This study examines the effectiveness of using the Normalized Difference Vegetation Index (NDVI) derived from 1326 different Landsat Thematic Mapper and Enhanced Thematic Mapper images in finding isolated housing starts within the Commonwealth of Virginia's forests. Individual NDVI images were stacked by year for the years 1995-2011 and the yearly maximum for each pixel was extracted, resulting in a 17-year image stack of all yearly maxima (a 98.7% data reduction). Using location data from housing starts and well permits, known previously forested housing starts were isolated from all other forest disturbance types. Samples from housing starts and other forest disturbances, as well as from undisturbed forest, were used to derive vegetation index thresholds enabling separation of disturbed from undisturbed forest. Disturbances, once identified, were separated accurately (overall accuracy = 85.4 percent, F-statistic = 0.86) into housing starts and other forest disturbances using a classification tree and only two variables from the Disturbance Detection and Diagnostics (D3) algorithm: the maximum NDVI in the available recovery period and the slope between the NDVI value at the time of the disturbance and the maximum NDVI in the available recovery period. Landsat time series stacks thus show promise for identifying even the small changes associated with exurban development. / Master of Science / The objective of this study was to determine whether low-density development in previously forested areas can be identified using a time series of maximum annual vegetation greenness derived from the Landsat earth observing satellite missions. The study area was the Commonwealth of Virginia, USA. This study used 1326 different Landsat satellite images from the years 1995 through 2011. Each image contained over 34 million pixels, which were converted to a value between 0 and 1 that indicated how vegetated they were (a higher value being more vegetated). When houses are constructed trees are removed, thus lowering (at least temporarily) the overall greenness in a given area. Using location data from housing starts and well permits, known previously forested housing starts were isolated from all other forest disturbance types. Samples from housing starts and other forest disturbances, as well as from undisturbed forest, were used to develop greenness thresholds enabling separation of disturbed from undisturbed forest. Disturbances, once identified, were separated accurately (overall accuracy = 85.4 percent) into housing starts and other (non-housing) disturbances using a classification tree and the highest greenness a pixel attained in the available years after being disturbed (indicating how much vegetation returned in the recovery period) as well as the slope from the year of the identified disturbance to the year that had the highest value in the available recovery period.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/86139 |
| Date | 30 May 2017 |
| Creators | House, Matthew Neal |
| Contributors | Forest Resources and Environmental Conservation, Wynne, Randolph H., Radtke, Philip J., Thomas, Valerie A. |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Detected Language | English |
| Type | Thesis |
| Format | ETD, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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