Mapping of Sonoran Desert Vegetation Communities of San Cristobal Valley and Southern Sentinel Plains, Barry M. Goldwater Range AND Variables Influencing Route Proliferation in the Barry M. Goldwater Range's San Cristobal Valley
AuthorWhitbeck, Douglas Craig
AdvisorFehmi, Jeffrey S.
MetadataShow full item record
PublisherThe University of Arizona.
RightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
AbstractThe vegetation associations in the Eastern San Cristobal Valley of Barry M. Goldwater Range-East (BMGR) were mapped using a combination of field surveys (relevés) and interpretation of aerial imagery in order to contribute to ongoing mapping efforts of Barry M. Goldwater Range-East. Throughout the San Cristobal Valley, 149 relevé samples were collected to characterize the vegetation associations. Seventeen vegetation associations were identified and mapped, including a new Larrea tridentata/Ambrosia dumosa/Grusonia kunzei (Creosote bush-White bursage-Devil's cholla) association. Accuracy assessment of the map was conducted using a contingency table finding the map to be 82% accurate. Route proliferation in the San Cristobal Valley of Barry M. Goldwater Range-East (BMGR) was also mapped and measured using remotely sensed imagery in geographic information systems and modeled with geographical variables in a multivariate regression. Throughout the San Cristobal Valley study site, 6,878 km of unauthorized routes were identified. Geographic explanatory variables distance from slopes greater than 34% (b = -3.252e-5, p<0.001) and the most influential variable distance from unauthorized routes (b = -0.006568, p<0.001) were tested for significance and influence in predicting unauthorized route density. The resulting model, built from the two significant geographic variables in a multivariate regression, was able to explain 57% of the variability in the data. The results from this study have shown that through the use of GIS and remote sensing, unauthorized route density can be predicted by geographic variables which can then be used to make future route management decisions.
Degree ProgramGraduate College