A Comparative Study between Weighted Overlay ...

Environmental Degradation and Disaster : A Vision Plan for Sustainable Development

Conclusion From the above discussion it becomes clarified that MMES has done their level best in the name of conserving and promoting ecotourism in Manas National Park since the inception of their organization. The role and contribution in restoring the glory of the park is a didactic model to entire people of the world in giving lesson and sensitizing them on the importance of having sustainable environment by conserving flora and fauna in the forest. What they have done is indeed a matter of glorification and appreciation as the past glory of Manas National Park, to certain extent, may not be brought back in their absence. It also makes us realized that, the participation of local people is of utmost importance in order to save the forest from the poachers and timber smugglers. It should also be notified that the park was endangered in the list of World Heritage site since 1992 immediately after the declaration of its National Park status because of rampant destruction done to it. However, with the untiring efforts and various activities dedicated by the volunteers of MMES in the name of conservation and promotion of ecotourism along with the help from forest department of BTC, the park re-gained the status of World Heritage Site in the year 2011 from being endangered (Basumatary, Chandra Kanta, Halur, 2014).

References 1. Data Source: Manas Maozigendri Ecotourism Society (MMES), Records maintained. 2. Halur, (2014) A souvenir of the 22nd National Children’s Science Congress. 3. Paul AK & Narzary B (2011) Let the World Know about Bodoland, Repaired Edition, 2011, Published by G.B.D’s, Guwahati. Printed at Technoprint, Pathsala, Assam, pp.37-39. 4. Sharma, Dutta V et al., (2009), Environmental Education and Disaster Management, CBS; 1ST edition, New Delhi. 5. Website: http://baksa.gov.in/manas.html, Date of visit: 25th May 2016 6. Website: https://en.wikipedia.org/wiki/Manas_National_Park, Date of visit: 25th May 2016

A Comparative Study between Weighted Overlay Model (WOM) and Frequency Ratio Model (FRM) to Assess Landslide Susceptibility in the Lish River basin of Eastern Darjeeling Himalaya Biplab Mandal* and Sujit Mandal* Introduction Landslides and floods are the most serious natural problems that undermine the economic and cultural development of the Lish basin. Records since 1929 show a sharp acceleration in the rate of devastating slide occurrences (total no. 135 covering an area of 1.5 sq.km) along with lesser slips leading to great loss of life and heavy damage to land and property. The situation has deteriorated further in recent times, the last two decades having witnessed the worst landslides on hill-slopes (total no. 64 covering an area of 4.52 sq.km). The destructions and damages of settlements, communication lines, tea garden area, and agricultural land are the common phenomena in Darjiling Himalaya which triggers to environmental disasters. The changes in the land use character in the mountain slope are caused due to these environmental hazards. The upper surface of the Darjiling Himalaya is composed of heavily disintegrated and decomposed materials. The accumulation of the materials and its increasing weight as a result of drainage concentration and saturation caused due to continuous rainfall for few days basically invites slope failure in the southern escarpment slope of Darjiling Himalaya. Bbasu and Ghatwar (1988) studied in detail the causes and consequences of landslides in Darjiling Himalaya. The devastating landslip of June, 2015 and destruction of lives and properties in Darjiling caused as a result of few day continuous heavy rainfall. RS & GIS based landslide hazard zonation approach had been studied by Muthu and Petrou (2007); and Caiyan and Jianping (2009). Rowbothan and Dudycha, 1998; Donati and Turrini 2002; Lee et al., 2004b; Lee and Pradhan, *

Research Scholar, Department of Geography, University of Gour Banga Email: [email protected] ** Associate Professor and Head, Department of Geography, University of Gour Banga Email: [email protected]

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2006, 2007; Sarkar and Kanungo (2004); Pande, Dabral, Chowdhury and Yadav (2008); and Nithya and Prasanna (2010) and Mandal and Maiti (2011) have studied and applied the probabilistic model for landslide susceptibility and risk evaluation. Guzzetti et al. (1999) summarized many landslide hazard evaluation studies. Jibson et al. (2000); and Zhou et al. (2002) applied the probabilistic models for landslide risk and hazard analysis. Atkinson and Massari, (1998); introduced the logistic regression model for landslide hazard mapping. Landslide Susceptibility mapping using either multivariate or bivariate statistical approach analyzes the historical link between landslide controlling factors and the distribution of landslides (Guzzetti et al., 1999). Besides, there are also a lot of model in landslide susceptibility mapping, such as frequency ratio, artificial neural network, decision tree, weights of evidence, fuzzy logic (Bagherzadeh and Mansouri Daneshvar, 2012; Ilanloo, 2011; Poudyal et al., 2010; Pourghasemi et al., 2013; Tsai et al., 2013).Lee and Sambath (2006), Lee and Pradhan, (2007) opined that frequency ratio model provides a correlation between the historical slide locations and various influencing factors under consideration. Intarawichian and Dasananda (2011) applied frequency ratio model to analyze slope instability and ascribed the model as a popular quantitative method. In the present study area remote sensing Technique and GIS tools are used on nine landslide inducing parameter like lithology, geomorphology, soil, relief, slope angle, slope aspect, slope curvature, drainage density, NDVI, landuse and land coverture assess the magnitude of susceptibility to landslide and its spatial distribution. The quantitative analysis of landslide inducing attributes like slope, aspect, amplitude of relief, drainage density, lithology, Geomorphology and landuse is of great significance for the scientific management of mountain river basin. Preparation of Landslide Zonation Map is an important technique which figure out spatial distribution of landslides and helps to take site specific proper remedial measures in a rational manner. In the present study the interaction of different factors are studied separately and ultimately final coordination is made through Landslide Potentiality Index Value (LPIV) and Landslide Susceptibility Index Value (LSIV).

A Comparative Study between Weighted Overlay Model

way it receives at least 75 tributaries; important among them are the ChunKhola, Phang-Khola, Lish-Nadi, Turung-Khola, Rato-Khola, Tik-khola, PabringKhola, Lama-Khola and the Char-Khola etc. Materials and Methods A landslide susceptibility map is prepared applying remote sensing technique and GIS tool to show the spatial distribution of the interactions of causative factors, and to draw a relationship between range of individual factors and magnitude of landslide for understanding the relative importance of the factor with the help of LPIV (Landslide Potentiality Index Value).The digitization of all the landslide inducing factors are made and the concerned data layers are prepared based on SOI Topo-sheetusing Arc View and ARC GIS Software. Firstly, the contour map is prepared and digitized from the SOI Topo-sheet and is subsequently transformed to Digital Elevation Model or to GRID/Raster Surface at 30×30 m resolution of the corresponding Satellite Image LANDSAT (2015). Finally, slope, curvature and aspect maps are prepared from DEM and designed in value domain using filtering technique. The lithological map of the concerned study area is prepared after NATMO Kolkata (Eastern Region). Drainage density map (the length of drainage in km/sq.km) is prepared on the grid resolution of 30×30 m.

The Study Area Lish river basin, covering a geographical area 51.72 sq. km. is a sub basin of Tista river basin which is located mainly Eastern Darjeeling Himalaya (Fig.1). It is extended between 26° 52' 30'' N to 27° 00' 00'' N latitudes and 88° 30' 00'' E to 88° 34' 30'' E longitudes. The River Lish originating from Lalegoan (lat. 26° 59' N and long. 88° 33' E) at the altitude of 1820 m traverses a distance of about 21.20 km to join the mighty river Tista at Shaugoan (lat. 26°49' N and 88°33' E). On the

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Fig.1: Location Map of the Study Area

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All the generated maps are reclassified with weighted values depending on their degree of magnitude to landslide occurrences. Finally, two landslide susceptibility maps were carved out applying Weighted Overlay Analysis Model and Frequency ratio model. Landslide Susceptibility by WOM = [Lithology*21 + Drainage Density *14+Slope Angle*13 + Elevation*12 + Slope Curvature*10 + Relative Relief*08 +Slope Aspect*07 +NDVI*06+Soil*05+Geomorphology*04]………………(eq.2) To obtain frequency ratio (FR) for each class of all the data layers a combination has been established between landslide inventory map and criterion maps using the following principle and finally landslide susceptibility coefficient values were derived and susceptibility map was made.. eq.3

Fig.2: Landslide Inventory Map of the Lish river basin. Land use/land cover map is prepared evaluating LANDSAT image data, SOI Topo-Sheet using supervised classification technique and following maximum likelihood method. To bring out the degree of importance of the triggering factors, Landslide Potentiality Index Value [LPIV] for each range of the concerned factors is calculated. × 100—-———————— (1) = number of pixels/cells or grid without landslide

: The number of pixels containing slide in each class (i), : Total number of pixels having class (i) in the whole watershed : Total number of pixels containing landslide, : Total number of pixels in the whole area of the watershed. To obtain landslide susceptibility index (LSI), frequency ration of each range/ class of all the landslide triggering factors were summed (eq.) after Pradahan, 2010.

= number of pixels/cells or grid with landslide. To extract the number of pixels with and without landslide, a landslide occurrence map (Fig.2) is prepared evaluating SOI Topo-sheet and Satellite Image LANDSAT digitized and converted into raster values through ARC GIS Software.

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…………………...eq.4 Where, LSI= Landslide susceptibility index; Fr= Frequency ratio/ rating to each class/range of landslide triggering factor.

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Results and Discussion Weighted Overlay Model and landslide susceptibility The zone of very susceptible to catastrophic slope failure is located at Northern part of the Lish river basin Maximum of the existing landslides are also located in those areas and thus demanding more attention from the habitants, planners and administrators (Fig.3). The area already affected by huge landslide and so immediate attention is needed for site specific slope management for these regions. Study shows that around 22% area of the Lish river basin is attributed with frequency ration value of more than ‘1’ which indicates greater the chances of landslide probability in the same area. 56.55 % are of the basin is registered with moderate landslide susceptibility with frequency ration value of 1.03. High landslide susceptible area of 25.59 % is registered with the frequency ratio of 1.22 which shows high landslide probability.

Fig.3: Landslide Susceptibility Mapping of the Lish River basin using Weighted Overlay Model

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Frequency Ratio Model and Landslide Susceptibility Landslide Susceptibility map of the Lish river basin is classified into 6 categories such as very low, low, moderate, moderately high, high and very high. The study revealed that there is a positive relationship between Landslide Susceptibility and Frequency ratio. Lower the Frequency Ratio value means, the low probability of landslide. On the other hand higher the Frequency Ratio value, grater the probability of landslide. Maximum Landslide occurs in moderate (26.72%) to moderately high (25.30%) landslide susceptibility classes covering near about 52% of the total landslides affected area. So the Lish River is moderately high landslide prone basin. The Lish river basin is dominated by moderate landslide susceptibility which is followed by moderately high, low, high, very low and very high (Fig.16). The derived success rate curve revealed that more than 60% area is under moderate to moderately high landslide susceptibility (Fig.4).

Fig.4: Landslide Susceptibility Mapping of the Lish River basin using Frequency Ratio Model

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Conclusion To minimize the damage caused due to landslides, the identification of potential landslide zones is of utmost important. In the present study, the application of frequency ratio model and preparation of landslide susceptible zones will provide a great support to planner and policy makers for ensuring further developmental activities in the Lish river basin. The relationship between all the factors map, landslide inventory map and landslide susceptibility map of the Lish river basin revealed that steep slope segment, moderate to high drainage density, high positive and high negative slope curvature, fragile lithology, southsouth east and south west facing slope, and settlement area are attributed with high frequency ration and high landslide susceptibility. Mountain slope segment having all these characteristics are to be avoided for further construction for development so that future environmental hazards and disasters can be arrested. References Atkinson PM, Massari R (1998) Generalized linear modeling of susceptibility to landsliding in the central Apennines, Italy. Computer & Geosciences 24:373–385 Basu SR, Ghatwar L (1988) Landslide in the Lish Basin of the Eastern Himalayas and their Control, Proceeding of the International Symposium on Geomorphology and Environmental Management, Allahabad. January 17-20(1987), 428-443 (1989) Bagherzadeh A, MansouriDaneshvar MR (2012) Mapping of landslide hazard zonationusing GIS at Golestan watershed, northeast of Iran. Arabian Journal of Geosciences 6: 3377-3388 Caiyan WU and Jianping Q (2009) Relationship between landslides and lithology in the Three Gorges Reservoir area based on GIS AND Information Value Model. Higher Education Press and Springer-Verlag 4(2): 165-170

A Comparative Study between Weighted Overlay Model Ilanloo M (2011) A comparative study of fuzzy logic approach for landslide susceptibility mapping using GIS: An experience of Karaj dam basin in Iran. Procedia - Social and Behavioral Sciences 19: 668-676 Jibson WR, Edwin LH, John AM (2000) A method for producing digital probabilistic seismic landslide hazard maps. Engineering Geology (58) pp. 271–289 Lee S, Pradhan B (2006) Landslide hazard assessment at Cameron Highland Malaysia using frequency ratio and logistic regression models. Geophy Res Abstracts 8: SRef ID: 1607-7962/gra/EGU06-A-03241 Lee S, Pradhan B (2007) Landslide hazard mapping at Selangor, Malaysia using frequencyratio and logistic regression models. Landslides 4(1): 33-41 Lee S, Choi J and Min K (2004b) Probabilistic landslide hazard mapping using GIS and remote sensing data at Boun, Korea. International J Remote Sensing 25:2037 2052 Mandal S and Maiti R (2011) Landslide Susceptibility Analysis of Shivkhola Watershed, Darjeeling: A Remote Sensing & GIS Based Analytical Hierarchy Process (AHP). JIndian Soc. Remote Sensing DOI 10.10007/s 12524-011-0160-9 Muthu K, Petrou M (2007) Landslide hazard mapping using anExpertSystem and a GIS. Transactions on Geoscience and Remote Sensing 45 (2):522-531 Nithya ES, Prasanna RP (2010) An Integrated Approach with GIS and Remote Sensing Technique for Landslide Zonation. International Journal of Geomatics and Geosciences 1(1) Pandey A, Dabral PP, Chowdhary VM and Yadav NK (2008) Landslide Hazard ZonationUsing Remote Sensing and GIS: A Case study of Dikrong river basin, Arunachal Pradesh, India. Environ Geol. 54: 1517-1529 Rowbotham D, Dudycha DN (1998) GIS Modelling of slope stability in Phewa Tal Watershed, Nepal. Geomorphology 26:151-170

Donati L, Turrini MC (2002) An objective and method to rank the importance of the factors predisposing to landslides with the GIS methodology, application to an area of the Apennines (Valnerina; Perugia, Italy). Engineering Geology 63 (3-4): 277–289

Sarkar S, Kanungo DP (2004) An Integrated Approach for Landslide SusceptibilityMapping Using Remote Sensing and GIS. Photogrammetric Engineering & Remote Sensing 70(5): 617-625

Guzzetti F, Carrara A, Cardinali M and Reichenbach P (1999) Landslide Hazard Evaluation: A review of current techniques and their application in a multi-scale study, Central Italy. Journal of Geomorphology 31: Elsevier, London, pp. 181-216

Zhou CH, Lee CF, Li J, and Xu ZW (2002) On the spatial relationship between landslide and causative factors on Lantau Island, Hong Kong. Geomorphology 43: 197-207

Intarawichian N, Dasananda S (2011) Frequency Ratio model based landslide susceptibility mapping in lower Mae Chaem watershed, Northern Thailand. Environ Earth Sci 64:2271-2285

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