Developing an early warning system for a very slow landslide based on displacement monitoring

Macciotta, Renato; Hendry, Michael T.; Martin, C. Derek

doi:10.1007/s11069-015-2110-2

Cited by 80 publications

(46 citation statements)

References 14 publications

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“…Sun et al (2015) used ALOS L-band images acquired between January 2007 and May 2010 to conclude that Zhouqu giant landslide that caused over than 1700 fatalities in 2010, shown instabilities before the event and Novellino et al (2015) present a MT-InSAR analysis to monitor landslide in Costa Della Gaveta (Italy) using TerraSAR-X images acquired between February 2010 and April 2011. Macciotta et al (2016) proposed an early warning system for a very slow landslide displacement monitoring by integrating MT-InSAR techniques with a set of in situ deployed techniques/equipment. Other authors focus also into the field of InSAR application for landslide early warning and conclude about a possibility of monitoring slow creeping slope movements and their dynamics while for a specific landslide, a high acquisition frequency of ground-based SAR instrument is more suitable (Del Ventisette et al, 2015).…”

Section: Sar Interferometry For Landslide Identificationmentioning

confidence: 99%

Potential of Sentinel-1a for Nation-Wide Routine Updates of Active Landslide Maps

Lázecký¹,

Comut²,

Nikolaeva³

et al. 2016

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

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ABSTRACT:Slope deformation is one of the typical geohazards that causes an extensive economic damage in mountainous regions. As such, they are usually intensively monitored by means of modern expertise commonly by national geological or emergency services. Resulting landslide susceptibility maps, or landslide inventories, offer an overview of areas affected by previously activated landslides as well as slopes known to be unstable currently. Current slope instabilities easily transform into a landslide after various triggering factors, such as an intensive rainfall or a melting snow cover. In these inventories, the majority of the existing landslide-affected slopes are marked as either stable or active, after a continuous investigative work of the experts in geology. In this paper we demonstrate the applicability of Sentinel-1A satellite SAR interferometry (InSAR) to assist by identifying slope movement activity and use the information to update national landslide inventories. This can be done reliably in cases of semi-arid regions or low vegetated slopes. We perform several analyses based on multitemporal InSAR techniques of Sentinel-1A data over selected areas prone to landslides.

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Section: Sar Interferometry For Landslide Identificationmentioning

confidence: 99%

Potential of Sentinel-1a for Nation-Wide Routine Updates of Active Landslide Maps

Lázecký¹,

Comut²,

Nikolaeva³

et al. 2016

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

View full text Add to dashboard Cite

show abstract

“…Due to its easy acquisition, quantification and high reliability, displacement monitoring data have become some of the most recognised evidence for landslide stability analysis and early warning. Macciotta et al (2016) suggested that velocity threshold be used as a criterion for an early warning system and that the annual horizontal displacement threshold for Ripley landslide (GPS 1) can be 90 mm and that between May and September can be 25 mm. Based on the analysis of a large number of displacement monitoring data, Xu and Zeng (2009) proposed that deformation acceleration be used as an indicator of landslide warning; the acceleration threshold of Jimingsi landslide was regarded as 0.45 mm d −2 and that of another landslide in Daye iron mine as 0.2 mm d −2 .…”

Section: Introductionmentioning

confidence: 99%

State fusion entropy for continuous and site-specific analysis of landslide stability changing regularities

Liu

Qin

et al. 2018

Nat. Hazards Earth Syst. Sci.

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Abstract. Stability analysis is of great significance to landslide hazard prevention, especially the dynamic stability. However, many existing stability analysis methods are difficult to analyse the continuous landslide stability and its changing regularities in a uniform criterion due to the unique landslide geological conditions. Based on the relationship between displacement monitoring data, deformation states and landslide stability, a state fusion entropy method is herein proposed to derive landslide instability through a comprehensive multi-attribute entropy analysis of deformation states, which are defined by a proposed joint clustering method combining K-means and a cloud model. Taking Xintan landslide as the detailed case study, cumulative state fusion entropy presents an obvious increasing trend after the landslide entered accelerative deformation stage and historical maxima match highly with landslide macroscopic deformation behaviours in key time nodes. Reasonable results are also obtained in its application to several other landslides in the Three Gorges Reservoir in China. Combined with field survey, state fusion entropy may serve for assessing landslide stability and judging landslide evolutionary stages.

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“…Constant deformation monitoring captures necessary information for landslide research. After summarizing the laws of long time land surface motion, early warnings can be communicated to local people [5].…”

Section: Introductionmentioning

confidence: 99%

A DLM-LSTM Framework for North-South Land Deformation Trend Analysis from Low-Cost GPS Sensor Time Series

Chen

et al. 2018

Journal of Sensors

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Landslides endanger regular industrial production and human safety. Displacement trend analysis gives us an explicit way to observe and forecast landslides. Although satellite-borne remote sensing methods such as synthetic aperture radar have gradually replaced manual measurement in detecting deformation trends, they fail to observe displacement in a north-south direction. Wireless low-cost GPS sensors have been developed to assist remote sensing methods in north-south deformation monitoring because of their high temporal resolution and wide usage. In our paper, a DLM-LSTM framework is developed to extract and predict north-south land deformation trends from meter accuracy GPS receivers. A dynamic linear model is introduced to model the relation between measurement and the state vector, including the trend, periodic variation, and autoregressive factors in a discontinuous low-cost latitude time series. The deformation trend with submeter-level accuracy is extracted by a Kalman filter and smoother. With validated input as in previous work, the power of an LSTM network is also shown in its ability to predict deformation trends in submeter-level accuracy. A submeter-level deformation trend is detected from wireless low-cost GPS sensors with meter-level navigation error. The framework will have broad application prospects in geological disaster monitoring.Coal mining in the Fushun Western Open-Pit Coal Mine (FWOCM), which is located at 41°50′38.33″ north latitude and 123°53′21.77″ east longitude, started in the early 20th century. Production of coal used to be a pillar of the Fushun economy. Decades of mining has caused frequent surface deformation and landslides that threaten the city, Fushun. An open pit with 6600 m east-to-west length and 2200 m north-to-south width stands among the residential districts [6,7]. Therefore, a landslide is a serious threat to miners and local people. According to our field investigation, deformation in the north-south direction is much worse than in the east-west direction. The velocity of displacement on the north and south sides is less than 3 m/year. A fault zone

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Developing an early warning system for a very slow landslide based on displacement monitoring

Cited by 80 publications

References 14 publications

Potential of Sentinel-1a for Nation-Wide Routine Updates of Active Landslide Maps

Potential of Sentinel-1a for Nation-Wide Routine Updates of Active Landslide Maps

State fusion entropy for continuous and site-specific analysis of landslide stability changing regularities

A DLM-LSTM Framework for North-South Land Deformation Trend Analysis from Low-Cost GPS Sensor Time Series

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