Detecting precursors of an imminent landslide  along the Jinsha River

Yang, Wentao; Liu, Lianyou; Shi, Peijun

doi:10.5194/nhess-20-3215-2020

Cited by 13 publications

(4 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There are 13 bands with a spatial resolution of 10, 20, and 60 m. Nearinfrared (NIR), red, green, and blue are four bands with 10 m in spatial resolution. The 10-m red band is the most frequently used data to detect surface deformation (Yang et al, 2020a;Qi et al, 2021).…”

Section: Deformation Derived By Pixel Offset Tracking and Time-series...mentioning

confidence: 99%

“…For this work, we selected images that were least affected by clouds. We used the COSI-Corr software, which was developed by a team in the California Institute of Technology (Leprince et al, 2007) and is a frequently used sub-pixel offset tracking method to derive surface deformation in Sentinel-2 images (Bontemps et al, 2018;Lacroix et al, 2018;Yang et al, 2020a;Qi et al, 2021). To derive surface deformation with the software, we must compose a pair of two images acquired at different dates.…”

Section: Deformation Derived By Pixel Offset Tracking and Time-series...mentioning

confidence: 99%

See 1 more Smart Citation

Impact of precipitation on Beishan landslide deformation from 1986 to 2023

Liu,

Yang,

et al. 2024

Front. Earth Sci.

Self Cite

View full text Add to dashboard Cite

Investigating the response of landslide activity to climate change is crucial for understanding the disastrous effects of climate change on high mountains. However, the lack of long-term, spatial–temporal consistent measurement of landslide activity prohibits the study of this relationship. In this work, we used two methods to derive the time series of a landslide’s deformation and study its relationship with precipitation in the northeastern Tibetan Plateau. The small baseline subset-interferometric synthetic aperture radar (SBAS-InSAR) method with Sentinel-1A images is first applied to derive time series of the landslide’s deformation from 2020 to 2021. A recently developed method to derive cumulative deformations of optical images was used with Landsat 5 and Sentinel-2 images to derive the long-term deformation time series from 1986 to 2023. Centimeter-scale deformations detected by using the InSAR method are mainly located in the upper and eastern parts of the landslide, whereas meter-scale deformations detected by using the optical method are in the middle of the landslide. Time-series results from both methods show that intra-annual initiations of the landslide’s deformation occurred in rainy months (from July to October). Although there seems to be no direct relations between inter-annual deformations and precipitation, significant displacements since 2020 occurred after exceptionally wet years from 2018 (with a record-breaking precipitation year in 2020). With optical images, we found that the maximum cumulative deformation of the landslide has been >35 m since 1986 with major deformations (>20 m) found after 2020, which may indicate an imminent risk to the Lijie town near the toe of the landslide. With climate change, increased precipitation is expected in future, which may trigger more similar landslides in the vicinity of this region. This work demonstrates an executable framework to assess landslide hazard risk under climate change.

show abstract

Section: Deformation Derived By Pixel Offset Tracking and Time-series...mentioning

confidence: 99%

Section: Deformation Derived By Pixel Offset Tracking and Time-series...mentioning

confidence: 99%

Impact of precipitation on Beishan landslide deformation from 1986 to 2023

Liu,

Yang,

et al. 2024

Front. Earth Sci.

Self Cite

View full text Add to dashboard Cite

show abstract

“…The landslide area is crossed by the N-NW-trending Jinsha tectonic belt, forming complex structural conditions with multiple faults and structural blocks and undermining the slope with long-term tectonic activities [124]. It is widely accepted that this landslide event occurred as a result of long-term slope deformation [74,124,[126][127][128][129][130][131]. Liu et al [132] collected 28 Advanced Land Observing Satellite (ALOS) SAR images from January 2007 to August 2018 to characterize the deformation history and temporal evolution of the Baige landslide and used a new offset tracking method to correct the SAR image deformation errors; they found that the maximum cumulative deformation of the Baige landslide in the radar line of sight reached approximately −60 m. Li et al [71] used ALOS and Sentinel-1 spaceborne SAR data from 2007 to 2018 to study the temporal evolution of the surface displacement of the Baige landslide before the first collapse.…”

Section: The Baige Landslidementioning

confidence: 99%

Remote Sensing Precursors Analysis for Giant Landslides

Lan

Liu

et al. 2022

Remote Sensing

View full text Add to dashboard Cite

Monitoring and early warning systems for landslides are urgently needed worldwide to effectively reduce the losses of life and property caused by these natural disasters. Detecting the precursors of giant landslides constitutes the premise of landslide monitoring and early warning, and remote sensing is a powerful means to achieve this goal. In this work, we aim to summarize the basic types and evolutionary principles of giant landslide precursors, describe the remote sensing methods capable of identifying those precursors, and present typical cases of related sliding. Based on a review of the literature and an analysis of remote sensing imagery, the three main types of remote sensing techniques for capturing the geomorphological, geotechnical, and geoenvironmental precursors of giant landslides are optical, synthetic aperture radar (SAR), and thermal infrared methods, respectively. Time-series optical remote sensing data from medium-resolution satellites can be used to obtain abundant information on geomorphological changes, such as the extension of cracks and erosion ditches, and band algebraic analysis, image enhancement, and segmentation techniques are valuable for focusing on the locations of geomorphological landslide precursors. SAR sensors have the ability to monitor the slight slope deformation caused by unfavorable geological structures and can provide precursor information on imminent failure several days before a landslide; furthermore, persistent scatterer interferometric SAR has significant advantages in large-scale surface displacement monitoring. Thermal infrared imagery can identify landslide precursors by monitoring geoenvironmental information, especially in permafrost regions where glaciers are widely distributed; the reason may be that freeze–thaw cycles and snowmelt caused by increased temperatures affect the stability of the surface. Optical, SAR, and thermal remote sensing all exhibit unique advantages and play an essential role in the identification of giant landslide precursors. The combined application of these three remote sensing technologies to obtain the synthetic geomorphological, geotechnical, and geoenvironmental precursors of giant landslides would greatly promote the development of landslide early warning systems.

show abstract

“…The Landsat-8 satellite payload consists of two instruments: Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS). The OLI provides nine bands at a spatial resolution of 30 m. TIRS provides two separate thermal infrared bands at a resolution of 100 m. Landsat series data are currently widely used in many research fields, such as natural resource protection [ 34 ], energy exploration [ 35 ], environmental management [ 36 ], and natural disaster monitoring [ 37 ].…”

Section: Study Area and Data Sourcesmentioning

confidence: 99%

Urban Form and Function Optimization for Reducing Carbon Emissions Based on Crowd-Sourced Spatio-Temporal Data

Cao

Qiu

Wang

et al. 2022

IJERPH

View full text Add to dashboard Cite

The low-carbon city has become an important global urban development-oriented goal. One important aspect of urban space is low-carbon urban planning, which has a vital role in urban carbon emissions. Which types of urban form and function allocations are conducive to reducing carbon emissions is therefore a key issue. In this study, the Futian and Luohu Districts of Shenzhen, Guangdong Province, China, are taken as an example to investigate this issue. Firstly, a “head/tail” breaks method based on the third fractal theory is adopted to obtain the minimum evaluation parcel of urban space. Then, the Landscape Shape Index (LSI), Fragmentation Index (C), Shannon’s Diversity Index (SHDI), and Density of Public Facilities (Den) are used to evaluate the form and function allocation of each parcel. In addition, the CO2 concentration distribution in this study area is acquired from remote sensing satellite data. Finally, the relationships between urban form, function allocation, and CO2 concentration are obtained. The results show that the lower the urban form index or the higher the urban function index, the less the CO2 concentration. To verify this conclusion, three experiments are designed and carried out. In experiment A, the CO2 concentration of the tested area is reduced by 14.31% by decreasing the LSI and C by 6.1% and 9.4%, respectively. In experiment B, the CO2 concentration is reduced by 15.15% by increasing the SHDI and Den by 16.3% and 12.1%, respectively. In experiment C, the CO2 concentration is reduced by 27.72% when the urban form and function are adjusted in the same was as in experiments A and B.

show abstract

Detecting precursors of an imminent landslide along the Jinsha River

Cited by 13 publications

References 27 publications

Impact of precipitation on Beishan landslide deformation from 1986 to 2023

Impact of precipitation on Beishan landslide deformation from 1986 to 2023

Remote Sensing Precursors Analysis for Giant Landslides

Urban Form and Function Optimization for Reducing Carbon Emissions Based on Crowd-Sourced Spatio-Temporal Data

Contact Info

Product

Resources

About