The use of satellite image data for vegetation mapping and forestry inventory in mountain environments has been greatly restricted by the scene digital values containing a mixture of variations which are due partly to surface cover (albedo) and partly to topography (illumination). Landsat Thematic Mapper (TM) data were selected for a portion of the Rocky Mountains in the Kananaskis Valley, Alberta, Canada and used to derive secondary data channels that sought to compensate for topographic information, by isolating the response due to surface cover. Two multispectral techniques were used: band ratioing and principal component analysis. In both cases, the generated channels represented the difference between the three spectral groups of bands for TM data: visible, near and middle (shortwave) infra-red. Multi-channel ratio and component combinations were used in an unsupervised clustering procedure that defined homogeneous ground areas. When the original pixel values were replaced by the average brightness for their respective clusters, the resulting image channels represented each of the multispectral bands as if the surface were flat. Selective principal components proved more successful than band ratioing, being less susceptible to noise and allowing input from more than two bands each. These component channels created a final classification which had a higher overall accuracy than those for studies in adjacent mountain environments using supervised classification and was comparable to those which also incorporated topographic data. The study was successful in identifying a different approach to topographic correction without having to acquire digital elevation models and considerable ground knowledge. This technique has much potential for expediting forest cover inventory and vegetation mapping in mountain environments and represents a new and promising application for principal component analysis.

Quantities of herbaceous vegetation of Sahelian rangelands in Niger and Mali were compared to vegetation indices (VI) derived from Landsat MSS and NOAA AVHRR LAC images. Field data was collected in 1985,1988 and 1989 in Niger and an appropriate sampling scheme for the study area was developed. Herbaceous vegetation could be estimated to within t 150 kgha 1 at an 80% confidence level up to 1300 kgha -1. Establishing site positions was found to be a primary obstacle when selecting suitable sampling areas. Suggested is the use of Landsat MSS image hard-copies in combination with a global positioning system. Landsat MSS and NOAA AVHRR LAC data were available for dates corresponding to field surveys of 1985 and 1988. While Landsat MSS scenes were geometrically corrected to maps, NOAA AVHRR images were registered to Landsat MSS with a simulated resolution of 1.1 km. Data from both satellites were radiometrically corrected and standardized to atmospheric conditions to the image with the highest relative scene contrast for each study area. These reference images were identified on the basis of bare soil spectral reflectance values and a binary decision tree. Five methods of resampling image data to represent field sites were applied. - The image data sampling methods were found to have a significant influence on spectral reflectance values attributed to a site and, consequently, on the relationship between ground and satellite VIs. Ratio, normalized difference and perpendicular VIs (RVI, NDVI and PVI) were computed for each step of pre-processing procedures. For Landsat MSS VIs were also derived from average spectral reflectance values of bands 3 and 4 to simulate NOAA AVHRR channel 2. VIs were compared for the same sensor, between sensors and related to field data by using linear and logarithmic regression analyses. RVIs and NDVIs achieved very similar results, while PVIs showed a more variable relationship to ground data. Overall, VIs from simulated NOAA AVHRR channel 2 values were found to be not superior to those derived from just band 4. NOAA AVHRR VIs could be related to Landsat MSS ratio VIs by a single regression line for 1985 and 1988 growing seasons for Niger and Mali survey sites. For the inter-calibration a simulation of the NOAA AVHRR pixel size was found to be better suited than the high resolution Landsat MSS data.

The conventional pixel-oriented classification is the most commonly used approach in remote sensing for land use product extraction. The object-oriented classification based on image segmentation is an alternative, which uses pixel context, texture and shapes, in addition to their spectral characteristics. This paper reports on a comparative study between supervised pixel-oriented and object-oriented classifications in a precision agriculture context using three hyperspectral images on our first study site, and a set of hyperspectral and multispectral images for a second site. The images for the first site, owned by the horticulture research and development centre (Agriculture Canada) at L'Acadie in southern Quebec, were acquired with the Compact Airborne Spectrographic Imager (CASI) sensor at three different altitudes, providing three different spatial resolutions: 1, 2 and 4 m. For the second site, located at the Indian Head Agriculture Research foundation in Saskatchewan, a Probe-1 hyperspectral image was acquired as well as a multispectral IKONOS image. After calibration and correcting the imagery, pixel-oriented classifications were carried out using the maximum likelihood algorithm and object-oriented classifications with a nearest neighbor classifier after region growing hierarchical segmentation. After segmentation, statistical comparison on the mean difference to neighbor objects confirmed that the segments had minimum mixing effects in respect to other segmentation levels and neighboring ground entities. After accuracy analysis on the classifications for the first site, the segmentation process allowed the use of a spatially coarser hyperspectral image (4 m with kappa of 0.8268) to achieve better results than pixel oriented classification of a spatially finer hyperspectral image (1 m with kappa of 0.7730), in the task of delineating agricultural classes. For the second site, results are still consistent. Object oriented results of the hyperspectral Probe-1 image (kappa of 0.9628) significantly exceed the pixel oriented results (kappa of 0.9217). Similarity is observed with IKONOS multispectral imagery (kappa of 0.9371 for object oriented and kappa of 0.8926 for pixel oriented). Image segmentation is therefore an important technique to achieve high accuracy in classification of land cover classes. Hyperspectral imagery also has a strong power of discrimination between many agricultural classes.

The work presented in this thesis documents the design, development and evaluation of a high performance stereoscopic telepresence system. Such a system offers the ability to enhance the operator perception of a remote and potentially hazardous environment as an aid to performing a remote task. To achieve this sensation of presence demands the design of a highly responsive remote camera system. A high performance stereo platform has been designed which utilises state- of-the-art cameras, servo drives and gearboxes. It possesses four degrees of freedom; pan, elevation and two camera vergence motions, all of which are controlled simultaneously in real-time by an open architecture controller. This has been developed on a PC/AT bus architecture and utilises a PID control regime. The controller can be easily interfaced to a range of input devices such as electromagnetic head tracking systems which provide the trajectory data for controlling the remote mechatronic platform. Experiments have been performed to evaluate both the mechatronic system and operator oriented performance aspects of the telepresence system. The mechatronic system investigations identify the overall system latency to be 80ms, which is considerably less than other current systems. The operator oriented evaluation demonstrates the necessity for a head tracked telepresence system with a head mounted display system. The need for a low latency period to achieve high operator performance and comfort during certain tasks is also established. This is evident during trajectory following experiments where the operator is required to track a highly dynamic target. The telepresence system has been fully evaluated and demonstrated to enhance operator spatial perception via a sensation of visual immersion in the remote environment.

<p> A detailed structural map of the central uplift of Martin Crater in western Thaumasia Planum, Mars, reveals highly folded and fractured geology throughout the 15-km diameter uplift. The stratigraphy in the central uplift of the crater has been rotated to near vertical dip and imaged by high-definition cameras aboard the Mars Reconnaissance Orbiter (MRO). These unique factors allow individual geologic beds in Martin Crater to be studied and located across the length of the uplift. </p><p> Bedding in Martin Crater primarily strikes SSE-NNW and dips near vertically. Many units are separated by a highly complex series of linear faults, creating megablocks of uplifted material. Faulting is dominantly left-slip in surface expression and strikes SW-NE, roughly perpendicular to bedding, and major fold axes plunge toward the SW. Coupled with infrared imagery of the ejecta blanket, which shows an &ldquo;exclusion zone&rdquo; northeast of the crater, these structural indicators provide strong support for a low-angle impactor (approximately 10&ndash;20&deg;) originating from the northeast. </p><p> Acoustic fluidization is the prevailing theoretical model put forth to explain complex crater uplift. The theory predicts that uplifted megablocks in craters are small, discrete, separated and highly randomized in orientation. However, megablocks in Martin Crater are tightly interlocked and often continuous in lithology across several kilometers. Thus, the model of acoustic fluidization, as it is currently formulated, does not appear to be supported by the structural evidence found in Martin Crater.</p><p>

<p> Thermokarst lakes are a dominant factor of landscape scale processes and permafrost dynamics in the otherwise continuous permafrost region of the Arctic Coastal Plain (ACP) of northern Alaska. Lakes cover greater than 20% of the landscape on the ACP and drained lake basins cover an additional 50 to 60% of the landscape. The formation, expansion, drainage, and reformation of thermokarst lakes has been described by some researchers as part of a natural cycle, the thaw lake cycle, that has reworked the ACP landscape during the course of the Holocene. Yet the factors and processes controlling contemporary thermokarst lake expansion remain poorly described. This thesis focuses on the factors controlling variation in extant thermokarst lake expansion rates in three ACP regions that vary with respect to landscape history, ground-ice content, and lake characteristics (i.e. size and depth). Through the use of historical aerial imagery, satellite imagery, and field-based data collection, this study identifies the controlling factors at multiple spatial and temporal scales to better understand the processes relating to thermokarst lake expansion. Comparison of 35 lakes across the ACP shows regional differences in expansion rate related to permafrost ice content ranging from an average expansion rate of 0.62 m/yr on the Younger Outer Coastal Plain where ice content is highest to 0.16 m/yr on the Inner Coastal Plain where ice content is lowest. Within each region, lakes vary in their expansion rates due to factors such as lake size, lake depth, and winter ice regime. On an individual level, lakes vary due to shoreline characteristics such as local bathymetry and bluff height. Predicting how thermokarst lakes will behave locally and on a landscape scale is increasingly important for managing habitat and water resources and informing models of land-climate interactions in the Arctic.</p><p>

<p> This dissertation describes the acquisition and analysis of tropical wave cloudiness. Tropical wave positions for the years 2003 through 2013 were extracted via text mining, from the National Hurricane Center&rsquo;s Tropical Weather Discussion, a bulletin released every six hours and published on-line. Tropical wave tracks were developed from these positions using the Multiple Hypothesis Tracking algorithm. Satellite data from the Atmospheric Infrared Sounder (AIRS) was downloaded from the NASA Mirador website based on time and position of tracked tropical waves. The AIRS data was mosaicked to provide complete coverage between satellite swaths. The AIRS Level 2 Cloud Fraction Standard product was used exclusively in the analysis. Cloud fraction data was divided into upper and lower levels as provided in the AIRS product. A cloud fraction ratio was also developed to provide some indication of the insulating quality of clouds. The analysis discovered secular trends of varying degrees and direction depending on location of tropical waves. The analysis also found significant periodic variability within cloud fraction values, much of which correlated to known global oscillations such as El Nino and the Madden-Julian Oscillation. However a number of periodic signals found within tropical wave cloudiness could not be correlated with any of the known global and non-earth oscillations tested against. Future research ideas in the conclusions include an examination of those uncorrelated periodic signals. Also included in the conclusions are theories about differences in correlations to periodic signals within a tropical wave core versus correlations that are seen in surrounding cloud patterns.</p><p>

M.Comm. / The problem of establishing and controlling remote access to corporate networks has become one of the most difficult issues facing network administrators and information security professionals (Tipton & Krause (b), 2001:99). As the connection devices become more powerful and less expensive, more and more personally owned devices make their way to the connected edge of the network. The rise of the Internet as an accepted vehicle for business use has dramatically increased the points of entry to the corporate network. As employees and third parties gain mobility through the use of laptops, smart phones and Personal Digital Assistants (PDAs), the threat to organisational security grows (Cartwright, 2001). There are three main reasons why remote access connections are often insecure: Lack of awareness regarding security risks associated with remote access; Protecting a remote access connection is a complex task which is unlikely to be done well unless a methodical approach is taken; and Remote locations are not under the direct control of the organisation and are inherently insecure (Information Security Forum (a), 1999). The purpose of this short dissertation is to: Provide an overview of remote access, including concepts and definitions; Identify the risks associated with remote access to corporate networks; Identify possible controls to mitigate these risks; and highlight possible future trends regarding remote access.

Policy analyses of agricultural and environmental systems are often limited due to data constraints. Measurement campaigns can be costly, especially when the area of interest includes oceans, forests, agricultural regions or other dispersed spatial domains. Satellite based remote sensing offers a way to increase the spatial and temporal resolution of policy analysis concerning these systems. However, there are key limitations to the implementation of satellite data. Uncertainty in data derived from remote-sensing can be significant, and traditional methods of policy analysis for managing uncertainty on large datasets can be computationally expensive. Moreover, while satellite data can increasingly offer estimates of some parameters such as weather or crop use, other information regarding demographic or economic data is unlikely to be estimated using these techniques. Managing these challenges in practical policy analysis remains a challenge. In this dissertation, I conduct five case studies which rely heavily on data sourced from orbital sensors. First, I assess the magnitude of climate and anthropogenic stress on coral reef ecosystems. Second, I conduct an impact assessment of soil salinity on California agriculture. Third, I measure the propensity of growers to adapt their cropping practices to soil salinization in agriculture. Fourth, I analyze whether small-scale desalination units could be applied on farms in California in order mitigate the effects of drought and salinization as well as prevent agricultural drainage from entering vulnerable ecosystems. And fifth, I assess the feasibility of satellite-based remote sensing for salinity measurement at global scale. Through these case studies, I confront both the challenges and benefits associated with implementing satellite based-remote sensing for improved policy analysis.

The goal of this thesis aims two fundamental points. The first consists in making the development on the optimal spectral index to characterize the dynamics of a forest cover of a fir tree independently of the internal (underlying soil, topography, saturation and linearity) and external disturbing factors (BRDF and atmosphere) to covers starting from the data of simulation. The second point makes it possible to compare for the first time the potential of three spectral resolutions different (fine, average and broad) from sensors MODIS-EOS, VEGETATION-SPOT and AVHRR-NOAA to minimize the disturbing effects on the indices of vegetation in forest medium. The results obtained show that in general, the influence of the disturbing effects on the indexes of vegetation are complex. There is not only one component which dominates the met variations, but rather, of the combined influences of several factors. (Abstract shortened by UMI.)