ALBERT

Description: The tools of geodesy have the potential to transform the Ocean Observing System. Geodetic observations are unique in the way that these methods produce accurate, quantitative, and integrated observations of gravity, ocean circulation, sea surface height, ocean bottom pressure, and mass exchanges among the ocean, cryosphere, and land. These observations have made fundamental contributions to the monitoring and understanding of physical ocean processes. In particular, geodesy is the fundamental science to enable determination of an accurate geoid model, allowing estimate of absolute surface geostrophic currents, which are necessary to quantify ocean’s heat transport. The present geodetic satellites can measure sea level, its mass component and their changes, both of which are vital for understanding global climate change. Continuation of current satellite missions and the development of new geodetic technologies can be expected to further support accurate monitoring of the ocean. The Global Geodetic Observing System (GGOS) of the International Association of Geodesy (IAG) provides the means for integrating the geodetic techniques that monitor the Earth's time-variable surface geometry (including ocean, hydrologic, land, and ice surfaces), gravity field, and Earth rotation/orientation into a consistent system for measuring ocean surface topography, ocean currents, ocean mass and volume changes. This system depends on both globally coordinated ground-based networks of tracking stations as well as an uninterrupted series of satellite missions. GGOS works with the Group on Earth Observations (GEO), the Committee on Earth Observation Satellites (CEOS) and space agencies to ensure the availability of the necessary expertise and infrastructure. In this white paper, we summarize the community consensus of critical oceanographic observables currently enabled by geodetic systems, and the requirements to continue such measurements. Achieving this potential will depend on merging the remote sensing techniques with in situ measurements of key variables as an integral part of the Ocean Observing System.

Description: In the geosciences, fine-scale detail of geomorphic surfaces, commonly parameterized as roughness, is growing in importance as a source of information for modeling natural phenomena and classifying features of interest. Terrestrial light detection and ranging (LiDAR) scanning (TLS), now well known to geologists, is a natural choice for collecting geospatial data. While many recent studies have investigated methodologies for estimating surface roughness from point clouds, research on the influence of instrumental bias on those point clouds and the resulting roughness estimates is scant. A scale-dependent bias in TLS range measurements could affect the outcome of studies relying on high-resolution surface morphology. Growing numbers of research applications in geomorphology, neotectonics, and other disciplines seek to measure the roughness of surfaces with local topographic variations (referred to as asperities) on the order of a few centimeters or less in size. These asperities may manifest as bed forms or pebbles in a streambed, or wavy textures on fault-slip surfaces. In order to assess the feasibility of applying TLS point cloud data sets to the task of measuring centimeter-scale surface roughness, we evaluated the relationship between roughness values of dimensionally controlled test targets measured with TLS scans and numerical simulations. We measured and simulated instrument rangefinder noise to estimate its influence on surface roughness measurements, which was found to decrease with increasing real surface roughness. The size of the area sampled by a single point measurement (effective radius) was also estimated. The ratio of the effective radius to the radius of surface asperities was found to correlate with the disparity between measured and expected roughness. Rangefinder noise was found to overestimate expected roughness by up to ~5%, and the smoothing effect of the measurement size disparity was found to underestimate expected roughness by up to 20%. Based on these results, it is evident that TLS point cloud geometry is correlated with instrument parameters, scan range, and the morphology of the real surface. As different geological applications of TLS may call for relative or absolute measurements of roughness at widely different scales, the presence of these biases imposes constraints on choice of instrument and scan network design. A general solution for such measurement biases lies in the development of calibration processes for TLS roughness measurement strategies, for which the results of this study establish a theoretical basis.

Description: 〈span〉〈div〉ABSTRACT〈/div〉Terrestrial laser scanning (TLS) is a surveying technology that has seen increasing use in the field of geosciences in recent years. One potential application for this technology is to aid in quantitative stratigraphy. Given a point cloud containing multiple lithologies, the points associated with a specific lithology can be analyzed to quantify the geometric characteristics of that lithology, such as apparent dip, thickness, and spacing. In this study, a semi-automated work flow to perform such a characterization is presented and applied to a case study from an oil sands pit mine in the Athabasca region of Alberta, Canada. The results obtained using data collected with mobile and static TLS systems are compared to evaluate the effects of the various measurements and resolutions on the resulting stratigraphic statistics. In addition, mobile data collected for a small portion of the pit that was actively being mined are compared over time to evaluate changes in sedimentary layering in the direction perpendicular to the pit face. This component of the study highlights the impact of data quality on the resulting interpretations and represents a potential methodology for enhancing three-dimensional quantitative spatial modeling in a sedimentary environment.〈/span〉

Description: This research explores persistence of new firm formation at the UK NUTS II level for the 1994–2007 period. The results obtained herewith suggest that interregional differences in new firm formation and their determinants are time persistent. The evidence produced shows that past new firm formation rates determine future ones and that, depending on the econometric specification, human capital, local industry structure, sources of external economies and local economic conditions and wealth are significant determinants. The analysis of new firm formation distribution dynamics suggests that whatever changes may arise in the external shape of distribution are not significant and intra-distribution mobility is limited.

Description: 〈span〉〈div〉Abstract〈/div〉Terrestrial laser scanning (TLS) is a surveying technology that has seen increasing use in the field of geosciences in recent years. One potential application for this technology is to aid in quantitative stratigraphy. Given a point cloud containing multiple lithologies, the points associated with a specific lithology can be analyzed to quantify the geometric characteristics of that lithology, such as apparent dip, thickness, and spacing. In this study, a semiautomated work flow to perform such a characterization is presented and applied to a case study from an oil sands pit mine in the Athabasca region of Alberta, Canada. The results obtained using data collected with mobile and static TLS systems are compared to evaluate the effects of the various measurements and resolutions on the resulting stratigraphic statistics. In addition, mobile data collected for a small portion of the pit that was actively being mined are compared over time to evaluate changes in sedimentary layering in the direction perpendicular to the pit face. This component of the study highlights the impact of data quality on the resulting interpretations and represents a potential methodology for enhancing three-dimensional quantitative spatial modeling in a sedimentary environment.〈/span〉

Description: Terrestrial light detection and ranging (LiDAR) data can be acquired from either static or mobile platforms. The latter presents some challenges in terms of resolution and accuracy, but the opportunity to cover a larger region and repeat surveys often prevails in practice. This paper presents a machine learning algorithm (MLA) for automated lithological classification of individual points within LiDAR point clouds based on intensity and geometry information. Two example data sets were collected by static and mobile platforms in an oil sands pit mine and the MLA was trained to distinguish sandstone and mudstone laminations. The type of approach presented here has the potential to be developed and applied for geological mapping applications such as reservoir characterization or underground excavation face mapping.