The Detection of Active Sinkholes by Airborne Differential LiDAR DEMs and InSAR Cloud Computing Tools

InSAR (Interferometric Synthetic Aperture Radar) cloud computing and the subtraction of LiDAR (Light Detection and Ranging) DEMs (Digital Elevation Models) are innovative approaches to detect subsidence in karst areas. InSAR cloud computing allows for analyzing C-band Envisat and Sentinel S1 SAR images through web platforms to produce displacement maps of the Earth’s surface in an easy manner. The subtraction of serial LiDAR DEMs results in the same product but with a different level of accuracy and precision than InSAR maps. Here, we analyze the capability of these products to detect active sinkholes in the mantled evaporite karst of the Ebro Valley (NE Spain). We found that the capability of the displacement maps produced with open access, high-resolution airborne LiDAR DEMs was up to four times higher than InSAR displacement maps generated by the Geohazard Exploitation Platform (GEP). Differential LiDAR maps provide accurate information about the location, active sectors, maximum subsidence rate and growing trend of the most rapid and damaging sinkholes. Unfortunately, artifacts and the subsidence detection limit established at −4 cm/yr entailed important limitations in the precise mapping of the sinkhole edges and the detection of slow-moving sinkholes and small collapses. Although InSAR maps provided by GEP show a worse performance when identifying active sinkholes, in some cases they can serve as a complementary technique to overcome LiDAR limitations in urban areas.

USING TAILORED GRAPHICAL TOOLS TO IMPROVE THE QUALITY OF DISPLACEMENT MAPS AT CTTC: THE VETOOLS

The Geomatics Division of the Centre Tecnològic de Telecomunicacions de Catalunya (CTTC) has been producing terrain displacement maps for more than 15 years using the PSIG software chain, developed entirely by the members of the aforesaid group. PSIG has reached a high level of maturity, being highly automated but also offering the user the possibility to fine-tune the set of critical parameters controlling the process. However, large areas with a high level of diversity often pose problems in finding a good quality solution using a single set of parameters. Improving the quality of the final terrain displacement map means being able to process very local and critical areas using specific sets of parameters; however, identifying such areas is a difficult process without suitable analysis tools. The VETools, a new software project still under development, but very close to its completion, target this problem. With the VETools it is possible to visualize the results produced by a previous PSIG global processing, analysing their quality, thus making possible to identify the local, critical areas, allowing the user to interactively experiment with specific sets of parameters for these areas, reprocessing them and reviewing the local results as many times as desired and, finally, merging all of them in a single, unique solution whose level of quality is appropriate for the whole area of interest. This work presents the current state of development of the VETools, describing their features and the challenges overcome.

Seasonal Surface Subsidence and Frost Heave Detected by C-Band DInSAR in a High Arctic Environment, Cape Bounty, Melville Island, Nunavut, Canada

Differential interferometry of synthetic aperture radar (DInSAR) can be used to generate high-precision surface displacement maps in continuous permafrost environments, capturing isotropic surface subsidence and uplift associated with the seasonal freeze and thaw cycle. We generated seasonal displacement maps using DInSAR with ultrafine-beam Radarsat-2 data for the summers of 2013, 2015, and 2019 at Cape Bounty, Melville Island, and examined them in combination with a land-cover classification, meteorological data, topographic data, optical satellite imagery, and in situ measures of soil moisture, soil temperature, and depth to the frost table. Over the three years studied, displacement magnitudes (estimated uncertainty ± 1 cm) of up to 10 cm per 48-day DInSAR stack were detected. However, generally, the displacement was far smaller (up to 4 cm). Surface displacement was found to be most extensive and of the greatest magnitude in low-lying, wet, and steeply sloping areas. The few areas where large vertical displacements (>2.5 cm) were detected in multiple years were clustered in wet, low lying areas, on steep slopes or ridges, or close to the coast. DInSAR also captured the expansion of two medium-sized retrogressive thaw slumps (RTS), exhibiting widespread negative surface change in the slump floor.

AUGMENTING DATA USING GAUSSIAN MIXTURE EMBEDDING FOR IMPROVING LAND COVER SEGMENTATION

The use of convolutional neural networks improved greatly data synthesis in the last years and have been widely used for data augmentation in scenarios where very imbalanced data is observed, such as land cover segmentation. Balancing the proportion of classes for training segmentation models can be very challenging considering that samples where all classes are reasonably represented might constitute a small portion of a training set, and techniques for augmenting this small amount of data such as rotation, scaling and translation might be not sufficient for efficient training. In this context, this paper proposes a methodology to perform data augmentation from few samples to improve the performance of CNN-based land cover semantic segmentation. First, we estimate the latent data representation of selected training samples by means of a mixture of Gaussians, using an encoder-decoder CNN. Then, we change the latent embedding used to generate the mixture parameters, at random and in training time, to generate new mixture models slightly different from the original. Finally, we compute the displacement maps between the original and the modified mixture models, and use them to elastically deform the original images, creating new realistic samples out of the original ones. Our disentangled approach allows the spatial modification of displacement maps to preserve objects where deformation is undesired, like buildings and cars, where geometry is highly discriminant. With this simple pipeline, we managed to augment samples in training time, and improve the overall performance of two basal semantic segmentation CNN architectures for land cover semantic segmentation.

Joint seismic and geodetic transdimensional earthquake source optimization guided by multi-array teleseismic backprojection

<p>Earthquakes have been observed to initiate and terminate near geometrical irregularities (bends, step-overs, branching of secondary faults). Rupture segmentationinfluences the seismic radiation and therefore, the related seismic hazard. Good imaging of rupture segmentation helps to characterize fault geometries at depth for follow-up tectonic, stress-field or other analyses. From reported earthquake source models it appears that large earthquakes with magnitudes above 7 are most often segmented, while earthquakes with magnitudes below 6.5 most often are not. If this observationreflects nature or if it is rather an artifact of our abilities to well observe and infer earthquake sources can not be answered without an objective strategy to constrain rupture complexity. However, data-driven analyses of rupture segmentation are not often conducted in source modeling as it is mostly pre-defined through a given and fixed number of sources. </p><p>We, here, propose a segmentation-sensitive source analysis by combining a model-independent teleseismic back-projection and image segmentation methods with a kinematic fault inversion. Our approach is twofold. We first develop a time-domain multi-array back-projection of teleseismic data with robust estimations of uncertainties based on bootstrapping of the travel-time models and array weights (Palantiri software, https://braunfuss.github.io/Palantiri/). Backprojection has proven to be a powerful tool to infer rupture propagation from teleseismic data and identify irregularities of the rupture process over time.</p><p>We then model the earthquake sources with the results obtained from the backprojection and additional information obtained from the application of image segmentation methods to the InSAR displacement maps. For this second step, we use a combination of different observations (teleseismic waveforms and surface displacement maps based on InSAR) to increase the resolution on the spatio-temporal evolution of fault slip. We develop a novel Informational criterion based transdimensional optimization scheme to model an adequate representation of the source complexity. We present our method on two cases study: the 2016 Muji Mw 6.7 earthquake (Pamir) and the 2008-2009 Qaidam (Tibet) sequence of earthquakes. We find that the 2008 Qaidam earthquake ruptures one segment, the 2016 Muji earthquake on two segments and the Qaidam 2009 earthquake on two or three segments.</p><p>This work is based on the open-source, python-based Pyrocko toolbox and is conducted within the project “Bridging Geodesy and Seismology” (www.bridges.uni-kiel.de<http://www.bridges.uni-kiel.de>) funded by the DFG through an Emmy-Noether grant.</p><p> </p>

Global data-base of co-seismic interferograms generated via unsupervised Sentinel-1 DInSAR processing

<p>Differential Synthetic Aperture Radar Interferometry (DInSAR) is one of the key methods to investigate, with centimeters to millimeters accuracy, the Earth surface displacements, as those occurred during natural and man-made hazards.</p><p>Nowadays, with the increasing of SAR data availability provided by Sentinel-1 (S1) constellation of Copernicus European Program, the radar Earth Observation (EO) scenario is moving from the historical analysis to operational functionalities. Indeed, the S1 mission, by using the Terrain Observation by Progressive Scans (TOPS) technique, has been designed with the specific aim of natural hazards monitoring via SAR Interferometry guaranteeing a very large coverage of the illuminated scene (250km of swath). These characteristics sum up with the free & open access data policy, the global scale acquisition plan and the high system reliability thus providing a set of peculiarities that make S1 a game changer in the context of operational EO scenario.</p><p>By taking benefit of the S1 characteristics, an unsupervised and cloud-based tool for the automatic generation of co-seismic ground displacement maps has been recently proposed. The tool is triggered by the significant (i.e. bigger than a defined magnitude) seismic events reported in the online catalogues of the United States Geological Survey (USGS) and the National Institute of Geophysics and Volcanology of Italy (INGV). The system permits to generate not only the co-seismic displacement maps but also the pre- and post- seismic ones, up to 30 days after the monitored event.</p><p>Although it was conceived to generate displacement maps relevant to the upcoming earthquakes, as an operational service for the Civil Protection departments, the implemented tool has also been applied to the study of historical events imaged by the S1 data. This allowed us to generate a global data-base of DInSAR-based co-seismic displacement maps.</p><p>Accordingly, the implementation of such data-base will be presented, with particular emphasis on the exploited computing infrastructure solutions (namely the AWS Cloud Computing environment), the used algorithmic strategies and the achieved interferometric results.</p><p>Moreover, the whole data-base of DInSAR products will be made available through the European Plate Observing System (EPOS) Research Infrastructure, thus making them freely and openly accessible to the European and international solid Earth community.</p><p>The implemented global data-base will be helpful for investigating the dynamics of surface deformation in the seismic zones around the Earth. Indeed, it will contribute to the study of global tectonic earthquake activity through the integration of DInSAR information with other geophysical parameters.</p><p>This work has been partially supported by the 2019-2021 IREA-CNR and Italian Civil Protection Department agreement, the EPOS-IP and EPOS-SP projects of the <span>European Union Horizon 2020 R&I program (grant agreement 676564 and 871121) and the I-AMICA (PONa3_00363) project</span>.</p>

Tools for fast analysis of InSAR based displacement maps

<p>The use of satellite interferometry (InSAR) is exponentially growing for the detection and monitoring of geohazard related movements. InSAR technique allows to process large areas and to extract high number of displacement measurements at low cost. By the way, the outputs consist of high volumes of information whose interpretation can be complex and time-consuming, mostly for users who are not familiar with radar data. Moreover, the use of InSAR have been moving from local to national, and now we are going towards a European application. In this scenario, the development of methodologies and tools to automatize the extraction of significant information and to facilitate the interpretation of the results, is more and more needed in order to increase their operational use. In this work we present a series of tools developed in the framework of the projects DEMOS (CGL2017- 83704-P), Momit (S2R-H2020/777630), Safety (ECHO/SUB/2015/718679) and U-Geohaz (UCPM-2017-PP-AG/783169). The so-called ADA (Active Displacement Areas) tools have been developed with the aim of ease the management, the use and the interpretation of wide areas results. Starting from the semi-automatic extraction of the most significant Active Displacement Areas (ADAFinder tool) we move to an automatic preliminary assessment of the phenomena that is behind the detected movement (ADAClassifier tool). All these tools go in the same direction of the European Ground Motion Service (EU-GMS) project, which will provide consistent, regular and reliable information regarding natural and anthropogenic ground motion phenomena all over Europe.</p>

Fusion and mining of glacier surface flow velocity time series

<p>Glacier response to climate change results in natural hazards, sea level rise and changes in freshwater resources. To evaluate this response, glacier surface flow velocity constitutes a crucial parameter to study. Nowadays, more and more velocity maps at regional or global scales issued from satellite SAR and/or optical images tend to be available online or on-demand. Such amount of data requires appropriate data fusion strategies in order to generate displacement time series with improved precision and spatio-temporal coverage. The improved displacement time series can then be used by advanced multi-temporal analysis approaches for further physical interpretations of the phenomenon under observation. In this work, time series of Sentinel-2 (10~m resolution, every 5 days), Landsat-8 (15~m resolution, every 16 days) and Venus (5~m resolution, every 2 days) images acquired between January 2017 and September 2018, over the Fox glacier in the Southern Alps of New Zealand are investigated. Velocities are generated with an offset tracking technique using an automatic processing chain for every possible repeat cycles (2 days-100 days and 300 days to 400 days). Thousands of velocity maps are available, and they are subject to both uncertainty and data gaps. In order to produce a displacement time series as precise/complete as possible , we propose three fusion strategies: 1) use all the available Sentinel-2 displacement maps with different time spans. The goal is to construct a time series of displacement with respect to a common master by means of an inversion 2) take only Sentinel-2 displacement maps with as small time spans as possible, at the same time, keep as much as possible redundancy in the network to be able to construct a common master displacement time series by inversion 3) follow the previous strategy but use all available displacement maps from 3 sensors, with different temporal sampling and measurement precision taken into account. Afterwards, the common master displacement time series will be analysed by a data mining approach in order to extract unusual spatio-temporal patterns in the time series.</p>