Using Sentinel-2 time series to detect slope movement before the Jinsha River landslide

Yang, Wentao; Wang, Yunqi; Sun, Shao; Wang, Yujie; Ma, Chao

doi:10.1007/s10346-019-01178-8

Cited by 52 publications

(41 citation statements)

References 26 publications

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“…This part of the slope has an aspect of the southeast, with azimuth between 112.5° and 157.5° (Figure 1c). This downstream landslide around the Mindu town has a similar monsoon climate with the Baige landslide and most of the rain occurs from May to October (Yang et al 2019).…”

Section: Methodsmentioning

confidence: 95%

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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Section: Methodsmentioning

confidence: 95%

Deriving slope movements for an imminent landslide along the Jinsha river

Yang

Liu

Shi

2020

Preprint

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“…To derive slope movement, we used the red band because its wavelength is longer than those of other visible bands and is less influenced by the atmosphere. Compared to the near infrared, this band is less sensitive to vegetation and is more reliable for measuring slope deformation (Yang et al, 2019). We used the Level-1C product, which is orthorectified before distribution (Gascon et al, 2017).…”

Section: Study Areamentioning

confidence: 99%

“…The latter type is good at detecting larger slope movements that are visible on images (Bradley et al, 2019;Lacroix et al, 2020). In recent years, image correlation methods have been proposed and widely used to detect subpixel slope displacements in optical images (Bontemps et al, 2018;Lacroix et al, 2018Lacroix et al, , 2019Yang et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Detecting precursors of an imminent landslide along the Jinsha River

Yang

Liu

Shi

2020

Nat. Hazards Earth Syst. Sci.

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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 dam-breached mega floods. Usually, there are slope deformations before major landslides occur, and detecting precursors such as slope movement before major landslides is important for preventing possible disasters. In this work, we applied multi-temporal optical remote sensing images (Landsat 7 and Sentinel-2) and an image correlation method to detect subpixel slope deformations of a slope near the town of Mindu in the Tibet Autonomous Region. This slope is located on the right bank of the Jinsha River, ∼80 km downstream from the famous Baige landslide. We used a DEM-derived aspect to restrain background noise in image correlation results. We found the slope remained stable from November 2015 to November 2018 and moved significantly from November 2018. We used more data to analyse slope movement in 2019 and found retrogressive slope movements with increasingly large deformations near the riverbank. 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 foot of the slope moved in dry seasons, whereas the entire slope was activated in rainy seasons. Until 24 August 2019, the size of the slope with displacements larger than 3 m was 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 threaten the Jinsha River. Further modelling works should be carried out to check if the imminent landslide could dam the Jinsha River and measures should be taken to mitigate possible dam breach flood disasters. This work illustrates the potential of using optical remote sensing to monitor slope deformations over remote mountain regions.

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“…Such medium- resolution SITS data provide valuable information about the status and dynamics of the Earth surface, supporting analyses of the functional and structural characteristics of land covers as well as identifying change events [2,3]. For this reason, SITS have been widely used in various application domains, such as ecology [4], agriculture [5], forest [6], land management [7], disaster monitoring [8] risk assessment [9], etc. In the meantime, new challenges are also being introduced by the question of how to extract valuable knowledge and meaningful information to exploit such abundant data.…”

Section: Introductionmentioning

confidence: 99%

Self-Supervised Pre-Training of Transformers for Satellite Image Time Series Classification

Yuan¹,

Liu²

2020

Preprint

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<div>Satellite image time series (SITS) classification is a major research topic in remote sensing and is relevant for a wide range of applications. Deep learning approaches have been commonly employed for SITS classification and have provided state-of-the-art performance. However, deep learning methods suffer from overfitting when labeled data is scarce. To address this problem, we propose a novel self-supervised pre-training scheme to initialize a Transformer-based network by utilizing large-scale unlabeled data. In detail, the model is asked to predict randomly contaminated observations given an entire time series of a pixel. The main idea of our proposal is to leverage the inherent temporal structure of satellite time series to learn general-purpose spectral-temporal representations related to land cover semantics. Once pre-training is completed, the pre-trained network can be further adapted to various SITS classification tasks by fine-tuning all the model parameters on small-scale task-related labeled data. In this way, the general knowledge and representations about SITS can be transferred to a label-scarce task, thereby improving the generalization performance of the model as well as reducing the risk of overfitting. Comprehensive experiments have been carried out on three benchmark datasets over large study areas. Experimental results demonstrate the effectiveness of the proposed method, leading to a classification accuracy increment up to 2.38% to 5.27%. The code and the pre-trained model will be available at https://github.com/linlei1214/SITS-BERT upon publication.</div><div><b>This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.</b></div>

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Using Sentinel-2 time series to detect slope movement before the Jinsha River landslide

Cited by 52 publications

References 26 publications

Deriving slope movements for an imminent landslide along the Jinsha river

Deriving slope movements for an imminent landslide along the Jinsha river

Detecting precursors of an imminent landslide along the Jinsha River

Self-Supervised Pre-Training of Transformers for Satellite Image Time Series Classification

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