scholarly journals Effects of different land use and land cover data on the landslide susceptibility zonation of road networks

2019 ◽  
Vol 19 (3) ◽  
pp. 471-487 ◽  
Author(s):  
Bruno M. Meneses ◽  
Susana Pereira ◽  
Eusébio Reis
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Landslide Susceptibility ◽  
Road Network ◽  
Slope Angle ◽  
Information Value ◽  
Slope Aspect ◽  
The Road ◽  
Landslide Susceptibility Zonation ◽  
On The Road

Abstract. This work evaluates the influence of land use and land cover (LUC) data with different properties on the landslide susceptibility zonation of the road network in the Zêzere watershed (Portugal). The information value method was used to assess the landslide susceptibility using two models: one including detailed LUC data (the Portuguese Land Cover Map – COS) and the other including more generalized LUC data (the CORINE Land Cover – CLC). A set of fixed independent layers was considered as landslide predisposing factors (slope angle, slope aspect, slope curvature, slope-over-area ratio, soil, and lithology) while COS and CLC were used to find the differences in the landslide susceptibility zonation. A landslide inventory was used as a dependent layer, including 259 shallow landslides obtained from the photointerpretation of orthophotos from 2005, and further validated in three sample areas. The landslide susceptibility maps were assigned to the road network data and resulted in two landslide susceptibility road network maps. The models' performance was evaluated with prediction and success rate curves and the area under the curve (AUC). The landslide susceptibility results obtained in the two models present a high accuracy in terms of the AUC (>90 %), but the model with more detailed LUC data (COS) produces better results in the landslide susceptibility zonation on the road network with the highest landslide susceptibility.

scholarly journals Land use and land cover geoinformation properties and its influence on the landslide susceptibility zonation of road network

2017 ◽  
Author(s):  
Bruno M. Meneses ◽  
Susana Pereira ◽  
Eusébio Reis
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Landslide Susceptibility ◽  
Road Network ◽  
Slope Angle ◽  
Information Value ◽  
Slope Aspect ◽  
The Road ◽  
Landslide Susceptibility Zonation ◽  
On The Road

Abstract. This paper evaluates the influence of land use and land cover (LUC) geoinformation with different properties on landslide susceptibility zonation of the road network in Zêzere watershed (Portugal). The Information Value method was used to assess landslide susceptibility using two models: one including detailed LUC geoinformation (Portuguese Land Cover Map – COS) and other including more generalized LUC geoinformation (Corine Land Cover – CLC). A set of six fixed independent layers were considered as landslide predisposing factors (slope angle, slope aspect, slope curvature, slope over area ratio, soil, and lithology), while COS and CLC were used to find the differences in the landslide susceptibility zonation. A landslide inventory was used as dependent layer, including 259 shallow landslides obtained from photo-interpretation of orthophotos of 2005 and further validated in three sample areas (128 landslides). The landslide susceptibility maps were merged into road network geoinformation, and resulted in two landslide susceptibility road network maps. Models performance was evaluated with success rate curves and area under the curve. Landslide susceptibility results obtained in the two models are very good, but in comparison the model obtained with more detailed LUC geoinformation (COS) produces better results in the landslide susceptibility zonation and on the road network detection with the highest landslide susceptibility. This last map also provides more detailed information about the locals where the next landslides will probably occur with possible road network disturbances.

scholarly journals Landslide Susceptibility Zonation Mapping in Post- Earthquake Scenario in Gorkha District

2018 ◽  
Vol 15 ◽  
pp. 45-56 ◽  
Author(s):  
Him Lal Shrestha ◽  
Mahesh Poudel
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Landslide Susceptibility ◽  
Remote Sensing Data ◽  
Mitigation Measures ◽  
Ordinal Scale ◽  
Slope Aspect ◽  
Landsat Images ◽  
Earthquake Scenario ◽  
Landslide Susceptibility Zonation

Landslide hazard zonation map is prepared to assist planners to implement mitigation measures so that further damage and loss can be minimized. In this study, post 25 April 2015 earthquake remote sensing data were used to prepare landslide inventory. Landsat images after the earthquake were downloaded from the National Aeronautics and Space Administration (NASA) website and processed using ArcGIS, ERDAS imagine and Analytical Hierarchy Process (AHP) as an extension in ArcGIS. The study was carried out in Gorkha district as this was the epicenter of the main earthquake of 25 April 2015 and consequently was highly affected by earthquake triggered landslide. The digital imagery was processed to analyze land use/land cover type. Geological features were analyzed using the criteria like color, tone, topography, stream drainage, etc. Primary topographic features like slope, aspect, elevation, etc. were generated from Digital Elevation Model (DEM). Seismological data (magnitude and epicenter) were obtained from Department of Seismology. For Landslide Susceptibility Zonation (LSZ) different thematic maps like Land Use and Land Cover (LULC) map, slope map, aspect map, lithological map, buffer map (distance from road and river/water source), soil map, and seismological map were assigned relative weights on the ordinal scale to obtain Landslide Susceptibility Index (LSI). Threshold values were selected according to breaks in LSI frequency and a LSZ map was prepared which shows very low, low, moderate, high, very high hazard zones in Gorkha district.

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 Assessing the spatial variability of coefficients of landslide predictors in different regions of Romania using logistic regression

2013 ◽  
Vol 13 (12) ◽  
pp. 3339-3355 ◽  
Author(s):  
M. C. Mărgărint ◽  
A. Grozavu ◽  
C. V. Patriche
Keyword(s):  
Land Use ◽  
Logistic Regression ◽  
Spatial Variability ◽  
Landslide Susceptibility ◽  
Scientific Literature ◽  
Characteristic Curve ◽  
Slope Angle ◽  
Slope Aspect ◽  
Hazard Mapping ◽  
Correct Identification

Abstract. In landslide susceptibility assessment, an important issue is the correct identification of significant contributing factors, which leads to the improvement of predictions regarding this type of geomorphologic processes. In the scientific literature, different weightings are assigned to these factors, but contain large variations. This study aims to identify the spatial variability and range of variation for the coefficients of landslide predictors in different geographical conditions. Four sectors of 15 km × 15 km (225 km2) were selected for analysis from representative regions in Romania in terms of spatial extent of landslides, situated both on the hilly areas (the Transylvanian Plateau and Moldavian Plateau) and lower mountain region (Subcarpathians). The following factors were taken into consideration: elevation, slope angle, slope height, terrain curvature (mean, plan and profile), distance from drainage network, slope aspect, land use, and lithology. For each sector, landslide inventory, digital elevation model and thematic layers of the mentioned predictors were achieved and integrated in a georeferenced environment. The logistic regression was applied separately for the four study sectors as the statistical method for assessing terrain landsliding susceptibility. Maps of landslide susceptibility were produced, the values of which were classified by using the natural breaks method (Jenks). The accuracy of the logistic regression outcomes was evaluated using the ROC (receiver operating characteristic) curve and AUC (area under the curve) parameter, which show values between 0.852 and 0.922 for training samples, and between 0.851 and 0.940 for validation samples. The values of coefficients are generally confined within the limits specified by the scientific literature. In each sector, landslide susceptibility is essentially related to some specific predictors, such as the slope angle, land use, slope height, and lithology. The study points out that the coefficients assigned to the landslide predictors through logistic regression are capable to reveal some important characteristics in landslide manifestation. The study also shows that the logistic regression could be an alternative method to the current Romanian methodology for landslide susceptibility and hazard mapping.

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 Assessing the spatial variability of weights of landslide causal factors in different regions from Romania using logistic regression

2013 ◽  
Vol 1 (2) ◽  
pp. 1749-1774 ◽  
Author(s):  
M. C. Mărgărint ◽  
A. Grozavu ◽  
C. V. Patriche
Keyword(s):  
Land Use ◽  
Logistic Regression ◽  
Spatial Variability ◽  
Landslide Susceptibility ◽  
Scientific Literature ◽  
Slope Angle ◽  
Slope Aspect ◽  
Correct Identification ◽  
Channel Network ◽  
Causal Factors

Abstract. In landslide susceptibility assessment, an important issue is the correct identification of significant causal factors, which leads to the improvement of predictions regarding this type of geomorphological processes. In the scientific literature, different weightings are assigned to these factors, but with large variations. This study aims to identify the spatial variability and range of variation of landslide causal factors in different geographical conditions. Four square sectors of 15 km × 15 km (225 km2) were selected for analysis from representative regions in Romania in terms of spatial extent of landslides, situated both in hilly areas (Transylvanian Plateau and Moldavian Plateau) and lower mountain region (Subcarpathians). The following factors were taken into consideration: elevation, slope angle, slope height, terrain curvature (mean, plan and profile), distance from drainage network, slope aspect, surface lithology and land use. For each sector, landslide inventory, digital elevation model and thematic layers of the mentioned predictors were achieved and integrated in georeferenced environment. The logistic regression was applied separately for the four study sectors, as statistical method for assessing terrain landsliding susceptibility. Maps of landslide susceptibility were achieved, the values of which were classified using the natural breaks method (Jenks). The accuracy of logistic regression outcomes was evaluated using the ROC curve and AUC parameter, which show values between 0.852 and 0.922. The values of factor weights are generally placed within the limits specified by the scientific literature. For all study sectors, the prevailing factors for landslide susceptibility are slope angle, land use and slope height above channel network. The study points out that the weights assigned to the causal factors through logistic regression are capable to reveal some important regional characteristics in landslides manifestation.

scholarly journals Estimation of the susceptibility of a road network to shallow landslides with the integration of the sediment connectivity

2018 ◽  
Author(s):  
Massimiliano Bordoni ◽  
M. Giuseppina Persichillo ◽  
Claudia Meisina ◽  
Stefano Crema ◽  
Marco Cavalli ◽  
...  
Keyword(s):  
Land Use ◽  
Road Network ◽  
Slope Angle ◽  
Drainage System ◽  
Shallow Landslides ◽  
Mitigation Measures ◽  
Explanatory Variables ◽  
The Road ◽  
Genetic Algorithm Method ◽  
Sediment Connectivity

Abstract. Landslides causes severe damages to the road network of a hit zone, in terms of both direct (partial or complete destruction of a road trait, blockages) and indirect (traffic restriction, cut-off of a certain area) costs. Thus, the identification of the parts of the road network which are more susceptible to landslides is fundamental to reduce the risk to the population potentially exposed and the money expense caused by road damaging. For these reasons, this paper aimed to develop and test a data-driven model based on the Genetic Algorithm Method for the identification of road sectors that are susceptible to be hit by shallow landslides triggered in slopes upstream to the infrastructure. This work also analyzed the importance of considering or not the sediment connectivity on the estimation of the susceptibility. The study was carried out in a catchment of north-eastern Oltrepò Pavese (northern Italy), where several shallow landslides affected roads in the last 8 years. The random partition of the dataset used for building the model in two parts (training and test subsets), within a 100-fold bootstrap procedure, allowed to select the most significant explanatory variables, providing a better description of the occurrence and distribution of the road sectors potentially susceptible to damages induced by shallow landslides. The presented methodology allows the identification, in a robust and reliable way, of the most susceptible road sectors that could be hit by sediments delivered by landslides. The best predictive capability was obtained using a model which took into account also the index of connectivity, calculated according to a linear relationship. Most susceptible road traits resulted to be located below steep slopes with a limited height (lower than 50 m), where sediment connectivity is high. Different scenarios of land use were implemented in order to estimate possible changes in road susceptibility. Land use classes of the study area were characterized by similar connectivity features with a consequent loss of variations also on the susceptibility of the road networks according to different scenarios of distribution of land cover. Larger effects on sediment connectivity and, as a consequence on road susceptibility, could be due to modifications in the morphology of the slopes (e.g. drainage system, modification of the slope angle) caused by the abandonment or by the recovery of cultivations. The results of this research demonstrate the ability of the developed methodology in the assessment of susceptible roads. This could give to the managers of an infrastructure information on the criticality of the different road traits, thereby allowing attention and economic budgets to be shifted towards the most critical assets, where structural and non-structural mitigation measures could be implemented.

scholarly journals Landslide Susceptibility Mapping Using GIS-based Information Value and Frequency Ratio Methods in Gindeberet area, West Shewa Zone, Oromia Region, Ethiopia

2021 ◽  
Author(s):  
Alembante Genene ◽  
Matebie Meten
Keyword(s):  
Land Use ◽  
Land Use Planning ◽  
Landslide Susceptibility ◽  
Frequency Ratio ◽  
Image Interpretation ◽  
Active Surface ◽  
Google Earth ◽  
Information Value ◽  
Landslide Susceptibility Mapping ◽  
Slope Aspect

Abstract The study area is found in Gindeberet district of West Shewa zone in Oromia Regional State of Ethiopia.This area is highly susceptible to active surface processes due to the presence of rugged morphology with steep scarps, sharp ridges, cliffs, deep gorges and valleys. This study aimed to identify and evaluate the causative factors and to prepare the landslide susceptibility maps (LSMs) of the study area. Two bivariate statistical models i.e. Information value(IV) and the Frequency ratio(FR), were used. First, active, reactivated and passive landslides and scarps were identified using Google Earth image interpretation and extensive field survey for landslide inventory. A total of 580 landslide were randomly selected into two datasets in which (80%)460 landslides were used for modeling and (20%)116 landslidesfor validation. conditioning factors (slope, aspect, curvature, distance from stream, distance from lineaments, lithology, rainfall and land use) were combined with a training landslide dataset in a ArcGIS to generate LSMs which weredivided into verylow, low, moderate, high and veryhigh susceptibility zones. LSMs for IV and FR models were validated using the Area under(ROC) curve showing a success rate of 0.836 and 0.835 respectively and a predictive rate of 0.817 and 0.818 respectively wich showed a good performance of both models. The resulting LSMs can be used for land use planning and management.

scholarly journals Modeling of Landslide susceptibility in a part of Abay Basin, northwestern Ethiopia

2020 ◽  
Vol 12 (1) ◽  
pp. 1440-1467
Author(s):  
Azemeraw Wubalem
Keyword(s):  
Land Use ◽  
Landslide Susceptibility ◽  
Vegetation Index ◽  
Statistical Significance ◽  
Google Earth ◽  
Landslide Hazard ◽  
Information Value ◽  
Landslide Susceptibility Mapping ◽  
Slope Aspect ◽  
Landslide Occurrence

AbstractThe study area in northwestern Ethiopia is one of the most landslide-prone regions, which is characterized by frequent high landslide occurrences. To predict future landslide occurrence, preparing a landslide susceptibility mapping is imperative to manage the landslide hazard and reduce damages of properties and loss of lives. Geographic information system (GIS)-based frequency ratio (FR), information value (IV), certainty factor (CF), and logistic regression (LR) methods were applied. The landslide inventory map is prepared from historical records and Google Earth imagery interpretation. Thus, 717 landslides were mapped, of which 502 (70%) landslides were used to build landslide susceptibility models, and the remaining 215 (30%) landslides were used to model validation. Eleven factors such as lithology, land use/cover, distance to drainage, distance to lineament, normalized difference vegetation index, drainage density, rainfall, soil type, slope, aspect, and curvature were evaluated and their relationship with landslide occurrence was analyzed using the GIS tool. Then, landslide susceptibility maps of the study area are categorized into very low, low, moderate, high, and very high susceptibility classes. The four models were validated by the area under the curve (AUC) and landslide density. The results for the AUC are 93.9% for the CF model, which is better than 93.2% using IV, 92.7% using the FR model, and 87.9% using the LR model. Moreover, the statistical significance test between the models was performed using LR analysis by SPSS software. The result showed that the LR and CF models have higher statistical significance than the FR and IV methods. Although all statistical models indicated higher prediction accuracy, based on their statistical significance analysis result (Table 5), the LR model is relatively better followed by the CF model for regional land use planning, landslide hazard mitigation, and prevention purposes.

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