Monitoring of landslide activity at the Sirobagarh landslide, Uttarakhand, India, using LiDAR, SAR interferometry and geodetic surveys

Tiwari, Ashutosh; Narayan, Avadh Bihari; Dwivedi, Ramji; Dikshit, Onkar; Nagarajan, B.

doi:10.1080/10106049.2018.1524516

Cited by 21 publications

(10 citation statements)

References 29 publications

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“…In the case of D-InSAR, the topographic component is removed by using a pre-existing DEM to leave only the phase difference associated to the displacement [48]. Although, a DEM sampled at SAR resolution would be necessary to completely remove the topography, lower resolution global DEMs, available in open access, are suitable when studying ground deformations (e.g., SRTM DEM [49][50][51][52][53][54][55]). The orbital contribution can be removed as we selected SAR images acquired along a same orbit, which we assume to be correctly georeferenced.…”

Section: Radar Remote Sensingmentioning

confidence: 99%

“…To do this, the DEM of the Shuttle Radar Topography Mission (SRTM, 30 m resolution), was subtracted from the preliminary product. During the last decades, many researchers used SRTM DEMs for their InSAR processing [49][50][51][52][53][54][55]. As the same topographic reference (DEM) is used for each pair of images, it does not affect the measurement of relative displacements of a few centimeters.…”

Section: Radar Remote Sensingmentioning

confidence: 99%

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Monitoring the Recent Activity of Landslides in the Mailuu-Suu Valley (Kyrgyzstan) Using Radar and Optical Remote Sensing Techniques

et al. 2020

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Central Asian mountain regions are prone to multiple types of natural hazards, often causing damage due to the impact of mass movements. In spring 2017, Kyrgyzstan suffered significant losses from a massive landslide activation event, during which also two of the largest deep-seated mass movements of the former mining area of Mailuu-Suu—the Koytash and Tektonik landslides—were reactivated. This study consists of the use of optical and radar satellite data to highlight deformation zones and identify displacements prior to the collapse of Koytash and to the more superficial deformation on Tektonik. Especially for the first one, the comparison of Digital Elevation Models of 2011 and 2017 (respectively, satellite and unmanned aerial vehicle (UAV) imagery-based) highlights areas of depletion and accumulation, in the scarp and near the toe, respectively. The Differential Synthetic Aperture Radar Interferometry analysis identified slow displacements during the months preceding the reactivation in April 2017, indicating the long-term sliding activity of Koytash and Tektonik. This was confirmed by the computation of deformation time series, showing a positive velocity anomaly on the upper part of both landslides. Furthermore, the analysis of the Normalized Difference Vegetation Index revealed land cover changes associated with the sliding process between June 2016 and October 2017. In addition, in situ data from a local meteorological station highlighted the important contribution of precipitation as a trigger of the collapse. The multidirectional approach used in this study demonstrated the efficiency of applying multiple remote sensing techniques, combined with a meteorological analysis, to identify triggering factors and monitor the activity of landslides.

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Section: Radar Remote Sensingmentioning

confidence: 99%

Section: Radar Remote Sensingmentioning

confidence: 99%

Monitoring the Recent Activity of Landslides in the Mailuu-Suu Valley (Kyrgyzstan) Using Radar and Optical Remote Sensing Techniques

et al. 2020

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“…Analysis of deformations events like landslide, earthquake, land subsidence etc (Tiwari et al, 2018) It assesses infrastructure damages and ground changes.…”

Section: Optical Sar Lidarmentioning

confidence: 99%

A Comparative Assessment of Remote Sensing Imaging Techniques: Optical, Sar and Lidar

Agrawal

Khairnar

2019

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

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Abstract. Remote sensing is a popular technique that is using in the mapping and monitoring of earth features. Early remote sensing was predominantly passive, i.e. it depends upon the sun for energy. Till the last decade, optical imaging is mainly used in remote sensing works. Over the time, significant innovations and improvements have been made in the active remote sensing which resulted in the form of sophisticated imaging techniques like Synthetic Aperture RADAR (SAR) and Light Detection And Ranging (LiDAR). These advancements promoted the use of multi-sensor and multi-source data in remote sensing projects. This highlights the need to review these imaging techniques. Therefore, this review work is carried out. In this review, different imaging techniques are discussed and compared. Optical, SAR and LiDAR techniques are first summarized and then they are compared on the basis of technical specification and working. Finally, an in-depth assessment is made on the applicability of these imaging techniques in different fields.

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“…These thresholds can be used to determine the temporal probability of the occurrence of landslides as a part of a landslide early warning system (LEWS). The thresholds can be estimated using empirical methods [9][10][11][12], probabilistic methods [8,13,14], and/or physical methods [15,16], and we can use other tools like geographic information system (GIS) [17] and global positioning system (GPS) [18,19] and new technologies in order to make the calculation processes automatic [20,21]. When long-term monitoring data is not available for the study area, it is often difficult to identify the triggering rainfall corresponding to landslides [22].…”

Section: Introductionmentioning

confidence: 99%

IoT-Based Geotechnical Monitoring of Unstable Slopes for Landslide Early Warning in the Darjeeling Himalayas

Abraham

Satyam

Pradhan

et al. 2020

Sensors

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In hilly areas across the world, landslides have been an increasing menace, causing loss of lives and properties. The damages instigated by landslides in the recent past call for attention from authorities for disaster risk reduction measures. Development of an effective landslide early warning system (LEWS) is an important risk reduction approach by which the authorities and public in general can be presaged about future landslide events. The Indian Himalayas are among the most landslide-prone areas in the world, and attempts have been made to determine the rainfall thresholds for possible occurrence of landslides in the region. The established thresholds proved to be effective in predicting most of the landslide events and the major drawback observed is the increased number of false alarms. For an LEWS to be successfully operational, it is obligatory to reduce the number of false alarms using physical monitoring. Therefore, to improve the efficiency of the LEWS and to make the thresholds serviceable, the slopes are monitored using a sensor network. In this study, micro-electro-mechanical systems (MEMS)-based tilt sensors and volumetric water content sensors were used to monitor the active slopes in Chibo, in the Darjeeling Himalayas. The Internet of Things (IoT)-based network uses wireless modules for communication between individual sensors to the data logger and from the data logger to an internet database. The slopes are on the banks of mountain rivulets (jhoras) known as the sinking zones of Kalimpong. The locality is highly affected by surface displacements in the monsoon season due to incessant rains and improper drainage. Real-time field monitoring for the study area is being conducted for the first time to evaluate the applicability of tilt sensors in the region. The sensors are embedded within the soil to measure the tilting angles and moisture content at shallow depths. The slopes were monitored continuously during three monsoon seasons (2017–2019), and the data from the sensors were compared with the field observations and rainfall data for the evaluation. The relationship between change in tilt rate, volumetric water content, and rainfall are explored in the study, and the records prove the significance of considering long-term rainfall conditions rather than immediate rainfall events in developing rainfall thresholds for the region.

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Monitoring of landslide activity at the Sirobagarh landslide, Uttarakhand, India, using LiDAR, SAR interferometry and geodetic surveys

Cited by 21 publications

References 29 publications

Monitoring the Recent Activity of Landslides in the Mailuu-Suu Valley (Kyrgyzstan) Using Radar and Optical Remote Sensing Techniques

Monitoring the Recent Activity of Landslides in the Mailuu-Suu Valley (Kyrgyzstan) Using Radar and Optical Remote Sensing Techniques

A Comparative Assessment of Remote Sensing Imaging Techniques: Optical, Sar and Lidar

IoT-Based Geotechnical Monitoring of Unstable Slopes for Landslide Early Warning in the Darjeeling Himalayas

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