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dc.contributor.advisorBell, Scott M.en_US
dc.creatorGovind, Arunen_US
dc.date.accessioned2015-08-21T12:00:21Z
dc.date.available2015-08-21T12:00:21Z
dc.date.created2015-05en_US
dc.date.issued2015-08-20en_US
dc.date.submittedMay 2015en_US
dc.identifier.urihttp://hdl.handle.net/10388/ETD-2015-05-2139en_US
dc.description.abstractOptimal scale of study and scaling are fundamental to ecological research, and have been made easier with remotely sensed (RS) data. With access to RS data at multiple scales, it is important to identify how they compare and how effectively information at a specific scale will potentially transfer between scales. Therefore, my research compared the spatial, spectral, and temporal aspects of scale of RS data to study biophysical properties and spatio-temporal dynamics of the northern mixed prairie vegetation. I collected ground cover, dominant species, aboveground biomass, and leaf area index (LAI) from 41 sites and along 3 transects in the West Block of Grasslands National Park of Canada (GNPC; +49°, -107°) between June-July of 2006 and 2007. Narrowband (VIn) and broadband vegetation indices (VIb) were derived from RS data at multiple scales acquired through field spectroradiometry (1 m) and satellite imagery (10, 20, 30 m). VIs were upscaled from their native scales to coarser scales for spatial comparison, and time-series imagery at ~5-year intervals was used for temporal comparison. Results showed VIn, VIb, and LAI captured the spatial variation of plant biophysical properties along topographical gradients and their spatial scales ranged from 35-200 m. Among the scales compared, RS data at finer scales showed stronger ability than coarser scales to estimate ground vegetation. VIn were found to be better predictors than VIb in estimating LAI. Upscaling at all spatial scales showed similar weakening trends for LAI prediction using VIb, however spatial regression methods were necessary to minimize spatial effects in the RS data sets and to improve the prediction results. Multiple endmember spectral mixture analysis (MESMA) successfully captured the spatial heterogeneity of vegetation and effective modeling of sub-pixel spectral variability to produce improved vegetation maps. However, the efficiency of spectral unmixing was found to be highly dependent on the identification of optimal type and number of region-specific endmembers, and comparison of spectral unmixing on imagery at different scales showed spectral resolution to be important over spatial resolution. With the development of a comprehensive endmember library, MESMA may be used as a standard tool for identifying spatio-temporal changes in time-series imagery. Climatic variables were found to affect the success of unmixing, with lower success for years of climatic extremes. Change-detection analysis showed the success of biodiversity conservation practices of GNPC since establishment of the park and suggests that its management strategies are effective in maintaining vegetation heterogeneity in the region. Overall, my research has advanced the understanding of RS of the northern mixed prairie vegetation, especially in the context of effects of scale and scaling. From an eco-management perspective, this research has provided cost- and time-effective methods for vegetation mapping and monitoring. Data and techniques tested in this study will be even more useful with hyperspectral imagery should they become available for the northern mixed prairie.en_US
dc.language.isoengen_US
dc.subjectRemote sensingen_US
dc.subjectscaleen_US
dc.subjectvegetation, mixed prairieen_US
dc.subjectsatellite imageryen_US
dc.subjectLandsaten_US
dc.titleMulti-scalar remote sensing of the northern mixed prairie vegetationen_US
thesis.degree.departmentGeography and Planningen_US
thesis.degree.disciplineGeographyen_US
thesis.degree.grantorUniversity of Saskatchewanen_US
thesis.degree.levelDoctoralen_US
thesis.degree.nameDoctor of Philosophy (Ph.D.)en_US
dc.type.materialtexten_US
dc.type.genreThesisen_US
dc.contributor.committeeMemberGuo, Xulinen_US
dc.contributor.committeeMemberLamb, Ericen_US
dc.contributor.committeeMemberZeng, Weipingen_US
dc.contributor.committeeMemberde Boer, Dirken_US


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