Landslide susceptibility assessment based on an incomplete landslide inventory in the Jilong Valley, Tibet, Chinese Himalayas

Du, Juan; Glade, Thomas; Woldai, T.; Chai, Bo; Zeng, Bin

doi:10.1016/j.enggeo.2020.105572

Cited by 102 publications

(48 citation statements)

References 64 publications

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“…Moreover, inventories should be regularly updated, complemented by historical databases to consider the geographical distribution of past (Svennevig 2019) and recent landslides in different time periods (location of the hazard initiation and extension, age…). The regular updating of inventories is also complicated and requires large effort (Bell et al 2012;Burns and Madin 2009;Burns et al 2012;Galli et al 2008;Guzzetti et al 2012) especially for inaccessible high altitude areas (Du et al 2020). But they are essential for Landslide Susceptibility Mapping (LSM) for risk mitigation and planning; the accurate detection of landslide locations highly influence the landslide susceptibility analysis (Galli et al 2008, Song et al 2012.…”

Section: Earth Observation Data and Methods For Landslide Detection Amentioning

confidence: 99%

“…In the selected papers for this review, the radiometric analysis of optical images mainly consists of index calculations to discriminate areas covered by vegetation from exposed bare soils (Du et al 2020) and to detect anomalies in vegetation cover (Fig. 1).…”

Section: Radiometric Analysismentioning

confidence: 99%

“…Interpretation of aerial photos or satellite images are approaches commonly used to identify past and recent mass movements (Chigira et al 2004;Catani et al 2005;Ardizzone et al 2007;Galli et al 2008;Yang and Chen 2010;Song et al 2012;Chen et al 2014;Xu et al 2014;Zhang et al 2014;Ciampalini et al 2015;Fressard et al 2016;Fan et al 2017Fan et al , 2018Roulland et al 2019;Bui et al 2019;Görüm 2019;Lewkowicz and Way 2019;Pánek et al 2019;Pham et al 2019;Wang et al 2019;Du et al 2020). Based on morphological features of the landscape and visible 'anomalies', visual interpretation of images can be faster than the ground survey approach to identify mass movement.…”

Section: Data Interpretationmentioning

confidence: 99%

“…(Yang and Chen 2010;Song et al 2012;Yang et al 2013;Behling et al 2014;Moosavi et al 2014;Achour and Pourghasemi 2019;Arabameri et al 2019;Ghorbanzadeh et al 2019;Wang et al 2019;Bui et al 2020;Du et al 2020;Fang et al 2020;Hong et al 2015;Hu et al 2020;Huang et al 2020).…”

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Remote Sensing for Assessing Landslides and Associated Hazards

et al. 2020

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Multi-platform remote sensing using space-, airborne and ground-based sensors has become essential tools for landslide assessment and disaster-risk prevention. Over the last 30 years, the multiplicity of Earth Observation satellites mission ensures uninterrupted optical and radar imagery archives. With the popularization of Unmanned Aerial Vehicles, free optical and radar imagery with high revisiting time, ground and aerial possibilities to perform high-resolution 3D point clouds and derived digital elevation models, it can make it difficult to choose the appropriate method for risk assessment. The aim of this paper is to review the mainstream remote-sensing methods commonly employed for landslide assessment, as well as processing. The purpose is to understand how remote-sensing techniques can be useful for landslide hazard detection and monitoring taking into consideration several constraints such as field location or costs of surveys. First we focus on the suitability of terrestrial, aerial and spaceborne systems that have been widely used for landslide assessment to underline their benefits and drawbacks for data acquisition, processing and interpretation. Several examples of application are presented such as Interferometry Synthetic Aperture Radar (InSAR), lasergrammetry, Terrestrial Optical Photogrammetry. Some of these techniques are unsuitable for slow moving landslides, others limited to large areas and others to local investigations. It can be complicated to select the most appropriate system. Today, the key for understanding landslides is the complementarity of methods and the automation of the data processing. All the mentioned approaches can be coupled (from field monitoring to satellite images analysis) to improve risk management, and the real challenge is to improve automatic solution for landslide recognition and monitoring for the implementation of near real-time emergency systems.

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Section: Earth Observation Data and Methods For Landslide Detection Amentioning

confidence: 99%

Section: Radiometric Analysismentioning

confidence: 99%

Section: Data Interpretationmentioning

confidence: 99%

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Remote Sensing for Assessing Landslides and Associated Hazards

et al. 2020

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“…To monitor slope movement over large mountain regions, remote sensing techniques have been an efficient way (Du et al 2020). Optical passive and microwave active radar remote sensing are the two most frequently used methods to detect slope displacements.…”

Section: Introductionmentioning

confidence: 99%

Deriving slope movements for an imminent landslide along the Jinsha river

Yang

Liu

Shi

2020

Preprint

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Abstract. Landslides are major hazards that may pose serious threats to mountain communities. Even landslides in remote mountains could have non-negligible impacts on populous regions by blocking large rivers and forming megafloods. Usually, there are slope deformations before major landslides occur, and detecting precursors over large mountain regions is important for screening possible landslide disasters. In this work, we applied multi-temporal optical remote sensing images (Landsat 7 and Sentinel-2) and an image correlation method to detect sub-pixel slope deformations of a slope. Along the Jinsha river, this slope is located downstream the famous Baige landslide near the Mindu town, Tibet Autonomous Region. We used DEM derived aspect to restrain background noises in image correlation results. We found the slope remained stable from November 2015 to November 2018 and moved significantly from November 2018 to November 2019. We used more data to analyse slope movement in 2019 and found retrogressive slope movements with increasingly large deformations near the river bank. We also analysed spatial-temporal patterns of the slope deformation from October 2018 to February 2020 and found seasonal variations in slope deformations. Only the slope foot moved in dry seasons, whereas the entire slope activated in rainy seasons. Until 24 August 2019, the size of the slope with displacements larger than 3 m is similar to that of the Baige landslide. However, the river width at the foot of this slope is much narrower than the river width at the foot of the Baige landslide. We speculate it may continue to slide down and could threaten the Jinsha river. Further modelling works should be done to check if the imminent landslide could dam the Jinsha river and measures be taken to mitigate possible dammed breach flood disasters. This work illustrates the potential of using optical remote sensing to monitor slope deformations over large remote mountain regions.

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