Comparison of Earthquake-Triggered Landslide Inventories: A Case Study of the 2015 Gorkha Earthquake, Nepal

Meena, Sansar Raj; Piralilou, Sepideh Tavakkoli

doi:10.3390/geosciences9100437

Cited by 31 publications

(24 citation statements)

References 58 publications

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“…they do not follow standardised approaches or guidelines for data collection. This heterogeneity also contributes to a lack of information exchange and hinders collaboration because the landslide information of one organisation may not fulfil another organisation's requirements (Meena et al, 2018). In addition to public authorities, some scientific organisations also address landslide risk management, mainly focusing on testing new landslide documentation with remote sensing.…”

Section: Introductionmentioning

confidence: 99%

Nepalese landslide information system (NELIS): a conceptual framework for a web-based geographical information system for enhanced landslide risk management in Nepal

Meena

Albrecht

Hölbling

et al. 2021

Nat. Hazards Earth Syst. Sci.

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Abstract. Comprehensive and sustainable landslide risk management, including the identification of areas susceptible to landslides, requires responsible organisations to collaborate efficiently. Landslide risk management efforts are often made after major triggering events, such as hazard mitigation after the 2015 Gorkha earthquake in Nepal. There is also a lack of knowledge sharing and collaboration among stakeholders to cope with major disaster events, in addition to a lack of efficiency and continuity. There should be a system to allow for landslide information to be easily updated after an event. For a variety of users of landslide information in Nepal, the availability and extraction of landslide data from a common database are a vital requirement. In this study, we investigate the requirements to propose a concept for a web-based Nepalese landslide information system (NELIS) that provides users with a platform to share information about landslide events to strengthen collaboration. The system will be defined as a web GIS (geographic information system) that supports responsible organisations in addressing and managing different user requirements of people working with landslides, thereby improving the current state of landslide hazard and risk management in Nepal. The overall aim of this study is to propose a conceptual framework and design of NELIS. A system like NELIS could benefit stakeholders involved in data collection and landslide risk management in their efforts to report and provide landslide information. Moreover, such a system would allow for detailed and structured landslide documentation and consequently provide valuable information regarding susceptibility and hazard and risk mapping. For the reporting of landslides directly to the system, a web portal is proposed. Based on field surveys, a literature review and stakeholder interviews, a structure of the landslide database and a conceptual framework for the NELIS platform are proposed.

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Section: Introductionmentioning

confidence: 99%

Nepalese landslide information system (NELIS): a conceptual framework for a web-based geographical information system for enhanced landslide risk management in Nepal

Meena

Albrecht

Hölbling

et al. 2021

Nat. Hazards Earth Syst. Sci.

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“…Advances in technologies like remote sensing (RS), earth observation (EO) and geographic information systems (GIS) have significantly contributed to mitigation and planning measures for future hazards (Ghorbanzadeh et al 2018a(Ghorbanzadeh et al , 2019. In recent years, RS and GIS have been extensively used to evaluate natural hazards (Van Westen et al 2003;Oh and Pradhan 2011;Meena and Tavakkoli Piralilou 2019). RS, along with GIS, has been used in many studies to analyze flood susceptibility (Rahmati et al 2015a;Tehrany et al 2015a;Arabameri et al 2019;Tien Bui et al 2019).…”

Section: Introductionmentioning

confidence: 99%

A novel per pixel and object-based ensemble approach for flood susceptibility mapping

Nachappa

Meena

2020

Geomatics, Natural Hazards and Risk

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Conducting flood susceptibility assessments is critical for the identification of flood hazard zones and the mitigation of the detrimental impacts of floods in the future through improved flood management measures. The significance of this study is that we create ensemble methods using the per-pixel approaches of frequency ratio (FR), analytical hierarchical process (AHP), and evidence belief function (EBF) used for weightings with the object-based 'geons' approach used for aggregation to create a flood susceptibility map for the East Rapti Basin in Nepal. We selected eight flood conditioning factors considered to be relevant in the study area. The flood inventory data for the East Rapti basin was derived from past flood inventory datasets held in the regional database system by the International Centre for Integrated Mountain Development (ICIMOD). The flood inventory was classified into training and validation datasets based on the widely used split ratio of 70/30. The Receiver Operating Characteristic (ROC) was used to determine the accuracy of the flood susceptibility maps. The AUC results indicated that the combined per-pixel and object-based geon approaches yielded better results than the per-pixel approaches alone. Our results showed that the object-based geon approach creates meaningful regional units that are beneficial for future planning.

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“…Regardless of the type of methodology applied for landslide susceptibility mapping, reliable inventory data sets play an essential role in this process. A landslide inventory data set, including precise boundaries, spatial locations, and distributions, can be produced by conducting field surveys using the global positioning system (GPS), which is an expensive and, in some cases, dangerous approach due to the rough topography and instability [10,11]. Therefore, Earth observation (EO) products are considered a low cost and useful data source for landslide inventory data set production [12].…”

Section: Introductionmentioning

confidence: 99%

Landslide Detection Using Multi-Scale Image Segmentation and Different Machine Learning Models in the Higher Himalayas

et al. 2019

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Landslides represent a severe hazard in many areas of the world. Accurate landslide maps are needed to document the occurrence and extent of landslides and to investigate their distribution, types, and the pattern of slope failures. Landslide maps are also crucial for determining landslide susceptibility and risk. Satellite data have been widely used for such investigations—next to data from airborne or unmanned aerial vehicle (UAV)-borne campaigns and Digital Elevation Models (DEMs). We have developed a methodology that incorporates object-based image analysis (OBIA) with three machine learning (ML) methods, namely, the multilayer perceptron neural network (MLP-NN) and random forest (RF), for landslide detection. We identified the optimal scale parameters (SP) and used them for multi-scale segmentation and further analysis. We evaluated the resulting objects using the object pureness index (OPI), object matching index (OMI), and object fitness index (OFI) measures. We then applied two different methods to optimize the landslide detection task: (a) an ensemble method of stacking that combines the different ML methods for improving the performance, and (b) Dempster–Shafer theory (DST), to combine the multi-scale segmentation and classification results. Through the combination of three ML methods and the multi-scale approach, the framework enhanced landslide detection when it was tested for detecting earthquake-triggered landslides in Rasuwa district, Nepal. PlanetScope optical satellite images and a DEM were used, along with the derived landslide conditioning factors. Different accuracy assessment measures were used to compare the results against a field-based landslide inventory. All ML methods yielded the highest overall accuracies ranging from 83.3% to 87.2% when using objects with the optimal SP compared to other SPs. However, applying DST to combine the multi-scale results of each ML method significantly increased the overall accuracies to almost 90%. Overall, the integration of OBIA with ML methods resulted in appropriate landslide detections, but using the optimal SP and ML method is crucial for success.

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Comparison of Earthquake-Triggered Landslide Inventories: A Case Study of the 2015 Gorkha Earthquake, Nepal

Cited by 31 publications

References 58 publications

Nepalese landslide information system (NELIS): a conceptual framework for a web-based geographical information system for enhanced landslide risk management in Nepal

Nepalese landslide information system (NELIS): a conceptual framework for a web-based geographical information system for enhanced landslide risk management in Nepal

A novel per pixel and object-based ensemble approach for flood susceptibility mapping

Landslide Detection Using Multi-Scale Image Segmentation and Different Machine Learning Models in the Higher Himalayas

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