Change detection is currently a topic of great interest to theoretic geographic researchers. The necessity to map, monitor, and model land cover change is also important to a variety of applied fields as varied as urban planning and military intelligence. This research compares five algorithms to map urban land cover change in the greater Las Vegas, Nevada metropolitan area. Landsat Thematic Mapper imagery acquired on May 1990 and May 2000 was used as the primary data. The change detection methods yielded simple maps of change vs. no change. These algorithms included image differencing, image ratioing, image regression, vegetation index differencing, and principal components analysis. Each of these techniques accurately identified areas of land cover with moderate levels of accuracy and produced overall change detection accuracy values between 60% and 76% depending on the method. The highest accuracy was obtained by the image ratioing method using the red spectral band (76%). As expected, the determination of change detection thresholds for each technique was critical to the accuracy produced by the algorithm. Moreover, the type of statistic used in optimizing that threshold was also a significant impacting the final accuracy. The approach of using a set of ground points to calibrate the change detection threshold proved to have significant merit.
| Identifer | oai:union.ndltd.org:BGMYU2/oai:scholarsarchive.byu.edu:etd-4538 |
| Date | 07 December 2012 |
| Creators | Weidemann, Bonnie Diane |
| Publisher | BYU ScholarsArchive |
| Source Sets | Brigham Young University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Theses and Dissertations |
| Rights | http://lib.byu.edu/about/copyright/ |
Page generated in 0.0021 seconds