scholarly journals Gis-based Gully Erosion Susceptibility Modelling, Adapting Bivariate Statistical Method and Ahp Approach in Gombe Town and Environs Northeast Nigeria

2020 ◽  
Author(s):  
Ogbonnaya Igwe ◽  
Ikechukwu John Ugwuoke ◽  
Onwuka Solomon ◽  
Ozioko Obinna
Keyword(s):  
Density Distribution ◽  
Soil Texture ◽  
Slope Angle ◽  
Gully Erosion ◽  
Susceptibility Map ◽  
Large Area ◽  
Topographic Wetness Index ◽  
High Resolution Imagery ◽  
Bivariate Statistical Method ◽  
Very High

Abstract Gully erosion is a major environmental problem in Gombe town, large area of land is becoming unsuitable for human settlement, hence the need for gully erosion susceptibility map of the study area.To generate gully inventory map, detailed field exercise was carried out, during this investigation one hundred gullies were identified and studied extensively within the study area of about 550 km2. In addition to the mapped gullies, Google EarthPro with high resolution imagery was used to locate the spatial extents of fifty (50) more gullies. Ten gully erosion predisposing factors were carefully selected considering the information obtained from literature, and multiple field survey of the study area, the factors include: elevation, slope angle, curvature, aspect, topographic wetness index (TWI),soil texture, geology, drainage buffer, road buffer and landuse.In this study, a GIS-based Frequency Ratio (FR) and Analytical Hierarchy process (AHP) models were employed to predict areas prone to gully erosion in Gombe town and environs.The result obtained from FR shows that drainage, soil texture and slope have highest correlation with gully occurrence, while AHP modelrevealed that drainage buffer, soil texture, geology have high correlation with the formation of gully. Gully erosion susceptibility maps (GESM) were produced and reclassified into very high, high, moderate and low zones.The overall accuracies of both models weretested by means of area under curve(AUC) values and gully density distribution. FR and AHP model have AUC values of 0.73 and 0.72 respectively, the outcome indicates that both models have high prediction accuracy. The gully erosion density distribution values revealed that gullies are concentrated in the very high susceptibility class and it decreases towards the low class, Therefore the GESM produced using these models in this study area is reliable and can be used for land management and future planning

scholarly journals GIS-based gully erosion susceptibility modeling, adapting bivariate statistical method and AHP approach in Gombe town and environs Northeast Nigeria

2020 ◽  
Author(s):  
Ogbonnaya Igwe ◽  
Ikechukwu John Ugwuoke ◽  
Onwuka Solomon ◽  
Ozioko Obinna
Keyword(s):  
Density Distribution ◽  
Soil Texture ◽  
Slope Angle ◽  
Area Under The Curve ◽  
Gully Erosion ◽  
Susceptibility Map ◽  
Large Area ◽  
Topographic Wetness Index ◽  
Ahp Model ◽  
Very High

Abstract Gully erosion is a major environmental problem in Gombe town, a large area of land is becoming unsuitable for human settlement, hence the need for gully erosion susceptibility map of the study area. To generate a gully inventory map, detailed field exercise was carried out, during this investigation one hundred gullies were identified and studied extensively within the study area of about 550 km2. In addition to the mapped gullies, Google EarthPro with high-resolution imagery was used to locate the spatial extents of fifty (50) more gullies. Ten gully erosion predisposing factors were carefully selected considering the information obtained from literature, and multiple field survey of the study area, the factors include elevation, slope angle, curvature, aspect, topographic wetness index (TWI), soil texture, geology, drainage buffer, road buffer and landuse. In this study, a GIS-based Frequency Ratio (FR) and Analytical Hierarchy Process (AHP) models were employed to predict areas prone to gully erosion in Gombe town and environs. The result obtained from FR shows that drainage, soil texture, and slope have the highest correlation with gully occurrence, while the AHP model revealed that drainage buffer, soil texture, geology have a high correlation with the formation of a gully. Gully erosion susceptibility maps (GESM) were produced and reclassified into very high, high, moderate, and low zones. The overall accuracies of both models were tested utilizing area under the curve (AUC) values and gully density distribution. FR and AHP model have AUC values of 0.73 and 0.72 respectively, the outcome indicates that both models have high prediction accuracy. The gully erosion density distribution values revealed that gullies are concentrated in the very high susceptibility class and it decreases towards the low class, Therefore the GESM produced using these models in this study area is reliable and can be used for land management and future planning

scholarly journals GIS-based gully erosion susceptibility modeling, adapting bivariate statistical method and AHP approach in Gombe town and environs Northeast Nigeria

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Ogbonnaya Igwe ◽  
Ugwuoke Ikechukwu John ◽  
Onwuka Solomon ◽  
Ozioko Obinna
Keyword(s):  
Soil Texture ◽  
Slope Angle ◽  
Area Under The Curve ◽  
Gully Erosion ◽  
Susceptibility Map ◽  
Large Area ◽  
Topographic Wetness Index ◽  
Ahp Model ◽  
Susceptibility Modeling ◽  
Very High

AbstractGully erosion is a major environmental problem in Gombe town, a large area of land is becoming unsuitable for human settlement, hence the need for a gully erosion susceptibility map of the study area. To generate a gully inventory map, a detailed field exercise was carried out, during this investigation one hundred gullies were identified and studied extensively within the study area of about 550 km2. In addition to the mapped gullies, Google EarthPro with high-resolution imagery was used to locate the spatial extents of fifty (50) more gullies. Ten gully erosion predisposing factors were carefully selected considering the information obtained from literature, and multiple field survey of the study area, the factors include elevation, slope angle, curvature, aspect, topographic wetness index (TWI), soil texture, geology, drainage buffer, road buffer and landuse. In this study, a GIS-based Frequency Ratio (FR) and Analytical Hierarchy Process (AHP) models were employed to predict areas prone to gully erosion in Gombe town and environs. The result obtained from FR shows that drainage, soil texture, and slope have the highest correlation with gully occurrence, while the AHP model revealed that drainage buffer, soil texture, geology have a high correlation with the formation of a gully. Gully erosion susceptibility maps (GESM) were produced and reclassified into very high, high, moderate, and low zones. The overall accuracies of both models were tested utilizing area under the curve (AUC) values and gully density distribution.FR and AHP model have AUC values of 0.73 and 0.72 respectively, the outcome indicates that both models have high prediction accuracy. The gully erosion density distribution values revealed that gullies are concentrated in the very high susceptibility class and it decreases towards the low class, therefore the GESM produced using these models in this study area is reliable and can be used for land management and future planning.

scholarly journals GIS-based gully erosion susceptibility modeling, adapting bivariate statistical method and AHP approach in Gombe town and environs Northeast Nigeria

2020 ◽  
Author(s):  
Ogbonnaya Igwe ◽  
Ikechukwu John Ugwuoke ◽  
Onwuka Solomon ◽  
Ozioko Obinna
Keyword(s):  
Soil Texture ◽  
Slope Angle ◽  
Area Under The Curve ◽  
Gully Erosion ◽  
Susceptibility Map ◽  
Large Area ◽  
Topographic Wetness Index ◽  
Ahp Model ◽  
Susceptibility Modeling ◽  
Very High

Abstract Gully erosion is a major environmental problem in Gombe town, a large area of land is becoming unsuitable for human settlement, hence the need for a gully erosion susceptibility map of the study area. To generate a gully inventory map, a detailed field exercise was carried out, during this investigation one hundred gullies were identified and studied extensively within the study area of about 550 km2. In addition to the mapped gullies, Google EarthPro with high-resolution imagery was used to locate the spatial extents of fifty (50) more gullies. Ten gully erosion predisposing factors were carefully selected considering the information obtained from literature, and multiple field survey of the study area, the factors include elevation, slope angle, curvature, aspect, topographic wetness index (TWI), soil texture, geology, drainage buffer, road buffer and landuse. In this study, a GIS-based Frequency Ratio (FR) and Analytical Hierarchy Process (AHP) models were employed to predict areas prone to gully erosion in Gombe town and environs. The result obtained from FR shows that drainage, soil texture, and slope have the highest correlation with gully occurrence, while the AHP model revealed that drainage buffer, soil texture, geology have a high correlation with the formation of a gully. Gully erosion susceptibility maps (GESM) were produced and reclassified into very high, high, moderate, and low zones. The overall accuracies of both models were tested utilizing area under the curve (AUC) values and gully density distribution.FR and AHP model have AUC values of 0.73 and 0.72 respectively, the outcome indicates that both models have high prediction accuracy. The gully erosion density distribution values revealed that gullies are concentrated in the very high susceptibility class and it decreases towards the low class, therefore the GESM produced using these models in this study area is reliable and can be used for land management and future planning.

scholarly journals Landslides susceptibility modelling using Multivariate Logistic Regression Model in the Sahla Watershed in Northern Morocco

2021 ◽  
Vol 33 ◽  
Author(s):  
Mohammed El-Fengour ◽  
Hanifa El Motaki ◽  
Aissa El Bouzidi
Keyword(s):  
Logistic Regression ◽  
Landslide Susceptibility ◽  
Slope Angle ◽  
Google Earth ◽  
Slope Aspect ◽  
Multivariate Logistic Regression Model ◽  
Susceptibility Map ◽  
Potential Hazard ◽  
Topographic Wetness Index ◽  
Landslide Occurrence

This study aimed to assess landslide susceptibility in the Sahla watershed in northern Morocco. Landslides hazard is the most frequent phenomenon in this part of the state due to its mountainous precarious environment. The abundance of rainfall makes this area suffer mass movements led to a notable adverse impact on the nearby settlements and infrastructures. There were 93 identified landslide scars. Landslide inventories were collected from Google Earth image interpretations. They were prepared out of landslide events in the past, and future landslide occurrence was predicted by correlating landslide predisposing factors. In this paper, landslide inventories are divided into two groups, one for landslide training and the other for validation. The Landslide Susceptibility Map (LSM) is prepared by Logistic Regression (LR) Statistical Method. Lithology, stream density, land use, slope curvature, elevation, topographic wetness index, slope aspect, and slope angle were used as conditioning factors. The Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC) was employed to examine the performance of the model. In the analysis, the LR model results in 96% accuracy in the AUC. The LSM consists of the predicted landslide area. Hence it can be used to reduce the potential hazard linked with the landslides in the Sahla watershed area in Rif Mountains in northern Morocco.

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.

Analysis of factors affecting the Spatial Distribution of co-seismic landslides triggered by the 2011 (Mw 6.9) Sikkim earthquake

2021 ◽  
Author(s):  
Saurav Kumar ◽  
Sengupta Aniruddha
Keyword(s):  
Slope Failure ◽  
Slope Angle ◽  
Slope Aspect ◽  
Susceptibility Map ◽  
Past Study ◽  
Topographic Wetness Index ◽  
Landslide Occurrence ◽  
Seismic Landslides ◽  
Sikkim Earthquake ◽  
Seismic Landslide

<p>The Himalayan region is known as an earthquake-triggered landslides prone area. It is characterized by high seismicity, large relative relief, steep slopes, and dense precipitation. These seismically triggered landslides are likely to affect substantial societal impacts, including loss of life, damage to houses, public buildings, various lifeline structures like highways, railways tracks, etc. Further, they obstruct post-earthquake emergency response efforts. A past study by Martha et al. 2014 reported that an earthquake of Mw 6.9 in 2011 triggered 1196 landslides in Sikkim which is a part of the eastern Himalayas. The slope failure events are controlled by several factors, which can be grouped into four main classes: seismology, topography, lithology, and hydrology. Each class contains several sub-factors. Having in-depth knowledge of these factors and their influence on the density of landslide events in the affected area due to the 2011 Sikkim earthquake is essential to realize the level of threat of co-seismic landslide due to future earthquakes. Eight landslide controlling factors is considered in this analysis including peak ground acceleration (PGA), slope, aspect, elevation, curvature, lithology, distance from rivers, and topographic wetness index (TWI). Further, the frequency ratio model using the GIS framework is applied to evaluate the contribution of each landslide controlling factor to landslide occurrence. Scatter plots between the number of landslides per km<sup>2</sup> (LN) and percentage of landslide area (LA) and causative factors indicate that distance from the river, slope angle, and PGA are the dominant factors that control the landslides. The results of the above analysis showed that the majority of co-seismic landslides occurred at slope >30°, preferably in East, Southeast, and South directions and near river within a distance of 1500 m. The detailed study of interactions among these factors can improve the understanding of the mechanisms of co-seismic landslide occurrence in Sikkim and will be useful for producing a co-seismic landslide susceptibility map of the area.</p>

scholarly journals Landslide Susceptibility Zonation Using Geospatial Technologies and Multi Criteria Evaluation Techniques in Upper Didessa Sub-basin, Southwest Ethiopia

2020 ◽  
Author(s):  
Redwan Sultan Mohammednur ◽  
Dessalegn Obsi Gemeda ◽  
Kiros Tsegay Deribew
Keyword(s):  
Landslide Susceptibility ◽  
Causative Factor ◽  
Mitigation Strategies ◽  
Slope Aspect ◽  
Susceptibility Map ◽  
Topographic Wetness Index ◽  
Multi Criteria Evaluation ◽  
Hypsometric Integral ◽  
Expert Interview ◽  
Very High

Abstract Landslide is a serious geo-hazard that poses destruction and loses of life in different part of the world. The severity of the problem is higher in developing countries like Ethiopia. This study is aimed at assessing the spatial landslide susceptibility in the upper Didessa sub-basin using GIS and multi criteria evaluation (MCE) techniques. In order to reach this objective both primary (field survey) and secondary data (expert interview, literature, remote sensing data, digital soil map and geological map) were obtained from various source. Eleven landslide causative factor identified in this research are slope, aspect, drainage density, topographic wetness index (TWI), stream power index (SPI), topographic ruggedness index, hypsometric integral, lithology, LULC, soil texture, and distance from road. The analytical hierarchy process (AHP) method was employed to identify the weight of each indicator from the pairwise comparison matrix. The weighted linear combination was then used to generate landslide susceptibility map (LSM). Based on landslide susceptibility, the study area was classified into very high, high, moderate, low, and very low susceptibility zones. Finally, based on the eleven-landslide causative factor analysis, about 24% of the study area is moderately susceptible, while 12% and 6% were classified as high and very high susceptibility to landslides, respectively. The results of this study could help decision makers for future landslide hazardous preventions and mitigation strategies.

scholarly journals Fire Susceptibility Mapping in the Northeast Forests and Rangelands of Iran using New and Ensemble Data Mining Models

2021 ◽  
Author(s):  
Saeedeh Eskandari ◽  
Hamid Reza Pourghasemi ◽  
John P. Tiefenbacher
Keyword(s):  
Data Mining ◽  
Slope Angle ◽  
Area Under The Curve ◽  
Susceptibility Mapping ◽  
Wind Effect ◽  
Susceptibility Map ◽  
Ensemble Model ◽  
Topographic Wetness Index ◽  
Susceptibility Maps ◽  
Golestan Province

Abstract Fires have increased in the northeastern Iran as its semiarid climate landscape is being desiccated by human activities. To combat fire outbreaks in any region, one must map fire susceptibility with accurate and efficient models. This research mapped fire susceptibility in the forests and rangelands of northeastern Iran’s Golestan Province using new data mining models. Fire effective factors data describing elevation, slope angle, annual mean rainfall, annual mean temperature, wind effect, topographic wetness index (TWI), plan curvature, distance from river, distance to road, and distance to village were obtained from several sources. The relative importance of each variable was determined with a random forest algorithm. Fire susceptibility maps were produced in R 3.3.3 software using GAM, MARS, SVM algorithms and a new ens­emble of the three models: GAM-MARS-SVM. Validation of the four fire susceptibility maps was performed with the area under the curve. Results show that distance from village, annual mean rainfall and elevation were of greatest importance in predicting fire susceptibility. The new GAM-MARS-SVM ensemble model achieved the highest fire susceptibility mapping precision. The fire susceptibility map produced using the GAM-MARS-SVM ensemble model best detected the high fire risk areas in Golestan Province.

scholarly journals Landslide susceptibility mapping of SE Serbia using GIS

2019 ◽  
Vol 80 (2) ◽  
pp. 105-116
Author(s):  
Sonja Djokanovic
Keyword(s):  
Landslide Susceptibility ◽  
Slope Angle ◽  
Landslide Susceptibility Mapping ◽  
Susceptibility Map ◽  
Susceptibility Assessment ◽  
High Susceptibility ◽  
Landslide Susceptibility Map ◽  
Analytic Hierarchy ◽  
Hierarchy Process ◽  
Very High

Landslides represent a great problem in Serbia. According to current estimates 30-35 % of Serbia is affected by landslides. In this paper a landslide susceptibility analysis is done for SE Serbia. Study area covers 1507 km2. Relief is hilly or mountainous and characterized by high altitude differences. Analysis is done by geographic information system (GIS) and evaluation by analytic hierarchy process (AHP). For susceptibility assessment are used four factors: lithology, slope angle, distance to rivers and distance to faults. The most landslides are formed on slope steepness less than 30?. There is four classes of susceptibility in study area. Zone of very high susceptibility make 63.9 % of the study area. Zone of high susceptibility covers 15.7 % of the study area. The moderate class occupies 37.4% and zone classified as having low susceptibility accounts for 10 % of study area. Final landslide susceptibility map of SE Serbia is satisfactory.

Mapping and Monitoring of the Land Use/Cover Changes in the Wider Area of ltanos, Crete, Using Very High Resolution EO Imagery With Specific Interest in Archaeological Sites

2019 ◽  
Author(s):  
Sawyer Reid stippa ◽  
George Petropoulos ◽  
Leonidas Toulios ◽  
Prashant K. Srivastava
Keyword(s):  
High Resolution ◽  
Spatial Resolution ◽  
Satellite Images ◽  
Archaeological Site ◽  
Digital Photography ◽  
Archaeological Sites ◽  
Large Area ◽  
High Resolution Imagery ◽  
Site Mapping ◽  
Rule Sets

Archaeological site mapping is important for both understanding the history as well as protecting them from excavation during the developmental activities. As archaeological sites generally spread over a large area, use of high spatial resolution remote sensing imagery is becoming increasingly applicable in the world. The main objective of this study was to map the land cover of the Itanos area of Crete and of its changes, with specific focus on the detection of the landscape’s archaeological features. Six satellite images were acquired from the Pleiades and WorldView-2 satellites over a period of 3 years. In addition, digital photography of two known archaeological sites was used for validation. An Object Based Image Analysis (OBIA) classification was subsequently developed using the five acquired satellite images. Two rule-sets were created, one using the standard four bands which both satellites have and another for the two WorldView-2 images their four extra bands included. Validation of the thematic maps produced from the classification scenarios confirmed a difference in accuracy amongst the five images. Comparing the results of a 4-band rule-set versus the 8-band showed a slight increase in classification accuracy using extra bands. The resultant classifications showed a good level of accuracy exceeding 70%. Yet, separating the archaeological sites from the open spaces with little or no vegetation proved challenging. This was mainly due to the high spectral similarity between rocks and the archaeological ruins. The satellite data spatial resolution allowed for the accuracy in defining larger archaeological sites, but still was a difficulty in distinguishing smaller areas of interest. The digital photography data provided a very good 3D representation for the archaeological sites, assisting as well in validating the satellite-derived classification maps. All in all, our study provided further evidence that use of high resolution imagery may allow for archaeological sites to be located, but only where they are of a suitable size archaeological features.

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