Sujit Mandal

Assessment of mountain slope instability in the Lish River basin of Eastern Darjeeling Himalaya using frequency ratio model (FRM)

Landslides are the most destructive natural hazards that undermine the economic and cultural development in Darjeeling Himalaya. To prepare landslide susceptibility map of the Lish River basin of Eastern Darjeeling Himalaya frequency ratio model (FRM) was applied based on remote sensing and GIS tools and an integration of ten landslide triggering parameters or factors like Geomorphology, lithology, slope angle, slope aspect, slope curvature, drainage density, NDVI, Relative Relief, land use and land cover (LULC) and soli map were made. The frequency ratio model (FRM) was used to derive class frequency ratio or class weight incorporating both pixels with and without landslides and to determine the relative importance of individual classes. All the data layers were prepared in consultation with SOI Topo-sheet (78B/9), Google earth image, Landsat 8 OLI (operational land imager), GDEM (Global Digital Elevation Model) image with the help of Arc View and ARC GIS Software (10.1). A Weighted linear combination model was performed to combine frequency ratio/class rating values and to determine the landslide susceptibility index (LSI) value on GIS software tools. Greater the value of ‘LSI’, higher is the propensity of landslide susceptibility over the space. Then, the Lish River basin was classified into six landslide susceptibility zones, i.e., very low, low, moderate, moderately high, high and very high considering the ranges of LSI.

Landslide susceptibility mapping using modified information value model in the Lish river basin of Darjiling Himalaya

Abstract The spatial distribution of mountain slope instability deals with the potential zones for landslides occurrences. In the present study, information value model was modified to make the modified information value model using RS & GIS to assess landslide susceptibility of the Lish river basin of Eastern Darjeeling Himalaya. Eleven important causative factors of slope instability like slope, aspect, curvature, lithology, geomorphology, soil, NDVI, drainage density, relative relief, LULC, elevation were considered and corresponding thematic data layers were generated in Arc GIS (10.1) environments. 87 very small to large various types landslide locations were identified with the help GPS through extensive field survey and incorporating Google earth image (2015). The entire thematic data layers were extracted from ASTER GDEM, Topographical maps (78 B/9; 1: 50,000), LANDSAT 8 OLI satellite image, Google earth image (2015) etc. All the thematic data layers were integrated on GIS environment to generate the landslide susceptibility map of the study area. The Lish river basin was classified into six landslide susceptibility zones i.e. very low, low, moderate, moderately high, high and very high considering the ranges of landslide susceptibility index. Finally, an accuracy assessment was done in Arc GIS by ground truth verification of 54 training sites having landslides from Google earth image (2015) for each landslide susceptibility class and compared with probability model which demonstrates the overall accuracy of the present study is 87.04% and Kappa coefficient is 84.41%.

RS & GIS-based landslide susceptibility mapping of the Balason River basin, Darjeeling Himalaya, using logistic regression (LR) model

ABSTRACT The present study deals with the preparation of a landslide susceptibility map of the Balason River basin, Darjeeling Himalaya, using a logistic regression model based on Geographic Information System and Remote Sensing. The landslide inventory map was prepared with a total of 295 landslide locations extracted from various satellite images and intensive field survey. Topographical maps, satellite images, geological, geomorphological, soil, rainfall and seismic data were collected, processed and constructed into a spatial database in a GIS environment. The chosen landslide-conditioning factors were altitude, slope aspect, slope angle, slope curvature, geology, geomorphology, soil, land use/land cover, normalised differential vegetation index, drainage density, lineament number density, distance from lineament, distance to drainage, stream power index, topographic wetted index, rainfall and peak ground acceleration. The produced landslide susceptibility map satisfied the decision rules and −2 Log likelihood, Cox & Snell R-Square and Nagelkerke R-Square values proved that all the independent variables were statistically significant. The receiver operating characteristic curve showed that the prediction accuracy of the landslide probability map was 96.10%. The proposed LR method can be used in other hazard/disaster studies and decision-making.

Application of frequency ratio (FR) model in spatial prediction of landslides in the Balason river basin, Darjeeling Himalaya

The present study aims to prepare landslide susceptibility map using frequency ratio (FR) model in the Balason river basin of Darjeeling Himalaya. Along with keeping in mind that, can the produced landslide susceptibility map yields acceptable landslide prediction accuracy or not. For that reliable landslide inventory map was prepared with a total of 295 landslide locations. The chosen landslide conditioning factors were altitude, aspect, slope angle, curvature, geology, geomorphology, soil, land use/land cover, normalized differential vegetation index, drainage density, lineament density, distance from lineament, distance to drainage, stream power index and topographic wetted index. To estimate FR value for each class of all the landslide conditioning factors pixels affected by landslide (%) and total pixels (%) were taken into account. FR model was applied to integrate all the data layers on GIS platform. The derived susceptibility map was divided into five categories i.e. very low, low moderate, high and very high which cover 17.52, 18.71, 30.35, 24.49 and 8.92% area of the basin respectively. The area under curve value of receiver operating characteristics indicates the prediction accuracy of the prepared map was 94.2% that is highly desirable. FR plots represent that there were positive relationship between landslide susceptibility classes and FR value. The prepared map will helping developers and planners to implement slope management plans, land use plans and other development action plans in this area.

Modeling and mapping landslide susceptibility zones using GIS based multivariate binary logistic regression (LR) model in the Rorachu river basin of eastern Sikkim Himalaya, India

Multivariate binary logistic regression (LR) model was used for the assessment of landslide susceptibility in the Rorachu river basin of eastern Sikkim Himalaya. For this purpose, a spatial database of 13 factors such as rainfall, slope, aspect, curvature, relief, drainage density, distance from drainage, distance from lineament, distance from road, geology, soil, Normalized Difference Vegetation Index (NDVI), and land use/land cover was constructed under Geographical Information System (GIS) environment. A landslide inventory map was prepared and converted into binary raster coded by 0 for absence and 1 for the presence of landslide. Total 946 landslide pixels were found out of which 725 landslide pixels (76.63%) were used as training dataset for the model and the model was validated using all landslide pixels. The coefficient value of geology was maximum followed by NDVI, soil and land use/land. The calculated probability value was used as Landslide Susceptibility Index (LSI). On the basis of the LSI value, the landslide susceptibility map was divided into five distinct categories of very low, low, moderate, high and very high susceptibility. The susceptibility classes depict that, 9.18% of total area was under very high and high susceptibility contains 75.48% area of total landslide. Finally the accuracy of the model was assessed by area under curve (AUC) of receiver operating characteristics (ROC) curve and landslide density method. The AUC value of 0.947 indicates a very good quality of the model and landslide density shows that the density is increasing with LSI classes.

Monitoring forest dynamics and landslide susceptibility in Mechi–Balason interfluves of Darjiling Himalaya, West Bengal using forest canopy density model (FCDM) and Landslide Susceptibility Index model (LSIM)

Mapping and monitoring of forest extent is a common requirement of regional forest inventories and public land natural resource management in India. The present study involves the assessment of forest dynamicity using forest canopy density model and the preparation of Landslide Susceptibility Index map using Landslide Susceptibility Index model in Mechi–Balason interfluves of Mirik Block of Darjiling Himalaya to find out the relationship between areal coverage of the forest and landslides. The forest canopy density model and forest pattern map of the study was prepared considering different forest parameter such as Bareness Index, Scaled Shadow Index, NDVI, fractional vegetation cover and forest canopy density on GIS platform. All these parameter were integrated applying Multi Criteria Decision Making Approach and Rank Sum Method to develop regeneration, degeneration and unchanged forest area map of the study area. Finally this forest map was incorporated with landslide susceptibility value to draw a conclusion about the role of forest cover in landslide. It is found that the Landslide Susceptibility Index value is higher in the degenerated portion of the forest and LSI is lower in the regenerated portion of the forest. So it can be concluded that landslide can be one of the major factor of forest dynamicity apart from this the other natural and anthropogenic factors may responsible for the forest dynamicity of the study area.

Artificial Neural Network (ANN) Model and Landslide Susceptibility

The present study is dealt with the preparation of landslide susceptibility map of Darjeeling Himalaya with the help of GIS tools and artificial neural network (ANN) model. Fifteen landslide causative factors, i.e. elevation, slope aspect, slope angle, slope curvature, geology, soil, lineament density, distance to lineament, drainage density, distance to drainage, stream power index (SPI), topographic wetted index (TWI), rainfall, normalized differential vegetation index (NDVI) and land use and land cover (LULC) were considered to produce the landslide susceptibility zonation map. To generate all these aforesaid causative factors map, topographical maps, geological map, soil map, satellite imageries, Google earth images and some other authorized maps were processed and constructed into a spatial data base using GIS and image processing techniques. The back-propagation method was applied to estimate factor’s weight and the landslide hazard indices were derived with the help of trained back-propagation weights. Then, the landslide susceptibility zonation map of Darjeeling Himalaya was made using GIS tool and classified into five, i.e. very low, low, moderate, high, and very low landslide susceptibility. To validate the prepared landslide susceptibility map, landslide inventory was used and accuracy result was obtained after processing ROC curve. The accuracy of the landslide susceptibility map was 81.5% which is desirable.

Weighted Overlay Analysis (WOA) Model, Certainty Factor (CF) Model and Analytical Hierarchy Process (AHP) Model in Landslide Susceptibility Studies

The present study is dealt with the application of weighted overlay analysis (WOA) model, certainty factor (CF) model, analytical hierarchy process (AHP) model for the preparation of landslide susceptibility zonation map of Darjeeling Himalaya. To perform three models, various data layers with regard to elevation, slope aspect, slope angle, slope curvature, geology, soil, lineament density, distance to lineament, drainage density, distance to drainage, stream power index (SPI), topographic wetted index (TWI), rainfall, normalized differential vegetation index (NDVI) and land use and land cover (LULC) were taken into account. For the preparation of various data layers, topographical maps, Google earth images, SRTM DEM, http://www.worldclim.org, satellite image (Landsat TM) and some authorized data were being processed on GIS environment (ArcMap 10.1). The prepared landslide susceptibility maps of Darjeeling Himalaya were classified into five, i.e. very low, low, moderate, high, and very high landslide susceptibility. To validate three landslide susceptibility zonation maps derived from WOA, CF, and AHP models, ROC Curve and frequency ratio plot methods were incorporated. ROC curve showed the level of accuracy of each landslide susceptibility map. The study revealed that WOA, CF, and AHP were with the accuracy level of 65.4%, 81.2%, and 67.5%. Frequency ratio plots sugessted that moderate, high, and very high landslide susceptibility zones in Darjeeling Himalaya are experienced with greater probability landslide phenomena.

Knowledge-Driven Statistical Approach for Landslide Susceptibility Assessment Using GIS and Fuzzy Logic (FL) Approach

The present study is dealt with the application of fuzzy logic and preparation of landslide susceptibility zonation map of Darjeeling Himalaya on GIS environment. To accomplish fuzzy logic, several data layers such as elevation, slope, aspect, curvature, drainage density, distance to drainage, lineament density, distance to lineament, lithology, land use and land cover, soil, stream power index (SPI), topographic wetness index (TWI), and rainfall were made in consultation with topographical map, Google earth images, satellite imageries, and some other authorized maps. For computing fuzzy membership value and developing the model frequency ratio and cosine amplitude, values were derived corresponding to each class of the landslide causative factor. Then, fuzzy gamma operator value of 0.975 was used to prepare landslide susceptibility zonation map of Darjeeling Himalaya considering frequency ratio and cosine amplitude membership value. The accuracy study based on ROC curve revealed that the FR membership value based fuzzy gamma operator and landslide susceptibility map having the accuracy result of 80.9% and cosine amplitude membership value based landslide susceptibility having the validation result of 67.9%.

Comparison Between Statistical Models: A Review and Evaluation

The development of various models and their application in studies have brought a significant change in the subject discipline of geography. In the present study, various geomorphic and geohydrologic parameters, i.e. elevation, slope aspect, slope angle, slope curvature, geology, soil, lineament density, distance to lineament, drainage density, distance to drainage, stream power index (SPI), topographic wetted index (TWI), rainfall, normalized differential vegetation index (NDVI) and land use and land cover (LULC) were considered, and their integration was made on GIS environment to prepare landslide susceptibility zonation map of Darjeeling Himalaya, India. To generate all data layers, Google earth imagery, toposheet and GPS field survey data (2015–2016); geological and soil map; SRTM DEM (30 m spatial resolution); Landsat TM Image, Feb. 2009 (30 m spatial resolution), rainfall data (1950–2010) and some other information were processed with the help of GIS. To integrate all the data layers and to prepare landslide susceptibility map, several models such as frequency ratio (FR) model, modified information value (MIV) model, logistic regression (LR) model, artificial neural network (ANN) model, weighted overlay analysis (WOA) model, certainty factor (CF) model, analytical hierarchy process (AHP) model and fuzzy logic (FL) approach were applied. The prepared landslide susceptibility maps using all the models were classified into five, i.e. very low, low, moderate, high, and very high. All the developed landslide susceptibility maps of Darjeeling Himalaya were being validated using receiver operating characteristics (ROC) curve). The study concluded that artificial neural network model (ANN), certainty factor (CF) model, and frequency ratio-based fuzzy logic approach are most reliable statistical techniques in the assessment and prediction of landslide susceptibility in Darjeeling Himalaya because of high level of accuracy in comparison to models applied in the study.