Anthropogenic influences and climate change are warming rivers in New Brunswick and threatening the cold water habitats of native salmonids. When ambient river temperatures in summer exceed the tolerance level of Atlantic salmon and brook trout, individuals behaviourally thermoregulate by seeking out cold water refugia. These critical thermal habitats are often created by tributaries and concentrated groundwater discharge. Thermal infrared imagery was used to map cold water anomalies along a 53 km reach of the Cains River on 23 July 2008. Although efficient and useful for mapping surface temperature of a continuous stream reach, the fish did not use all identified thermal anomalies as refugia. Overall, 100 % of observed large brook trout >35 cm in length were found in 30 % of the TIR-mapped cold water anomalies. Ninety eight percent of observed small brook trout 8 – 30 cm in length were found in 80 % of the mapped cold water anomalies and their densities within anomalies were significantly higher than densities outside of anomalies. Fifty nine percent of observed salmon parr were found in 65 % of the mapped anomalies; however, they were dispersed within study sites and their densities were not significantly different within anomalies compared to outside of the anomalies. No brook trout were observed at the seven noncold water study sites that were investigated. Preference curves for various habitat variables including velocity, temperature, depth, substrate, and deep water availability near cold water anomalies were developed based on field investigations during high temperature events (ambient river temperature >21 oC). Combined with thermal imagery, managers can use the physical descriptions of thermal refugia developed here as a tool to help conserve and restore critical thermal refugia for Atlantic salmon and brook trout on the Cains River, and potentially similar river systems.

No description available.

The purpose of this study was to examine the effects of a competition routine intervention on youth swimmers’ competition anxiety, confidence and performance. Participants of this study were youth swimmers (n = 4; female) between the ages of 12-16. Through the use of a multiple-baseline, single-subject design, participants received a competition routine intervention involving imagery, positive self-talk and a relaxation deep-breathing technique. Mixed methods were used, with participants completing the Competitive State Anxiety Inventory for Children questionnaire prior to their swim race and a reflective questionnaire following their swim race. Participants also took part in a follow-up interview upon completing the study. Participants showed no considerable decrease in cognitive and somatic anxiety, or increase in self-confidence or performance following the intervention. However, reflective questionnaire and interview results showed that participants perceived a positive improvement in all of these areas and found the intervention beneficial.

Accurate, fast detection of clouds in satellite imagery has many applications, for example Numerical Weather Prediction (NWP) and climate studies of both the atmosphere and of the Earth’s surface temperature. Most operational techniques for cloud detection rely on the differences between observations of cloud and of clear-sky being more or less constant in space and in time. In reality, this is not the case - different clouds have different spectral properties, and different cloud types are more or less likely in different places and at different times, depending on atmospheric conditions and on the Earth’s surface properties. Observations of clear sky also vary in space and time, depending on atmospheric and surface conditions, and on the presence or absence of aerosol particles. The Bayesian approach adopted in this project allows pixel-specific physical information (for example from NWP) to be used to predict pixel-specific observations of clear sky. A physically-based, spatially- and temporally-specific probability that each pixel contains a cloud observation is then calculated. An advantage of this approach is that identification of ambiguously classed pixels from a probabilistic result is straightforward, in contrast to the binary result generally produced by operational techniques. This project has developed and validated the Bayesian approach to cloud detection, and has extended the range of applications for which it is suitable, achieving skills scores that match or exceed those achieved by operational methods in every case. High temperature gradients can make observations of clear sky around ocean fronts, particularly at thermal wavelengths, appear similar to cloud observations. To address this potential source of ambiguous cloud detection results, a region of imagery acquired by the AATSR sensor which was noted to contain some ocean fronts, was selected. Pixels in the region were clustered according to their spectral properties with the aim of separating pixels that correspond to different thermal regimes of the ocean. The mean spectral properties of pixels in each cluster were then processed using the Bayesian cloud detection technique and the resulting posterior probability of clear then assigned to individual pixels. Several clustering methods were investigated, and the most appropriate, which allowed pixels to be associated with multiple clusters, with a normalized vector of ‘membership strengths’, was used to conduct a case study. The distribution of final calculated probabilities of clear became markedly more bimodal when clustering was included, indicating fewer ambiguous classifications, but at the cost of some single pixel clouds being missed. While further investigations could provide a solution to this, the computational expense of the clustering method made this impractical to include in the work of this project. This new Bayesian approach to cloud detection has been successfully developed by this project to a point where it has been released under public license. Initially designed as a tool to aid retrieval of sea surface temperature from night-time imagery, this project has extended the Bayesian technique to be suitable for imagery acquired over land as well as sea, and for day-time as well as for night-time imagery. This was achieved using the land surface emissivity and surface reflectance parameter products available from the MODIS sensor. This project added a visible Radiative Transfer Model (RTM), developed at University of Edinburgh, and a kernel-based surface reflectance model, adapted here from that used by the MODIS sensor, to the cloud detection algorithm. In addition, the cloud detection algorithm was adapted to be more flexible, making its implementation for data from the SEVIRI sensor straightforward. A database of ‘difficult’ cloud and clear targets, in which a wide range of both spatial and temporal locations was represented, was provided by M´et´eo-France and used in this work to validate the extensions made to the cloud detection scheme and to compare the skill of the Bayesian approach with that of operational approaches. For night land and sea imagery, the Bayesian technique, with the improvements and extensions developed by this project, achieved skills scores 10% and 13% higher than M´et´eo-France respectively. For daytime sea imagery, the skills scores were within 1% of each other for both approaches, while for land imagery the Bayesian method achieved a 2% higher skills score. The main strength of the Bayesian technique is the physical basis of the differentiation between clear and cloud observations. Using NWP information to predict pixel-specific observations for clear-sky is relatively straightforward, but making such predictions for cloud observations is more complicated. The technique therefore relies on an empirical distribution rather than a pixel-specific prediction for cloud observations. To try and address this, this project developed a means of predicting cloudy observations through the fast forward-modelling of pixel-specific NWP information. All cloud fields in the pixel-specific NWP data were set to 0, and clouds were added to the profile at discrete intervals through the atmosphere, with cloud water- and ice- path (cwp, cip) also set to values spaced exponentially at discrete intervals up to saturation, and with cloud pixel fraction set to 25%, 50%, 75% and 100%. Only single-level, single-phase clouds were modelled, with the justification that the resulting distribution of predicted observations, once smoothed through considerations of uncertainties, is likely to include observations that would correspond to multi-phase and multi-level clouds. A fast RTM was run on the profile information for each of these individual clouds and cloud altitude-, cloud pixel fraction- and channel-specific relationships between cwp (and similarly cip) and predicted observations were calculated from the results of the RTM. These relationships were used to infer predicted observations for clouds with cwp/cip values other than those explicitly forward modelled. The parameters used to define the relationships were interpolated to define relationships for predicted observations of cloud at 10m vertical intervals through the atmosphere, with pixel coverage ranging from 25% to 100% in increments of 1%. A distribution of predicted cloud observations is then achieved without explicit forward-modelling of an impractical number of atmospheric states. Weights are applied to the representation of individual clouds within the final Probability Density Function (PDF) in order to make the distribution of predicted observations realistic, according to the pixel-specific NWP data, and to distributions seen in a global reference dataset of NWP profiles from the European Centre for Medium Range Weather Forecasting (ECMWF). The distribution is then convolved with uncertainties in forward-modelling, in the NWP data, and with sensor noise to create the final PDF in observation space, from which the conditional probability that the pixel observation corresponds to a cloud observation can be read. Although the relatively fast computational implementation of the technique was achieved, the results are disappointingly poor for the SEVIRI-acquired dataset, provided by M´et´eo-France, against which validation was carried out. This is thought to be explained by both the uncertainties in the NWP data, and the forward-modelling dependence on those uncertainties, being poorly understood, and treated too optimistically in the algorithm. Including more errors in the convolution introduces the problem of quantifying those errors (a non-trivial task), and would increase the processing time, making implementation impractical. In addition, if the uncertianties considered are too high then a PDF flatter than the empirical distribution currently used would be produced, making the technique less useful.

Technologies in digital cinema are quickly changing the way contemporary filmmakers create films and how audiences currently perceive them. As we move onward into the digital turn, it becomes ever more apparent that the medium of film has been emancipated from its dependence on the photograph. Directors are no longer required to capture the objectively real as it sits before the photographic lens, but can essentially construct it via groundbreaking advancements in computer-generated imagery, motion capture technology, and digital 3D camera systems and display technologies. Since the origins of film, spectators and filmmakers have assumed an existing relationship between reality and the photographic image. Yet digital film technologies now provide us with hyper-facsimiles of reality that are perceived as photographic, but are often created by way of computer processes. Digital cinema currently allows the viewer to inhabit and interact with cinematic realities in unprecedented ways, and it is this contemporary paradigmatic shift from the analog to the digital that has catalyzed fundamentally new ways of looking at the filmic image. In this paper, I will examine the perceptual complexities of contemporary digital film through the lens of these cinematic technologies by examining their impact on the viewer’s experience.

No description available.

While many studies have examined the effects of mental training programs on anxiety and concentration as they pertain to performance, none have examined the effects simultaneously as they relate to performance. Few studies have examined mental training programs, especially individualized mental imagery scripts, as they relate to the athlete's attentional profile and level of trait anxiety. The purpose of this study was to determine if a mental training program alters attentional profiles and trait anxiety. Additionally, this study determined if a mental training program had a transfer effect on performance. The following null hypotheses were examined: 1) There would not be a significant difference in attentional profiles before or after a mental training program; 2) there would not be a significant difference in trait anxiety scores before or after a mental training program; 3) there would not be a significant difference in an athlete's performance before or after a mental training program. The subjects (N = 20) in this study were comprised of Ball State University Women's Softball members. The age range of the subjects was between 18 and 23. The instruments used in this study were the Test of Attentional and Interpersonal Styles Self-Assessment (TAIS-SA) (Nideffer, 1980) for attentional styles and the Sport Competition Anxiety Test (SCAT) (Martens, 1977) for competitive traitanxiety. Batting performance was recorded by the investigator as the subjects took batting practice (from a pitching machine) during the normal course of practice. Each subject was given a log sheet and was asked to keep a record of the number of times she used the mental training program per week. This study was a pretest, posttest design, and dependent t-tests (p < .05) were calculated for the six subscales of the TAIS, the SCAT, and performance. There was a statistically significant difference from the pretest to the posttest on the levels of anxiety as measured by the SCAT and on hitting performance. While all scores on the six subscales of the TAIS-SA changed, they were not statistically significant. / School of Physical Education

Introduction: Visibility within advertising can generate acceptance and normalization in society, hence it is a powerful media that is of utmost importance for the homosexual group. Portrayals in advertising equal an admission as citizens, which homosexual women are currently neglected as media circumscribe male homosexuality as norm. Knowledge of how homosexual women identifies with portrayals in advertising is currently sparse. Aim: The overall aim of this study is to satiate the gap of knowledge of portrayals of homosexual women in advertising and how the dual identity of gender and sexuality influence the interpretation of advertisements targeting homosexual women. Method: A combination of quantitative and qualitative methodology. The sample includes self-identified homosexual women recruited consecutively during a two-month period. In total 113 homosexual women participated in the study. To provide a realistic interpretation, actual print advertisements portraying female homosexuals were used as stimuli within the contexts of mainstream media and gay media. Results: The female homosexual group appears to be heterogeneous since the perception of the individual sexual identity and lifestyle was not aligned but rather dispersed. However, the participants’ perception of the sexual identity and lifestyle of the female homosexual group is that is a homogenous group. Conclusion: Within the female homosexual group a stigmatization exist regarding gender behaviour that does not correspond with the recurrent portrayal in advertising of homosexual women as a homogenous group.

The purpose of this study was to examine the effects of a competition routine intervention on youth swimmers’ competition anxiety, confidence and performance. Participants of this study were youth swimmers (n = 4; female) between the ages of 12-16. Through the use of a multiple-baseline, single-subject design, participants received a competition routine intervention involving imagery, positive self-talk and a relaxation deep-breathing technique. Mixed methods were used, with participants completing the Competitive State Anxiety Inventory for Children questionnaire prior to their swim race and a reflective questionnaire following their swim race. Participants also took part in a follow-up interview upon completing the study. Participants showed no considerable decrease in cognitive and somatic anxiety, or increase in self-confidence or performance following the intervention. However, reflective questionnaire and interview results showed that participants perceived a positive improvement in all of these areas and found the intervention beneficial.

[Abstract]: Since early settlement, agriculture has been one of the main industries contributing to the livelihoods of most rural communities in Australia. The wheat grain industry is Australia’s second largest agricultural export commodity, with an average value of $3.5 billion per annum. Climate variability and change, higher input costs, and world commodity markets have put increased pressure on the sustainability of the grain industry. This has lead to an increasing demand for accurate, objective and near real-time crop production information by industry. To generate such production estimates, it is essential to determine crop area planted at the desired spatial and temporal scales. However, such information at regional scale is currently not available in Australia.The aim of this study was to determine broadacre crop area estimates through the use of multi-temporal satellite imagery for major Australian winter crops. Specifically, the objectives were to: (i) assess the ability of a range of approaches to using multi-temporal Moderate Resolution Imaging Spectroradiometer (MODIS) imagery to estimate total end-of-season winter crop area; (ii) determine the discriminative ability of such remote sensing approaches in estimating planted area for wheat, barley and chickpea within a specific cropping season; (iii) develop and evaluate the methodology for determining the predictability of crop area estimates well before harvest; and (iv) validate the ability of multi-temporal MODIS approaches to determine the pre-harvest and end-of-season winter crop area estimates for different seasons and regions.MODIS enhanced vegetation index (EVI) was used as a surrogate measure for crop canopy health and architecture, for two contiguous shires in the Darling Downs region of Queensland, Australia. Multi-temporal approaches comprising principal component analysis (PCA), harmonic analysis of time series (HANTS), multi-date MODIS EVI during the crop growth period (MEVI), and two curve fitting procedures (CF1, CF2) were derived and applied. These approaches were validated against the traditional single-date approach. Early-season crop area estimates were derived through the development and application of a metric, i.e. accumulation of consecutive 16-day EVI values greater than or equal to 500, at different periods before flowering. Using ground truth data, image classification was conducted by applying supervised (maximum likelihood) and unsupervised (K-means) classification algorithms. The percent correctly classified and kappa coefficient statistics from the error matrix were used to assess pixel-scale accuracy, while shire-scale accuracy was determined using the percent error (PE) statistic. A simple linear regression of actual shire-scale data against predicted data was used to assess accuracy across regions and seasons. Actual shire-scale data was acquired from government statistical reports for the period 2000, 2001, 2003 and 2004 for the Darling Downs, and 2005 and 2006 for the entire Queensland cropping region.Results for 2003 and 2004 showed that multi-temporal HANTS, MEVI, CF1, CF2 and PCA methods achieved high overall accuracies ranging from 85% to 97% to discriminate between crops and non-crops. The accuracies for discriminating between specific crops at pixel scale were less, but still moderate, especially for wheat and barley (lowest at 57%). The HANTS approach had the smallest mean absolute percent error of 27% at shire-scale compared to other multi-temporal approaches. For early-season prediction, the 16-day EVI values greater than or equal to 500 metric showed high accuracy (94% to 98%) at a pixel scale and high R2 (0.96) for predicting total winter crop area planted.The rigour of the HANTS and the 16-day EVI values greater than or equal to 500 approaches was assessed when extrapolating over the entire Queensland cropping region for the 2005 and 2006 season. The combined early-season estimate of July and August produced high accuracy at pixel and regional scales with percent error of 8.6% and 26% below the industry estimates for 2005 and 2006 season, respectively. These satellite-derived crop area estimates were available at least four months before harvest, and deemed that such information will be highly sought after by industry in managing their risk. In discriminating among crops at pixel and regional scale, the HANTS approach showed high accuracy. Specific area estimates for wheat, barley and chickpea were, respectively, 9.9%, -5.2% and 10.9% (for 2005) and -2.8%, -78% and 64% (for 2006). Closer investigation suggested that the higher error in 2006 area estimates for barley and chickpea has emanated from the industry figures collected by the government.Area estimates of total winter crop, wheat, barley and chickpea resulted in R2 values of 0.92, 0.89, 0.82 and 0.52, when contrasted against the actual shire-scale data. A significantly high R2 (0.87) was achieved for total winter crop area estimates in Augusts across all shires for the 2006 season. Furthermore, the HANTS approach showed high accuracy in discriminating cropping area from non-cropping area and highlighted the need for accurate and up-to-date land use maps.This thesis concluded that time-series MODIS EVI imagery can be applied successfully to firstly, determine end-of-season crop area estimates at shire scale. Secondly, capturing canopy green-up through a novel metric (i.e. 16-day EVI values greater than or equal to 500) can be utilised effectively to determine early-season crop area estimates well before harvest. Finally, the extrapolability of these approaches to determine total and specific winter crop area estimates showed good utility across larger areas and seasons. Hence, it is envisaged that this technology is transferable to different regions across Australia. The utility of the remote sensing techniques developed in this study will depend on the risk agri-industry operates at within their decision and operating regimes. Trade-off between risk and value will depend on the accuracy and timing of the disseminated crop production forecast.