Remote sensing and GIS in support of sustainable agricultural development
| dc.contributor.advisor | Franklin, Steven E. | en_US |
| dc.contributor.committeeMember | Guo, Xulin | en_US |
| dc.contributor.committeeMember | Cunfer, Geoff | en_US |
| dc.contributor.committeeMember | Treitz, Paul | en_US |
| dc.contributor.committeeMember | Maule, Charles | en_US |
| dc.creator | Duro, Dennis | en_US |
| dc.date.accessioned | 2013-01-03T22:30:43Z | |
| dc.date.available | 2013-01-03T22:30:43Z | |
| dc.date.created | 2012-03 | en_US |
| dc.date.issued | 2012-05-22 | en_US |
| dc.date.submitted | March 2012 | en_US |
| dc.description.abstract | Over the coming decades it is expected that the vast amounts of area currently in agricultural production will face growing pressure to intensify as world populations continue to grow, and the demand for a more Western-based diet increases. Coupled with the potential consequences of climate change, and the increasing costs involved with current energy-intensive agricultural production methods, meeting goals of environmental and socioeconomic sustainability will become ever more challenging. At a minimum, meeting such goals will require a greater understanding of rates of change, both over time and space, to properly assess how present demand may affect the needs of future generations. As agriculture represents a fundamental component of modern society, and the most ubiquitous form of human induced landscape change on the planet, it follows that mapping and tracking changes in such environments represents a crucial first step towards meeting the goal of sustainability. In anticipation of the mounting need for consistent and timely information related to agricultural development, this thesis proposes several advances in the field of geomatics, with specific contributions in the areas of remote sensing and spatial analysis: First, the relative strengths of several supervised machine learning algorithms used to classify remotely sensed imagery were assessed using two image analysis approaches: pixel-based and object-based. Second, a feature selection process, based on a Random Forest classifier, was applied to a large data set to reduce the overall number of object-based predictor variables used by a classification model without sacrificing overall classification accuracy. Third, a hybrid object-based change detection method was introduced with the ability to handle disparate image sources, generate per-class change thresholds, and minimize map updating errors. Fourth, a spatial disaggregation procedure was performed on coarse scale agricultural census data to render an indicator of agricultural development in a spatially explicit manner across a 9,000 km2 watershed in southwest Saskatchewan for three time periods spanning several decades. The combination of methodologies introduced represents an overall analytical framework suitable for supporting the sustainable development of agricultural environments. | en_US |
| dc.identifier.uri | http://hdl.handle.net/10388/ETD-2012-03-390 | en_US |
| dc.language.iso | eng | en_US |
| dc.subject | remote sensing | en_US |
| dc.subject | agriculture | en_US |
| dc.subject | land cover | en_US |
| dc.subject | classification | en_US |
| dc.subject | change detection | en_US |
| dc.subject | spatial disaggregation | en_US |
| dc.title | Remote sensing and GIS in support of sustainable agricultural development | en_US |
| dc.type.genre | Thesis | en_US |
| dc.type.material | text | en_US |
| thesis.degree.department | School of Environment and Sustainability | en_US |
| thesis.degree.discipline | Environment and Sustainability | en_US |
| thesis.degree.grantor | University of Saskatchewan | en_US |
| thesis.degree.level | Doctoral | en_US |
| thesis.degree.name | Doctor of Philosophy (Ph.D.) | en_US |