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dc.contributor.advisorZhan, F. Benjamin
dc.contributor.authorSilvan-Cardenas, Jose Luis ( Orcid Icon 0000-0002-7091-4372 )en_US
dc.date.accessioned2012-02-24T10:12:28Z
dc.date.available2012-02-24T10:12:28Z
dc.date.issued2009-05en_US
dc.identifier.citationSilvan-Cardenas, J. L. (2009). Sub-pixel remote sensing for mapping and modelling invasive Tamarix: A case study in West Texas, 1993-2005 (Unpublished dissertation). Texas State University-San Marcos, San Marcos, Texas.
dc.identifier.urihttps://digital.library.txstate.edu/handle/10877/3387
dc.description.abstractA number of economical and environmental issues involved with invasive species worldwide is demanding the analysis of large-scale observations made through remote sensing. The saltcedar invasion in the Western US represents a high priority case where some remote sensing observations can play an important role in both controlling and researching the invasion process. Unfortunately, the analysis of these complex datasets still offers a number of challenges that hinders their widespread adoption. For example, recent studies in remote sensing of vegetation have proposed a simple parameterization of the light-canopy interaction that would allow more accurate estimations of the relative abundance of plant species. These non-linear mixture models are, however, difficult to invert and their application for large-scale studies is still to be assessed. Furthermore, the accuracy assessment of this new level of information represents a bottleneck for furthering the data analysis. This dissertation reviews and tests a number of existing and custom-developed methods for studying the saltcedar invasion along the Rio Grande in West Texas. These techniques include cross-comparison matrices for assessing the accuracy of sub-pixel land cover classifications (Chapter 2), methods for retrieving the sub-pixel canopy coverage of saltcedar and associated native species (Chapter 3), a post-classification change detection method based on sub-pixel maps (Chapter 4) and a metapopulation model for linking remote sensing land cover change to population dynamics (Chapter 5). It is shown how moderate resolution observations from Landsat satellite coupled with sub-pixel techniques can provide a cost-effective means to support control efforts by providing continuous monitoring of its distribution, as well as a way to study its invasion by providing information on location, abundance, and rate of change, which are particularly useful for a factorial analysis and for the assessment of its impacts on the water availability. A spatially explicit metapopulation model is introduced as a means to enable remote sensing to access population parameters along the riparian corridor and as hypothesis-testing framework for general invasion processes. The analysis of multitemporal Landsat images allowed to assess the status of invasion long a segment of the Rio Grande river, from Candelaria, Texas to Presidio, Texas.

The observations support prior in-situ observations on saltcedar dynamics and its competitive superiority over the native species. Although not extensively tested, the metapopulation model has led to the identification of conditions for invasiveness under competition and could be potentially useful to explain distribution patterns observed through remote sensing.
en_US
dc.formatText
dc.format.extent203 pages
dc.format.medium1 file (.pdf)
dc.language.isoen
dc.subjectRio Grandeen_US
dc.subjectSalt cedaren_US
dc.subjectLand coveren_US
dc.subjectChangeen_US
dc.subjectSub-pixel remote sensingen_US
dc.subjectPopulation dynamicsen_US
dc.subjectInvasive speciesen_US
dc.subject.classificationGeographyen_US
dc.titleSub-pixel Remote Sensing for Mapping and Modelling Invasive Tamarix: A Case Study in West Texas, 1993-2005en_US
txstate.documenttypeDissertation
dc.contributor.committeeMemberWang, Le
dc.contributor.committeeMemberFonstad, Mark
dc.contributor.committeeMemberCrews, Kelley A.
thesis.degree.departmentGeography
thesis.degree.disciplineGeography
thesis.degree.grantorTexas State University-San Marcos
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy
txstate.departmentGeography


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