In the era of the Internet of Things and Big Data modern cars have become mobile electronic systems or computers on wheels. Car sensors record a multitude of car and traffic related data as well as environmental parameters outside the vehicle. The data recorded are spatio-temporal by nature (floating car data) and can thus be classified as geodata. Their geospatial potential is, however, not fully exploited so far. In this paper, we present an approach to collect, process and visualize floating car data for traffic-and environment-related applications. It is demonstrated that cartographic visualization, in particular, is as effective means to make the enormous stocks of machine-recorded data available to human perception, exploration and analysis.

Arctic tundra landscapes are composed of a complex mosaic of patterned ground features, varying in soil moisture, vegetation composition, and surface hydrology over small spatial scales (10-100 m). The importance of microtopography and associated geomorphic landforms in influencing ecosystem structure and function is well founded, however, spatial data products describing local to regional scale distribution of patterned ground or polygonal tundra geomorphology are largely unavailable. Thus, our understanding of local impacts on regional scale processes (e.g., carbon dynamics) may be limited. We produced two key spatiotemporal datasets spanning the Arctic Coastal Plain of northern Alaska (similar to 60,000 km(2)) to evaluate climate-geomorphological controls on arctic tundra productivity change, using (1) a novel 30m classification of polygonal tundra geomorphology and (2) decadal-trends in surface greenness using the Landsat archive (1999-2014). These datasets can be easily integrated and adapted in an array of local to regional applications such as (1) upscaling plot-level measurements (e.g., carbon/energy fluxes), (2) mapping of soils, vegetation, or permafrost, and/or (3) initializing ecosystem biogeochemistry, hydrology, and/or habitat modeling.

Arctic permafrost landscapes are among the most vulnerable and dynamic landscapes globally, but due to their extent and remoteness most of the landscape changes remain unnoticed. In order to detect disturbances in these areas we developed an automated processing chain for the calculation and analysis of robust trends of key land surface indicators based on the full record of available Landsat TM, ETM +, and OLI data. The methodology was applied to the similar to 29,000 km(2) Lena Delta in Northeast Siberia, where robust trend parameters (slope, confidence intervals of the slope, and intercept) were calculated for Tasseled Cap Greenness, Wetness and Brightness, NDVI, and NDWI, and NDMI based on 204 Landsat scenes for the observation period between 1999 and 2014. The resulting datasets revealed regional greening trends within the Lena Delta with several localized hot-spots of change, particularly in the vicinity of the main river channels. With a 30-m spatial resolution various permafrost-thaw related processes and disturbances, such as thermokarst lake expansion and drainage, fluvial erosion, and coastal changes were detected within the Lena Delta region, many of which have not been noticed or described before. Such hotspots of permafrost change exhibit significantly different trend parameters compared to non-disturbed areas. The processed dataset, which is made freely available through the data archive PANGAEA, will be a useful resource for further process specific analysis by researchers and land managers. With the high level of automation and the use of the freely available Landsat archive data, the workflow is scalable and transferrable to other regions, which should enable the comparison of land surface changes in different permafrost affected regions and help to understand and quantify permafrost landscape dynamics. (C) 2016 Elsevier Inc. All rights reserved.

Direct anthropogenic influences on the Earth’s subsurface during drilling, extraction or injection activities, can affect land stability by causing subsidence, uplifts or lateral displacements. They can occur in localized as well as in uninhabited and inhabited regions. Thus the associated risks for humans, infrastructure, and environment must be minimized. To achieve this, appropriate surveillance methods must be found that can be used for simultaneous monitoring during such activities. Multi-temporal synthetic aperture radar interferometry (MT-InSAR) methods like the Persistent Scatterer Interferometry (PSI) and the Small BAseline Subsets (SBAS) have been developed as standard approaches for satellite-based surface displacement monitoring. With increasing spatial resolution and availability of SAR sensors in recent years, MT-InSAR can be valuable for the detection and mapping of even the smallest man-made displacements. This doctoral thesis aims at investigating the capacities of the mentioned standard methods for this purpose, and comprises three main objectives against the backdrop of a user-friendly surveillance service: (1) the spatial and temporal significance assessment against leveling, (2) the suitability evaluation of PSI and SBAS under different conditions, and (3) the analysis of the link between surface motion and subsurface processes. Two prominent case studies on anthropogenic induced subsurface processes in Germany serve as the basis for this goal. The first is the distinct urban uplift with severe damages at Staufen im Breisgau that has been associated since 2007 with a failure to implement a shallow geothermal energy supply for an individual building. The second case study considers the pilot project of geological carbon dioxide (CO2) storage at Ketzin, and comprises borehole drilling and fluid injection of more than 67 kt CO2 between 2008 and 2013. Leveling surveys at Staufen and comprehensive background knowledge of the underground processes gained from different kinds of in-situ measurements at both locations deliver a suitable basis for this comparative study and the above stated objectives. The differences in location setting, i.e. urban versus rural site character, were intended to investigate the limitations in the applicability of PSI and SBAS. For the MT-InSAR analysis, X-band images from the German TerraSAR-X and TanDEM-X satellites were acquired in the standard Stripmap mode with about 3 m spatial resolution in azimuth and range direction. Data acquisition lasted over a period of five years for Staufen (2008-2013), and four years for Ketzin (2009-2013). For the first approximation of the subsurface source, an inversion of the InSAR outcome in Staufen was applied. The modeled uplift based on complex hydromechanical simulations and a correlation analysis with bottomhole pressure data were used for comparison with MT-InSAR measurements at Ketzin. In response to the defined objectives of this thesis, a higher level of detail can be achieved in mapping surface displacements without in-situ effort by using MT-InSAR in comparison to leveling (1). A clear delineation of the elliptical shaped uplift border and its magnitudes at different parts was possible at Staufen, with the exception of a vegetated area in the northwest. Vegetation coverage and the associated temporal signal decorrelation are the main limitations of MT-InSAR as clearly demonstrated at the Ketzin test site. They result in insufficient measurement point density and unwrapping issues. Therefore, spatial resolutions of one meter or better are recommended to achieve an adequate point density for local displacement analysis and to apply signal noise reduction. Leveling measurements can provide a complementary data source here, but require much effort pertaining to personnel even at the local scale. Horizontal motions could be identified at Staufen by only comparing the temporal evolution of the 1D line of sight (LOS) InSAR measurements with the available leveling data. An exception was the independent LOS decomposition using ascending and descending data sets for the period 2012-2013. The full 3D displacement field representation failed due to insufficient orbit-related, north-south sensitivity of the satellite-based measurements. By using the dense temporal mapping capabilities of the TerraSAR-X/TanDEM-X satellites after every 11 days, the temporal displacement evolution could be captured as good as that with leveling. With respect to the tested methods and in the view of generality, SBAS should be preferred over PSI (2). SBAS delivered a higher point density, and was therefore less affected by phase unwrapping issues in both case studies. Linking surface motions with subsurface processes is possible when considering simplified geophysical models (3), but it still requires intensive research to gain a deep understanding.