The study of uncertainty in Geographic Information Systems (GIS) and environmental models has received increasing attention in recent years, due in part to the widespread use of GIS for resource management. This study used GIS-based techniques in order to compare several different forest inventory and forest cover datasets. These datasets pertain to an area of the boreal mixedwood forest in northeastern Alberta which covers roughly 73,000 km2, and which has recently been approved for logging. The datasets include two forest inventories based on aerial photographs, and a forest cover classification based on remotely sensed satellite data. Simple logical operations were used to transform the datasets to a form suitable for comparison. Standard GIS overlay techniques were used to compare the agreement among different datasets. Visualization techniques were used to display patterns of agreement in attribute space (contingency tables), and in geographic space (maps of uncertainty). Agreement between the two forest inventories was about 50% (Percent Correctly Classified), with a Kappa value of 0.4, for a classification based on species composition. In general, much of the misclassification was between ecologically similar types, particularly between different combinations of aspen and white spruce. Comparison of the forest inventories with the classified satellite image was done using a simplified land cover classification with five categories. Agreement was about 55% (Percent Correctly Classified), with a Kappa value of 0.3. Possible sources of discrepancy among datasets include change over time, differences in spatial scale, differences in category definitions, positional inaccuracy, boundary effects and misclassification. Analyses were conducted to characterize the effect of each of these sources of disagreement. The agreement was strongly affected by the distance to boundary, indicating a boundary effect extending to more than 100 meters. Differences in spatial scale accounted for a small proportion of discrepancy. None of the other possible sources had a measurable effect on the discrepancy. It was therefore inferred that misclassification accounted for a large proportion of the discrepancy. Estimated levels of uncertainty were propagated through models including simple growth and yield tables and a more complex harvest scheduling model. It was found that uncertainty in model outputs was strongly affected by uncertainty in inventory data, uncertainty in volume yield curves, and perhaps most importantly, by a poor understanding of disturbance and forest dynamics in the region. The results of the analysis show that these uncertainties may have significant economic and ecological implications.

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The study of uncertainty in Geographic Information Systems (GIS) and environmental models has received increasing attention in recent years, due in part to the widespread use of GIS for resource management. This study used GIS-based techniques in order to compare several different forest inventory and forest cover datasets. These datasets pertain to an area of the boreal mixedwood forest in northeastern Alberta which covers roughly 73,000 km2, and which has recently been approved for logging. The datasets include two forest inventories based on aerial photographs, and a forest cover classification based on remotely sensed satellite data. Simple logical operations were used to transform the datasets to a form suitable for comparison. Standard GIS overlay techniques were used to compare the agreement among different datasets. Visualization techniques were used to display patterns of agreement in attribute space (contingency tables), and in geographic space (maps of uncertainty). Agreement between the two forest inventories was about 50% (Percent Correctly Classified), with a Kappa value of 0.4, for a classification based on species composition. In general, much of the misclassification was between ecologically similar types, particularly between different combinations of aspen and white spruce. Comparison of the forest inventories with the classified satellite image was done using a simplified land cover classification with five categories. Agreement was about 55% (Percent Correctly Classified), with a Kappa value of 0.3. Possible sources of discrepancy among datasets include change over time, differences in spatial scale, differences in category definitions, positional inaccuracy, boundary effects and misclassification. Analyses were conducted to characterize the effect of each of these sources of disagreement. The agreement was strongly affected by the distance to boundary, indicating a boundary effect extending to more than 100 meters. Differences in spatial scale accounted for a small proportion of discrepancy. None of the other possible sources had a measurable effect on the discrepancy. It was therefore inferred that misclassification accounted for a large proportion of the discrepancy. Estimated levels of uncertainty were propagated through models including simple growth and yield tables and a more complex harvest scheduling model. It was found that uncertainty in model outputs was strongly affected by uncertainty in inventory data, uncertainty in volume yield curves, and perhaps most importantly, by a poor understanding of disturbance and forest dynamics in the region. The results of the analysis show that these uncertainties may have significant economic and ecological implications. / Arts, Faculty of / Geography, Department of / Graduate

Planning and control of time and other resources are crucial to the construction of large projects. Yet, current computerized techniques are unable to model the work patterns by which construction personnel plan a project. Furthermore, these methods are not capable of reflecting the day to day changes which must be monitored to control the construction site. The purpose of this thesis-is to promote the usability of computerized planning and scheduling through the development of the heuristic manner by which construction personnel perceive the project. Site studies held in cooperation with Poole Construction Limited and Foundation Company of Canada were performed using a computer scheduling system at the University of British Columbia which contained a prototype model of repetitive work. It provided insight to the process of repetition and rhythm by which projects are planned and to the requirements of the updating process necessary to monitor, and hence control the project. Two models evolved. The definition of the general repetitive structure was formulated to provide construction personnel with a tool by which to model the process of repetition. The definition of an updating process was formulated capable of monitoring daily progress on a construction site. Work performed with these models have shown them to be realistic in their approach to construction management. / Applied Science, Faculty of / Civil Engineering, Department of / Graduate

published_or_final_version / abstract / toc / Mechanical Engineering / Master / Master of Philosophy

published_or_final_version / Industrial and Manufacturing Systems Engineering / Doctoral / Doctor of Philosophy

published_or_final_version / Business Administration / Master / Master of Business Administration

published_or_final_version / Business Administration / Master / Master of Business Administration

published_or_final_version / Business Administration / Master / Master of Business Administration

Coal quality plays a crucial role both economically and environmentally in the operation of a coal-fired power plant. These plants are designed to operate most efficiently when coal of consistently good quality is used as the feed. For example, the ash content should be monitored to control slagging problems in the burners. For emission control purposes the sulfur content of the feed should be carefully scrutinized. This realization of the importance of coal quality to the end-user has made the prediction of coal quality an important concern. Computerized mine planning systems can be used to help suppliers predict the quality of future coal shipments. One such system was implemented at an underground coal mine to determine its performance in an operating environment. Following some extensive revisions to the system, the results of the implementation exercise showed the ability of this mine planning system to account for coal quality while producing a workable short-range plan. To be of direct use to the end-user, the mine planning results should be adjusted to account for mining dilution and subsequent beneficiation of the run-of-mine coal. To help power plant owners in predicting the quality of the power plant feed a computer program was developed to solve the blending problem faced by plants dependent on multiple sources of supply. This interactive program utilizes the predictions from the mine planning system to obtain a goal programming formulation of the blending problem. This type of problem formulation gives the program the flexibility needed to solve the blending problem in both long and short term time frames. Use of the program in both planning scenarios was illustrated by examples. The results showed the ability of the program to determine the best blend possible based on a set of user-specified objectives. Use of this interactive blending program in conjunction with the mine planning system provides the capability of computerized prediction of coal quality from mine mouth to blended plant feed. Implementation of these planning aids on a routine basis will assist the owners of coal-fired power plants in their attempts to maximize plant efficiency and maintain environmental standards.