ALBERT

Description: GPS satellite observations indicate that in the tectonically complex eastern Mediterranean and east African regions microplates rotate counterclockwise with respect to the neighboring African plate. Using 3D numerical models, Glerum relates these observations of crustal deformation to the dynamics of the lithosphere and the underlying mantle that may cause this deformation. Glerum first describes her additions to the ASPECT software necessary for numerically modeling the upper mantle and lithosphere dynamics of convergent and divergent plate boundaries. These additions include the tracking of multiple materials with different physical properties and nonlinear viscous as well as viscoplastic rheologies. The implementations of complex, multi-material rheologies are verified with well-known 2D benchmarks and multi-material viscoplasticity is applied in 3D time-dependent thermomechanical models of oceanic subduction. Subsequently, Glerum uses ASPECT to investigate the sensitivity of horizontal surface motions to individual geodynamic processes in the eastern Mediterranean. Identification of all mantle drivers that should participate in modeling attempts to explain observations of crustal flow is essential to fully exploit the information contained by surface motions about their driving processes. Glerum therefore employs 3D data-driven instantaneous dynamics models of compressible flow including a complete set of possible mantle drivers of surface deformation. The reference instantaneous flow model results indicate that mantle processes can explain a large part of the crustal motion of the Aegean-Anatolian microplate. Subsequent systematic perturbations of model properties with respect to this reference model help estimate the individual contributions of tectonic plate motions, slab pull and trench suction, and density-induced mantle flow interacting with the slab and overlying plates while moderated by the mantle’s bulk viscosity. In order of regional importance, the predicted crustal flow of the Aegean-Anatolian region is most sensitive to slab pull, followed by slab-mantle interaction and basal drag, mantle rheology, and the absolute plate motion reference frame. Lastly, Glerum demonstrates a possible mechanism for the counterclockwise rotation of the Victoria microplate in the East African Rift System, which is in striking contrast to the clockwise motion of the surrounding plates. 3D models of the divergent system show that Victoria’s rotation can be caused by the drag of the African and Somalian plates along the strong edges of the microplate, while the rift segments along inherited lithospheric weaknesses facilitate Victoria’s rotation. The amount of rotation is therefore primarily controlled by the distribution of preexisting stronger regions and the weaker Precambrian mobile belts that surround Victoria. The induced counterclockwise rotation of the microplate leads to a clockwise shift of the local extension direction from E-W to more WNW-ESE along the overlapping rift branches. Comparison of the resulting predicted stress field and tectonic regimes to observations helps to elucidate the interpretation of local stress and strain indicators and to reconcile different opening models used to interpret the East African Rift System.

Description: Voxel representations have been used for years in scientific computation and medical imaging. The main focus of our research is to provide easy access to methods for making large-scale voxel models of built environment for environmental modelling studies while ensuring they are spatially correct, meaning they correctly represent topological and semantic relations among objects. In this article, we present algorithms that generate voxels (volumetric pixels) out of point cloud, curve, or surface objects. The algorithms for voxelization of surfaces and curves are a customization of the topological voxelization approach [1]; we additionally provide an extension of this method for voxelization of point clouds. The developed software has the following advantages: • It provides easy management of connectivity levels in the resulting voxels. • It is not dependant on any external library except for primitive types and constructs; therefore, it is easy to integrate them in any application. • One of the algorithms is implemented in C++ and C for platform independence and efficiency.

Description: Powered by WebGL, some renderers have recently become available for the visualization of point cloud data over the web, for example Plasio or Potree. We have extended Potree to be able to visualize massive point clouds and we have successfully used it with the second national Lidar survey of the Netherlands, AHN2, with 640 billion points. In addition to the visualization, the publicly available service at http://ahn2.pointclouds.nl/ also features a multi-resolution download tool, a geographic name search bar, a measurement toolkit, a 2D orientation map with field of view depiction, a demo mode and the tuning of the visualization parameters. Potree relies on reorganizing the point cloud data into an multi-resolution octree data structure. However, this reorganization is very time consuming for massive data sets. Hence, we have used a divide and conquer approach to decrease the octree creation time. To achieve such performance improvement we divided the entire space into smaller cells, generated an octree for each of them in a distributed manner and then we merged them into a single massive octree. The merging is possible because the extent of all the nodes of the octrees is known and fixed. All the developed tools are free and open-source (FOSS) and they can be used to visualize over the web other massive point clouds.

Description: For more than 40 years, remote sensing satellite missions are globally scanning the earth´s surface. They are ideal instruments for monitoring spatio-temporal changes. A comprehensive analysis of this data has the potential to support solutions to major global change challenges related to climate change, population growth, water scarcity, or loss of biodiversity. However, a comprehensive analysis of these remote sensing data is a challenging task: (a) there is a lack of Big Data-adapted analysis tools, (b) the number of available sensors will steadily increase over the next years and (c) technological advancements allow to measure data at higher spatial, spectral, and temporal resolutions than ever before. These developments create an urgent need to better analyze huge and heterogeneous data volumes in support of e.g. global change research. The interdisciplinary research consortium of the “GeoMultiSens” project focuses on developing an open source, scalable and modular Big Data system that combines data from different sensors and analyzes data in the petabyte range (1015 Byte). The most important modules are: (1) data acquisition, (2) pre-processing and homogenization, (3) storage, (4) analysis, and (5) visual exploration. The data acquisition module enables users to specify a region and time interval of interest, to identify the available remote sensing scenes in different data archives, to assess how these scenes are distributed in space and time, and to decide which scenes to use for a specific analysis. The homogenization module uses novel and state-of-the-art algorithms that combine the selected remote sensing scenes from different sensors into a common data set. The data storage module optimises storage and processing of petabytes of data in a parallel and failure tolerant manner. The core technology of the data storage module is XtreemFS (http://www.xtreemfs.org). The analysis module implements image classification and time series analysis algorithms. The visual exploration module supports users in assessing the analysis results. All modules are adapted to a map-reduce processing scheme to allow a very fast information retrieval and parallel computing within the processing system Flink (http://flink.apache.org). Finally, a Visual Analytics approach integrates the individual modules and provides a visual interface to each step in the analysis pipeline. The Big Data system “GeoMultiSens” will store and process remotely sensed data from space-borne multispectral sensors of high and medium spatial resolution such as Sentinel-2, Landsat 5/7/8, Spot 1-6, ASTER, ALOS AVNIR-2 and RapidEye. Our poster presents the overall scientific concept of the Big Data system “GeoMultiSens” and technical details of the most important modules. We discuss scientific challenges of the Big Data system “GeoMultiSens” and present our ideas to address these scientific challenges.

Description: Landslides are a worldwide natural hazard causing thousands of fatalities and severe monetary losses every year. To predict and thus reduce the landslide risk in the future, a profound knowledge about the past and recent landslide activity is of utmost importance. For this purpose, the records about the landslide activity have to be as complete as possible in time and space, in order to derive spatial and temporal probabilities of landslide occurrence as a crucial prerequisite of landslide hazard and risk assessment. However, for most regions of the world such comprehensive landslide records are not available, because the conventional manual mapping of landslides is an extremely time-consuming and labor-intensive task. This study presents an automated approach for efficient multi-temporal identification of landslides at regional scale based on optical remote sensing time series data. The developed approach allows for retrospective analysis of long-term landslide occurrence and for monitoring recent landslide activity. In case of the long-term analysis, a combined usage of multiple optical sensors is required to achieve best possible temporal data coverage for the longest possible time span. For this study, such a database has been established for a landslide-affected area of 12000 km² in Southern Kyrgyzstan, Central Asia. It consists of about 900 orthorectified multispectral satellite remote sensing datasets acquired by Landsat-(E)TM, SPOT, IRS-1C (LISS3), ASTER and RapidEye during the last 30 years. For monitoring the landslide activity of the last 5 years, high spatial and temporal resolution RapidEye data have been acquired in the frame of the RapidEye Science Archive (RESA) program. The developed approach comprises automated multi-sensor pre-processing and multi-temporal change detection methods allowing spatiotemporal identification of landslides in an object-based form. The change detection builds on the analysis of temporal NDVI-trajectories, representing footprints of vegetation changes over time. Landslide-specific trajectories are characterized by short-term vegetation cover destruction and longer-term revegetation rates resulting from landslide related disturbance and dislocation of the fertile soil cover. In combination with DEM-derivatives the developed approach enables automated identification of landslides of different sizes, shapes and in different stages of development under varying natural conditions. The multi-sensor long-term analysis of a 2500 km² region resulted in the identification of 1583 landslides ranging in size between 50 m² and 2.8 km². The highest overall landslide rates occurred in 2003 and 2004 exceeding the long-term annual average rate of 57 landslides per year by more than five times. For monitoring the recent landslide activity the approach has been applied to the RapidEye time series acquired between 2009 and 2015 for the whole 12000 km² study area. The combination of high spatial resolution (5 m) and frequent data acquisition (up to several days/weeks) of the RapidEye data has allowed for the systematic assessment of the whole variety of landslide processes also including small slope failures, which often represent precursors for subsequent larger and more hazardous landslides. Thus, the approach can provide valuable information in the context of early warning. Currently, the applicability of the approach is investigated for assessing the aftereffects of the disastrous Nepal earthquakes of April and May 2015 that triggered thousands of landslides. First results have shown the general transferability of the approach to the differing natural environment of Nepal and its general suitability to operate within a rapid response system. Together with the newly available Sentinel-2 data, this approach has the potential to be developed into a globally applicable landslide mapper, which will open up new opportunities to analyze spatiotemporal landslide activity over large areas facilitating further development of probabilistic landslide hazard and risk assessments.

Description: The remote sensing analyses in the BMBF-SPACES collaborative project Geoarchives - Signals of Climate and Landscape Change preserved in Southern African Geoarchives - focuses on the use of recent and upcoming Earth Observation Tools for the study of climate and land use changes and its impact on the ecosystem. It aims at demonstrating the potential of recently available advanced optical remote sensing imagery with its extended spectral coverage and temporal resolution for the identification and mapping of sediment features associated with paleo-environmental archives as well as their recent dynamic. In this study we focus on the analyses of two ecosystems of major interest, the Kalahari salt pans as well as the lagoons at Namibia’s west coast, that present high dynamic caused by combined hydrological and surface processes linked to climatic events. Multitemporal remote sensing techniques allow us to derive the recent surface dynamic of the salt pans and also provide opportunities to get a detailed understanding of the spatiotemporal development of the coastal lagoons. Furthermore spaceborne hyperspectral analysis can give insight to the current surface mineralogy of the salt pans on a physical basis and provide the intra pan distribution of evaporites. The soils and sediments of the Kalahari salt pans such as the Omongwa pan are a potentially significant storage of global carbon and also function as an important terrestrial climate archive. Thus far the surface distribution of evaporites have been only assessed mono-temporally and on a coarse regional scale, but the dynamic of the salt pans, especially the formation of evaporites, is still uncertain and poorly understood. For the salt pan analyses a change detection is applied using the Iterative-reweighted Multivariate Alteration Detection (IR-MAD) method to identify and investigate surface changes based on a Landsat time-series covering the period 1984-2015. Furthermore the current spatial distribution of evaporites is obtained using of EO-1 Hyperion hyperspectral imagery linked with geochemical field data. Results reveal a highly heterogeneous dynamic of the pan surface, which seems to be associated with varying surface crust types, halite or gypsum dominated. The lagoons at Namibia’s west coast such as of Sandwich Harbour and Walvis Bay, are important habitats and also serve as a natural barrier to protect shipping and ports on an otherwise inhospitable coastline. Several studies have shown that these lagoons are highly dynamic and are known to have altered their shape in historical time. These changes occur due to sediment transport forced by aeolian processes or either by longshore or cross-shore drifts. A profound understanding of the spatiotemporal variations in the sand spits is of high relevance. In the lagoon environment the Landsat time-series is used to separate sand spits from open water. This way, changes in morphology of the sand spit are identified over time. The results reveal the presence of long-term and short-term changes as well as the presence of stable parts in the sand spits. These findings are linked to temporal patterns of forcing processes such as wind, tidal and ocean current data.

Description: The Singular Value Decomposition (SVD) is a mathematical procedure with multiple applications in the geosciences. For instance, it is used in dimensionality reduction and as a support operator for various analytical tasks applicable to spatio-temporal data. Performing SVD analyses on large datasets, however, can be computationally costly, time consuming, and sometimes practically infeasible. However, techniques exist to arrive at the same output, or at a close approximation, which requires far less effort. This article examines several such techniques in relation to the inherent scale of the structure within the data. When the values of a dataset vary slowly, e.g., in a spatial field of temperature over a country, there is autocorrelation and the field contains large scale structure. Datasets do not need a high resolution to describe such fields and their analysis can benefit from alternative SVD techniques based on rank deficiency, coarsening, or matrix factorization approaches. We use both simulated Gaussian Random Fields with various levels of autocorrelation and real-world geospatial datasets to illustrate our study while examining the accuracy of various SVD techniques. As the main result, this article provides researchers with a decision tree indicating which technique to use when and predicting the resulting level of accuracy based on the dataset’s structure scale.

Description: 3D digital city models form the basis for flow simulations (e.g. wind flow and water runoff), urban planning, under-and over-ground formation analysis, and they are very important for automated anomaly detection on man made structures. They consist of large collections of semantically rich objects which have many properties such as material and color. Such user's data structure perception is leading to complex storage schemas. The number of table relations to manage and the large data storage footprint drawbacks are then extended with the fact that not all the systems have a “real” 3D data type. In this work we would like to show our efforts to develop a new kind of Spatial Data Management System (SDBMS) where topological and geometric functionality for 3D raster manipulation will become part of the relational kernel and not an add-on. With it spatial analysis tailored to different use case scenarios is done on-demand and fast enough to support real-time interaction in modern risk management systems.

Description: The Hadoop Distributed File System (HDFS) has become an important data repository in the enterprise as the center for all business analytics, from SQL queries and machine learning to reporting. At the same time, enterprise data warehouses (EDWs) continue to support critical business analytics. This has created the need for a new generation of a special federation between Hadoop-like big data platforms and EDWs, which we call the hybrid warehouse. There are many applications that require correlating data stored in HDFS with EDW data, such as the analysis that associates click logs stored in HDFS with the sales data stored in the database. All existing solutions reach out to HDFS and read the data into the EDW to perform the joins, assuming that the Hadoop side does not have efficient SQL support. In this article, we show that it is actually better to do most data processing on the HDFS side, provided that we can leverage a sophisticated execution engine for joins on the Hadoop side. We identify the best hybrid warehouse architecture by studying various algorithms to join database and HDFS tables. We utilize Bloom filters to minimize the data movement and exploit the massive parallelism in both systems to the fullest extent possible. We describe a new zigzag join algorithm and show that it is a robust join algorithm for hybrid warehouses that performs well in almost all cases. We further develop a sophisticated cost model for the various join algorithms and show that it can facilitate query optimization in the hybrid warehouse to correctly choose the right algorithm under different predicate and join selectivities.

Description: 3D digital city models, important for urban planning, are currently constructed from massive point clouds obtained through airborne LiDAR (Light Detection and Ranging). They are semantically enriched with information obtained from auxiliary GIS data like Cadastral data which contains information about the boundaries of properties, road networks, rivers, lakes etc. Technical advances in the LiDAR data acquisition systems made possible the rapid acquisition of high resolution topographical information for an entire country. Such data sets are now reaching the trillion points barrier. To cope with this data deluge and provide up-to-date 3D digital city models on demand current geospatial management strategies should be re-thought. This work presents a column-oriented Spatial Database Management System which provides in-situ data access, effective data skipping, efficient spatial operations, and interactive data visualization. Its efficiency and scalability is demonstrated using a dense LiDAR scan of The Netherlands consisting of 640 billion points and the latest Cadastral information, and compared with PostGIS.