LiDAR and InSAR analysis of deformation in the Krafla rift zone, NE Iceland

Current models of fault growth examine the relationship of fault length (L) to vertical displacement (D) where the faults exhibit the classic fault shape of gradually increasing vertical displacement from zero at the fault tips to a maximum displacement (Dmax) at the middle of the fault. These models cannot adequately explain displacement length observations at the Krafla fissure swarm, in Iceland's northern volcanic zone, where I observe that many of the faults with significant vertical displacements still retain fissure-like features, with no vertical displacement, along portions of their lengths. I have created a high resolution digital elevation model (DEM) of the Krafla region using airborne LiDAR and measured the displacement/length profiles of 775 faults, with lengths ranging from 10s to 1000s of metres. I have categorised the faults based on the proportion of the profile that was still fissure-like. Fully-developed faults (no fissure-like regions) were further grouped into those with profiles that had a flattened appearance (large regions of constant vertical displacment), those with a classical fault shape and those that show regions of fault linkage. I measured the Dmax/L ratio of each identifiable original fault within the linked fault profiles, evidencing that the majority of the original faults had reached the maximum D/L prior to linkage. I suggest that a fault can most easily accommodate stress by displacing regions that are still fissure-like, and that a fault would be more likely to accommodate stress by linkage once it has reached the maximum displacement for its fault length. My results demonstrate that there is a pattern of growth from fissure to fault in the Dmax/L ratio of the categorised faults and propose a model for this growth. I suggest it is possible to better understand how faults grow in their earliest stages of development and that the proposed model can be incorporated as an early stage of fault growth for current models which only model behaviour of a fault once it has acquired the classical D/L profile. The range in the distribution of the published Dmax/L data is mainly attributed to tectonic setting, rock type and resolution limiting the choice of sample rate and fault length range. Using the LiDAR data I have examined the effect that data resolution has on the interpretation of the D/L relationship. I have resampled the LiDAR point data to produce two additional DEMs of 10 m and 30 m resolution, from which I have measured 90 and 40 fault profiles respectively. I have compared (Dmax)/L for all of these fault profiles with those of the published data. I have shown that by varying resolution the interpretation of the (Dmax)/L relationship gives trends for each resolution that together account for the spread in results of the combined published data for the length of faults measured. I have proposed that it may be possible to identify whether a measured fault is a single structure or if it is actually a segmented structure, when measured at a higher resolution, based on its location in the Dmax/L published distribution. The currently available surface displacement data in post-rifting Krafla and interrifting Askja are limited either to single point time series of displacement or regional displacement maps that are averaged over time and do not provide details of changes in rate through time. I have created a 24-epoch InSAR time series from ERS-1 and ERS-2 satellite SAR images over the 16-year period between 1992-2008. Using this I have extracted time series at 39 locations, both along- and across-axis at Krafla and Askja, and have identified trends in displacement rates over time. I have produced cumulative displacement profiles, based on the trends in displacement rate, both along- and acrossaxis and identifed key periods of displacement behaviour in the NVZ. I suggest that Krafla has three possible major sources of surface displacement: the shallow magma chamber under the Krafla caldera provides a decaying surface deflation between 1992 and 1999 and two possible deeper sources further north, the first between the Krafla and Fremrinamar fissure swarms creating uplift between 1992 and 1999 and the second beneath the Theistareykir volcanic centre between 2004 and 2008. In Askja I observe that the displacement rate in the caldera, previously thought to be a slowly decaying inflation, incurred a significant increase in rate to ~30 mm/yr in 1996-2004 followed by a decrease in rate to ~10 mm/yr

Methods for Real-time Visualization and Interaction with Landforms

This thesis presents methods to enrich data modeling and analysis in the geoscience domain with a particular focus on geomorphological applications. First, a short overview of the relevant characteristics of the used remote sensing data and basics of its processing and visualization are provided. Then, two new methods for the visualization of vector-based maps on digital elevation models (DEMs) are presented. The first method uses a texture-based approach that generates a texture from the input maps at runtime taking into account the current viewpoint. In contrast to that, the second method utilizes the stencil buffer to create a mask in image space that is then used to render the map on top of the DEM. A particular challenge in this context is posed by the view-dependent level-of-detail representation of the terrain geometry. After suitable visualization methods for vector-based maps have been investigated, two landform mapping tools for the interactive generation of such maps are presented. The user can carry out the mapping directly on the textured digital elevation model and thus benefit from the 3D visualization of the relief. Additionally, semi-automatic image segmentation techniques are applied in order to reduce the amount of user interaction required and thus make the mapping process more efficient and convenient. The challenge in the adaption of the methods lies in the transfer of the algorithms to the quadtree representation of the data and in the application of out-of-core and hierarchical methods to ensure interactive performance. Although high-resolution remote sensing data are often available today, their effective resolution at steep slopes is rather low due to the oblique acquisition angle. For this reason, remote sensing data are suitable to only a limited extent for visualization as well as landform mapping purposes. To provide an easy way to supply additional imagery, an algorithm for registering uncalibrated photos to a textured digital elevation model is presented. A particular challenge in registering the images is posed by large variations in the photos concerning resolution, lighting conditions, seasonal changes, etc. The registered photos can be used to increase the visual quality of the textured DEM, in particular at steep slopes. To this end, a method is presented that combines several georegistered photos to textures for the DEM. The difficulty in this compositing process is to create a consistent appearance and avoid visible seams between the photos. In addition to that, the photos also provide valuable means to improve landform mapping. To this end, an extension of the landform mapping methods is presented that allows the utilization of the registered photos during mapping. This way, a detailed and exact mapping becomes feasible even at steep slopes

Urban Deformation Monitoring using Persistent Scatterer Interferometry and SAR tomography

This book focuses on remote sensing for urban deformation monitoring. In particular, it highlights how deformation monitoring in urban areas can be carried out using Persistent Scatterer Interferometry (PSI) and Synthetic Aperture Radar (SAR) Tomography (TomoSAR). Several contributions show the capabilities of Interferometric SAR (InSAR) and PSI techniques for urban deformation monitoring. Some of them show the advantages of TomoSAR in un-mixing multiple scatterers for urban mapping and monitoring. This book is dedicated to the technical and scientific community interested in urban applications. It is useful for choosing the appropriate technique and gaining an assessment of the expected performance. The book will also be useful to researchers, as it provides information on the state-of-the-art and new trends in this fiel

Cell-Based Deformation Monitoring via 3D Point Clouds

Deformation is one of the most important phenomena in environmental science and engineering. Deformation of artificial and natural objects happens worldwide, such as structural deformation, landslide, subsidence, erosion, and rockfall. Monitoring and assessment of such deformation process is not only scientifically interesting, but also beneficial to hazard/risk control and prediction. In addition, it is also useful for regional planning and development. Deformation monitoring was driven by geodetic observations in the field of traditional geodetic surveying, based on the measurement of sparse points in a control network. Recently, with the rapid development of terrestrial LiDAR techniques, millions of points with associated three-dimensional coordinates (known as "3D point clouds") can be promptly captured in a few minutes. Compared to traditional surveying, terrestrial LiDAR offers great potential for deformation monitoring, because of various advantages such as fast data capture, high data density, and precise 3D object representation. By analysing 3D point clouds, the objective of this thesis is to provide an effective and efficient approach for deformation monitoring. Towards this goal, this thesis designs a new concept of "deformation map" for deformation representation and a novel "cell-based approach" for deformation computation. The main outcome of this thesis is a novel and rich approach that is able to automatically and incrementally compute a deformation map that enables a better understanding of structural and natural hazards with heterogeneous deformation characteristics. This work includes several dedicated contributions as follows. Hybrid Deformation Modelling. This thesis firstly provides a comprehensive investigation on the modelling requirements of various deformation phenomena. The requirements concern three main aspects, i.e., what has deformation (deformation object), which type of deformation, and how to describe deformation. Based on this detailed requirement analysis, we propose a rich and hybrid deformation model. This model is composed of meta-deformation, sub-deformation and deformation map, corresponding to deformation for a small cell, for a partial area, and for the whole object, respectively. Cell-based Deformation Computation. In order to automatically and incrementally extract heterogeneous deformation of the whole monitored object, we bring the "cell" concept into deformation monitoring. This thesis builds a cell-based deformation computing framework, which consists of three key steps: split, detect, and merge. Split is to divide the space of the object into many cells (uniform or irregular); detect is to extract the meta-deformation for individual cells by analysing the inside point clouds at two epochs; and merge is to group adjacent cells with similar deformation together and to form a consistent sub-deformation. As the final result, an informative deformation map is computed for describing the deformation for the whole object. Evaluation of Cell-based Approach. To evaluate such hybrid modelling and cell-based deformation computation, this thesis extensively studies both synthetic and real-life point cloud datasets: (1) by imitating a landslide scenario, we generate synthetic data using Matlab programming and practical settings, and compare the cell-based approach with traditional non-cell based geodetic methods; (2) by analysing two real-life cases of deformation in Switzerland, we further validate our approach and compare the results with third party sources (e.g., results provided by a surveying company, results computed by using a commercial software like 3DReshaper). Extension of Cell-based Approach. At the last stages of this thesis work, we particularly focus on providing several technical extensions to enhance this cell-based deformation monitoring approach. The main extensions include: (1) supporting dynamic cells instead of uniform cells when splitting the entire object space, (2) finding cell correspondence for the deformation scenarios that have large deformation like rockfalls, (3) movement tracking with data-driven cells which have irregular cell shape that can be automatically determined by the deformation boundary itself, (4) designing an adaptive modelling strategy that is able to accordingly select a suitable model for detecting meta-deformation of cells, and (5) computing deformation evolution for a monitored object with more than two epochs of point cloud datasets

3D Remote Sensing Applications in Forest Ecology: Composition, Structure and Function

Dear Colleagues, The composition, structure and function of forest ecosystems are the key features characterizing their ecological properties, and can thus be crucially shaped and changed by various biotic and abiotic factors on multiple spatial scales. The magnitude and extent of these changes in recent decades calls for enhanced mitigation and adaption measures. Remote sensing data and methods are the main complementary sources of up-to-date synoptic and objective information of forest ecology. Due to the inherent 3D nature of forest ecosystems, the analysis of 3D sources of remote sensing data is considered to be most appropriate for recreating the forest’s compositional, structural and functional dynamics. In this Special Issue of Forests, we published a set of state-of-the-art scientific works including experimental studies, methodological developments and model validations, all dealing with the general topic of 3D remote sensing-assisted applications in forest ecology. We showed applications in forest ecology from a broad collection of method and sensor combinations, including fusion schemes. All in all, the studies and their focuses are as broad as a forest’s ecology or the field of remote sensing and, thus, reflect the very diverse usages and directions toward which future research and practice will be directed

Assessing the role of EO in biodiversity monitoring: options for integrating in-situ observations with EO within the context of the EBONE concept

The European Biodiversity Observation Network (EBONE) is a European contribution on terrestrial monitoring to GEO BON, the Group on Earth Observations Biodiversity Observation Network. EBONE’s aims are to develop a system of biodiversity observation at regional, national and European levels by assessing existing approaches in terms of their validity and applicability starting in Europe, then expanding to regions in Africa. The objective of EBONE is to deliver: 1. A sound scientific basis for the production of statistical estimates of stock and change of key indicators; 2. The development of a system for estimating past changes and forecasting and testing policy options and management strategies for threatened ecosystems and species; 3. A proposal for a cost-effective biodiversity monitoring system. There is a consensus that Earth Observation (EO) has a role to play in monitoring biodiversity. With its capacity to observe detailed spatial patterns and variability across large areas at regular intervals, our instinct suggests that EO could deliver the type of spatial and temporal coverage that is beyond reach with in-situ efforts. Furthermore, when considering the emerging networks of in-situ observations, the prospect of enhancing the quality of the information whilst reducing cost through integration is compelling. This report gives a realistic assessment of the role of EO in biodiversity monitoring and the options for integrating in-situ observations with EO within the context of the EBONE concept (cfr. EBONE-ID1.4). The assessment is mainly based on a set of targeted pilot studies. Building on this assessment, the report then presents a series of recommendations on the best options for using EO in an effective, consistent and sustainable biodiversity monitoring scheme. The issues that we faced were many: 1. Integration can be interpreted in different ways. One possible interpretation is: the combined use of independent data sets to deliver a different but improved data set; another is: the use of one data set to complement another dataset. 2. The targeted improvement will vary with stakeholder group: some will seek for more efficiency, others for more reliable estimates (accuracy and/or precision); others for more detail in space and/or time or more of everything. 3. Integration requires a link between the datasets (EO and in-situ). The strength of the link between reflected electromagnetic radiation and the habitats and their biodiversity observed in-situ is function of many variables, for example: the spatial scale of the observations; timing of the observations; the adopted nomenclature for classification; the complexity of the landscape in terms of composition, spatial structure and the physical environment; the habitat and land cover types under consideration. 4. The type of the EO data available varies (function of e.g. budget, size and location of region, cloudiness, national and/or international investment in airborne campaigns or space technology) which determines its capability to deliver the required output. EO and in-situ could be combined in different ways, depending on the type of integration we wanted to achieve and the targeted improvement. We aimed for an improvement in accuracy (i.e. the reduction in error of our indicator estimate calculated for an environmental zone). Furthermore, EO would also provide the spatial patterns for correlated in-situ data. EBONE in its initial development, focused on three main indicators covering: (i) the extent and change of habitats of European interest in the context of a general habitat assessment; (ii) abundance and distribution of selected species (birds, butterflies and plants); and (iii) fragmentation of natural and semi-natural areas. For habitat extent, we decided that it did not matter how in-situ was integrated with EO as long as we could demonstrate that acceptable accuracies could be achieved and the precision could consistently be improved. The nomenclature used to map habitats in-situ was the General Habitat Classification. We considered the following options where the EO and in-situ play different roles: using in-situ samples to re-calibrate a habitat map independently derived from EO; improving the accuracy of in-situ sampled habitat statistics, by post-stratification with correlated EO data; and using in-situ samples to train the classification of EO data into habitat types where the EO data delivers full coverage or a larger number of samples. For some of the above cases we also considered the impact that the sampling strategy employed to deliver the samples would have on the accuracy and precision achieved. Restricted access to European wide species data prevented work on the indicator ‘abundance and distribution of species’. With respect to the indicator ‘fragmentation’, we investigated ways of delivering EO derived measures of habitat patterns that are meaningful to sampled in-situ observations

Measuring and modelling the crustal response to the 2011 eruption of Nabro volcano, Eritrea

Nabro volcano, situated to the east of the Afar Rift Zone, erupted on 12 June 2011. Eruptions at such off-rift volcanoes are infrequent, and consequently the plumbing systems are poorly understood. In this thesis I present post-eruption InSAR and seismic data to delineate the plumbing system of Nabro. I also investigate the temporal evolution of the system. I discuss my findings in reference to the tectonics of the Afar Rift Zone, off-rift volcanism and compare the findings to volcanoes world wide. I present 6 weeks of continuous seismic activity from an array of 7 seismic stations deployed following the eruption. For the analysis I locate and relocate hypocentres, determine focal mechanisms, calculate b-values and cross-correlate waveforms. I have relocated the hypocentres of 456 earthquakes and used the spatial pattern to interpret the local and regional crustal response to the eruption. The shallow earthquakes beneath Nabro's caldera delineate a NE-SW thrust fault which dips 45 degrees to the SE and extends across the caldera floor. This accommodates the stress change following the eruption, rather than movement on ring faults. The NE-SW fault plane is not associated with measurable surface deformation, indicating that it does not contribute much to the caldera deformation. A 10 km deep cluster highlights potential magma migration pathways directly beneath Nabro. On the flanks of the volcano, a linear pattern of earthquakes illuminate possible minor faults. There is also a cluster of earthquakes beneath Mallahle caldera at a depth of 6 km; the b-value for this cluster is 0.97 and is lower than that for clusters under Nabro (b=1.3). This implies that the earthquakes generated at Mallahle are not dominated by magmatic processes and occur in rock with a stronger rheology. Therefore, the seismicity I observed is likely due to changes in the stress field resulting from the subsidence at Nabro, and not caused by magma movement beneath Mallahle. TerraSAR-X and COSMO-SkyMed were both tasked to prioritise the acquisition of SAR data over the volcanic centre. During the following 15 months, Nabro was imaged 129 times by these satellites, with an acquisition every 5 days on average. I processed the 25 images acquired by TerraSAR-X between 1 July 2011 and 5 October 2012 on descending orbit 046, to create 34 interferograms. I complemented these with 19 images from ascending orbit 130 spanning 6 July 2011 to 10 October 2012 from ascending orbit 130, which I used to create 21 interferograms. I produced velocity ratemaps and time series using pi-RATE, showing subsidence of 25 cm/yr offset by 2 km to the SW of Nabro's caldera. COSMO-SkyMed satellite also imaged the volcano on a descending track between 26 June 2011 and 18 July 2012 within the Italian Space Agency project SAR4Volcanoes: a total of 64 images were acquired and used to produce 171 interferograms. I combined the InSAR data sets using a modelling approach to produce a detailed time series of the deflation of a Mogi source at 6.4 km depth. The time series shows that the volcano continued to subside for the entire period of observation, with the most rapid subsidence in the first 12 weeks, followed by subsidence at a slowly declining rate. I assessed the impact of atmosphere delays, using the outputs from ERA-Interim (ERA-I), a global atmospheric model computed by the European Centre for Medium-range Weather Forecasting (ECMWF), to correct each SAR acquisition. The atmospheric correction noticeably reduced the scatter in the time series, and removed the two atmospheric artefacts apparent in the COSMO-SkyMed time series. This result highlighted the importance of applying atmospheric corrections using independent sources of information. This contrasts with a standard approach of filtering in space and time which did not completely remove these atmospheric errors. Without the ERA-I correction the time series appeared to show pulses of recharge; with the correction continued subsidence is observed. I explore mechanisms that might explain the long-lived subsidence at Nabro volcano. In particular, I tested models of thermal contraction, degassing, fluid migration and viscoelasticity. Degassing is the most likely cause of deformation, although contraction due to cooling may also contribute. The long term post-eruption subsidence is unusual in comparison to other active volcanoes. I suggest that the low magma supply rate, combined with the high rate of passive degassing, induces an overall subsidence of the ground surface above Nabro

Automatic Detection of Volcanic Unrest Using Interferometric Synthetic Aperture Radar

A diverse set of hazards are posed by the world's 1500 subaerial volcanoes, yet the majority of them remain unmonitored. Measurements of deformation provide a way to monitor volcanoes, and synthetic aperture RaDAR (SAR) provides a powerful tool to measure deformation at the majority of the world's subaerial volcanoes. This is due to recent changes in how regularly SAR data are acquired, how they are distributed to the scientific community, and how quickly they can be processed to create time series of interferograms. However, for interferometric SAR (InSAR) to be used to monitor the world's volcanoes, an algorithm is required to automatically detect signs of deformation-generating volcanic unrest in a time series of interferograms, as the volume of new interferograms produced each week precludes this task being achieved by human interpreters. In this thesis, I introduce two complementary methods that can be used to detect signs of volcanic unrest. The first method centres on the use of blind signal separation (BSS) methods to isolate signals of geophysical interest from nuisance signals, such as those due to changes in the refractive index of the atmosphere between two SAR acquisitions. This is achieved through first comparing which of non-negative matrix factorisation (NMF), principal component analysis (PCA), and independent component analysis (ICA) are best suited for solving BSS problems involving time series of InSAR data, and how InSAR data should best be arranged for its use with these methods. I find that NMF can be used with InSAR data, providing the time series is formatted in a novel way that reduces the likelihood of any pixels having negative values. However, when NMF, PCA, and ICA are applied to a set of synthetic data, I find that the most accurate recovery of signals of interest is achieved when ICA is set to recover spatially independent sources (termed sICA). I find that the best results are produced by sICA when interferograms are ordered as a simple ``daisy chain'' of short temporal baselines, and when sICA is set to recover around 1-3 more sources than were thought to have contributed to the time series. However, I also show that in cases such as deformation centred under a stratovolcano, the overlapping nature of a topographically correlated atmospheric phase screen (APS) signal and a deformation signal produces a pair of signals that are no longer spatially statistically independent, and so cannot be recovered accurately by sICA. To validate these results, I apply sICA to a time series of Sentinel-1 interferograms that span the 2015 eruption of Wolf volcano (Galapagos archipelago, Ecuador) and automatically isolate three signals of geophysical interest, which I validate by comparing with the results of other studies. I also apply the sICA algorithm to a time series of interferograms that image Mt Etna, and through isolating signals that are likely to be due to instability of the east flank of the volcano, show that the method can be applied to stratovolcanoes to recover useful signals. Utilising the ability of sICA to isolate signals of interest, I introduce a prototype detection algorithm that tracks changes in the behaviour of a subaerial volcano, and show that it could have been used to detect the onset of the 2015 eruption of Wolf. However, for use in an detection algorithm that is to be applied globally, the signals recovered by sICA cannot be manually validated through comparison with other studies. Therefore, I seek to incorporate a module into my detection algorithm that is able to quantify the significance of the sources recovered by sICA. I achieve this through extensively modernising the ICASO algorithm to create a new algorithm, ICASAR, that is optimised for use with InSAR time series. This algorithm allows me to assess the significance of signals recovered by sICA at a given volcano, and to then prioritise the tracking of any changes they exhibit when they are used in my detection algorithm. To further develop the detection algorithm, I create two synthetic time series that characterise the different types of unrest that could occur at a volcanic centre. The first features the introduction of a new signal, and my algorithm is able to detect when this signal enters the time series by tracking how well the baseline sources are able to fit new interferograms. The second features the change in rate of a signal that was present during the baseline stage, and my algorithm is able to detect when this change in rate occurs by tracking how sources recovered from the baseline data are used through time. To further test the algorithm, I extended the Sentinel-1 time series I used to study the 2015 eruption of Wolf to include the 2018 eruption of Sierra Negra, and I find that my algorithm is able to detect the increase in inflation that precedes the eruption, and the eruption itself. I also perform a small study into the pre-eruptive inflation seen at Sierra Negra using the deformation signal and its time history that are outputted by ICASAR. A Bayesian inversion is performed using the GBIS software package, in which the inflation signal is modelled as a horizontal rectangular dislocation with variable opening and uniform overpressure. Coupled with the time history of the inflation signal provided by ICASAR, this allows me to determine the temporal evolution of the pre-eruptive overpressure since the beginning of the Sentinel-1 time series in 2014. To extend this back to the end of the previous eruption in 2005, I use GPS data that spans the entire interruptive period. I find that the total interruptive pressure change is ~13.5 MPa, which is significantly larger than the values required for tensile failure of an elastic medium overlying an inflating body. I conclude that it is likely that one or more processes occurred to reduce the overpressure within the sill, and that the change in rate of inflation prior to the final failure of the sill is unlikely to be coincidental. The second method I develop to detect volcanic deformation in a time series of interferograms uses a convolutional neural network (CNN) to classify and locate deformation signals as each new interferogram is added to the time series. I achieve this through building a model that uses the five convolutional blocks of a previously state-of-the-art classification and localisation model, VGG16, but incorporates a classification output/head, and a localisation output/head. In order to train the model, I perform transfer learning and utilise the weights made freely available for the convolutional blocks of a version of VGG16 that was trained to classify natural images. I then synthesise a set of training data, but find that better performance is achieved on a testing set of Sentinel-1 interferograms when the model is trained with a mixture of both synthetic and real data. I conclude that CNNs can be built that are able to differentiate between different styles of volcanic deformation, and that they can perform localisation by globally reasoning with a 224 x 224 pixel interferogram without the need for a sliding window approach. The results I present in this thesis show that many machine learning methods can be applied to both time series of interferograms, and individual interferograms. sICA provides a powerful tool to separate some geophysical signals from atmospheric ones, and the ICASAR algorithm that I develop allows a user to evaluate the significance of the results provided by sICA. I incorporate these methods into an deformation detection algorithm, and show that this could be used to detect several types of volcanic unrest using data produced by the latest generation of SAR satellites. Additionally, the CNN I develop is able to differentiate between deformation signals in a single interferogram, and provides a complementary way to monitor volcanoes using InSAR

The global tree carrying capacity (keynote)

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