Thèses (M.Sc.)--Université de Sherbrooke (Canada), 2004. / Titre de l'écran-titre (visionné le 20 juin 2006). Publié aussi en version papier.

Thesis (M.S.)--Rochester Institute of Technology, 2008. / Typescript. Includes bibliographical references (p. 239).

Thèse (M.Sc.)--Université Laval, 2004 . / Bibliogr.: f. [89]-104. Publié aussi en version électronique.

Thesis (M.Sc. (Geography)) - Rhodes University, 2007.

M.Sc. (Geography) / The aim of this research is to determine if, by means of Landsat MSS digital data, urban land use classes can be identified and separated, and if changes in land use (urban sprawl) can be detected, over a period of time. Regional authorities function at inter-municipal scale. In order for these instittitions to perform these functions, they need to have access to standardized data (standardized in scale, time and interpretation) in order to obtain a global view of the total area under their authority. Remotely sensed digital data have the potential to fulfil these needs. A secondary objective will then also be to make an evaluation of the various applications of the results to the relevant authorities.

Chromaticity analysis of LANDSAT Multispectral Scanner (MSS) imagery of Chilko Lake, B.C. reveals a. locus whose shape has not been previously reported. To investigate the cause of this and to come to a broader understanding of chromaticity analysis for MSS and Thematic Mapper (TM) data, an optical water quality model has been used. The model is composed of a four component reflectance model (R-model), an interface model and an atmospheric model. The R-model was calibrated for Chilko Lake by determining the specific absorption and backscattering spectra for suspended minerals (SM), chlorophyll-a uncorrected for phaeophytins (C) and yellow substance (YS). The fourth component is water. The model reproduces the observed locus shape and indicates that it is primarily a function of SM, with the unreported lower limb on MSS imagery caused by SM gradients with concentrations less than 1-2 mg/L. The effects of C, YS and SM cannot be separated on plots of chromaticity coordinates X and Y for either MSS or TM data. In addition, haze or wind gradients, if they occur over water with low levels of SM, would look similar to the lower limb on MSS XY plots. However, if brightness is used in combination with X, the model predicts that C and YS, though themselves inseparable, can be differentiated from SM at all but the lowest concentrations of SM. Furthermore, haze and wind gradients can be distinguished from the lower limb. Thus the addition of brightness to chromaticity analysis has the potential to significantly improve the technique. The model was tested by comparing simulated chromaticity results with results from actual images (one TM image and three MSS images) for which ground truth had been collected. Qualitative predictions regarding haze and water quality patchiness were confirmed. Correlation analysis with R² values from 0.81 to 0.95 also strongly confirmed predictions regarding SM, but showed that the model is systematically underestimating SM. Correlation tests for a combined C and YS factor (CYS) were inconclusive because of the systematic modeling error, but classification maps provide weak evidence that CYS is behaving qualitatively as predicted and that CYS can be differentiated from SM. The modeling error is thought to originate in atmospheric assumptions which are not met. The R-model which is fundamental to the study has been tested and is not a major source of error. The study concludes that the model is qualitatively correct and that the use of brightness improves chromaticity analysis by allowing separation of CYS and SM, though further work should be undertaken to verify these results. Maps of CYS and SM in Chilko Lake reveal that CYS tends to be higher along the western shore and where the hypolimnion is exposed. SM are highest near stream mouths. The distribution patterns are related to physical processes within the lake and provide a synoptic view of the connection between water quality parameters and circulation which would be difficult to achieve in any other way. / Forestry, Faculty of / Graduate

The improved spatial resolution of the data from the SPOT satellite provides a substantially better basis for monitoring urban land use and growth with remote sensing than Landsat data. The purpose of this study is to delineate the road network in 20-m resolution SPOT-images of urban areas automatically. The roads appear as linear features. However, most edge and line detectors are not effective in detecting roads in these images because of the low signal to noise ratio, low contrast and blur in the imagery. For the automatic recognition of roads, a new line detector based on surface modelling is developed. A line can be approximated by a piecewise straight curve composed of short linear line-elements, called linels, each characterized by a direction, a length and a position. The approach to linel detection is to fit a directional surface that models the ideal local intensity profile of a linel in the least square sense. A Gaussian surface with a direction of invariance forms an adequate basis for modelling the ideal local intensity profile of the roads. The residual of the least squares fit as well as the parameters of the fit surface characterize the linel detected. The reliable performance of this line operator makes the problems of linking linels more manageable. / Master of Science

Although the maximum likelihood classifier is a popular classification technique, there is an inherent problem associated with the 100% classification of a scene. This is because there will inevitably be pixels within a study area that have a low probability of belonging to any of the predefined categories. The focus of this research was to locate these low probability pixels and observe their affect on classification accuracy. This was done by performing supervised classifications at various threshold levels using two methods of classification training combined category training site statistics and separated category training site statistics. In general, it was found that a majority of the scene was classified at very low probabilities but the accuracy of the resulting classifications was much greater than the low probabilities would suggest. / Master of Science

Digital Landsat cover-type classifications have often proved less accurate than hoped for, or have been less detailed than needed. Recent research efforts have used additional data to supplement the four bands of Landsat MSS data in an attempt to increase the accuracies of computer classifications. The goal of this study was to evaluate the use of vegetation-related ancillary variables for improving the performance of a Landsat classification of the Great Dismal Swamp. Ancillary data considered to be related to the distribution of vegetation types in the swamp were registered with Landsat multispectral scanner data to a 50 meter UTM grid. The ancillary variables were peat depths and elevations from field surveys, and spectral texture values from the Landsat data. Discriminant analyses of a sample of pixels were performed to investigate the ability of spectral and ancillary data, separately and in combination, to discriminate between vegetation cover types. A layered classification procedure was developed that used discriminant analysis of ancillary data after a previous unsupervised spectral classification. This was compared to a spectral stratification classification and a straightforward unsupervised classification of spectral data alone. The layered procedure resulted in an accuracy of 21.46% for level III classes and 41.71% for level II classes. The accuracies for level III and level II classifications using the unsupervised procedure were 41.58% and 63.77%, respectively. Some possible explanations of the seemingly contradictory results were posed, and alternative procedures suggested. / Master of Science

The multispectral scanners (MSS) of the LANDSAT satellites collect solar radiation of different wavelengths reflected from the earth's surface. While the different greytone values of a given band of a LANDSAT image of simple terrain are due almost entirely to the various reflectivity values of the features of the earth's surface (such as vegetation, mineral deposits, or bodies of water), the same is not true of areas of complex topography. In mountainous areas, the mixture of light and dark regions in a LANDSAT image may be due to shadow effects as well as the reflectivity values for those wavelengths of light of the various surface features. In this research, an illumination model is developed to help understand features observed in a LANDSAT scene of a mountainous area. The illumination model is defined and its implementation in the GIPSY (General Image Processing System) system is discussed. The application of the model to a particular LANDSAT scene is described including the development of an elevation model from the LANDSAT data. Finally, the illumination model image is compared with the LANDSAT scene and the results are discussed. / Master of Science