Investigation of a Small Landslide in the Qinghai-Tibet Plateau by InSAR and Absolute Deformation Model

Hao, Jinkai; Wu, Tonghua; Wu, Xiaodong; Hu, Guojie; Zou, Defu; Zhu, Xiaofan; Zhao, Lin; Li, Ren; Xie, Changwei; Ni, Jie; Cheng, Yang; Li, Xiangfei; Ma, Weiguang

doi:10.3390/rs11182126

Cited by 28 publications

(15 citation statements)

References 71 publications

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“…Based on the annual deformation rate after conduction of hot spot analysis, 517 landslides with evident deformation and certain threats to railways were interpreted (Figure 8). Many detected landslides have been validated by field investigation and published articles, such as those pertaining to the Yigong landslide, Lancang River, Jinsha River, and Yalong River landslide groups [11,12,[38][39][40][41][42]. By comparing the identification results of hot spot analysis and traditional manual visual interpretation, it was found that traditional visual interpretation resulted in the exclusion of data on 178 missed landslides and 58 misjudged landslides.…”

Section: Active Landslide Inventorymentioning

confidence: 99%

Landslide Detection in the Linzhi–Ya’an Section along the Sichuan–Tibet Railway Based on InSAR and Hot Spot Analysis Methods

et al. 2021

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Construction of the 998.64-km Linzhi–Ya’an section of the Sichuan‒Tibet Railway has been influenced by landslide disasters, threatening the safety of Sichuan‒Tibet railway projects. Landslide identification and deformation analysis in this area are urgently needed. In this context, it was the first time that 164 advanced land-observing satellite-2 (ALOS-2) phased array type L-band synthetic aperture radar-2 (PALSAR-2) images were collected to detect landslide disasters along the entire Linzhi‒Ya’an section. Interferogram stacking and small baseline interferometry methods were used to derive the deformation rate and time-series deformation from 2014‒2020. After that, the hot spot analysis method was introduced to conduct spatial clustering analysis of the annual deformation rate, and the effective deformation area was quickly extracted. Finally, 517 landslide disasters along the Linzhi‒Ya’an route were detected by integrating observed deformation, Google Earth optical images, and external geological data. The main factors controlling the spatial landslide distribution were analyzed. In the vertical direction, the spatial landslide distribution was mainly concentrated in the elevation range of 3000‒5000 m, the slope range of 10‒40°, and the aspect of northeast and east. In the horizontal direction, landslides were concentrated near rivers, and were also closely related to earthquake-prone areas, fault zones, and high-precipitation areas. In short, rainfall, freeze–thaw weathering, seismic activity, and fault zones are the main factors inducing landslides along this route. This research provides scientific support for the construction and operation of the Linzhi‒Ya’an section of the Sichuan‒Tibet Railway.

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Section: Active Landslide Inventorymentioning

confidence: 99%

Landslide Detection in the Linzhi–Ya’an Section along the Sichuan–Tibet Railway Based on InSAR and Hot Spot Analysis Methods

et al. 2021

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“…The size and the resolution of detected damage depend on the spatial resolution of satellite data: from several meters to several tens of meters. Moreover, interferometric synthetic aperture radar (InSAR) enables us to estimate the amount of displacement within a few centimeters (e.g., [39][40][41]). Using these approaches, mass movement can be detected as pixel-by-pixel changes.…”

Section: Discussionmentioning

confidence: 99%

Topographic Analysis of Landslide Distribution Using AW3D30 Data

Nonomura

Hasegawa

Kanbara³

et al. 2020

Geosciences

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Landslides cause serious damage to society, and some occur as reactivations of old landslides in response to earthquakes and/or rainfall. Landslide distributions are therefore useful when siting engineering projects such as road and tunnel constructions. Although several methods have been proposed to extract landslides from topographic data on the basis of their morphological features (crown, main scarp, and main body), such morphological features are gradually eroded by heavy precipitation or landslide recurrence. Therefore, conventional methods cannot always identify areas influenced by recurrent landslides. In this study, we investigated the relationship between ridgeline continuity and landslide distribution using AW3D30, which is a global digital surface model (DSM) dataset produced from the Panchromatic Remote-sensing Instrument for Stereo Mapping (PRISM) onboard the Advanced Land Observing Satellite (ALOS) launched by the Japan Aerospace Exploration Agency (JAXA) in 2013. The relationship between the area of landslides and the number of ridge pixels was analyzed, and we propose a method for estimating the upper bound distribution of landslide topographies based on extracted ridgelines data using the Data Envelopment Analysis (DEA) function on the R statistical software packages. The upper bound on the area of landslides decreases as the number of ridge pixels increases. The same trend was seen in all the five sites, and the upper bound derived from one site is hardly exceeded by those derived from all other sites. By using the upper bound distribution function, the landslide distribution will not be missed.

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“…where λ is the wavelength, B ⊥ denotes the perpendicular baseline, θ and R denote the incidence angle and slant range distance, respectively, and t represents the time interval between two SAR scenes. In recent studies, The seasonal subsidence with a short time interval was successfully monitored based on the PSI (StaMPS-InSAR) [60] or DInSAR [61] method using Sentinel-1 data. The linear model with a short time interval used in seasonal subsidence monitoring is acceptable.…”

Section: Nsbas Methodsmentioning

confidence: 99%

Active Layer Thickness Retrieval Over the Qinghai-Tibet Plateau Using Sentinel-1 Multitemporal InSAR Monitored Permafrost Subsidence and Temporal-Spatial Multilayer Soil Moisture Data

Zhang

Wang

et al. 2020

IEEE Access

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Increasing near-surface temperature over the Qinghai-Tibet Plateau (QTP) has led to permafrost degradation and increasing active layer thickness (ALT). In this study, the ALT was estimated based on ground subsidence monitored by multitemporal interferometric synthetic aperture radar (MT-InSAR) and temporal-spatial multilayer soil moisture data. For the ground subsidence monitoring, a modified Stefan piecewise elevation change model based on air temperature data was integrated into a new small baseline subset (NSBAS) chain. A total of 33 scenes of Sentinel-1 data (S-1) were collected over one year to build the MT-InSAR analysis network. Moreover, both soil moisture active/passive (SMAP) L4 surface and root zone soil moisture data and ERA-Interim reanalysis data were used to build an ALT retrieval model. In particular, the global-scaled soil moisture data (SMAP and ERA-Interim) fraction was separated based on the Sentinel-1 amplitude-based land cover classification results and in situ soil moisture data. A typical ALT estimation method based on the point scale groundwater information was also performed to evaluate the performance of the proposed method. Based on the validation of the ground-based ALT observations, the proposed method outperformed the traditional point scale groundwater information-based method, with a correlation coefficient of 0.67, RMSE of 0.70 and ubRMSE of 0.51, respectively. The ERA-Interim-based estimation results were underestimated due to the overestimation of the ERA-Interim soil moisture data. Obvious differences were observed between the ALT of the alpine meadow areas and alpine desert areas. Our results demonstrate that the combination of temporal-spatial multilayer soil moisture data and the MT-InSAR method with S-1 images is a promising approach for the large-scale characterization of ALT.

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Investigation of a Small Landslide in the Qinghai-Tibet Plateau by InSAR and Absolute Deformation Model

Cited by 28 publications

References 71 publications

Landslide Detection in the Linzhi–Ya’an Section along the Sichuan–Tibet Railway Based on InSAR and Hot Spot Analysis Methods

Landslide Detection in the Linzhi–Ya’an Section along the Sichuan–Tibet Railway Based on InSAR and Hot Spot Analysis Methods

Topographic Analysis of Landslide Distribution Using AW3D30 Data

Active Layer Thickness Retrieval Over the Qinghai-Tibet Plateau Using Sentinel-1 Multitemporal InSAR Monitored Permafrost Subsidence and Temporal-Spatial Multilayer Soil Moisture Data

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