservation techniques. It is therefore concluded that, the GIS technique is suitable for an identification of water conservation structures.     

Routing of Road Network through Least Cost Pathway Algorithm to Minimize Impacts on Environment

In line with growth and sustainable development of the country and fundamental evolution of economic and social matters, the role of road networks becomes ever more significant. The network provides access to hot spots and movement of materials which are necessary for such a development. To address sustainable road development, GIS techniques were used to determine the optimum routes. All important parameters like slope, geology, landslide, etc. were selected, collated in a data base and used to create a cost layer. Then, using the Least Cost Pathway Algorithm, the four routes based on different cost layers were designed in GIS. These cost layers were calculated based on variable threshold values related to each criterion. Finally the optimum way was selected according to distance from landslide zone, fault lines, high slope areas and proximity to economical centres. As conclusion and in comparison, we found that currently designed road without regard to environmental factors may lead to selection of ways that will pass through inhibited zones increasing the likelihood of damage to the environment and economical costs.     

基于神经网络和模糊评判的滑坡敏感性分析

滑坡的敏感性涉及到很多因素,如滑坡体的坡度、坡的朝向、坡度的类型、岩石特性、海拔高度、植被覆盖等特征。神经网络具有非线性映射能力,利用这些与滑坡发生紧密相关的因素作为网络的输入,构造一个具有反映滑坡敏感性的评价网络,输出端为敏感性分析的结果。本文针对某具体地区,提取相关因素,构造评价指标体系并量化,利用该地区样本集数据对滑坡敏感性评价神经网络进行训练,用训练后的网络对实例并结合模糊评判进行了相互验证,结果说明利用神经网络和模糊评判进行滑坡敏感性分析是可行的。     

Investigating the Promotion Possibility of No-Hunting Areas to the Protected Areas- a Case Study

Protected areas are established to conserve unique features and biodiversity of the nature. Accordingly, wherever has one of the natural, ecological and/or cultural values it should be considered a protected area. Kave-Deh No-hunting Area is located on extremely east of Tehran Province in an area of 94,961 ha. Due to rich and diverse land cover, distinctive wildlife species, and unique monuments the area was selected as a case study to examine the possibility of its promotion to the protected area using Spatial Multi Criteria Evaluation (SMCE) Method. For this purpose, the relevant criteria were identified by Delphi method. After finalization of the most important criteria by Delphi panelists, the map layers were prepared at the scale of 1:100000, in the environment of GIS Software. Afterwards, the map layers were divided into factors and constraints of which factors were standardized by S-shaped membership functions of fuzzy logic. The dimensionless factor maps were weighted using Analytical Hierarchy Process (AHP) Method in the environment of Expert Choice Software. Subsequently, a mathematical equation was extracted to conduct the land suitability analysis. The Weighted Linear Combination (WLC) Method was applied to overlay the map layers and obtain the final ‘nature conservation’ land use map. The final land suitability map showed that 34,687 ha of whole study area (equal to 37 %) have the potentiality for promotion to the protected area.     

Assessment of groundwater potential zones using GIS technique

A case study was conducted to find out the groundwater potential zones in Kattakulathur block, Tamil Nadu, India with an aerial extent of 360.60 km². The thematic maps such as geology, geomorphology, soil hydrological group, land use / land cover and drainage map were prepared for the study area. The Digital Elevation Model (DEM) has been generated from the 10 m interval contour lines (which is derived from SOI, Toposheet 1:25000 scale) and obtained the slope (%) of the study area. The groundwater potential zones were obtained by overlaying all the thematic maps in terms of weighted overlay methods using the spatial analysis tool in ArcGIS 9.2. During weighted overlay analysis, the ranking has been given for each individual parameter of each thematic map and weights were assigned according to the influence such as soil −25%, geomorphology − 25%, land use/land cover −25%, slope − 15%, lineament − 5% and drainage / streams − 5% and find out the potential zones in terms of good, moderate and poor zones with the area of 49.70 km², 261.61 km² and 46.04 km² respectively. The potential zone wise study area was overlaid with village boundary map and the village wise groundwater potential zones with three categories such as good, moderate and poor zones were obtained. This GIS based output result was validated by conducting field survey by randomly selecting wells in different villages using GPS instruments. The coordinates of each well location were obtained by GPS and plotted in the GIS platform and it was clearly shown that the well coordinates were exactly seated with the classified zones.     

Shallow Landslide Susceptibility Mapping Using Stereo Air Photos and Thematic Maps

Overlay (or heuristic) models have been shown to be a reasonable method to generate landslide susceptibility maps. However, overlay models are subjective and rely on expert judgment. The method developed here employs an overlay model but it tries to estimate certain parameters, such as slope angle or distance to streams, in a more quantitative manner. Each individual factor believed to have an influence on mass wasting processes is scaled to a value between 0 and 1. All different factors are then multiplied, producing the degree of stability, D_f, which lies on a ratio scale, rather than a nominal or interval scale. Thus, a pseudo probability of failure can be obtained. The data required are derived partly from thematic maps and partly from stereo air photos. The air photo stereo pair is used to automatically derive a digital elevation model which is then used to create an orthoimage of an area. The air photos are also used to develop a land use map, an important component in estimating landslide susceptibility. This methodology was tested on a section of the Santa Monica Mountains near Malibu, California. The resulting map both visually and statistically appears to successfully identify problematic areas. Although the methodology was primarily developed to identify shallow slides, the crest of a large, deep-seated landslide was also recognized. The general methodology should be easily adaptable to other regions or could be applied with a different set of factors than those considered in this specific case.     

Effects of Landslide Sampling Strategies on the Prediction Skill of Landslide Susceptibility Modelings

In this study, landslide susceptibility assessments were achieved using logistic regression, in a 523 km² area around the Eastern Mediterranean region of Southern Turkey. In reliable landslide susceptibility modeling, among others, an appropriate landslide sampling technique is always essential. In susceptibility assessments, two different random selection methods, ranging 78–83% for the train and 17–22% validation set in landslide affected areas, were applied. For the first, the landslides were selected based on their identity numbers considering the whole polygon while in the second, random grid cells of equal size of the former one was selected in any part of the landslides. Three random selections for the landslide free grid cells of equal proportion were also applied for each of the landslide affected data set. Among the landslide preparatory factors; geology, landform classification, land use, elevation, slope, plan curvature, profile curvature, slope length factor, solar radiation, stream power index, slope second derivate, topographic wetness index, heat load index, mean slope, slope position, roughness, dissection, surface relief ratio, linear aspect, slope/aspect ratio have been considered. The results showed that the susceptibility maps produced using the random selections considering the entire landslide polygons have higher performances by means of success and prediction rates.     

Landslide susceptibility mapping in a hilly terrain using remote sensing and GIS

Remote sensing and Geographic Information System (GIS) are well suited to landslide studies. The aim of this study is to prepare a landslide susceptibility map of a part of Ooty region, Tamil Nadu, India, where landslides are common. The area of the coverage is approximately 10 × 14 km in a hilly region where planting tea, vegetables and cash crops are in practice. Hence, deforestation, formation of new settlements and changing land use practices are always in progress. Land use and land cover maps are prepared from Indian Remote Sensing Satellite (IRS 1C - LISS III) imagery. Digital Elevation Model (DEM) was developed using 20 m interval contours, available in the topographic map. Field studies such as local enquiry, land use verification, landslide location identification were carried out. Analysis was carried out with GIS software by assigning rank and weights for each input data. The output shows the possible landslide areas, which are grouped for preparation of landslide susceptibility maps.