Earthquake-induced landslide mapping in the western Himalayas using medium resolution ASTER imagery

Lodhi, Mahtab A.

doi:10.1080/01431161.2010.502158

Cited by 12 publications

(5 citation statements)

References 31 publications

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“…Geographic Information Systems (GIS) and remote sensing are useful to develop landslide inventories and to evaluate the spatial and temporal distribution of landslides (Akbar and Ha 2011;Ayalew and Yamagishi 2005;Chalkias et al 2014;Lee and Sambath 2006;Quan and Lee 2012;Reis et al 2012). Landslide inventories are mainly developed through visual interpretation of satellite images and aerial photographs (Saba et al 2010;Scaioni et al 2014), but can also be developed using object based image classification (Lodhi 2011;Martha et al 2010). The influence of causative factors such as topography, geology, tectonics, drainage, land cover/land use and anthropogenic activity are evaluated to determine the spatial distribution and density of landslides (Kamp et al 2008), which is subsequently can be used for landslide susceptibility mapping.…”

Section: Introductionmentioning

confidence: 99%

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

2018

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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.

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Section: Introductionmentioning

confidence: 99%

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

2018

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“…Many studies have compared the characteristics of earthquake and rainfall-triggered landslide inventories and focus on mapping landslides triggered by rainfall after major earthquakes (Wei et al 2018). World widely the earthquake has widespread thousands of large landslides such as Chi Chi earthquake (Weissel and Stark 2001), the 2005 Kashmir earthquake (Kamp et al 2008;Lodhi 2011;Shafique et al 2016), Wenchuan earthquake of 2008 (Chigira et al 2010;Gorum et al 2011) and Kumamoto earthquake of 2016 (Xu et al 2018). Due to the shaking of earth during Earthquake the mountainous surface lose their shear strength which occur the large landslides in the earthquake effected area.…”

Section: Introductionmentioning

confidence: 99%

“…Landslide inventory developing and susceptibility mapping is very important to separate the area of susceptible and stable area to mitigate the property and infrastructure in the area. For developing landslide inventory different techniques are applied by different researchers like manual digitization, Pixel based classification of landslides and also the Object based image classification techniques (Martha et al 2010;Lodhi 2011;Basharat et al 2016). Spatial resolution of satellite imagery is important to define the magnitude and extent of landslide area, the fine resolution data can detect small landslides but can only work on small area due to high prices of imagery (Ali et al 2017).…”

Section: Introductionmentioning

confidence: 99%

“…Spatial resolution of satellite imagery is important to define the magnitude and extent of landslide area, the fine resolution data can detect small landslides but can only work on small area due to high prices of imagery (Ali et al 2017). Different spatial resolution data has been used for this study area for developing landside susceptibility map like ASTER has been used by (Kamp et al 2010) Landsat is used by (Lodhi 2011). Landslide susceptibility mapping is the distribution of the area into different zones on the basis of the probability of the occurrence of the landslide according to the factors which are taken during the modelling.…”

Section: Introductionmentioning

confidence: 99%

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Utilization of Fine Resolution Satellite Data for Landslide Susceptibility Modelling: A Case Study of Kashmir Earthquake Induced Landslides

Ali

Chu

Ullah

et al. 2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. The 2005 Kashmir earthquake has triggered thousands of landslides which devastated most of the livelihood and other infrastructure in the area. Landslide inventory and subsequently landslide susceptibility mapping is one of the main prerequisite for taking mitigation measure against landslide effects. This study has focused on developing most updated and realistic landslide inventory and Susceptibility mapping. The high resolution data of Worldveiw-2 having spatial resolution of 0.4 m is used for landslide inventory. Support Vector Machine (SVM) classifier was used for landslide inventory developing. Total 51460 number of landslides were classified using semi-automatic technique with covering area of 265 Km2, smallest landslide mapped is covering area of 2.01 m2 and the maximum covered area of single landslide is 3.01 Km2. Nine influential causative factors are used for landslide susceptibility mapping. Those causative factors include slope, aspect, profile curvature, elevation, distance from fault lines, distance from streams and geology. Logistic regression model was used for the Landslides susceptibility modelling. From model the highest coefficient was assigned to geology which shows that the geology has higher influence in the area. For landslide susceptibility mapping the 70 % of the data was used and 30% is used for the validation of the model. The prediction accuracy of the model in this study is 92 % using validation data. This landslide susceptibility map can be used for land use planning and also for the mitigation measure during any disaster.

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“…Geodetic techniques can provide 2D and even 3D spatial information in point or surface form. Remote sensing satellite imagery (Metternicht et al, ; Martha et al, ; Lodhi, ; Debella‐Gilo and Kääb, ), Global Positioning Systems (Malet et al, ; Mora et al, ), total stations (Petley et al, ), ground‐based SAR interferometry (Tarchi et al, ), terrestrial laser scanning (Travelletti et al, ; Pesci et al, ), airborne laser scanning (Bell et al, ), unmanned aerial vehicles (Niethammer et al, ), airborne photogrammetry (Chadwick et al, ; Baldi et al, ; Dewitte et al, ) and high speed digital cameras (Dewez et al, ) have been utilised for the pre‐ and post‐analysis of landslide events, risk assessment and long‐term monitoring tasks.…”

Section: Introductionmentioning

confidence: 99%

Photogrammetric Monitoring of an Artificially Generated Shallow Landslide

Akça

2013

The Photogrammetric Record

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An artificial rainfall event was applied to a forested slope in Ruedlingen, northern Switzerland. The experiment triggered a landslide which resulted in mobilising about 130 m 3 of debris. The event was monitored by a photogrammetric network of four cameras, operating at 5 to 8 frames per second, in order to quantify spatial and temporal changes by tracking tennis balls pegged into the ground. Image measurements were performed using automated image matching methods, implemented through a software package developed in-house. Threedimensional coordinates of the target points were estimated by running a customised type of bundle adjustment, achieving a positioning precision of AE 1Á8 cm.

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Earthquake-induced landslide mapping in the western Himalayas using medium resolution ASTER imagery

Cited by 12 publications

References 31 publications

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

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

Utilization of Fine Resolution Satellite Data for Landslide Susceptibility Modelling: A Case Study of Kashmir Earthquake Induced Landslides

Photogrammetric Monitoring of an Artificially Generated Shallow Landslide

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