Decentralized wind power as part of the relief for an overstrained grid. A case study on Northern Senja, Norway

The most significant factor in wind turbine siting is the wind conditions. Those often determine the economic and ecologic success of a project. Especially in topographically complex areas micro siting can be difficult and costly. Small and medium scale projects often lack the knowledge and resources for an extended in situ assessment. A combination of modelled wind data and the use of a geographic information system (GIS) could be an economical competitive approach to find and compare different wind power sites over a larger defined region. This thesis looks at the small community of Northern Senja, a sparsely populated island in Northern Norway. It evaluates the possibility of community scale wind power (maximum 1MW nominal power) with the help of numerical weather prediction (NWP) wind data. The challenge therein lies in the incapability of mesoscale data to predict the influence of the island’s highly complex topography on the wind flow. This mesoscale data is therefore interpolated to a finer grid and corrected for the effect of using a smoothed terrain model. Production maps for a set of predetermined turbines are created with these corrected data and – together with non-wind related criteria – suitable wind power sites determined. One idea behind this approach is to use free accessible satellite data and to work economical on computational resources. It is possible to correct the wind speed for height differences, but the method seems to underestimate the shear effects of the complex topography that leads to a probable overestimation of the expected production. Better tuning with the help of real life measurements, which currently are lacking, and an improved implementation of orographic roughness are proposed to resolve that challenge

Natural and anthropogenic controls of landslides on Vancouver Island

Empirically-based models of landslide distribution and susceptibility are currently the most commonly used approach for mapping probabilities of landslide initiation and analyzing their association with natural and anthropogenic environmental factors. In general, these models statistically estimate susceptibility based on the predisposition of an area to experience a landslide given a range of environmental factors, which may include land use, topography, hydrology and other spatial attributes. Novel statistical approaches include the generalized additive model (GAM), a non-parametric regression technique, which is used in this study to explore the relationship of landslide initiation to topography, rainfall and forest land cover and logging roads on Vancouver Island, British Columbia. The analysis is centered on an inventory of 639 landslides of winter 2006/07. Data sources representing potentially relevant environmental conditions of landslide initiation are based on: terrain analysis derived from a 20-m CDED digital elevation model; forest land cover classified from Landsat TM scenes for the summer before the 2006 rainy season; geostatistically interpolated antecedent rainfall patterns representing different temporal scales of rainfall (a major storm, winter and annual rainfall); and the main lithological units of surface geology. In order to assess the incremental effect of these data sources to predict landslide susceptibility, predictive performances of models based on GAMs are compared using spatial cross-validation estimates of the area under the ROC curve (AUROC), and variable selection frequencies are used to determine the prevalence of non-parametric associations to landslides. In addition to topographic variables, forest land cover (e.g., deforestation), and logging roads showed a strong association with landslide initiation, followed by rainfall patterns and the very general lithological classification as less important controls of landscape-scale landslide activity in this area. Annual rainfall patterns are found not to contribute significantly to model prediction improvement and may lead to model overfitting. Comparisons to generalized linear models (i.e., logistic regression) indicate that GAMs are significantly better for modeling landslide susceptibility. Overall, based on the model predictions, the most susceptible 4% of the study area had 29 times higher density of landslide initiation points than the least susceptible 73% of the study area (0.156 versus 0.005 landslides/km2)

Road Estimation Using GPS Traces and Real Time Kinematic Data

Advance Driver Assistance System (ADAS) are becoming the main issue in today’s automotive industry. The new generation of ADAS aims at focusing on more details and obtaining more accuracy. To achieve this objective, the research and development parts of the automobile industry intend to utilize Global Positioning System (GPS) by integrating it with other existing tools in ADAS. There are several driving assistance systems which are served by a digital map as a primary or a secondary sensor. The traditional techniques of digital map generation are expensive and time consuming and require extensive manual effort. Therefore, having frequently updated maps is an issue. Furthermore, the existing commercial digital maps are not highly accurate. This Master thesis presents several algorithms for automatically converting raw Universal Serial Bus (USB)-GPS and Real Time Kinematic (RTK) GPS traces into a routable road network. The traces are gathered by driving 20 times on a highway. This work begins by pruning raw GPS traces using four different algorithms. The first step tries to minimize the number of outliers. After the traces are smoothed, they tend to consolidate into smooth paths. So in order to merge all 20 trips together and estimate the road network a Trace Merging algorithm is applied. Finally, a Non-Uniform Rational B-Spline (NURBS) curve is implemented as an approximation curve to smooth the road shape and decrease the effect of noisy data further. Since the RTK-GPS receiver provides highly accurate data, the curve resulted from its GPS data is the most sufficient road shape. Therefore, it is used as a ground truth to compare the result of each pruning algorithm based on data from USB-GPS. Lastly, the results of this work are demonstrated and a quality evaluation is done for all methods

The Effects of Impervious Surfaces and Forests on Water Quality in a Southern Appalachian Headwater Catchment: A Geospatial Modeling Approach

The water quality of streams is impacted by the land cover types that occur within their watersheds and stream corridors. Research has indicated that impervious surfaces (roads, roofs, and parking lots) exert significant stress on stream system health by increasing storm runoff and transporting pollutants into streams. Forests, on the other hand, serve to protect water quality by slowing runoff, which allows rainfall to percolate into the ground, and absorbing pollutants. This thesis research examined the effects of impervious surfaces and forests on water quality in the headwaters of the New River in Watauga County. Results demonstrated that these effects are clearly identifiable and statistically significant. Limiting the amount of impervious surfaces that occur within 100 meters of streams and establishing 50 meter forested stream buffer zones could improve water quality and help preserve stream system health

Space-time exposure modelling of troposheric O3 in Europe

Exposure models need to be developed which can be applied at the continental scale, while still reflecting local variations in exposure conditions. Land use regression (LUR) has been widely adopted to describe the spatial variations in air pollutants over the longer term but not for short-term time-variable exposures. This study, therefore, aimed to develop and validate a space-time O3 model applicable to epidemiological studies investigating the health effects of short-term (e.g. daily) O3 exposures at the small-area scale. A geographical information system (GIS) was developed, incorporating data from 1211 O3 monitoring sites across Western Europe and a range of predictors, stored as 100m grids, including land cover, roads, topography and meteorology. The spatial model consisted of a LUR model representing the long-term average for years 2001-2007. The monitoring sites were classified, using multivariate statistical techniques, into 13 site types based on a set of descriptive indicators, then 13 temporal models represented by time functions were produced – one for each site type. These were linked to the spatial model using probability of group membership as a weighting factor. Finally, local meteorological data were incorporated to produce the full space-time model to predict daily concentrations for point locations. The spatial and temporal models were individually evaluated based on agreement with measurement data from a reserved subset of 20% of the monitoring sites. The performance of the spatial model was similar to other continental LUR models (R2=0.67; RMSE=7.64 μg/m3), while performance of the temporal models ranged from 0.3 to 0.5 (R2). Including local meteorological data into the full spatial-temporal model improved correlation with the concentrations measured at 30 monitoring sites in the Netherlands (R2= 0.42 without; R2=0.53 with meteorology). Modelling daily O3 over large areas at a fine spatial scale is possible using this approach. Overall model performance was further improved as the temporal period was aggregated to weekly or monthly. The model was applied to mothers in two birth cohorts in the European Study of Cohorts for Air Pollution Effects (ESCAPE) to provide daily O3 exposure estimates, which can be aggregated as needed to provide individualised exposures based on date of birth

Vegetation Dynamics Revealed by Remote Sensing and Its Feedback to Regional and Global Climate

This book focuses on some significant progress in vegetation dynamics and their response to climate change revealed by remote sensing data. The development of satellite remote sensing and its derived products offer fantastic opportunities to investigate vegetation changes and their feedback to regional and global climate systems. Special attention is given in the book to vegetation changes and their drivers, the effects of extreme climate events on vegetation, land surface albedo associated with vegetation changes, plant fingerprints, and vegetation dynamics in climate modeling

The design and simulation of traffic networks in virtual environments

For over half a century, researchers from a diverse set of disciplines have been studying the behaviour of traffic flow to better understand the causes of traffic congestion, accidents, and related phenomena. As the global population continues to rise, there is an increasing demand for more efficient and effective transportation infrastructures that are able to accommodate a greater number of civilians without compromising travel times, journey quality, cost, or accessibility. With recent advances in computing technology, transportation infrastructures are now typically developed using design and simulation packages that enable engineers to accurately model large-scale road networks and evaluate their designs through visual simulation. However, as these projects increase in scale and complexity, methodologies to intuitively design more complex and realistic simulations are highly desirable. The need of such technology translates across to the entertainment industry, where traffic simulations are integrated into computer games, television, film, and virtual tourism applications to enhance the realism and believability of the simulated scenario. In this thesis two significant challenges related to the design and simulation of traffic networks for use in virtual environments are presented. The first challenge is the development of intuitive techniques to assist the design and construction of high-fidelity three-dimensional road networks for use in both urban and rural virtual environments. The second challenge considers the implementation of computational models to accurately simulate the behaviour of drivers and pedestrians in transportation networks, in real time. An overview of the literature in the field is presented in this work with novel contributions relating to the challenges defined above

DEVELOPMENT OF DEM-BASED METHOD FOR MAPPING STREAM POWER DISTRIBUTION OF SOUTHERN ONTARIO STREAMS

Mapping of stream power along a stream system, a known determinant of channel form and dynamics, is a valuable component of geomorphic stream assessment procedures that, unlike current methods, is physically-based, time- and cost-effective, objective and repeatable. Continuous maps of stream power can be obtained by extracting channel slope from DEMs and combining them with a discharge-drainage area function. Using the case of Highland Creek, a highly urbanized basin in Scarborough Ontario for which extensive data and background information is available, it is shown that reliable and precise stream power maps can be obtained from the Ontario provincial DEM. Local stream power variation can be seen to match known features of the channel and both reach-scale and overall trends in stream power match those from a ID computational model (HEC-RAS). Stream power maxima and minima also coincide with known areas o f channel instability and deposition

Application of remote sensing to selected problems within the state of California

There are no author-identified significant results in this report