Landslide Susceptibility Index Map Research Articles

Landslide susceptibility evaluation in the Beas River Basin of North-Western Himalaya: A geospatial analysis employing the Analytical Hierarchy Process (AHP) method

In the North-Western Himalayas, particularly within the Beas Basin of Himachal Pradesh, landslide incidents are frequent, primarily due to the unique interplay of adverse geological conditions, heavy rainfall, and human factors. These incidents result in substantial loss of life and property each year. To mitigate such issues, systematic landslide research is essential, encompassing aspects like inventory mapping and risk assessment. This study leverages the Analytical Hierarchy Process (AHP) for an in-depth Landslide Susceptibility Index (LSI) mapping in the Beas River basin, employing remote sensing data to analyze key factors contributing to the region's instability. The process involved a detailed selection and mapping of landslide conditioning variables, guided by validated landslide inventory data and high-resolution remote sensing images, ensuring an accurate representation of the basin's geographical variations. The creation of the landslide susceptibility map utilized the weighted overlay approach, categorizing the area into five levels of susceptibility: very low, low, moderate, high, and very high. This classification incorporated ten critical factors influencing landslide occurrence, including elevation, slope aspect, slope angle, distance from drainage, lithology, distance from lineament, geomorphology, rainfall, and land use/land cover (LULC). The LSI was calculated through the Weighted Linear Combination (WLC) technique, leveraging the weights and ratings derived via the AHP method. This analysis revealed that approximately 634.1 square kilometers, or 12.8% of the region, face very high landslide susceptibility, followed by 22.6% at high, 25.8% with moderate, 24.4% at low, and 14.5% at very low susceptibility. The LSI map's accuracy in predicting landslides was affirmed through Receiver Operating Characteristic (ROC) and Area Under Curve (AUC) evaluations, showcasing an 86.3% precision rate. This classification facilitates focused interventions in high-risk areas, guiding planners in landslide-conscious development and infrastructure planning. It directs engineers toward engineering solutions like slope stabilization and drainage improvements to mitigate landslide effects. Moreover, this approach supports the creation of evacuation and emergency response plans, bolstering community resilience to landslide threats in the river basin.

Assessment of Landslide Susceptibility using the AHP and GIS Techniques for the Zurbatiya Region, East of Iraq

The present study adopts AHP and GIS techniques to produce a landslide susceptibility index map of the Zurbatiya region. The research area is located in eastern Iraq, within Wasit Governorate. The local terrain predispositions were represented by the landslide conditioning parameters that were chosen: slope angles, geology, slope aspect, topographic elevation, distance from faults, and distance from valleys. The spatial resolution used for the raster-based study is 12.5×12.5 m. The AHP scale determines the relative importance of each element, with the slope angle having the largest weight (9) then geology and slope (7), aspect and elevation (5), and distance from faults and distance from valleys having the lowest relative importance (3). Depending on the landslide susceptibility index map, the low landslide susceptibility class has the highest percentage (41.2%) for the whole region, followed by the moderate landslide susceptibility class, which its share (39.9%), then the high landslide susceptibility class, with its share (14.8%), where, the very high share (3.9%). The landslide susceptibility index map was tested within the fieldwork stage in which 14 stations are selected within these four zones (low, moderate, high, and very high) where stations 1, 3, 4, 6 and 7 are distributed within the very high zone, station (8) within the high zone, stations 2, 9, 10 and 12 within the moderate zone and stations 11,13 and 14 within the low zone. Finally, it can be seen that they are an exact match when comparing the landslide susceptibility index map with the fieldwork stage, which is based on the previous failures and which are expected to occur. Toppling and Rock fall found within in the study area, in addition to the rolling that occurs later for the separated blocks by failure.

Landslide susceptibility mapping of Penang Island, Malaysia, using remote sensing and multi-geophysical methods

Malaysia is one of the countries in the world experiencing landslides yearly due to natural events and human activities. Penang Island is Malaysia’s second most developed state and the largest by population. It is prone to landslides with devastating environmental impacts. Hence, the need to characterize its near-surface soil-rock conditions. This study uses remotely sensed data via frequency ratio (FR) techniques to identify landslide-prone areas based on different categories of landslide causative factors. To further understand the conditions and hydrodynamics of the soil-rock profiles causing landslides, electrical resistivity tomography and seismic refraction tomography were carried out at a landslide-suspected section in the study area. Also, the satellite-derived Bouguer gravity anomaly modeling was performed to map the varied gravity anomalies associated with landslide-triggering factors in lithologic units. The multi-geophysical models offer strongly correlated results with the causative remote sensed maps and the landslide susceptibility index (LSI) map. The likelihood of landslides occurring in the area, as suggested by the area under curve modeling of LSI data, yielded a high predicted success rate of 83.47%. Hence, prospective landslides were identified at the hilly and elevated sections, while the less susceptible sections were identified on flat reliefs. Landslides may also be triggered, for instance, at steep sections with varied contractive soil bodies and shallow structures. Most importantly, leveraging the LSI map would help the necessary agencies to forestall and mitigate future landslide occurrences in the area.

Landslide susceptibility prediction using frequency ratio model: a case study of Uttarakhand, Himalaya (India)

The purpose of this study is to develop a landslide susceptibility prediction model by applying the Frequency Ratio (FR) model and remote sensing data sets for the Northern part of Uttarakhand, India. First, a landslide inventory was carried out from the interpretation of satellite images. Thereafter, the landslide inventory points were randomly separated into training and validation datasets. Subsequently, the significant landslide causative factors such as slope, lithology, lineament density, land use/land cover, drainage density, aspect, elevation, road buffer, normalized differential vegetation index (NDVI), stream power index, and topographic wetness index were identified to run the model set up. Next, applying the FR statistical model in a GIS environment for development of landslide susceptibility index map and divided into five distinct landslide susceptibility zones (very low, low, moderate, high, and very high). To validate the results, the Receiver Operating Characteristics (ROC) curve were developed to check the accurrancy of the model, and it was observed that the prediction value of the FR model was reasonably accurate (86.1% at 95% confidence level). The output LSI map would be helpful for the government and planners to map and monitor potential landslide areas and mitigate the hazards.

Assessing landslide susceptibility using geospatial technology in Bonga town, southwestern Ethiopia

ABSTRACT This study aims to investigate the main factors that contribute to frequent landslides in Bonga town using GIS and remote sensing. To this end, a frequency ratio model in integration with geospatial technology was applied. The first input parameter (landslide inventory data) was collected and randomly categorized into training and validation. The second input parameters were causative landslide factors such as slope, aspect, curvature, elevation, land use, lithology, stream distance, and distance to the road. The contributing rates of landslide causative factors were calculated using the frequency ratio model based on causative factors and landslide training data. The study found that distance to the road, distance to the stream, elevation, and aspect were the most important factors for the occurrence of landslides in Bonga town. Land use and slope were the moderate contributing factors to landslide occurrence, while curvature and lithology were the least important contributing factors. The landslide susceptibility index map shows that the largest portion of the study area falls under the moderate (42.3%,) and high (34.3%) susceptibility zones. Hence, different intervention strategies for saving lives and assets should be developed swiftly based on the vulnerability level of each site.

Large-scale Mapping of Landslide and Debris Flow using Flowr Model with Statistical and Machine Learning Methods

The main purpose of this article is to establish a susceptibility zonation map of the landslides and debris flows in Phin Ngan commune, Bat Xat district, Lao Cai province on a large scale using statistical methods and machine learning combined with the FlowR model. First, the five Landslide Susceptibility Index (LSI) maps were established from two statistical models (Logistic Regression - LR, Discriminant Analysis – DA) and three machine learning models (Bayesian Network – BN, Artificial Neural Network – ANN, Support Vector Machine – SVM) were generated based on seven maps of landslide conditioning factors (slope, curvature, stream power index-SPI, topographic wetness index-TWI, sediment transportation index-STI, land use/land cover and weathering crust). Next, the five LSI maps will be evaluated for performance with the value of Area Under the Curve (AUC) according to the Receiver Operating Characteristic (ROC) curve. After that, a susceptibility map of debris flow established with FlowR software was combined with the five LSI maps created from five statistical and machine learning methods to generate a susceptibility zonation map of landslides and debris flows in the study area. The area percentage of the locations with ​​landslides and debris flows located in the zones of susceptibility (very low, low, medium, high, very high), which were created from five combined methods: BN-FlowR, LR-FlowR, DA-FlowR, ANN-FlowR, and SVM-FlowR, were compared and evaluated. The results indicate that the integrated models have given outputs with good forecasting ability. They are also very useful in land-use planning as well as the prevention and mitigation of risks due to landslides and debris flows in the research area and other similar mountainous areas.

Study on landslide susceptibility mapping based on rock\u2013soil characteristic factors

This study introduces four rock–soil characteristics factors, that is, Lithology, Rock Structure, Rock Infiltration, and Rock Weathering, which based on the properties of rock formations, to predict Landslide Susceptibility Mapping (LSM) in Three Gorges Reservoir Area from Zigui to Badong. Logistic regression, artificial neural network, support vector machine is used in LSM modeling. The study consists of three main steps. In the first step, these four factors are combined with the 11 basic factors to form different factor combinations. The second step randomly selects training (70% of the total) and validation (30%) datasets out of grid cells corresponding to landslide and non-landslide locations in the study area. The final step constructs the LSM models to obtain different landslide susceptibility index maps and landslide susceptibility zoning maps. The specific category precision, receiver operating characteristic curve, and 5 other statistical evaluation methods are used for quantitative evaluations. The evaluation results show that, in most cases, the result based on Rock Structure are better than the result obtained by traditional method based on Lithology, have the best performance. To further study the influence of rock–soil characteristic factors on the LSM, these four factors are divided into “Intrinsic attribute factors” and “External participation factors” in accordance with the participation of external factors, to generate the LSMs. The evaluation results show that the result based on Intrinsic attribute factors are better than the result based on External participation factors, indicating the significance of Intrinsic attribute factors in LSM. The method proposed in this study can effectively improve the scientificity, accuracy, and validity of LSM.

Landslide susceptibility assessment using frequency ratio technique – A case study of NH67 road corridor in the Nilgiris district, Tamilnadu, India

The purpose of this study is to develop landslide susceptibility mapping for NH67 road corridor in the Nilgiris district of Tamilnadu, India, using the Geographic information system (GIS) and multivariate statistical approach. In November 2009, North-East monsoon triggered numerous landslides in the Nilgiris district of Tamilnadu, resulting in a high death toll and considerable damage to property. The landslide-controlling parameters namely lithology, soil type, slope gradient, normalized difference vegetation index (NDVI), proximity to faults, and proximity to road, rainfall and streams were extracted from the spatial database, and the weight coefficient of each factor was computed. Then the landslide hazard was analyzed using the frequency ratio method. For verification of the model, the results of the analyses were then compared with the field verified landslide locations. It was concluded that about 3.86% and 16.23 % of the study area are in the very high and high susceptible zone. Based on this, landslide susceptibility map was then classified as very high, high, moderate, low and very low. In the course of model validation, the results were validated using the ROC curve. The AUC of Frequency ratio model was 67.5%. Therefore, the landslide susceptibility index map of the study area is considered valuable for the decision-makers of the landslide-prone region. This landslide susceptibility map can be used to carry out mitigation measures to reduce the susceptibility associated with landslide hazard.

Application of Frequency Ratio and Logistic Regression Model in the Assessment of Landslide Susceptibility Mapping for Nilgiris District, Tamilnadu, India

This study aims to develop landslide susceptibility mapping for Nilgiris district of Tamil Nadu, India using GIS and remote sensing. The Northeast monsoons often trigger numerous landslides in the Nilgiris district of Tami Nadu, resulting in a high death toll and considerable property damage. The landslide causative factors namely elevation, slope, aspect, land use/land cover, rainfall, geology, geomorphology, soil, distance to lineament, distance to road, distance to stream, stream power index and topographic wetness index extracted from the spatial database were incorporated in the analysis, where the correlation between each parameter and landslides were computed using the frequency ratio model and logistic regression model. Finally, the resultant landslide susceptibility zonation maps were obtained from both the models, which were classified as very high, high, moderate, low and very low landslide susceptible zones. The results convey that about 8.78% and 23.22% of the study area were found to be prone to very high landslide susceptibility zones based on the frequency ratio and logistic regression model, respectively. The model validation was carried out using the Receiver Operating Characteristic (ROC) curve by correlating the information obtained from the field verification. The obtained Area Under the Curve (AUC) value of the frequency ratio and logistic regression model was 82.3% and 84.2%, respectively. Therefore, the landslide susceptibility index map of Nilgiris district is claimed to be valuable for the decision-makers to reduce the susceptibility associated with landslide hazard.

PS-InSAR based validated landslide susceptibility modelling: a case study of Ghizer valley, Northern Pakistan

Northern Pakistan is a rugged mountainous area that is seismically active, high gradients, disintegrated lithology, and glaciers in the high peaks. District Ghizer lies among the most vulnerable areas and experience landslides every year due to different causative factors. This study has carried out to prepare a detailed landslide inventory and to develop a susceptibility model for the area. The most followed and probabilistic approach, Frequency Ratio (FR) model and a semi-qualitative Analytical Hierarchy Process (AHP) approach were applied to find the correlation between causative factors and mapped landslides. Persistent Scatterer Interferometry (PSI) Interferometric Synthetic Aperture Radar (InSAR) technique was applied to check deformation movement in the susceptible zones of extracted models, which showed the high Line of Sight (LOS) deformation velocity in high susceptible zones of both models. The extracted Landslide Susceptibility Index (LSI) models showed 82.82% and 73.43% of prediction accuracy for FR and AHP method calculated by Area Under Curve (AUC) of Receiver operating characteristic (ROC) method. The models revealed Slope, barrenness, and Geology are the main causative factors of landslide activities in the study area. Finally, both Landslide susceptibility index maps were classified into five susceptibility classes. As the study area is very prone to landslide disasters so these susceptibility models will be helpful to delineate hazardous zones for the medication of future landslides disasters in the area as well as it can be used as a tool in the planning strategies by decision-makers in development projects in the area.

Landslide susceptibility mapping using information value and logistic regression models in Goncha Siso Eneses area, northwestern Ethiopia

Goncha Siso Eneses area of East Gojam Zone in northwestern Ethiopia is one of the most landslide-prone regions, which is characterized by frequent landslide occurrences causing fatalities and damages in cultivated and non-cultivated lands, infrastructure and properties. Hence, preparing a landslide susceptibility map is very helpful in reducing the damages in infrastructure and properties and loss of animal and human lives. In this study, GIS-based information value and logistic regression models were applied. A reliable and detailed landslide inventory with 894 landslides was prepared through detailed fieldwork and Google Earth image interpretation. These landslides were randomly divided into training data set for model development and testing data set for model validation. Nine landslide causative factors like slope, curvature, aspect, lithology, distance to stream, distance to lineament, distance to spring, rainfall and land use/cover were integrated with training landslides to determine the weight(s) of each landslide factor and factor classes using Information Value and Logistic Regression models, respectively. The landslide susceptibility index map was then produced by summing the weights of all the landslide factors using raster calculator of the spatial analyst tool in GIS. To evaluate the performance of the information value and logistic regression models for landslide susceptibility modeling, the relative landslide density index and area under the curve (AUC) of the receiver operating characteristic curves were performed on both the training and testing landslide data sets. The model has an AUC accuracy of 88.9% success rate and 85.9% prediction rate for information value model whereas 81.8% success rate and 80.2% predictive rate for logistic regression model.

A comparative study on the integrative ability of the analytical hierarchy process, weights of evidence and logistic regression methods with the Flow-R model for landslide susceptibility assessment

The main purpose of the present study is to compare the results of the integrated models, the weights of evidence (WoE), analytical hierarchical process (AHP) and logistic regression (LR) models combine with a Flow-R model for landslide susceptibility assessment in Ha Giang city, Vietnam and the surrounding areas. First, three landslide susceptibility index (LSI) maps were calculated using thirteen landslide conditioning factor maps. Then, a runout map was generated by Flow-R model using the digital elevation model and the slope map of the study area. Secondly, the success rate curve, prediction rate curve and area under the curves (AUC) were calculated to assess prediction capability. The final landslide susceptibility maps were produced by the integration of the LSI maps with the runout map that was extracted from FlowR model. The validation results showed that the AUC of LSI maps in the integrated models, AHP-FlowR, WoE-FlowR and LR-FlowR, were 82.89%, 88.15% and 86.53% with prediction accuracies of 80.41%, 84.94% and 85.71%, respectively. The combined models of WoE-FlowR (88.15%) and LR-FlowR (86.53%) have shown the highest prediction capability. In general, the integrated models provide reasonable results that may be aided in prevention planning and land use planning purposes.

Sustainable Management of a Mountain Community Vulnerable to Geohazards: A Case Study of Maolin District, Taiwan

This study investigates community-based landslide mitigation planning. The combination of a landslide susceptibility index (LSI) model, landslide inventory datasets, and field work is used to identify hazard-prone areas in Maolin District, Taiwan. Furthermore, to identify the challenges and opportunities affecting the sustainable development of mountain communities, a pilot survey was conducted in three such communities (Dona Village, Wanshan Village, and Maolin Village). The results reveal that there are two types of significant mass movement in such areas: debris avalanche and debris flow. The results also show that the LSI map and multi-temporal landslide inventory datasets correlate with landslide locations. Meander is identified as an important factor in landslide activity. The questionnaire results show that the residents of the study area lack awareness of and access to information related to landslide activity. Similarly, the local residents favor increased environmental protection, working within their community, and additional government spending in regard to managing geohazards. To increase the resilience of the community, an improved landslide susceptibility map is proposed based on the output of the results. Thus, this research improves upon the process of identifying, supporting, and bettering the management of communities prone to landslides.

Landslide susceptibility mapping by remote sensing and geomorphological data: case studies on the Sorrentina Peninsula (Southern Italy)

The Sorrentina Peninsula is a densely populated area with high touristic impact. It is located in a morphologically complex zone of Southern Italy frequently affected by dangerous and calamitous landslides. This work contributes to the prevention of such natural disasters by applying a GIS-based interdisciplinary approach aimed to map the areas more potentially prone to trigger slope instability phenomena. We have developed the Landslide Susceptibility Index (LSI) combining five weighted and ranked susceptibility parameters on a GIS platform. These parameters are recognized in the literature as the main predisposing factors for triggering landslides. This work combines analyses conducted on Remote Sensing, Geo-Lithology and Morphometry data and it is organized in the following logical steps: i) Multi-temporal InSAR technique was applied to Envisat-ASAR (2003–2010) and COSMO-SkyMed (2013–2015) datasets to obtain the ground displacement time series and the relative mean ground velocity maps. InSAR allowed the detection of the areas that are subjected to ground deformation and the main affected municipalities; ii) Such deformation areas were investigated through airborne photo interpretation to identify the presence of geomorphological peculiarities connected to potential slope instability. Subsequently, some of these peculiarities were checked on the field; iii) In these deformation areas the susceptibility parameters were mapped in the entire territory of Amalfi and Conca dei Marini and then investigated with a multivariate analysis to derive the classes and the respective weights used in the LSI calculation. The resulting LSI map classifies the two municipalities with high spatial resolution (2m) according to five classes of instability. The map highlights that the high/very high susceptibility zones cover 6% of the investigated territory and correspond to potential landslide source areas characterized by 25°-70° slope angles. A spatial analysis between the map of the historical landslides and the areas classified according to susceptibility allowed testing of the reliability of the LSI Index, resulting in 85% prediction accuracy.

Evaluation of landslide susceptibility models: A comparative study on the part of Western Ghat Region, India

The present research focuses on evaluating the effectiveness of landslide susceptibility using fuzzy gamma operator (FGO) and analytical hierarchy process (AHP) model in part of Western Ghat, India. The landslide inventory data and various causative factors were used to generate the landslide susceptibility index (LSI) map using geospatial technology. The LSI map classified into five classes and finally validated by Area Under Curve (AUC) method for accuracy assessment. The AUC result of success and prediction rates depicts that FGO model has high accuracy value than AHP model. However, both models are highly correlated (r = 0.976). Among the all causative factors slope, lithology, land use land cover, geomorphology and lineament proximity are strongly correlated (r = >0.9) with the landslide susceptibility between the two models. The research outcome will be helpful for future spatial planning in the study region.

Assessing LNRF, FR, and AHP models in landslide susceptibility mapping index: a comparative study of Nojian watershed in Lorestan province, Iran

Landslides and slope instabilities are major risks for human activities which often lead to economic losses and human fatalities all over the world. The main purpose of this study is to evaluate and compare the results of Landslide Nominal Risk Factor (LNRF), Frequency Ratio (FR), and Analytical Hierarchy Process (AHP) models in mapping Landslide Susceptibility Index (LSI). The study case, Nojian watershed with an area of 344.91 km2, is located in Lorestan province of Iran. The procedure was as follows: first, the effective factors of the landslide basin were prepared for each layer in the GIS software. Then, the layers and the landslides of the basin were also prepared using aerial photographs, satellite images, and fieldwork. Next, the effective factors of the layers were overlapped with the map of landslide distribution to specify the role of units in such distribution. Finally, nine factors including lithology, slope, aspect, altitude, distance from the fault, distance from river, fault land use, rainfall, and altitude were found to be effective elements in landslide occurrence of the basin. The final maps of LSI were prepared based on seven factors using LNRF, FR, and AHP models in GIS. The index of the quality sum (Qs) was also used to assess the accuracy of the LSI maps. The results of the three models with LNRF (40%), FR (39%), and AHP (44%) indicated that the whole study area was located in the classes of high to very high hazard. The Qs values for the three models above were also found to be 0.51, 0.70 and 0.70, respectively. In comparison, according to the amount of Qs, the results of AHP and FR models have slightly better performed than the LNRF model in determining the LSI maps in the study area. Finally, the study watershed was classified into five classes based on LSI as very low, low, moderate, high, and very high. The landslide susceptibility maps can be helpful to select sites and mitigate landslide hazards in the study area and the regions with similar conditions.

Landslide inventory and susceptibility modelling using geospatial tools, in Hunza-Nagar valley, northern Pakistan

A comprehensive landslide inventory and susceptibility maps are prerequisite for developing and implementing landslide mitigation strategies. Landslide susceptibility maps for the landslides prone regions in northern Pakistan are rarely available. The Hunza-Nagar valley in northern Pakistan is known for its frequent and devastating landslides. In this paper, we have developed a landslide inventory map for Hunza-Nagar valley by using the visual interpretation of the SPOT-5 satellite imagery and mapped a total of 172 landslides. The landslide inventory was subsequently divided into modelling and validation data sets. For the development of landslide susceptibility map seven discrete landslide causative factors were correlated with the landslide inventory map using weight of evidence and frequency ratio statistical models. Four different models of conditional independence were used for the selection of landslide causative factors. The produced landslides susceptibility maps were validated by the success rate and area under curves criteria. The prediction power of the models was also validated with the prediction rate curve. The validation results shows that the success rate curves of the weight of evidence and the frequency models are 82% and 79%, respectively. The prediction accuracy results obtained from this study are 84% for weight of evidence model and 80% for the frequency ratio model. Finally, the landslide susceptibility index maps were classified into five different varying susceptibility zones. The validation and prediction result indicates that the weight of evidence and frequency ratio model are reliable to produce an accurate landslide susceptibility map, which may be helpful for landslides management strategies.

A Comparative Study of the Frequency Ratio and Evidential Belief Function Models for Landslide Susceptibility Mapping

The goal of this study was to analyze landslide susceptibility using two different models and compare the results. For this purpose, a landslide inventory map was produced from a field survey, and the inventory was divided into two groups for training and validation, respectively. Sixteen landslide conditioning factors were considered. The relationships between landslide occurrence and landslide conditioning factors were analyzed using the FR (Frequency Ratio) and EBF (Evidential Belief Function) models. The LSI (Landslide Susceptibility Index) maps that were produced were validated using the ROC (Relative Operating Characteristics) curve and the SCAI (Seed Cell Area Index). The AUC (Area under the ROC Curve) values of the FR and EBF LSI maps were 80.6% and 79.5%, with prediction accuracies of 72.7% and 71.8%, respectively. Additionally, in the low and very low susceptibility zones, the FR LSI map had higher SCAI values compared to the EBF LSI map, as high as 0.47%p. These results indicate that both models were reasonably accurate, however that the FR LSI map had a slightly higher accuracy for landslide susceptibility mapping in the study area.

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.

GIS based landslide susceptibility mapping of northern areas of Pakistan, a case study of Shigar and Shyok Basins

ABSTRACTThis study presents the use of geographical information system (GIS) datasets and methods to investigate landslide susceptibility in the rugged mountainous terrain of Shigar and Shyok Basins in northern areas of Pakistan. Study area is situated in Karakorum mountainous region where catastrophic landslides occur frequently and pose a serious threat to local living conditions. Landslide susceptibility index maps were prepared by combining four main indicators groups: (1) human induced parameters, which contain Landsat 8 imagery and distance from roads; (2) topographical parameters, which include slope, aspect and plan curvature; (3) hydrological parameters, which contain stream power index, topographic wetness index and distance from drainage and (4) geological parameters, which consist of lithology and distance from major thrusts and faults. These layers were prepared in GIS environment, and analytical hierarchy process (AHP) based heuristic approach was adopted to generate final landslide susceptibility map for this preliminary regional level landslide hazard study. Three different susceptibility zones have been identified in the region. AHP weights with 0.04 consistency ratio were used in the preparation of final map. The final landslide susceptibility map of the study area indicates that the low, moderate, and high landslide susceptibility classes are respectively covering 23.2% (4745 km2), 45% (6737 km2), 23.2% (3444 km2) when distance from roads, distance from drainage and distance from major thrusts and faults parameters were not incorporated in the analysis. Including distance from roads, distance from drainage and distance from major thrusts and faults parameters layers to the final landslide susceptibility analysis shows that moderate susceptibility class is the predominant landslide susceptibility category covering 88% (13,412 km2) of the study area characterized by steep slopes (30°–45°), low positive plan curvature (0–1) and weak lithology.