Satellite radar interferometry for monitoring subsidence induced by longwall mining activity using Radarsat-2, Sentinel-1 and ALOS-2 data

Ng, Alex Hay‐Man; Liu, Ge; Du, Zheyuan; Wang, Shuren; Ma, C.

doi:10.1016/j.jag.2017.05.009

Cited by 80 publications

(53 citation statements)

References 20 publications

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“…The constellation of the two latest European Space Agency (ESA) radar satellites, Sentinel-1A and 1B, forms a revisit period of six days for the C-band sensors with a wavelength equal to 5.6 cm, which leads to a theoretical maximum deformation rate of 85 cm/year detectable by PSI. The maximum detectable rate of the subsidence trough is dependent also on the special resolution of the radar image [19]. Ng et al [14] suggested the consecutive DInSAR method with a shorter temporal baseline as the most appropriate for detection of fast terrain motions.…”

Section: Insar Deformationsmentioning

confidence: 99%

“…Ng et al [14] suggested the consecutive DInSAR method with a shorter temporal baseline as the most appropriate for detection of fast terrain motions. A simulated test of the capabilities of the different operating SAR satellites [19] demonstrates that shorter temporal baselines in combination with a higher spatial resolution will determine a more precise subsidence peak. A study that compared the PSI and DInSAR results in the location of the current investigation, namely the Upper Silesian Coal Basin (USCB) in Poland, was presented [20].…”

Section: Insar Deformationsmentioning

confidence: 99%

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Mining Deformation Life Cycle in the Light of InSAR and Deformation Models

et al. 2019

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The Sentinel-1 constellation provides an effective new radar instrument with a short revisit time of six days for the monitoring of intensive mining surface deformations. Our goal is to investigate in detail and to bring new comprehension of the mine life cycle. The dynamics of mining, especially in the case of horizontally evolving longwall technology, exhibit rapid surface changes. We use the classical approach of differential radar interferometry (DInSAR) with short temporal baselines (six days), which results in deformation maps with a low decorrelation between the satellite images. For the same time intervals, we compare the radar results with prediction models based on the Knothe–Budryk theory for mining subsidence. The validation of the results with ground levelling measurements reveals a high level of resemblance of the DInSAR subsidence maps (−0.04 m bias with respect to the levelling). On the other hand, aside from the explicable exaggeration, the location of the subsidence trough needs improvement in the forecasted deformations (0.2 km shift in location, a deformation velocity four times higher than in DInSAR). In addition, a time lag between DInSAR (compatible with extraction) and prediction is revealed. The model improvement can be achieved by including the DInSAR results in the elaboration of the model parameters.

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Section: Insar Deformationsmentioning

confidence: 99%

Section: Insar Deformationsmentioning

confidence: 99%

Mining Deformation Life Cycle in the Light of InSAR and Deformation Models

et al. 2019

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“…In general, the maximum subsidence between two nearby points can be derived through a simplest model (Du, Ge, Ng, & Li, ; Kampes, ; Ng, Ge, Du, Wang, & Ma, ).

({, \begin{array}{c} V_{max, italicvert} = \pm \frac{365}{Δ T_{\max}} \cdot \frac{λ}{4} \cdot \frac{1}{cos θ} \\ {\overset{⌢}{V}}_{max, italicvert} = \frac{D}{g_{italicres}} \cdot V_{max, italicvert} \end{array})

where Δ T max is the maximum temporal baseline of all interferograms, λ is the wavelength, θ is the incidence angle, g res is the spatial resolution, D is the actual ground distance between two given positions, V max, vert is the expected maximum detectable deformation rate between adjacent pixels in vertical direction, and

{\overset{⌢}{V}}_{max, italicvert}

is the maximum detectable deformation rate; the unit is in metres per year.…”

Section: Methodsmentioning

confidence: 99%

“…In general, the maximum subsidence between two nearby points can be derived through a simplest model Kampes, 2006;Ng, Ge, Du, Wang, & Ma, 2017).…”

Section: Maximum Subsidence Estimationmentioning

confidence: 99%

Long‐term subsidence in Mexico City from 2004 to 2018 revealed by five synthetic aperture radar sensors

Liu

et al. 2019

Land Degrad Dev

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Anthropogenic land subsidence is an example of changes to the natural environment due to human activities and is one of the key factors in causing land degradation at a range of scales. Previous studies assessing land subsidence in the Valley of Mexico either focused on regional scale or short (noncontinuous) temporal scale. In this study, long‐term land subsidence (~15 years) is mapped in Mexico City (Mexico) using two interferometric synthetic aperture radar (InSAR) methods, namely, GEOS (Geoscience and Earth Observing Systems Group)‐Advance Time‐series Analysis and GEOS‐Small Baseline Subset. An inverse distance weighted‐based integration module and maximum likelihood regression‐based M estimator are introduced to further enhance these two methods. The land subsidence was continuously mapped using ENVISAT (2004–2007), ALOS‐1 (2007–2011), COSMO‐SkyMed (2011–2014), ALOS‐2 (2014–2018), and SENTINEL‐1 (2015–2017) data sets. A comparison between InSAR time‐series and GPS measurement shows that the subsidence rates are consistent over 2004–2018. The subsidence map over 15 years was generated finding a maximum subsidence over 4.5 m. By comparing our InSAR results with a land use map, we find that the subsidence centre in Mexico City is mostly located in the residential regions with the consumption of groundwater contributing considerably to the local subsidence rate. A total volume of 1.20 × 108 m3 of the land in Ciudad Nezahualcoyotl subsided/degraded. A continuing subsidence process limits the potential land use causing serious land degradation. Our results may be used to assist disaster reduction plans.

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“…The subsidence of a mining sector is the sinking of the surface, due to the extraction of rocks or minerals from an underground mine [1]. The detection and monitoring of land subsidence make it possible to detect temporary changes in the sinking pattern and provide important information on the dynamics of this process.…”

Section: Introductionmentioning

confidence: 99%

Monitoring of Mining Subsidence in a Sector of Central Chile through the Processing of a Time Series of Sentinel 1 Images Using Differential Interferometry Techniques (DInSAR)

Vidal-Páez

Derauw

Fernández-Sarría

et al. 2019

The II Geomatics Engineering Conference

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The objective of this work is to monitor the mining subsidence in a mountain range sector of central Chile between 2014 and 2018 through the processing of a time series of Sentinel 1 images using differential interferometry techniques (DInSAR). As a pretest, nine interferometric pairs were considered and processed using SNAP and SNAPHU software. Coherence levels obtained in complex topography sectors were low, due to both temporal and geometrical decorrelation. However, it is proposed that data from the PAZ project (X-band) and the Argentinean SAOCOM satellite (L-band) be used in the future.

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Satellite radar interferometry for monitoring subsidence induced by longwall mining activity using Radarsat-2, Sentinel-1 and ALOS-2 data

Cited by 80 publications

References 20 publications

Mining Deformation Life Cycle in the Light of InSAR and Deformation Models

Mining Deformation Life Cycle in the Light of InSAR and Deformation Models

Long‐term subsidence in Mexico City from 2004 to 2018 revealed by five synthetic aperture radar sensors

Monitoring of Mining Subsidence in a Sector of Central Chile through the Processing of a Time Series of Sentinel 1 Images Using Differential Interferometry Techniques (DInSAR)

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