Landslide susceptibility mapping using GIS and Remote Sensing: A case study of the Rangamati District, Bangladesh

2020 ◽  
Author(s):  
Afruja Begum ◽  
Md Shofiqul Islam ◽  
Md. Muyeed Hasan
Keyword(s):  
Remote Sensing ◽  
Water Pressure ◽  
Research Work ◽  
Slope Angle ◽  
Steep Slope ◽  
Landslide Susceptibility Mapping ◽  
Dominant Factor ◽  
Natural Phenomenon ◽  
Slope Aspect ◽  
Gis And Remote Sensing

Abstract The landslide is a natural phenomenon and one of the most commonplace disasters in the Rangamati Hill tract area which appeals for better forecasting and specify the landslide susceptible zonation. This research work examines the application of GIS and Remote Sensing techniques based on different parameters such as altitude, slope angle, slope aspect, rainfall, land-use land-cover (LULC), geology and stream distance by heuristic model to identify the landslide susceptible zones for the study area. Among the parameters, rainfall, steep slope, geology and LULC are the dominant factor that triggering the landslide. Clayey or silty soils of the study area during heavy and prolong rainfall behave a flow of debris due to water pressure within the soil, resulting landslides. Steep slope has greater influences for weather zones of the rock-masses for susceptible landslides. Result and field observation indicate that the population density and LULC has a vital effect on landslide within the study area. However, landslide susceptible zones were created based on the susceptibility map of the study area which shows that about 19.43% of the area are at low susceptible zone, 56.55% of the area are at medium susceptible zone, 19.19% of the area are in the high susceptible zone and 4.81% of the area is at the very high susceptible zone.

scholarly journals Landslide Susceptibility Mapping Using Different GIS-Based Bivariate Models

Water ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 1402 ◽  
Author(s):  
Nohani ◽  
Moharrami ◽  
Sharafi ◽  
Khosravi ◽  
Pradhan ◽  
...  
Keyword(s):  
Land Use ◽  
Landslide Susceptibility ◽  
Vegetation Index ◽  
Slope Angle ◽  
Aerial Photographs ◽  
Landslide Susceptibility Mapping ◽  
Slope Aspect ◽  
Susceptibility Map ◽  
Conditioning Factors ◽  
Bivariate Models

Landslides are the most frequent phenomenon in the northern part of Iran, which cause considerable financial and life damages every year. One of the most widely used approaches to reduce these damages is preparing a landslide susceptibility map (LSM) using suitable methods and selecting the proper conditioning factors. The current study is aimed at comparing four bivariate models, namely the frequency ratio (FR), Shannon entropy (SE), weights of evidence (WoE), and evidential belief function (EBF), for a LSM of Klijanrestagh Watershed, Iran. Firstly, 109 locations of landslides were obtained from field surveys and interpretation of aerial photographs. Then, the locations were categorized into two groups of 70% (74 locations) and 30% (35 locations), randomly, for modeling and validation processes, respectively. Then, 10 conditioning factors of slope aspect, curvature, elevation, distance from fault, lithology, normalized difference vegetation index (NDVI), distance from the river, distance from the road, the slope angle, and land use were determined to construct the spatial database. From the results of multicollinearity, it was concluded that no collinearity existed between the 10 considered conditioning factors in the occurrence of landslides. The receiver operating characteristic (ROC) curve and the area under the curve (AUC) were used for validation of the four achieved LSMs. The AUC results introduced the success rates of 0.8, 0.86, 0.84, and 0.85 for EBF, WoE, SE, and FR, respectively. Also, they indicated that the rates of prediction were 0.84, 0.83, 0.82, and 0.79 for WoE, FR, SE, and EBF, respectively. Therefore, the WoE model, having the highest AUC, was the most accurate method among the four implemented methods in identifying the regions at risk of future landslides in the study area. The outcomes of this research are useful and essential for the government, planners, decision makers, researchers, and general land-use planners in the study area.

scholarly journals Landslide Susceptibility Mapping using Bivariate Statistical Models and GIS in Chattagram District, Bangladesh

2021 ◽  
Author(s):  
Md. Sharafat Chowdhury ◽  
Bibi Hafsa
Keyword(s):  
Statistical Models ◽  
Landslide Susceptibility ◽  
Slope Angle ◽  
Information Value ◽  
Weight Of Evidence ◽  
Landslide Susceptibility Mapping ◽  
Slope Aspect ◽  
Susceptibility Map ◽  
Topographic Wetness Index ◽  
Conditioning Factors

Abstract This study attempts to produce Landslide Susceptibility Map for Chattagram District of Bangladesh by using five GIS based bivariate statistical models, namely the Frequency Ratio (FR), Shanon’s Entropy (SE), Weight of Evidence (WofE), Information Value (IV) and Certainty Factor (CF). A secondary landslide inventory database was used to correlate the previous landslides with the landslide conditioning factors. Sixteen landslide conditioning factors of Slope Aspect, Slope Angle, Geology, Elevation, Plan Curvature, Profile Curvature, General Curvature, Topographic Wetness Index, Stream Power Index, Sediment Transport Index, Topographic Roughness Index, Distance to Stream, Distance to Anticline, Distance to Fault, Distance to Road and NDVI were used. The Area Under Curve (AUC) was used for validation of the LSMs. The predictive rate of AUC for FR, SE, WofE, IV and CF were 76.11%, 70.11%, 78.93%, 76.57% and 80.43% respectively. CF model indicates 15.04% of areas are highly susceptible to landslide. All the models showed that the high elevated areas are more susceptible to landslide where the low-lying river basin areas have a low probability of landslide occurrence. The findings of this research will contribute to land use planning, management and hazard mitigation of the CHT region.

scholarly journals Drought Risk Assessment in the Khushab Region of Pakistan Using Satellite Remote Sensing and Geospatial Methods

2019 ◽  
Vol 10 (1) ◽  
pp. 48-56 ◽  
Author(s):  
Muhammad Khubaib Abuzar ◽  
Muhammad Shafiq ◽  
Syed Amer Mahmood ◽  
Muhammad Irfan ◽  
Tayyaba Khalil ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Social Life ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Standardized Precipitation Index ◽  
Temporal Analysis ◽  
Agricultural Drought ◽  
Natural Phenomenon ◽  
Drought Risk ◽  
Gis And Remote Sensing

Drought is a harmful and slow natural phenomenon that has significant effects on the economy, social life,agriculture and environment of the country. Due to its slow process it is difficult to study this phenomenon. RemoteSensing and GIS tools play a key role in studying different hazards like droughts. The main objective of the study wasto investigate drought risk by using GIS and Remote Sensing techniques in district Khushab, Pakistan. Landsat ETMimages for the year 2003, 2009 and 2015 were utilized for spatial and temporal analysis of agricultural andmeteorological drought. Normalized difference vegetation index (NDVI) Standardized Precipitation Index (SPI) andrainfall anomaly indices were calculated to identify the drought prone areas in the study area. To monitormeteorological drought SPI values were used and NDVI was calculated for agricultural drought. These indices wereintegrated to compute the spatial and temporal drought maps. Three zones; no drought, slight drought and moderatedrought were identified. Final drought map shows that 30.21% of the area faces moderate drought, 28.36% faces slightdrought while nearly 41.3% faces no drought situation. Drought prevalence and severity is present more in the southernpart of Khushab district than the northern part. Most of the northern part is not under any type of drought. Thus, anoverall outcome of this study shows that risk areas can be assessed appropriately by integration of various data sourcesand thereby management plans can be prepared to deal with the hazard.

scholarly journals Evaluating landslide susceptibility using environmental factors, fuzzy membership functions and GIS

2013 ◽  
Vol 13 (1) ◽  
pp. 28-40
Keyword(s):  
Factor Analysis ◽  
Landslide Susceptibility ◽  
Slope Angle ◽  
Landslide Susceptibility Mapping ◽  
Slope Aspect ◽  
Fuzzy Membership ◽  
Susceptibility Map ◽  
Membership Functions ◽  
Conditioning Factors ◽  
Fuzzy Membership Functions

A methodology for landslide susceptibility assessment to delineate landslide prone areas is presented using factor analysis and fuzzy membership functions and Geographic Information Systems (GIS). A landslide inventory of 51 landslides was created in the mountainous part of Xanthi prefecture (North Greece) and the associated conditioning factors were determined for each landslide by field work. Six conditioning factors were evaluated: slope angle, slope aspect, land use, geology, distance to faults and topographical elevation. Fuzzy membership functions were defined for each factor using the landslide frequency data. Factor analysis provided weights (i.e., importance for landslide occurrences) for each one of the above conditioning factors, indicating the most important factors as geology and slope angle. An overlay and index method was adopted to produce the landslide susceptibility map. In this map 96% of the observed landslides are located in very high and high susceptibility zones, indicating a suitable approach for landslide susceptibility mapping.

Use of GIS and Remote Sensing for Landslide Susceptibility Mapping

2019 ◽  
pp. 384-398 ◽  
Author(s):  
Arzu Erener ◽  
Gulcan Sarp ◽  
Sebnem H. Duzgun
Keyword(s):  
Remote Sensing ◽  
Information Systems ◽  
Spatial Data ◽  
Landslide Susceptibility ◽  
Geographical Information Systems ◽  
Vegetation Index ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping ◽  
Gis And Remote Sensing ◽  
Data Layers

In recent years, geographical information systems (GISs) and remote sensing (RS) have proven to be common tools adopted for different studies in different scientific disciplines. GIS is defined as a set of tools for the input, storage, retrieval, manipulation, management, modeling, analysis, and output of spatial data. RS, on the other hand, can play a role in the production of a data and in the generation of thematic maps related to spatial studies. This study focuses on use of GIS and RS data for landslide susceptibility mapping. Five factors including normalized difference vegetation index (NDVI) and topographic wetness index (TWI), slope, lineament density, and distance to roads were used for the grid-based approach for landslide susceptibility mappings. Results of this study suggest that geographic information systems can effectively be used to obtain susceptibility maps by compiling and overlaying several data layers relevant to landslide hazards.

scholarly journals Hybrid Computational Intelligence Methods for Landslide Susceptibility Mapping

Symmetry ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 325 ◽  
Author(s):  
Guirong Wang ◽  
Xinxiang Lei ◽  
Wei Chen ◽  
Himan Shahabi ◽  
Ataollah Shirzadi
Keyword(s):  
Landslide Susceptibility ◽  
Radial Basis Function Network ◽  
Slope Angle ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping ◽  
Training Dataset ◽  
Validation Dataset ◽  
Slope Aspect ◽  
Susceptibility Maps ◽  
Landslide Susceptibility Maps

In this study, hybrid integration of MultiBoosting based on two artificial intelligence methods (the radial basis function network (RBFN) and credal decision tree (CDT) models) and geographic information systems (GIS) were used to establish landslide susceptibility maps, which were used to evaluate landslide susceptibility in Nanchuan County, China. First, the landslide inventory map was generated based on previous research results combined with GIS and aerial photos. Then, 298 landslides were identified, and the established dataset was divided into a training dataset (70%, 209 landslides) and a validation dataset (30%, 89 landslides) with ensured randomness, fairness, and symmetry of data segmentation. Sixteen landslide conditioning factors (altitude, profile curvature, plan curvature, slope aspect, slope angle, stream power index (SPI), topographical wetness index (TWI), sediment transport index (STI), distance to rivers, distance to roads, distance to faults, rainfall, NDVI, soil, land use, and lithology) were identified in the study area. Subsequently, the CDT, RBFN, and their ensembles with MultiBoosting (MCDT and MRBFN) were used in ArcGIS to generate the landslide susceptibility maps. The performances of the four landslide susceptibility maps were compared and verified based on the area under the curve (AUC). Finally, the verification results of the AUC evaluation show that the landslide susceptibility mapping generated by the MCDT model had the best performance.

scholarly journals Landslide Susceptibility Mapping and Assessment of Ketti Micro Watershed in Nilgiris District using Remote Sensing and Gis Techniques

2020 ◽  
Vol 13 (1) ◽  
pp. 142-148
Author(s):  
P. Kodanda Rama Rao ◽  
C. Rajakumar
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Field Study ◽  
Landslide Susceptibility ◽  
Remote Sensing And Gis ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping ◽  
Thematic Maps ◽  
Soil Thickness ◽  
Gis And Remote Sensing

The GIS and Remote sensing technologies have been useful in the field of mapping in recent days. It is possible to integrate spatial data’s of different layers to determine the influence of various factors on landslide incidences. Based on the parameters such as slope, geomorphology, lineament, aspect, and present land use and soil thickness various thematic maps were prepared. By assessing proper ranks and weights the final landslide susceptible map was prepared. These maps were validated during field study

scholarly journals Drought Risk Assessment in the Khushab Region of Pakistan Using Satellite Remote Sensing and Geospatial Methods

2019 ◽  
Vol 10 (1) ◽  
pp. 48-56
Author(s):  
Muhammad Khubaib Abuzar ◽  
Muhammad Shafiq ◽  
Syed Amer Mahmood ◽  
Muhammad Irfan ◽  
Tayyaba Khalil ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Social Life ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Standardized Precipitation Index ◽  
Temporal Analysis ◽  
Agricultural Drought ◽  
Natural Phenomenon ◽  
Drought Risk ◽  
Gis And Remote Sensing

Drought is a harmful and slow natural phenomenon that has significant effects on the economy, social life,agriculture and environment of the country. Due to its slow process it is difficult to study this phenomenon. RemoteSensing and GIS tools play a key role in studying different hazards like droughts. The main objective of the study wasto investigate drought risk by using GIS and Remote Sensing techniques in district Khushab, Pakistan. Landsat ETMimages for the year 2003, 2009 and 2015 were utilized for spatial and temporal analysis of agricultural andmeteorological drought. Normalized difference vegetation index (NDVI) Standardized Precipitation Index (SPI) andrainfall anomaly indices were calculated to identify the drought prone areas in the study area. To monitormeteorological drought SPI values were used and NDVI was calculated for agricultural drought. These indices wereintegrated to compute the spatial and temporal drought maps. Three zones; no drought, slight drought and moderatedrought were identified. Final drought map shows that 30.21% of the area faces moderate drought, 28.36% faces slightdrought while nearly 41.3% faces no drought situation. Drought prevalence and severity is present more in the southernpart of Khushab district than the northern part. Most of the northern part is not under any type of drought. Thus, anoverall outcome of this study shows that risk areas can be assessed appropriately by integration of various data sourcesand thereby management plans can be prepared to deal with the hazard.

Use of GIS and Remote Sensing for Landslide Susceptibility Mapping

2018 ◽  
pp. 3503-3514 ◽  
Author(s):  
Arzu Erener ◽  
Gulcan Sarp ◽  
Sebnem H. Duzgun
Keyword(s):  
Remote Sensing ◽  
Information Systems ◽  
Spatial Data ◽  
Landslide Susceptibility ◽  
Geographical Information Systems ◽  
Vegetation Index ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping ◽  
Gis And Remote Sensing ◽  
Data Layers

In recent years, geographical information systems (GISs) and Remote Sensing (RS) have proven to be common tools adopted for different studies in different scientific disciplines. GIS defined as a set of tools for the input, storage, retrieval, manipulation, management, modeling, analysis and output of spatial data. RS, on the other hand, can play a role in the production of a data and in the generation of thematic maps related to spatial studies. This study focuses on use of GIS and RS data for landslide susceptibility mapping. Five factors including Normalized Difference Vegetation Index (NDVI) and Topographic Wetness Index (TWI), slope; lineament density and distance to roads were used for the grid based approach for landslide susceptibility mappings. Results of this study suggest that geographic information systems can effectively be used to obtain susceptibility maps by compiling and overlaying several data layers relevant to landslide hazards.

scholarly journals Application of a GIS-Based Slope Unit Method for Landslide Susceptibility Mapping in Helong City: Comparative Assessment of ICM, AHP, and RF Model

Symmetry ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 1848
Author(s):  
Chenglong Yu ◽  
Jianping Chen
Keyword(s):  
Landslide Susceptibility ◽  
Slope Angle ◽  
Area Under The Curve ◽  
Susceptibility Mapping ◽  
Mitigation Strategies ◽  
Landslide Susceptibility Mapping ◽  
Slope Aspect ◽  
Suitable Model ◽  
Mapping Unit ◽  
Slope Unit

Landslides are one of the most extensive geological disasters in the world. The objective of this study was to assess the performances of different landslide susceptibility models information content method (ICM), analytical hierarchy process (AHP), and random forest (RF) model) and mapping unit (slope unit and grid unit) for landslide susceptibility mapping in the Helong city, Jilin province, northeastern China. First, a total of 159 landslides were mapped in the study area based on a geological hazard survey (1:50,000) of Helong city. Then, the slope units of the study area were divided by using the curvature watershed method. Next, eight influencing factors, namely, lithology, slope angle, slope aspect, rainfall, land use, seismic intensity, distance to river, and distance to fault, were selected to map the landslide susceptibility based on geological data, field survey, and landslide information. Afterward, landslide susceptibility modeling of landslide inventory data is performed for extracting and learning the symmetry latent in data patterns and relationships by three landslide susceptibility models and utilizing it to predict landslide susceptibility. Finally, the receiver operating characteristic (ROC) curve was used to compare the landslide susceptibility models. In addition, results based on grid units were calculated for comparison. The AUC (the area under the curve) result for ICM, AHP, and RF model was 87.1%, 80.5%, and 94.6% for slope units, and 83.4%, 70.9%, and 91.3% for grid units, respectively. Based on the overall assessments, the SU-RF model was the most suitable model for landslide susceptibility mapping. Consequently, these methods can be very useful for landslide hazard mitigation strategies.

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