Application of Unmanned Autonomous Vehicle Systems: Mapping Tamarix
| dc.contributor.advisor | Hardy, Thom | |
| dc.contributor.author | Clark, Laura Elizabeth | |
| dc.contributor.committeeMember | Jensen, Jennifer | |
| dc.contributor.committeeMember | Rast, Walter | |
| dc.date.accessioned | 2015-02-17T17:14:44Z | |
| dc.date.available | 2015-02-17T17:14:44Z | |
| dc.date.issued | 2014-12 | |
| dc.description.abstract | Tamarisk (Tamarix ramosissima), commonly known as saltcedar, is an invasive plant that has displaced numerous native riparian species in the southwestern US. Mapping Tamarix populations is essential for developing effective eradication programs. Innovative remote sensing technologies such as unmanned autonomous vehicles (UAV), can provide high spatial resolution imagery for assessing vegetative distributions. UAV are able to collect images at affordable rates, flexible schedules, and at no risk to the pilot; therefore, an economic comparison of UAV to satellite and piloted aircraft was assessed. Additionally, an assessment of the accuracy for identifying Tamarix using UAV remote sensing was evaluated. UAV imagery was obtained over 8.8 km2 of riparian corridor at the Matador Wildlife Management Area to identify Tamarix distribution. An unsupervised classification method was utilized to assess spatial surface features by analyzing spectral characteristics. An accuracy assessment of the feature classes was performed to evaluate the overall classification accuracy of the imagery. The accuracy assessment concluded an overall Kappa statistic of 0.62, with a Kappa statistic of 0.21 for Tamarix. Therefore, the classification accuracy is found to be moderate (0.40 > K < 0.79) for surface features and poor (K < 0.40) for Tamarix. Low accuracy for Tamarix was attributed to use of only RGB imagery (i.e., no NIR) and the unsupervised classification application. The results of this study indicate that UAV-based remote sensing is able to produce high resolution images, moderately accurate in identifying surface features, and cost-effective. Challenges and considerations for increasing Tamarix classification accuracy are addressed in future research recommendations. | |
| dc.description.department | Family and Consumer Sciences | |
| dc.format | Text | |
| dc.format.extent | 65 pages | |
| dc.format.medium | 1 file (.pdf) | |
| dc.identifier.citation | Clark, L. E. (2014). <i>Application of unmanned autonomous vehicle systems: Mapping Tamarix</i> (Unpublished thesis). Texas State University, San Marcos, Texas. | |
| dc.identifier.uri | https://hdl.handle.net/10877/5459 | |
| dc.language.iso | en | |
| dc.subject | Remote sensing | |
| dc.subject | UAV | |
| dc.subject | Drone | |
| dc.subject | Saltcedar | |
| dc.subject | Tamarix | |
| dc.subject.lcsh | Remote sensing | en_US |
| dc.subject.lcsh | Tamarisks | en_US |
| dc.subject.lcsh | Drone aircraft | en_US |
| dc.title | Application of Unmanned Autonomous Vehicle Systems: Mapping Tamarix | |
| dc.type | Thesis | |
| thesis.degree.department | Family and Consumer Science | |
| thesis.degree.discipline | Interdisciplinary Studies | |
| thesis.degree.grantor | Texas State University | |
| thesis.degree.level | Masters | |
| thesis.degree.name | Master of Science |