The role of Interferometric Synthetic Aperture Radar in Detecting, Mapping, Monitoring, and Modelling the Volcanic Activity of Piton de la Fournaise, La Réunion: A Review

Richter, Nicole; Froger, Jean‐Luc

doi:10.3390/rs12061019

Cited by 30 publications

(14 citation statements)

References 52 publications

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“…In recent decades, the interferometric synthetic aperture radar (SAR, InSAR) technique has been greatly developed and widely used to serve geohazard monitoring processes, such as those for mining subsidence [1][2][3], landslides [4][5][6], earthquakes [7][8][9], and volcano eruptions [10][11][12]. Especially when integrating multi-temporal SAR images with advanced time series InSAR (TS-InSAR) methods (e.g., persistent scatter (PS), small baseline subset (SBAS), and mixed PS/SBAS methods) [13][14][15][16][17], the inherent errors in a single interferogram (e.g., decorrelation noise and atmospheric delay) can be effectively mitigated, and simultaneously, the deformation time series of the study area can be obtained, which is of great significance for understanding the evolution process and mechanism of geohazards.…”

Section: Introductionmentioning

confidence: 99%

Constructing Adaptive Deformation Models for Estimating DEM Error in SBAS-InSAR Based on Hypothesis Testing

Liu

et al. 2021

Remote Sensing

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The Interferometric Synthetic Aperture Radar (InSAR) technique has been widely used to obtain the ground surface deformation of geohazards (e.g., mining subsidence and landslides). As one of the inherent errors in the interferometric phase, the digital elevation model (DEM) error is usually estimated with the help of an a priori deformation model. However, it is difficult to determine an a priori deformation model that can fit the deformation time series well, leading to possible bias in the estimation of DEM error and the deformation time series. In this paper, we propose a method that can construct an adaptive deformation model, based on a set of predefined functions and the hypothesis testing theory in the framework of the small baseline subset InSAR (SBAS-InSAR) method. Since it is difficult to fit the deformation time series over a long time span by using only one function, the phase time series is first divided into several groups with overlapping regions. In each group, the hypothesis testing theory is employed to adaptively select the optimal deformation model from the predefined functions. The parameters of adaptive deformation models and the DEM error can be modeled with the phase time series and solved by a least square method. Simulations and real data experiments in the Pingchuan mining area, Gaunsu Province, China, demonstrate that, compared to the state-of-the-art deformation modeling strategy (e.g., the linear deformation model and the function group deformation model), the proposed method can significantly improve the accuracy of DEM error estimation and can benefit the estimation of deformation time series.

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Section: Introductionmentioning

confidence: 99%

Constructing Adaptive Deformation Models for Estimating DEM Error in SBAS-InSAR Based on Hypothesis Testing

Liu

et al. 2021

Remote Sensing

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“…There are four types of satellite observations that are particularly relevant for volcano monitoring: 1) Deformation measurements made from radar imagery using a technique called Interferometric Synthetic Aperture Radar (InSAR) (e.g. Biggs et al 2014;Pritchard et al 2018;Richter and Froger 2020) 2) Measurements of active or passive degassing using either infrared or UV spectrometers (e.g. Carn et al 2016); 3) Measurements of thermal anomalies associated with erupted material (e.g.…”

Section: Methods: Satellite Data and Processingmentioning

confidence: 99%

Baseline monitoring of volcanic regions with little recent activity: application of Sentinel-1 InSAR to Turkish volcanoes

et al. 2021

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Volcanoes have dormancy periods that may last decades to centuries meaning that eruptions at volcanoes with no historical records of eruptions are common. Baseline monitoring to detect the early stages of reawakening is therefore important even in regions with little recent volcanic activity. Satellite techniques, such as InSAR, are ideally suited for routinely surveying large and inaccessible regions, but the large datasets typically require expert interpretation. Here we focus on Turkey where there are 10 Holocene volcanic systems, but no eruptions since 1855 and consequently little ground-based monitoring. We analyse data from the first five years of the European Space Agency Sentinel-1 mission which collects data over Turkey every 6 days on both ascending and descending passes. The high relief edifices of Turkey’s volcanoes cause two challenges: 1) snow cover during the winter months causes a loss of coherence and 2) topographically-correlated atmospheric artefacts could be misinterpreted as deformation. We propose mitigation strategies for both. The raw time series at Hasan Dag volcano shows uplift of ~ 10 cm between September 2017 and July 2018, but atmospheric corrections based on global weather models demonstrate that this is an artefact and reduce the scatter in the data to < 1 cm. We develop two image classification schemes for dealing with the large datasets: one is an easy to follow flowchart designed for non-specialist monitoring staff, and the other is an automated flagging system using a deep learning approach. We apply the deep learning scheme to a dataset of ~ 5000 images over the 10 Turkish volcanoes and find 4 possible signals, all of which are false positives. We conclude that there has been no cm-scale volcano deformation in Turkey in 2015–2020, but further analysis would be required to rule out slower rates of deformation (< 1 cm/yr). This study has demonstrated that InSAR techniques can be used for baseline monitoring in regions with few historical eruptions or little reported deformation.

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“…In addition, lava flow front locations and propagation rates are described in daily eruptive bulletins, where Harris et al ( 2019 ) explain how the flow contours are obtained from, amongst other methods, satellite or airborne images to allow tracking of flow field expansion. These, and other InSAR-derived attributes are available from the Observatoire InSAR de l’Océan Indien (Richter and Froger 2020 : https://opgc.uca.fr/volcanologie/oi2 ). Finally, time averaged discharge rate times series are available for Piton de la Fournaise from satellite-base monitoring systems: MIROVA (Coppola et al 2016 : https://www.mirovaweb.it/ ) and HOTVOLC (Gouhier et al 2016 : https://hotvolc.opgc.fr/www/index.php ), as well as MODVOLC (Wright et al 2002 : http://modis.higp.hawaii.edu/ ).…”

Section: Piton De La Fournaise: Preparedness Response and Recovery Gap Analysismentioning

confidence: 99%

Reappraisal of gap analysis for effusive crises at Piton de la Fournaise

et al. 2022

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Effective and rapid effusive crisis response is necessary to mitigate the risks associated with lava flows that could threaten or inundate inhabited or visited areas. At Piton de la Fournaise (La Réunion, France), well-established protocols between Observatoire Volcanologique du Piton de la Fournaise – Institut de Physique du Globe de Paris (OVPF-IPGP) and civil protection, and between scientists of a multinational array of institutes, allow effective tracking of eruptive crises and hazard management embracing all stakeholders. To assess the outstanding needs for such responses Tsang and Lindsay (J Appl Volcanol 9:9, 2020) applied a gap analysis to assess research gaps in terms of preparedness, response and recovery at 11 effusive centers, including Piton de la Fournaise. For Piton de la Fournaise, their gap analysis implied widespread gaps in the state of knowledge. However, their work relied on incomplete and erroneous data and methods, resulting in a gap analysis that significantly underrepresented this state of knowledge. We thus here re-build a correct database for Piton de la Fournaise, properly define the scope of an appropriate gap analysis, and provide a robust gap analysis, finding that there are, actually, very few gaps for Piton de la Fournaise. This is a result of the existence of a great quantity of published work in the peer-reviewed literature, as well as frequent reports documenting event impact in the local press and observatory reports. At Piton de la Fournaise, this latter (observatory-based) resource is largely due to the efforts of OVPF-IPGP who have a wealth of experience having responded to 81 eruptions since its creation in 1979 through the end of September 2021.Although welcome and necessary, especially if it is made by a group of scientists outside the local management of the volcanic risk (i.e., a neutral group), such gap analysis need to be sure to fully consider all available peer-reviewed literature, as well as newspaper reports, observatory releases and non-peer-reviewed eruption reports, so as to be complete and correct. Fundamentally, such an analysis needs to consider the information collected and produced by the volcano observatory charged with handling surveillance operations and reporting duties to civil protection for the volcano under analysis. As a very minimum, to ensure that a necessarily comprehensive and complete treatment of the scientific literature has been completed, we recommend that a third party expert, who is a recognized specialist in terms of research at the site considered, reviews and checks the material used for the gap analysis before final release of recommendations.

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The role of Interferometric Synthetic Aperture Radar in Detecting, Mapping, Monitoring, and Modelling the Volcanic Activity of Piton de la Fournaise, La Réunion: A Review

Cited by 30 publications

References 52 publications

Constructing Adaptive Deformation Models for Estimating DEM Error in SBAS-InSAR Based on Hypothesis Testing

Constructing Adaptive Deformation Models for Estimating DEM Error in SBAS-InSAR Based on Hypothesis Testing

Baseline monitoring of volcanic regions with little recent activity: application of Sentinel-1 InSAR to Turkish volcanoes

Reappraisal of gap analysis for effusive crises at Piton de la Fournaise

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