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Research Article
Open Access Peer-reviewed

Landslide Susceptibility Mapping in East Sikkim Region of Sikkim Himalaya Using High Resolution Remote Sensing Data and GIS techniques

Prakash Biswakarma, Binoy Kumar Barman, Varun Joshi , K. Srinivasa Rao
Applied Ecology and Environmental Sciences. 2020, 8(4), 143-153. DOI: 10.12691/aees-8-4-1
Received April 16, 2020; Revised May 18, 2020; Accepted May 25, 2020

Abstract

Occurrence of landslides is very common and frequent phenomenon in hilly terrain of Indian Himalayan region leading to severe environmental and socio-economic issues. The current research used the method of weighted parameter, Remote Sensing (RS) and Geographic Information System (GIS) for landslide susceptibility mapping in the study area, East Sikkim district of Sikkim Himalaya. The different thematic layers were produced from high-resolution terrain corrected ALOS PALSAR DEM of 12.5 meter spatial resolution, Sentinel-2A data of 10 meter spatial resolution multi-spectral satellite information, LANDSAT 8 multi-spectral satellite information and multiple other landslide-related sources such as rainfall distribution, slope and structural/linear features (faults, thrusts, roads). These thematic map layers were integrated in a GIS platform (ArcGIS10.7) to delineate vulnerable landslide prone zones. The weighted assigned values were used for assigning weightage ranging from 0 to 10 for various causative factors responsible for landslide occurrences using standard weighted overly techniques. Landslide susceptibility map of the entire research area is split into three categories i.e. low susceptibility, medium susceptibility and high susceptibility. The final map of the landslide susceptibility was further validated with GPS location information gathered from the field survey of active landslide locations. This research would be helpful in the study region for adequate planning of future development of infrastructure, landslide hazard prevention, and geo-environmental development.

1. Introduction

Landslide is a matter of serious concern all around the globe 1, 2. Landslide is defined as movement of a mass of rock, debris or earth materials down the slope in the influence of gravity 3. In terms of worldwide importance landslide holds the third position/rank among all the disasters in the world 4, 5. In the international scenario some of the most affected country in the world by landslides are China, Brazil, Indonesia, India, Nepal, Bangladesh and Vietnam etc. 6, 7. According to the various researches China, India, Nepal and Phillipines are the most affected countries by landslides based on the number of events and associated losses 8. Indian sub-continent is not an exception to landslide disaster as the Indian Himalayan Region (IHR) is critically vulnerable to landslides. Hilly terrains of India have always been the regular victim of the landslides with a huge loss of lives and properties. Landslides are one of the natural hazards affecting large part of India, particularly the Himalayas and the other hilly areas of the country. Landslides trigger different problems, i.e. the loss of human life, animal life, infrastructure and financial circumstances of the region. Indian Himalayan region (IHR) is having most complexed topography, also one of the youngest mountain chains of the world. From east to west it extends over 2400 km and from north to south width varies from 220 km to 330 km 9. Landslide has attracted the world wide attention due to the increasing human settlements in the mountainous regions and also because of growing consciousness of the societal economic impact 10. Landslides could be triggered under several conditions, e.g. intense rainfall, earthquake shaking, variation of water level, snowmelt, typhoon etc. 11, 12, 13, 14, 15, 16. Landslides are generally influenced by the action of structural, lithological, geomorphological, climatic, environmental, hydrological, seismological conditions etc. of the affected area. The present study area lies in the Sikkim Himalaya. Hundreds of landslides triggered due to the extended 1968 monsoon in and around the region of Sikkim and Darjeeling. Over 33,000 human lives were lost as a result of this incidence and caused enormous losses to property and infrastructure 17. The catastrophy of 1968 is considered one of the worst disasters of the century. The terror of 1968 is still alive in the mindset of the elderly people of the affected area. In the past Sikkim has undergone heavy loss of lives (human and livestock) due to landslides and similar losses takes place every year, though the severity varies with the intensity and location of the event. It also causes serious environmental impact in the affected areas. During monsoon months, the landslide intensity and the frequency increase. Most of the landslides triggers due to the super saturation of the slope forming materials. Landslides in the mountain regions are induced due to the construction of roads and the highways 18. With increase in the population the modern economic activities also increases in the mountainous region, thus paving a way for landslide to take place 19. The greatest damages which landslides effects are the transportation system i.e. roads and the highways which directly or indirectly affects a large number of people in the hilly terrain 20. Apart from this loss, other essential facilities like water supply, power and the communication are also being affected by the landslide activities. The different conditions which cause number of mass movements in Himalayan region are extreme rainfall in the monsoon season, geology, frequent tremors and the anthropogenic interferences 21, 22. Different geological factors contribute to the occurrence of landslide 23, at the same time it is also very important to understand that only by knowing the nature of the rock type alone, it is not possible to explain and predict the landslide 24. Numerous earlier researchers have proposed and had seen the potential of remote sensing and GIS technologies for LHZ studies 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36. Considering all the factors it is very necessary to prepare an updated Landslide susceptibility map for the study area. For the present study six very important factors are considered for triggering of landslides i.e. rainfall, slope angle, structural features, drainage density, lithology and land use/land cover. With the help of GIS environment, Landslide susceptibility map (LSM) prepared using thematic layers for each factor. This work has got much significance because thematic layers are prepared in high resolution remote sensing satellite data which is up to 10 m spatial resolution using Sentinel-2A multispectral satellite imagery. Such LSM would be very useful for the planners and the decision makers for taking better preventive measures and minimize the losses due to the slope failures.

2. Study Area

The present study carried out in the East Sikkim district (Figure 1) of the Sikkim Himalaya of Indian Himalayan region which lies in between N 27.274° - N 27.322° and E 88.778° - E 88.732°. The study area falls on the Survey of India (SoI) toposheet no. 78A/07; 78A/08; 78A/11; 78A/12; 78A/15 and 78A/16 covering a total area of 946.443 km2. The capital city of the state Gangtok lies in East Sikkim. The study area is also the most populous district among the four districts in the state. Due to the increasing population trend and the rapid urbanization in the hilly terrains the slopes here are at high risk. NH 10 which is the life line of Sikkim is the major road in the East Sikkim district. Due to reoccurring of slope failures NH 10 along with the other roads are badly affected and the state suffers huge economic losses especially during the monsoon months. Tourism which is the main source of economy usually hampered due to the road failure especially during the monsoon period. The state with the other agricultural products produces a good contribution of cardamom in the country. These agricultural products are also badly affected by the landslides. The climate of the region is broadly temperate and mainly governed by the monsoon. The study area receives rainfall throughout the year, the highest rainfall recorded between May to September. The maximum slope failure incidences are reported during this time of the year. July receives the highest rainfall. Elevation in the study area varies from 252 m to 4673 m and the temperature ranges between 16-25°C. The area also lies in the seismic zone IV and the incidents like earthquake and landslides are very common. The geological formations are as old as of Proterozoic which is characterized by biotite gneiss, mica schist, sillimanite granite gneiss, quartzite, garnet, kyanite sillimanite, biotite schist, garnetiferous mica schist, calc-silicate, carbonaceous schist, granite gneiss, interbanded chlorite-sericite schist, phyllite and quartzite, meta greywacke (quartzo feldspathic greywacke), pyritiferous black slate, biotite phyllite and mica schist 37. The study area is mostly covered by phyllites, schists, quartzites and gniesses.

3. Materials and Methods

The present work is framed in three parts i.e. pre-field analysis and interpretation, field work and post field analysis and interpretation. In the pre-field analysis the study area has been selected and relevant prerequisites data were collected. Survey of India (SoI) toposheets, rainfall data from the Center for Hydrometeorology and Remote Sensing (CHRS). For LULC classification map generation, Sentinel-2A multispectral satellite image were used. ALOS PALSAR DEM of 12.5 meters spatial resolution was utilized for slope map, drainage map and drainage density map, for the study area. Major roads, faults and thrusts were downloaded from Bhuvan Portal of NRSC. The automated lineament extraction from LANDSAT 8 Multispectral satellite data of 30 meters spatial resolution (Bands 2, 3, 4) downloaded from USGS Earth Explorer was used using PCI Geometica software. In the field work part extensive field surveys carried out in multiphases for the field check and identification of different features which were directly identified in the field. These features include identification of landslide sites and assessing the damage caused, identification of geology, geomorphology, slope, lineament, landuse/landcover, environmental losses etc. The field photographs of the some of the major landslides in the study area are shown in the Figure 2. The detailed landslide inventory of 15 major landslides in the study area is shown in Table 1. In the post-field interpretation, analysis of the data from the pre-field and the field study is carried out. Six major causative factors were taken for the present study. These factors are rainfall, slope, structure, lithology, land use/land cover and drainage density. There are no rules to consider the trigger factors in the mapping of landslide susceptibility, but the characteristics of the study area and the availability of data, lead towards the selection of thematic layers to be used 38, 39. Based on the features of the study region, six parameters mentioned below were regarded to be the main causative variables for landslides and their thematic maps for modeling were prepared in GIS.

3.1. Rainfall

Rainfall is a trigger factor for landslides and is closely linked to the trend of landslides 19, 40 as maximum landslide incidences take place during the heavy precipitation in the form of rainfall. A landslide becomes more destructive with the advent of the rainfall. The average rainfall recorded in the study area during the last one decade is 2473 mm out of which 1966 mm was the rainfall during May-September. Annual average rainfall data of 16 years from January 2003 to December 2018 was downloaded from the Center for Hydrometeorology and Remote Sensing (CHRS) which are based on PERSIANN-Cloud Classification System (PERSIANN-CCS), a real-time global high resolution (0.04° x 0.04° or 4km x 4km;) satellite precipitation product developed at the University of California, Irvine 41. These data were then converted from pixel to point in GIS environment and then generated the rainfall distribution map of the study area by IDW (inverse distance weighted) interpolation method (Figure 3).

3.2. Slope

The Slope map was extracted from Radiometrically Terrain-Corrected (RTC) ALOS PALSAR DEM of 12.5 meters spatial resolution was downloaded from Alaska Satellite Facility which were then resampled in GIS environment to 10 meters and were classified into three classes: 0°-30°, 30°-60° and 60°-81° (Figure 4) These classes represent the inclinations of the slope in the entire study area. Slope is an important factor determining the slope failures as steeper slopes are more prone to landslides.

3.3. Structure

The structure in this context is all the linear or the lineament structure located in the study area. The location of linear structure in the study area has been found affecting the stability of slope as many slope failures were existed in the vicinity of lineament structure in the study area. To determine the effect of lineaments on the slope failure activity it is very crucial to identify the lineament structures. The lineament map is shown in the Figure 5.

3.4. Lithology

An area's lithology is strongly linked to the occurrence of landslides as the intensity of the located lithology affects the stability of the slope. In the East Sikkim lithology is divided into five classes namely Daling group (mainly phyllites, schists, quartzites, etc.), Central Crystalline (mainly gneissic rocks , schists, etc.), Chungthang formation (mainly quartzites, schists, etc.), Lingtse gneiss (granite gneiss) and Permafrost area (mainly snow covered area) 37, 42 shown in Figure 6. Most of the slope failures are located in Daling group and Central crystalline as these are weak group of rocks.

3.5. Land Use/Land Cover (LULC)

Based on the land use/land cover, study area is divided into six classes: agricultural land, forest cover, built up area, water body, glacier/ice and barren rock (Figure 7). LULC is a very prominent parameter for any kind of landslide study. It is quite obvious that the vegetated land are much more stable than the barren land, because the roots of the vegetation cover holds the soil firmly and prevent the soil from movement and erosion. The area with dense vegetation is less prone to landslides with comparison to little or no vegetation 43. With the rapid urbanization in the hilly areas, the infrastructural and developmental activities are taking place very fast, making the slope highly vulnerable to landslides. For the present study barren lands are considered more prone to landslides.

3.6. Drainage Density

The drainage density of the study area is categorised into three classes i.e. low drainage density, medium drainage density and the high drainage density (Figure 8). Drainage density is always a crucial factor in determining slope failures. The area with the high drainage density has always the larger possibility of the slope failure. The area with higher drainage density has the higher water pressure which can fail easily compared to the area with the less drainage density. Also the area with high drainage density contains high moisture content which accelerates the weathering phenomenon and consequently vulnerable to landslides 44.

4. Results and Discussion

For the generation of landslide susceptibility map of the study area six parameters were considered i.e. rainfall, slope, structure, lithology, land use/land cover and drainage density. Using the weightage rating system, a landslide susceptibility map of the study area was prepared. Each class inside a thematic layer was allocated an ordinal rating from 0-10 as also adopted by 44 used in present study. Discussion of each parameter and assignment of their respective weights are carried as follows:

4.1. Rainfall

The rainfall map was created using CHRS PERSIANN-CCS satellite precipitation data developed at the University of California, Irvine 41. Based on this data, the study area is divided into four classes considering the amount of rainfall: 400-500 mm, 500-600mm, 600-700mm and 700-800mm. Due to the high-intensity rainfall, the study region is well known for frequent occurrence of landslides 45. The different class score and the final score given for the different class of rainfall based on the expert judgement (Table 2). High rainfall in the hilly region always increases the probability; hence high score is given for the same.

4.2. Slope

Slope is the connection between vertical elevation and horizontal run, expressed as a proportion of a slope from "toe" to "top." Slope map prepared from the ALOS PALSAR DEM of 12.5 meters spatial resolution is divided into three classes based on the slope angle. The classes are: 0°-30°, 30°-60° and 60°-81°. Based on the expert judgement the class score is given for these classes. Here the maximum score for the class is given for 30°-60° range. According to 46, the plane on which sliding takes place must strike parallel or almost parallel to the face of the slope (within roughly ±20). So this condition best suitable for the slope class 30°-60°, therefore maximum weightage is given for this class (Table 3). The other reason why this class is given the maximum weightage is the condition that water movement is the most frequent slope instability mechanism. The higher the steepness of the slope, the more probable it is that instead of infiltrating, rain will run off. Moreover, the steeper the slope, the quicker the water travels.

4.3. Structure

For the structural map three factors are considered for the present study, these are road, stream and fault. These three parameters are very significant in determining the slope failures of an area. The research area is tectonically active and numerous structural faults that enable the flowing water in the local drainages to divert its route; offering a simple mean of percolation into the rocks that further reduces their power and induces slope material motion. Therefore, it is essential to consider faults as one of the LSM factor. In order to evaluate the impact of the geological structures in the study area, faults buffer taken for 1000 m and the corresponding weight were assigned (Table 4). The closeness of the current landslides in the study region to the road network in the study area was determined by plotting 500 m buffer displaying the effect of road expansions and movement along the highways on study region slopes and the corresponding weight allocated to them. Similarly streams are important inducing factors for the landslides to take place. A stream buffer of 100 m taken for the present study and respective weight allocated (Table 4)

4.4. Lithology

Lithology is one of the key parameters for mapping landslide susceptibility. The study area contains a wide variety of rock units, grouped into five broad classifications i.e. Central crystalline, Daling group, Chungthang formation, Lingtse gneiss and Permafrost area. Lithology reflects the shear strength, permeability, weather sensitivity and other rock and soil characteristics that are accountable for the failure of the slope 47. The field observation shows that phyllites are extremely prone to landslide as they have undergone high weathering. The phyllites' flaky and fragile nature makes it susceptible to slipping due to triggering variables such as cloudburst, blasting, road building, etc. As found along the approach roads, the slides over phyllites leave a pile of clay and mica powdered over these roads and makes the approach very sticky and slippery following a downpour. Phyllitic lithology has typical features that it loses its shear strength by 25 percent and shows quenching-swelling properties when interacting with water, which gradually leads to rock instability 48. This is the typical nature of phyllites. Nevertheless, the slides with substrates of comparatively harder rock kinds such as quartzites dolomitic-calcareous, etc., show rolling rocks, sometimes large enough to block vehicle traffic and harm approach highways, energy lines, agricultural property, etc. Depending on all these factors, the highest weight for causative factor is given to Daling group and accordingly to other classes (Table 5).

4.5. Land Use/Land Cover

Agricultural land, forest cover, built up area, water body; glacier/ice and barren rock were demarcated for the study area. The underlying types, topography and hydrology control the pattern of land-use. Human settlements are predominantly in or around the shallow water areas. Agricultural practices are prevented and limited to low-relief fields underpinned by fragile rock formations such as schists, phyllite, weathered gneisses and broken quartzites. Forest on steeper or mild slopes is more frequent. Land cover, in particular vegetation, plays a significant part in the stabilization of slopes and landslides. Different kinds of LULC directly affect the occurrence of landfills by regulating the stability of the path 49. Anthropogenic activities have a significant impact on the landslide's reactivation or initiation 50, 51. Forest is the major land cover and agricultural land is the major land use in the study area. The different weights assigned based on the expert judgement are given in the Table 6.

4.6. Drainage Density

Mapping of the drainage and its drainage density is very important for the landslide susceptibility mapping of any study area. The value of drainage density measurements tells how well a drainage area is drained by the drainages and it is measured quantitatively as a proportion of channel width and basin region 52. Rozos et al., 53 used the parameter of drainage density and cited the indirect measurement of groundwater conditions in mountainous regions that play an active part in causing landslides. Field findings show that in the study area there is an increased incidence of landslides owing to erosion along the convex banks at sharp meander convexities of the Teesta River. The streams at higher altitudes are non-destructive in nature, but there are instances where landslides occur when roads are built across the small streams. The drainage density was extracted using the DEM information drainage network and categorized into three groups i.e. medium, low and high density (Table 7). It is quite obvious that the area with the high drainage have the maximum probability of slope failure.

4.7. Landslide Susceptible Zonation

The final landslide susceptibility map (LSM) was prepared and classified into low, medium and high susceptible areas by combining all control parameters and by providing distinct weights for all variables (Table 8). Detailed LSM of the study area is shown in Figure 9.

Landslide hazard index (LHI) for each grid cell is determined in the attribute table as per the following formula proposed by 54.

For the present study depending upon the conditions of the study area maximum weight value is given to the rainfall because the maximum landslide incidences are reported during the heavy rainfall followed by slope, structure, lithology, LU/LC and drainage density. The different classes of susceptibility map are outlined in detail below.


4.7.1. High Susceptible Zone

This zone is extremely sensitive geologically and is constantly threatened by landslides, especially during and after a severe downpour spell. This is because the region includes steep slopes with loose and unconsolidated materials, and proof of active or previous landslides is available. The area also involves regions close to faults and tectonically fragile regions. It also covers area where road cutting and other human operations are frequent. The high susceptible zone is therefore found predominantly in barren rocks. Because the high susceptible zone is extremely prone to landslides, no human-induced activity is suggested. This area also involves regions where, through weathered rock and soil debris, the probability of sliding debris is high. It includes an area of steep slopes that are susceptible to landslides when disturbed. Most of the landslides that pre-exist fall within this area. There may be significant instability within the area during and after an intense rainfall spell. The high risk area is also traversed by several lineaments, broken areas and fault planes. Areas that are constantly eroded by streams due to the smooth nature of the lithology and the loose overlying burden fall in this category. The high susceptible zone is also geologically volatile, and slope failure of any kind can be caused especially after heavy rain. This zone occupies 122.26 sq. km which is 13% of the entire study area. Such regions must be completely prevented for settlement or other developmental purposes and preferably left to regenerate natural vegetation in a timely manner to achieve natural stability.


4.7.2. Medium Susceptible Zones

This area includes regions with moderately thick vegetation, low angle of slope and relatively compact rocks. Although this area may include regions with steep slopes, the slope is less dangerous due to the orientation of the bedrock and the lack of overlying loose debris and human activities. The moderate susceptible zone within the study region is well spread and covers 571.54 sq.km which is 59.32% in the study area. Human settlements are common in this region.


4.7.3. Low Susceptible Zones

This area involves regions where it is usually unlikely that the combination of different control parameters will adversely affect the stability of the slope. The vegetation is comparatively thick and the angles of slopes are usually less than 30°. This area is primarily restricted to regions where there is less or no anthropogenic activity. There is no proof of instability within this area as far as the risk element is concerned, and mass movement is not anticipated unless major changes happen. Although in some areas the lithology may consist of soft rocks and overlying soil debris, due to low angle of slope the chances of slope failure are minimal. There are no instances of instability within this area as far as the risk element is concerned. This area is therefore appropriate for the implementation of development activities.

4.8. Validation

Landslide events that happened in the study region between 2017 to 2019 were taken for validation. The final susceptibility map of landslide prepared is overlain by recorded landslide occurrences and observed that out of 106 total landslide data 83% events fall on the high susceptible zone and medium susceptible zone and 17% events fall on the low susceptible zone (Figure 10).

Thus we have seen that weighted overlay techniques based on the expert opinion proves to be very useful in preparing landslide susceptibility map. Different researchers have worked using such techniques in the past 43, 55, 56, 57, 58, 59 but what makes this work more significant is the use of high resolution satellite imagery, and because of which a very good amount of accuracy achieved in the present study.

5. Conclusions

In our present study an accuracy of 83% for landslide susceptible zonation mapping was achieved. Almost all the landslides have been reported in the rainy season. The research shows that land use/land cover, rainfall, slope, drainage density, structure and lithology play an important role in landslide triggering. The ranking of the conditioning factors based on the present analysis and the landslide hazard index is highest for rainfall followed by structures, lithology, slope, LU/LC and drainage density. The total study area is divided into three susceptible zones i.e. Low, medium and the high susceptible zones comprising of 28%, 59% and 13% respectively. The methodology described here for landslide susceptible mapping includes generating thematic information layers, developing an appropriate numerical rating system, integrating spatial data and validating outcomes. It is analyzed that GIS application is extremely helpful for the generation of thematic information and their spatial data analysis, involving complex tasks. The numerical rating system enables to enhance performance assessment and optimization. Since the contributing variables to the landslide differ from region to region, however this rating may not apply to other areas of the Himalayas 60. During the implementation of a construction of project on the ground, the landslide susceptible mapping can assist in decision-making. Avoiding the high hazard area is always better, but if that is not feasible, corrective measures must be developed to minimize the likelihood of landslide events. It is also observed that through the afforestation schemes existing landslides can be recovered and potential landslides can be prevented. One of the common vegetation species found in the study area Alnus Nepalensis can be used as bio-engineering measures to minimise and prevent the landslides and other slope failures. The species grows rapidly in any kind of environment and holds the soil from preventing the slope from failure and erosion. The awareness regarding slope failures among the local dwellers is another important thing through which we can educate the villagers and minimize the loss of lives and properties.

Acknowledgements

The authors are very thankful to GBPNIHESD for financial support through GBPIHED project sanction letter No.: GBPI/IERP/14-15/06/43 dated 23 June 2015 and Dean, USEM, Guru Gobind Singh Indraprastha University, New Delhi for providing the facility in carrying out the research work.

Conflict of Interest

The authors declare that they have no conflict of interest.

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[29]  Kumar KV, Nair RR, Lakhera RC. (1993). Digital image enhancement for delineating active landslide areas. Asia-Pacific Remote Sensing Journal, 6(1), 63-66.
In article      
 
[30]  Lazzari M, Salvaneschi P. (1999). Embedding a Geographic Information System in a decision support system for landslide hazard monitoring. Natural Hazards, 20, 185-195.
In article      View Article
 
[31]  Lee S, Ryu JH, Won JS, Park HJ. (2003). Determination and application of the weights for landslide susceptibility mapping using an artificial neural network. Engineering Geology, 71(3-4), 289-302.
In article      View Article
 
[32]  Michael-Leiba M, Baynes F, Scott G, Granger K. (2003). Regional landslide risk to the Cairns community. Natural Hazards, 30, 233-249.
In article      View Article
 
[33]  Pieraccini M, Casagli N, Luzi G, Tarchi D, Mecatti D, Noferini L, Atzeni C. (2003). Landslide monitoring by ground-based radar interferometry: a field test in Valdarno (Italy). International Journal on Remote Sensing, 24(6), 1385-1391.
In article      View Article
 
[34]  Sakellariou MG, Ferentinou MD. (2001). GIS-based estimation of slope stability. Natural Hazards Rev 2(1), 12-21.
In article      View Article
 
[35]  Van Westen CJ, Getahun Lulie F. (2003). Analyzing the evolution of the Tessina landslide using aerial photographs and digital elevation models. Geomorphology, 54, 77-89.
In article      View Article
 
[36]  Yamaguchi Y, Tanaka S, Odajima T, Kamai T, Tsuchida S. (2003). Detection of a landslide movement as geometric misregistration in image matching of SPOT HRV data of two different dates. International Journal of Remote Sensing, 24(18), 3523-3534.
In article      View Article
 
[37]  Geology and Mineral Resources of Sikkim. (2012). Geological Survey of India Miscellaneous Pulication No.30, part XIX- Sikkim. Published by order of Government of India.
In article      
 
[38]  Ayalew L, Yamagishi H. (2005). The application of GIS based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko mountains Central Japan. Geomorphology 65 (1): 15-31.
In article      View Article
 
[39]  Sharma S, Mahajan AK. (2018). Information value based landslide susceptibility zonation of Dharamshala region, northwestern Himalaya, India. Spatial Information Research, 1-12.
In article      
 
[40]  Wang Q, Li W, Chen W, Bai H. (2015). GIS-based assessment of landslide susceptibility using certainty factor and index of entropy models for the Qianyang County of Baoji city, China. Journal of Earth System Science, 124(7):1399-1415.
In article      View Article
 
[41]  UCI (2019) https://chrs.web.uci.edu/resources.php.
In article      
 
[42]  Rawat MS (2015) Geo-environmental studies in a part of east sikkim with special reference to landslide. Ph.D thesis submitted to H. N. B. Garhwal University, Srinagar, Uttarakhand, India.
In article      
 
[43]  Gokceoglu C, Aksoy H, (1996). Landslide susceptibility mapping of the slopes in the residual soils of the Mengen region (Turkey) by deterministic stability analyses and image processing techniques. Engineering Geology. 44 (4): 147-161.
In article      View Article
 
[44]  Barman BK, Srinivasa Rao K, (2019) Landslide hazard susceptibility mapping of upper Tuirial watershed, Mizoram using Remote Sensing and GIS techniques. International Journal of Research and Analytical Reviews, 6 (1): 1624-1630.
In article      
 
[45]  Sharma AK, Joshi V, Kumar K. (2011). Landslide hazard zonation of Gangtok area, Sikkim Himalaya using remote sensing and GIS techniques. Journal of Geomatics, 5(2), 87-88.
In article      
 
[46]  Wyllie DC, Mah CW. (2005). Rock slope engineering—civil and mining, 4th edn. Spon.
In article      
 
[47]  Joshi V, Murthy TVR, Arya AS, Narayana A, Naithani AK, Garg JK. (2003). Landslide hazard zonation of Dharasu–Tehri–Ghansali area of Garhwal Himalaya, India using remote sensing and GIS techniques. Journal of Nepal Geological Society, 28, 85-94.
In article      
 
[48]  Saraf AK, Das J, Sharma K, Borgohain S, Naithani NP. (2018). Prospect of Early Detection of Earthquake and Reservoir Induced Landslides. Abstract volume of “International Conference on Climate Change and Disaster Risk Reduction” Oct. 26-28, 2018, 09-10.
In article      
 
[49]  Greenway DR. (1987). Vegetation and slope stability. In M. G. Anderson & K. S. Richards (Eds.), Slope stability (pp. 187-230). New York: Wiley.
In article      
 
[50]  van Den Eeckhaut M, Poesen J, van Gils M, van Rompaey A, Vandekerckhove L. (2009). How do humans interact with their environment in residential areas prone to landsliding? A case study from the Flemish Ardennes. Proceedings of the international conference on “landslide processes: from geomorphologic mapping to dynamic modelling” (19-24) Strasbourg, France, 6-7.
In article      
 
[51]  Vanacker V, Vanderschaeghe M, Govers G, Willems E, Poesen J, Deckers J, De Bievre B. (2003). Linking hydrological, infinite slope stability and land-use change models throughGIS for assessing the impact of deforestation on slope stability in high Andean watersheds. Geomorphology, 52, 299-315.
In article      View Article
 
[52]  Horton RE. (1945). Erosional development of streams and their drainage basins: Hydro physical approach to quantitative morphology. Geological Society of America Bulletin, 56, 275-370.
In article      View Article
 
[53]  Rozos D, Bathrellos GD, Skillodimou HD. (2011). Comparison of the implementation of rock engineering system and analytic hierarchy process methods, upon landslide susceptibility mapping, using GIS: a case study from the Eastern Achaia County of Peloponnesus, Greece. Environmental Earth Sciences, 63(1), 49-63.
In article      View Article
 
[54]  Pandey A, Dabaral PP, Chowdary VM, Yadav NK. (2007). Landslide Hazard Zonation using Remote Sensing and GIS: a case study of Dikrong river basin, Arunachal Pradesh, India. Environmental Geology 54(7), 1517-1529.
In article      View Article
 
[55]  Basharat M, Shah HR, Hameed N. (2016). Landslide susceptibility mapping using GIS and weighted overlay method: a case study from NW Himalayas, Pakistan. Arabian Journal of Geosciences, 9(4), 292.
In article      View Article
 
[56]  Gurugnanam B, Bagyaraj M, Kumaravel S, Vinoth M, Vasudevan S. (2012). GIS based weighted overlay analysis in landslide hazard zonation for decision makers using spatial query builder in parts of Kodaikanal taluk, South India. Journal of Geomatics, 6(1), 49.
In article      
 
[57]  Roslee R, Mickey AC, Simon N, Norhisham MN. (2017). Landslide susceptibility analysis (LSA) using weighted overlay method (WOM) along the Genting Sempah to Bentong Highway, Pahang. Malaysian Journal of Geosciences (MJG), 1(2), 13-19.
In article      View Article
 
[58]  Sadr MP, Hassan, H, Maghsoudi A. (2014). Slope Instability Assessment using a weighted overlay mapping method, A case study of Khorramabad-Doroud railway track, W Iran. Journal of Tethys, 2(3), 254-271.
In article      
 
[59]  Shit PK, Bhunia GS, Maiti R. (2016). Potential landslide susceptibility mapping using weighted overlay model (WOM). Modeling Earth Systems and Environment, 2(1), 21.
In article      View Article
 
[60]  Rawat MS, Uniyal DP, Dobhal R, Joshi V, Rawat BS, Bartwal A, Aswal A. (2015). Study of landslide hazard zonation in Mandakini Valley, Rudraprayag district, Uttarakhand using remote sensing and GIS. Current Science, 158-170.
In article      
 

Published with license by Science and Education Publishing, Copyright © 2020 Prakash Biswakarma, Binoy Kumar Barman, Varun Joshi and K. Srinivasa Rao

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Normal Style
Prakash Biswakarma, Binoy Kumar Barman, Varun Joshi, K. Srinivasa Rao. Landslide Susceptibility Mapping in East Sikkim Region of Sikkim Himalaya Using High Resolution Remote Sensing Data and GIS techniques. Applied Ecology and Environmental Sciences. Vol. 8, No. 4, 2020, pp 143-153. http://pubs.sciepub.com/aees/8/4/1
MLA Style
Biswakarma, Prakash, et al. "Landslide Susceptibility Mapping in East Sikkim Region of Sikkim Himalaya Using High Resolution Remote Sensing Data and GIS techniques." Applied Ecology and Environmental Sciences 8.4 (2020): 143-153.
APA Style
Biswakarma, P. , Barman, B. K. , Joshi, V. , & Rao, K. S. (2020). Landslide Susceptibility Mapping in East Sikkim Region of Sikkim Himalaya Using High Resolution Remote Sensing Data and GIS techniques. Applied Ecology and Environmental Sciences, 8(4), 143-153.
Chicago Style
Biswakarma, Prakash, Binoy Kumar Barman, Varun Joshi, and K. Srinivasa Rao. "Landslide Susceptibility Mapping in East Sikkim Region of Sikkim Himalaya Using High Resolution Remote Sensing Data and GIS techniques." Applied Ecology and Environmental Sciences 8, no. 4 (2020): 143-153.
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[22]  Sarkar S, Roy AK, Martha TR. (2013). Landslide susceptibility assessment using information value method in parts of the Darjeeling Himalayas. Journal of the Geological Society of India, 82(4), 351-362.
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[25]  Casson B, Delacourt C, Baratoux D, Allemand P. (2003). Seventeen years of the ‘‘La Clapie`re’’ landslide evolution analysed from ortho-rectified aerial photographs. Environmental Geology,68, 123-139.
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[26]  Fraser A, Huggins P, Rees J, Cleverly P. (1997). A satellite remote sensing technique for geological structure horizon mapping. International Journal of Remote Sensing,18(7), 1607-1615.
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[27]  Glassey P, Barrell D, Forsyth J, Macleod R. (2003). The geology of Dunedin, New Zealand, and the management of geological hazards. Quaternary International, 103(1), 23-40.
In article      View Article
 
[28]  Herva´s J, Barredo JI, Rosin PL, Pasuto A, Mantovani F, Silvano S. (2003). Monitoring landslides from optical remotely sensed imagery: the case history of Tessina landslide, Italy. Geomorphology, 54, 63-67.
In article      View Article
 
[29]  Kumar KV, Nair RR, Lakhera RC. (1993). Digital image enhancement for delineating active landslide areas. Asia-Pacific Remote Sensing Journal, 6(1), 63-66.
In article      
 
[30]  Lazzari M, Salvaneschi P. (1999). Embedding a Geographic Information System in a decision support system for landslide hazard monitoring. Natural Hazards, 20, 185-195.
In article      View Article
 
[31]  Lee S, Ryu JH, Won JS, Park HJ. (2003). Determination and application of the weights for landslide susceptibility mapping using an artificial neural network. Engineering Geology, 71(3-4), 289-302.
In article      View Article
 
[32]  Michael-Leiba M, Baynes F, Scott G, Granger K. (2003). Regional landslide risk to the Cairns community. Natural Hazards, 30, 233-249.
In article      View Article
 
[33]  Pieraccini M, Casagli N, Luzi G, Tarchi D, Mecatti D, Noferini L, Atzeni C. (2003). Landslide monitoring by ground-based radar interferometry: a field test in Valdarno (Italy). International Journal on Remote Sensing, 24(6), 1385-1391.
In article      View Article
 
[34]  Sakellariou MG, Ferentinou MD. (2001). GIS-based estimation of slope stability. Natural Hazards Rev 2(1), 12-21.
In article      View Article
 
[35]  Van Westen CJ, Getahun Lulie F. (2003). Analyzing the evolution of the Tessina landslide using aerial photographs and digital elevation models. Geomorphology, 54, 77-89.
In article      View Article
 
[36]  Yamaguchi Y, Tanaka S, Odajima T, Kamai T, Tsuchida S. (2003). Detection of a landslide movement as geometric misregistration in image matching of SPOT HRV data of two different dates. International Journal of Remote Sensing, 24(18), 3523-3534.
In article      View Article
 
[37]  Geology and Mineral Resources of Sikkim. (2012). Geological Survey of India Miscellaneous Pulication No.30, part XIX- Sikkim. Published by order of Government of India.
In article      
 
[38]  Ayalew L, Yamagishi H. (2005). The application of GIS based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko mountains Central Japan. Geomorphology 65 (1): 15-31.
In article      View Article
 
[39]  Sharma S, Mahajan AK. (2018). Information value based landslide susceptibility zonation of Dharamshala region, northwestern Himalaya, India. Spatial Information Research, 1-12.
In article      
 
[40]  Wang Q, Li W, Chen W, Bai H. (2015). GIS-based assessment of landslide susceptibility using certainty factor and index of entropy models for the Qianyang County of Baoji city, China. Journal of Earth System Science, 124(7):1399-1415.
In article      View Article
 
[41]  UCI (2019) https://chrs.web.uci.edu/resources.php.
In article      
 
[42]  Rawat MS (2015) Geo-environmental studies in a part of east sikkim with special reference to landslide. Ph.D thesis submitted to H. N. B. Garhwal University, Srinagar, Uttarakhand, India.
In article      
 
[43]  Gokceoglu C, Aksoy H, (1996). Landslide susceptibility mapping of the slopes in the residual soils of the Mengen region (Turkey) by deterministic stability analyses and image processing techniques. Engineering Geology. 44 (4): 147-161.
In article      View Article
 
[44]  Barman BK, Srinivasa Rao K, (2019) Landslide hazard susceptibility mapping of upper Tuirial watershed, Mizoram using Remote Sensing and GIS techniques. International Journal of Research and Analytical Reviews, 6 (1): 1624-1630.
In article      
 
[45]  Sharma AK, Joshi V, Kumar K. (2011). Landslide hazard zonation of Gangtok area, Sikkim Himalaya using remote sensing and GIS techniques. Journal of Geomatics, 5(2), 87-88.
In article      
 
[46]  Wyllie DC, Mah CW. (2005). Rock slope engineering—civil and mining, 4th edn. Spon.
In article      
 
[47]  Joshi V, Murthy TVR, Arya AS, Narayana A, Naithani AK, Garg JK. (2003). Landslide hazard zonation of Dharasu–Tehri–Ghansali area of Garhwal Himalaya, India using remote sensing and GIS techniques. Journal of Nepal Geological Society, 28, 85-94.
In article      
 
[48]  Saraf AK, Das J, Sharma K, Borgohain S, Naithani NP. (2018). Prospect of Early Detection of Earthquake and Reservoir Induced Landslides. Abstract volume of “International Conference on Climate Change and Disaster Risk Reduction” Oct. 26-28, 2018, 09-10.
In article      
 
[49]  Greenway DR. (1987). Vegetation and slope stability. In M. G. Anderson & K. S. Richards (Eds.), Slope stability (pp. 187-230). New York: Wiley.
In article      
 
[50]  van Den Eeckhaut M, Poesen J, van Gils M, van Rompaey A, Vandekerckhove L. (2009). How do humans interact with their environment in residential areas prone to landsliding? A case study from the Flemish Ardennes. Proceedings of the international conference on “landslide processes: from geomorphologic mapping to dynamic modelling” (19-24) Strasbourg, France, 6-7.
In article      
 
[51]  Vanacker V, Vanderschaeghe M, Govers G, Willems E, Poesen J, Deckers J, De Bievre B. (2003). Linking hydrological, infinite slope stability and land-use change models throughGIS for assessing the impact of deforestation on slope stability in high Andean watersheds. Geomorphology, 52, 299-315.
In article      View Article
 
[52]  Horton RE. (1945). Erosional development of streams and their drainage basins: Hydro physical approach to quantitative morphology. Geological Society of America Bulletin, 56, 275-370.
In article      View Article
 
[53]  Rozos D, Bathrellos GD, Skillodimou HD. (2011). Comparison of the implementation of rock engineering system and analytic hierarchy process methods, upon landslide susceptibility mapping, using GIS: a case study from the Eastern Achaia County of Peloponnesus, Greece. Environmental Earth Sciences, 63(1), 49-63.
In article      View Article
 
[54]  Pandey A, Dabaral PP, Chowdary VM, Yadav NK. (2007). Landslide Hazard Zonation using Remote Sensing and GIS: a case study of Dikrong river basin, Arunachal Pradesh, India. Environmental Geology 54(7), 1517-1529.
In article      View Article
 
[55]  Basharat M, Shah HR, Hameed N. (2016). Landslide susceptibility mapping using GIS and weighted overlay method: a case study from NW Himalayas, Pakistan. Arabian Journal of Geosciences, 9(4), 292.
In article      View Article
 
[56]  Gurugnanam B, Bagyaraj M, Kumaravel S, Vinoth M, Vasudevan S. (2012). GIS based weighted overlay analysis in landslide hazard zonation for decision makers using spatial query builder in parts of Kodaikanal taluk, South India. Journal of Geomatics, 6(1), 49.
In article      
 
[57]  Roslee R, Mickey AC, Simon N, Norhisham MN. (2017). Landslide susceptibility analysis (LSA) using weighted overlay method (WOM) along the Genting Sempah to Bentong Highway, Pahang. Malaysian Journal of Geosciences (MJG), 1(2), 13-19.
In article      View Article
 
[58]  Sadr MP, Hassan, H, Maghsoudi A. (2014). Slope Instability Assessment using a weighted overlay mapping method, A case study of Khorramabad-Doroud railway track, W Iran. Journal of Tethys, 2(3), 254-271.
In article      
 
[59]  Shit PK, Bhunia GS, Maiti R. (2016). Potential landslide susceptibility mapping using weighted overlay model (WOM). Modeling Earth Systems and Environment, 2(1), 21.
In article      View Article
 
[60]  Rawat MS, Uniyal DP, Dobhal R, Joshi V, Rawat BS, Bartwal A, Aswal A. (2015). Study of landslide hazard zonation in Mandakini Valley, Rudraprayag district, Uttarakhand using remote sensing and GIS. Current Science, 158-170.
In article