Amongst the most threatened ecosystems on Earth, mangrove forests are also one of the more difficult to work in due to their growth in mud and open water coastal zones and their dense tangled stems, branches and prop roots. Consequently, there has been an impetus to employ remotely sensed imagery as a means for rapid inventory of these coastal wetlands. To date, the majority of mangrove maps derived from satellite imagery utilize a simple mangrove classification scheme which does not distinguish mangrove species and may not be useful for conservation and management purposes. Although more elaborate satellite based mangrove classification schemes are being developed, given their enhanced complexity they deserve additional justification for end users. The purpose of this study was to statistically examine the appropriateness of one such classification scheme based on an inventory of field data. In January of 2007 and May of 2008, 61 field sample plots were selected in a stratified random fashion based on a previous classification of a degraded mangrove forest of the Isla La Palma (Sinaloa, Mexico) using Landsat TM5 data. Unlike other previous Landsat TM based classifications of this region, which simply identified the mangrove forests as one class, the mangroves were classified (i. e. mapped) according to four conditions; healthy tall, healthy dwarf, poor condition, and dead mangroves. Within each sample plot, all mangroves of diameter of breast height (dbh) greater than 2.5 cm were identified and their height, condition and dbh recorded. An estimated Leaf Area Index (LAI) value also was obtained for each sample and the shortest distance from the center of each sample plot to open flowing water was determined using a geographic information system (GIS) overlay procedure. These data were then used to calculate mean values for the four classes as well as to determine stem densities, basal areas, and the Shannon-Wiener diversity index. In order to assess the appropriateness of this mangrove classification scheme a discriminant analysis approach was then applied to these field data. The results indicate this forest has undergone severe degradation, with decreasing mean tree heights, mean dbh and species diversity. In regards to the discriminant analysis procedure, further classification of these field plots and cross-validation based on these significant variables provided high classification accuracy thus validating the appropriateness of the satellite based image classification scheme. Moreover, the discriminant analysis indicated that the estimated LAI, mean height, and mean dbh are significant in the separation of the classification of mangrove forest condition along these field sample plots.

The spectral separability of southern pines is a perplexing issue due to limited variance of spectral reflectance in species with similar morphological characteristics. Understory vegetation reflectance may exacerbate the ability to accurately identify various overstory tree species, specifically those of longleaf and loblolly pines in the southeastern US. In this study, identification of target level overstory crowns with varying degrees of understory vegetation cover based on fire return frequency was used to assess the role of understory reflectance on target crown species discernment. Seasonal variations of understory vegetation in late dormant and late growing seasons were compared for disparities in potential reflectance contribution from understory vegetation. Overall, the impact of understory vegetation was considered negligible in the spectral separability of longleaf and loblolly pines based on discriminant analysis results. Classification of WorldView-2 relative spectral profiles resulted in overall accuracies of 92% for dormant season and 96% for growing season imagery.

One of the basic goals of the physical anthropologist is to create a biological profile, consisting of sex, ancestry, age, and stature, from the skeletal material that they are presented with. This thesis seeks to explore size and shape differences related to sex and ancestry from the hyoid bones of the Robert J. Terry Anatomical Collection in order to gauge its usefulness in the process of developing a biological profile. A series of measurements were taken from 398 hyoids and analysis was conducted using a number of statistical methods. Independent samples t-tests were used to examine size differences between sexes and ancestries, while linear regression analysis and principle component analysis were used to examine shape differences. Discriminant function analysis was employed to test the ability of the hyoids to be classified by sex or ancestry. The ultimate goal of the thesis is to provide physical anthropologists with a series of discriminant function equations that can be used to estimate the sex and ancestry of a hyoid. Five equations ranging in accuracy from 83-88% were developed to determine sex of a hyoid, while four equations ranging in accuracy from 70-89% can be used to determine ancestry. In addition, the t-tests, regression analyses, and principle component analysis have identified several variations in size and shape between sexes and ancestries. These analyses have provided further knowledge as to the morphological form of the hyoid, as well as a method that can be easily used by physical anthropologists to assess sex and ancestry.

The objective of this study is to establish whether companies that utilise their resources more efficiently present specific characteristics in their financial profile, and whether on the basis of these characteristics a classification model can be constructed that includes, alongside resource utilisation measures, predictors related to other financial dimensions calculated from published information. The- research proceeds by examining the factors influencing companies' performance, and the reliabilty of published accounts. Discriminant analysis is chosen as the most appropriate technique of analysis. Its applications in the field of financial analysis are discussed -and an examination of the discriminant analysis technique is undertaken. For reasons of comparability and access to a large quantity of information, the analytical part of the study is based on data extracted from a computer readable tape provided by Extel Statistical Services Ltd. It starts by describing the financial variables to be used later on in the study, and proposing a classification framework that would be of assistance in identifying the financial dimensions of importance in relation to the problem under investigation. A discriminant model that correctly classifies 85 per cent of the companies is then constructed. It includes, besides measures of resources utilisation, measures of financial levarage, working capital management, cash position and stability of past performance. The-part of the analysis on the identification of potential well performing companies indicates that, although specific characteristics can be noticed up to five year before, it is only possible to construct a classification model with sufficient accuracy one year before a high level of performance is actually reached. Finally, an index of financial performance based on normal approximations of the z-score distributions from the model used to identify well performing companies is suggested and an assessment of the structural change experienced by companies rising from a less well to a well performaing status is presented. / Algerian Ministere de l'Hydraulique

No description available.

No description available.

Spectroradiometer data (350 – 2500 nm) were acquired in late summer 1999 over various forest sites in Appomattox Buckingham State Forest, Virginia, to assess the spectral differentiability among six major forestry tree species, loblolly pine (Pinus taeda), Virginia pine (Pinus virginiana), shortleaf pine (Pinus echinata), scarlet oak (Quercus coccinea), white oak (Quercus alba), and yellow poplar (Liriodendron tulipifera). Data were smoothed using both moving (9-point) and static (10 nm average) filters and curve shape was determined using first and second differences of resultant data sets. Stepwise discriminant analysis decreased the number of independent variables to those significant for spectral discrimination at -level of 0.0025. Canonical discriminant analysis and a normal discriminant analysis were performed on the data sets to test separability between and within taxonomic groups. The hardwood and pine groups were shown to be highly differentiable with a 100% cross-validation accuracy. The three pines were less differentiable, with cross-validation results varying from 61.64% to 84.25%, while spectral separability among the three hardwood species showed more promise, with classification accuracies ranging from 78.36% to 92.54%. The second difference of the 9-point weighted average filter was the most effective data set, with accuracies ranging from 84.25% to 100.00% for the separability tests. Overall, variables needed for spectral discrimination were well distributed across the 350 nm to 2500 nm spectral range, indicating the usefulness of the whole wavelength range for discriminating between taxonomic groups and among species. Derivative analysis was shown to be effective for between and within group spectral discrimination, given that the data were smoothed first. Given the caveat of the limited species diversity examined, results of this study indicate that leaf-on hyperspectral remotely sensed data will likely afford spectral discrimination between hardwoods and softwoods, while discrimination within taxonomic groups might be more problematic. / Master of Science

Bus transit driving is an occupation that requires high concentration in driving and is demanding due to work overload, time pressure, and responsibility for lives. In 2006, there were 103 fatal crashes involving transit buses. As the number of distraction-related crashes increases, it is important to conduct a transit distraction study to reduce future crashes. This thesis focused on the analysis of the likelihood of the operator distraction behaviors and the analysis to find a predictive model to classify different distraction categories. An ordinal logistic regression was carried out to evaluate how age, gender, driving experience of the operators, and their driving frequencies accounts for the likelihood of 17 potential distracted driving behaviors. The results of this analysis showed that there were only 5 best models (p-value of model fit less than 0.005 and p-value of parallel line test more than 0.005) that could be constructed, including: listening to the radio/ CD/DVD/MP3 player (D1); picking Up and Holding 2-way Radio (D5); listening to the Dispatch Office broadcast (D6); adjusting switches/controls on dashboard (D15); and utilizing mentor ranger (D16). On the other hand, a discriminant analysis was performed to predict how different transit operator driving behaviors when exposed by 10 different distraction activities and 16 predictors were considered in this analysis. The final results showed that there are 4 predictors that seem to be able to classify distraction groups across all 4 models; those include segment length, average duration of idling time/stop delay at speed interval 0—4 km/hr, frequency of speed transitions that deviate by ± 0 to 4 km/hr from its speed, and frequency of speed transitions that deviate by ± 8 to 12 km/hr from its speed. / Master of Science

Discriminant analysis is a procedure for identifying the relationships between qualitative criterion variables and quantitative predictor variables. Data bases of genetic polymorphisms are currently available that group such polymorphisms by ethnic origin or nationality. Such information could be useful to entities that base financial determinations upon predictions of disease or to medical researchers who wish to target prevention and treatment to population groups. While the use of genetic information to make such determinations is unlawful in states and confidentiality and privacy concerns abound, methods for human “redlining” may occur. Thus, it is necessary to investigate the efficacy of the relationship of certain genetic information to ethnicity to determine if a statistical analysis can provide information concerning such relationship. The use of the statistical technique of discriminant analysis provides a tool for examining such relationship.

The distributional properties of improvement-over-chance, I, effect sizes derived from linear and quadratic predictive discriminant analysis (PDA) and from logistic regression analysis (LRA) for the two-group univariate classification were examined. Data were generated under varying levels of four data conditions: population separation, variance pattern, sample size, and prior probabilities. None of the indices provided acceptable estimates of effect for all the conditions examined. There were only a small number of conditions under which both accuracy and precision were acceptable. The results indicate that the decision of which method to choose is primarily determined by variance pattern and prior probabilities. Under variance homogeneity, any of the methods may be recommended. However, LRA is recommended when priors are equal or extreme and linear PDA is recommended when priors are moderate. Under variance heterogeneity, selecting a recommended method is more complex. In many cases, more than one method could be used appropriately.