990 resultados para Kalgoorlie Terrain


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A combination of idealized numerical simulations and analytical theory is used to investigate the spacing between convective orographic rainbands over the Coastal Range of western Oregon. The simulations, which are idealized from an observed banded precipitation event over the Coastal Range, indicate that the atmospheric response to conditionally unstable flow over the mountain ridge depends strongly on the subridge-scale topographic forcing on the windward side of the ridge. When this small-scale terrain contains only a single scale (l) of terrain variability, the band spacing is identical to l, but when a spectrum of terrain scales are simultaneously present, the band spacing ranges between 5 and 10 km, a value that is consistent with observations. Based on the simulations, an inviscid linear model is developed to provide a physical basis for understanding the scale selection of the rainbands. This analytical model, which captures the transition from lee waves upstream of the orographic cloud to moist convection within it, reveals that the spacing of orographic rainbands depends on both the projection of lee-wave energy onto the unstable cap cloud and the growth rate of unstable perturbations within the cloud. The linear model is used in tandem with numerical simulations to determine the sensitivity of the band spacing to a number of environmental and terrain-related parameters.

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Although accuracy of digital elevation models (DEMs) can be quantified and measured in different ways, each is influenced by three main factors: terrain character, sampling strategy and interpolation method. These parameters, and their interaction, are discussed. The generation of DEMs from digitised contours is emphasised because this is the major source of DEMs, particularly within member countries of OEEPE. Such DEMs often exhibit unwelcome artifacts, depending on the interpolation method employed. The origin and magnitude of these effects and how they can be reduced to improve the accuracy of the DEMs are also discussed.

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LIght Detection And Ranging (LIDAR) data for terrain and land surveying has contributed to many environmental, engineering and civil applications. However, the analysis of Digital Surface Models (DSMs) from complex LIDAR data is still challenging. Commonly, the first task to investigate LIDAR data point clouds is to separate ground and object points as a preparatory step for further object classification. In this paper, the authors present a novel unsupervised segmentation algorithm-skewness balancing to separate object and ground points efficiently from high resolution LIDAR point clouds by exploiting statistical moments. The results presented in this paper have shown its robustness and its potential for commercial applications.

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Recent severe flooding in the UK has highlighted the need for better information on flood risk, increasing the pressure on engineers to enhance the capabilities of computer models for flood prediction. This paper evaluates the benefits to be gained from the use of remotely sensed data to support flood modelling. The remotely sensed data available can be used either to produce high-resolution digital terrain models (DTMs) (light detection and ranging (Lidar) data), or to generate accurate inundation mapping of past flood events (airborne synthetic aperture radar (SAR) data and aerial photography). The paper reports on the modelling of real flood events that occurred at two UK sites on the rivers Severn and Ouse. At these sites a combination of remotely sensed data and recorded hydrographs was available. It is concluded first that light detection and ranging Lidar generated DTMs support the generation of considerably better models and enhance the visualisation of model results and second that flood outlines obtained from airborne SAR or aerial images help develop an appreciation of the hydraulic behaviour of important model components, and facilitate model validation. The need for further research is highlighted by a number of limitations, namely: the difficulties in obtaining an adequate representation of hydraulically important features such as embankment crests and walls; uncertainties in the validation data; and difficulties in extracting flood outlines from airborne SAR images in urban areas.

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In this paper we consider the impedance boundary value problem for the Helmholtz equation in a half-plane with piecewise constant boundary data, a problem which models, for example, outdoor sound propagation over inhomogeneous. at terrain. To achieve good approximation at high frequencies with a relatively low number of degrees of freedom, we propose a novel Galerkin boundary element method, using a graded mesh with smaller elements adjacent to discontinuities in impedance and a special set of basis functions so that, on each element, the approximation space contains polynomials ( of degree.) multiplied by traces of plane waves on the boundary. We prove stability and convergence and show that the error in computing the total acoustic field is O( N-(v+1) log(1/2) N), where the number of degrees of freedom is proportional to N logN. This error estimate is independent of the wavenumber, and thus the number of degrees of freedom required to achieve a prescribed level of accuracy does not increase as the wavenumber tends to infinity.

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In this paper we show stability and convergence for a novel Galerkin boundary element method approach to the impedance boundary value problem for the Helmholtz equation in a half-plane with piecewise constant boundary data. This problem models, for example, outdoor sound propagation over inhomogeneous flat terrain. To achieve a good approximation with a relatively low number of degrees of freedom we employ a graded mesh with smaller elements adjacent to discontinuities in impedance, and a special set of basis functions for the Galerkin method so that, on each element, the approximation space consists of polynomials (of degree $\nu$) multiplied by traces of plane waves on the boundary. In the case where the impedance is constant outside an interval $[a,b]$, which only requires the discretization of $[a,b]$, we show theoretically and experimentally that the $L_2$ error in computing the acoustic field on $[a,b]$ is ${\cal O}(\log^{\nu+3/2}|k(b-a)| M^{-(\nu+1)})$, where $M$ is the number of degrees of freedom and $k$ is the wavenumber. This indicates that the proposed method is especially commendable for large intervals or a high wavenumber. In a final section we sketch how the same methodology extends to more general scattering problems.

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Flow and turbulence above urban terrain is more complex than above rural terrain, due to the different momentum and heat transfer characteristics that are affected by the presence of buildings (e.g. pressure variations around buildings). The applicability of similarity theory (as developed over rural terrain) is tested using observations of flow from a sonic anemometer located at 190.3 m height in London, U.K. using about 6500 h of data. Turbulence statistics—dimensionless wind speed and temperature, standard deviations and correlation coefficients for momentum and heat transfer—were analysed in three ways. First, turbulence statistics were plotted as a function only of a local stability parameter z/Λ (where Λ is the local Obukhov length and z is the height above ground); the σ_i/u_* values (i = u, v, w) for neutral conditions are 2.3, 1.85 and 1.35 respectively, similar to canonical values. Second, analysis of urban mixed-layer formulations during daytime convective conditions over London was undertaken, showing that atmospheric turbulence at high altitude over large cities might not behave dissimilarly from that over rural terrain. Third, correlation coefficients for heat and momentum were analyzed with respect to local stability. The results give confidence in using the framework of local similarity for turbulence measured over London, and perhaps other cities. However, the following caveats for our data are worth noting: (i) the terrain is reasonably flat, (ii) building heights vary little over a large area, and (iii) the sensor height is above the mean roughness sublayer depth.

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The development of shallow cellular convection in warm orographic clouds is investigated through idealized numerical simulations of moist flow over topography using a cloud-resolving numerical model. Buoyant instability, a necessary element for moist convection, is found to be diagnosed most accurately through analysis of the moist Brunt–Väisälä frequency (N_m) rather than the vertical profile of θ_e. In statically unstable orographic clouds (N_m^2) < 0), additional environmental and terrain-related factors are shown to have major effects on the amount of cellularity that occurs in 2D simulations. One of these factors, the basic-state wind shear, may suppress convection in 2D yet allow for longitudinal convective roll circulations in 3D. The presence of convective structures within an orographic cloud substantially enhanced the maximum rainfall rates, precipitation efficiencies, and precipitation accumulations in all simulations.

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Traffic collisions can be a major source of mortality in wild populations, and animals may be expected to exhibit behavioral mechanisms that reduce the risk associated with crossing roads. Animals living in urban areas in particular have to negotiate very dense road networks, often with high levels of traffic flow. We examined traffic-related mortality of red foxes (Vulpes vulpes) in the city of Bristol, UK, and the extent to which roads affected fox activity by comparing real and randomly generated patterns of movement. There were significant seasonal differences in the number of traffic-related fox deaths for different age and sex classes; peaks were associated with periods when individuals were likely to be moving through unfamiliar terrain and would have had to cross major roads. Mortality rates per unit road length increased with road magnitude. The number of roads crossed by foxes and the rate at which roads were crossed per hour of activity increased after midnight when traffic flow was lower. Adults and juveniles crossed 17% and 30% fewer roads, respectively, than expected from randomly generated movement. This highly mobile species appeared to reduce the mortality risk of minor category roads by changing its activity patterns, but it remained vulnerable to the effects of larger roads with higher traffic flows during periods associated with extraterritorial movements.

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Do we view the world differently if it is described to us in figurative rather than literal terms? An answer to this question would reveal something about both the conceptual representation of figurative language and the scope of top-down influences oil scene perception. Previous work has shown that participants will look longer at a path region of a picture when it is described with a type of figurative language called fictive motion (The road goes through the desert) rather than without (The road is in the desert). The current experiment provided evidence that such fictive motion descriptions affect eye movements by evoking mental representations of motion. If participants heard contextual information that would hinder actual motion, it influenced how they viewed a picture when it was described with fictive motion. Inspection times and eye movements scanning along the path increased during fictive motion descriptions when the terrain was first described as difficult (The desert is hilly) as compared to easy (The desert is flat); there were no such effects for descriptions without fictive motion. It is argued that fictive motion evokes a mental simulation of motion that is immediately integrated with visual processing, and hence figurative language can have a distinct effect on perception. (c) 2005 Elsevier B.V. All rights reserved.

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In this paper we present the novel concepts incorporated in a planetary surface exploration rover design that is currently under development. The Multitasking Rover (MTR) aims to demonstrate functionality that will cover many of the current and future needs such as rough-terrain mobility, modularity and upgradeability. The rover system has enhanced mobility characteristics. It operates in conjunction with Science Packs (SPs) and Tool Packs (TPs)-modules attached to the main frame of the rover, which are either special tools or science instruments and alter the operation capabilities of the system.

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In this paper, we address issues in segmentation Of remotely sensed LIDAR (LIght Detection And Ranging) data. The LIDAR data, which were captured by airborne laser scanner, contain 2.5 dimensional (2.5D) terrain surface height information, e.g. houses, vegetation, flat field, river, basin, etc. Our aim in this paper is to segment ground (flat field)from non-ground (houses and high vegetation) in hilly urban areas. By projecting the 2.5D data onto a surface, we obtain a texture map as a grey-level image. Based on the image, Gabor wavelet filters are applied to generate Gabor wavelet features. These features are then grouped into various windows. Among these windows, a combination of their first and second order of statistics is used as a measure to determine the surface properties. The test results have shown that ground areas can successfully be segmented from LIDAR data. Most buildings and high vegetation can be detected. In addition, Gabor wavelet transform can partially remove hill or slope effects in the original data by tuning Gabor parameters.

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In the past decade, airborne based LIght Detection And Ranging (LIDAR) has been recognised by both the commercial and public sectors as a reliable and accurate source for land surveying in environmental, engineering and civil applications. Commonly, the first task to investigate LIDAR point clouds is to separate ground and object points. Skewness Balancing has been proven to be an efficient non-parametric unsupervised classification algorithm to address this challenge. Initially developed for moderate terrain, this algorithm needs to be adapted to handle sloped terrain. This paper addresses the difficulty of object and ground point separation in LIDAR data in hilly terrain. A case study on a diverse LIDAR data set in terms of data provider, resolution and LIDAR echo has been carried out. Several sites in urban and rural areas with man-made structure and vegetation in moderate and hilly terrain have been investigated and three categories have been identified. A deeper investigation on an urban scene with a river bank has been selected to extend the existing algorithm. The results show that an iterative use of Skewness Balancing is suitable for sloped terrain.

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Purpose - This paper aims to address some of the needs of present and upcoming rover designs, and introduces novel concepts incorporated in a planetary surface exploration rover design that is currently under development. Design/methodology/approach - The Multitasking Rover (MTR) is a highly re-configurable system that aims to demonstrate functionality that will cover many of the current and future needs such as rough-terrain mobility, modularity and upgradeability. It comprises a surface mobility platform which is highly re-configurable, which offers centre of mass re-allocation and rough terrain stability, and also a set of science/tool packs - individual subsystems encapsulated in packs which the rover picks up, transports and deploys. Findings - Early testing of the suspension system suggests exceptional performance characteristics. Originality/value - Principles employed in the design of the MTR can be used in future rover systems to reduce associated mission costs and at the same time provide multiples the functionality.

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Light Detection And Ranging (LIDAR) is an important modality in terrain and land surveying for many environmental, engineering and civil applications. This paper presents the framework for a recently developed unsupervised classification algorithm called Skewness Balancing for object and ground point separation in airborne LIDAR data. The main advantages of the algorithm are threshold-freedom and independence from LIDAR data format and resolution, while preserving object and terrain details. The framework for Skewness Balancing has been built in this contribution with a prediction model in which unknown LIDAR tiles can be categorised as “hilly” or “moderate” terrains. Accuracy assessment of the model is carried out using cross-validation with an overall accuracy of 95%. An extension to the algorithm is developed to address the overclassification issue for hilly terrain. For moderate terrain, the results show that from the classified tiles detached objects (buildings and vegetation) and attached objects (bridges and motorway junctions) are separated from bare earth (ground, roads and yards) which makes Skewness Balancing ideal to be integrated into geographic information system (GIS) software packages.