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Browsing by Author "Vogeler, Jody, advisor"

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    Building on sustainable development goal indicator 11.3.1. for improved utility and guidance
    (Colorado State University. Libraries, 2023) Cardenas-Ritzert, Orion, author; Vogeler, Jody, advisor; McHale, Melissa, committee member; Leisz, Stephen, committee member
    The increased production of broad-coverage spatial datasets and investigation of these datasets by spatial analysis techniques allows for consistent examinations of urbanization patterns across the globe. Spatial data and analyses have proven valuable for sustainable urban development initiatives, including Sustainable Development Goal (SDG) 11 under the United Nation's 2030 Agenda for Sustainable Development. SDG Indicator 11.3.1 is a geospatially measured indicator implemented under SDG 11 for monitoring rates of urban expansion and population growth in a specific area over a period of time. Current methodological approaches and data inputs may hinder the application of SDG Indicator 11.3.1 at certain scales and extents. The overarching goal of this research is to build on the utility of SDG Indicator 11.3.1 by enhancing an existing urban delineation method for automated function, examining urban change at the urban agglomeration level across broad extents, highlighting hotspots of SDG Indicator 11.3.1, and evaluating the impacts of the spatial resolution of data inputs on SDG Indicator 11.3.1 and related outputs. In Chapter 1, we advanced an existing urban delineation method for the automatic identification of individual urban agglomerations across broad extents. We accomplished this by integrating various open-source datasets and tools with spatial analysis techniques. We used this methodology to examine SDG Indicator 11.3.1 and additional urban change metrics for urban agglomerations in Ethiopia, Nigeria, and South Africa over the 2016 to 2020 period. In Chapter 2, we applied our delineation methodology and examined the influence of spatial resolution of land use data on urban delineation, urban change metrics, and urban related land use change in Ethiopia over the 2016 to 2020 period. The results of Chapter 1 revealed trends of urban change and highlighted hotspots of SDG Indicator 11.3.1 at multiple levels across the three African countries. Chapter 2 revealed the implications of using varied spatial resolutions of land use maps when delineating urban areas, assessing SDG Indicator 11.3.1 and other urban change metrics, and examining urbanization-driven land use change.
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    Leveraging the Landsat archive to characterize plant species diversity and post-fire recovery in Great Basin shrublands
    (Colorado State University. Libraries, 2020) Jensen, Eric Robert, author; Vogeler, Jody, advisor; Newingham, Beth, committee member; Sibold, Jason, committee member
    Great Basin shrublands in the United States are rapidly converting to annual grass-dominated ecosystems, driven primarily by increased wildfire activity. Post-fire vegetation recovery trajectories vary spatially and temporally and are influenced by the effects of topography, climate, soils, and pre-fire vegetation. Our study leverages spatially continuous Landsat data alongside spatial models of environmental drivers to account for variability across space to evaluate important drivers of post-fire vegetation recovery. We first tested the spectral heterogeneity hypothesis, which suggests that variation in spectral values relates to plant species diversity, which, in turn, is theorized to be an important predictor of resilience to disturbance and resistance to invasive species. Weak relationships from the spectral heterogeneity tests led us to explicitly model plant species richness using both Landsat spectral data and environmental predictor variables. To evaluate drivers and patterns of post-fire vegetation recovery, we assessed how the number of times a site burned, post-fire seeding, and a suite of environmental predictor variables (including pre-fire species richness) affected pre- and post-fire plant functional groups using Landsat models. We also applied the suite of predictors to model vegetation recovery (15-year post-fire functional group dominance) and used the model to predict recovery for a contemporary fire, the Saddle Draw Fire from 2014. Our model of species richness had robust validation and evaluation of variable importance elucidated key drivers. While species richness may be important for aspects of ecological functioning not addressed in this study, it was not found among the most important drivers of post-fire recovery within Great Basin shrublands. However, the number of times burned affected post-fire recovery, which had a cumulative effect leading to increased annual herbaceous invasion and diminished perennial plant components. Meanwhile, on average post-fire seeding treatments had negligible influence upon post-fire perennial plant recovery. Post-fire recovery trajectories varied significantly across the fires evaluated in terms of both number of times burned and post-fire seeding. Models of post-fire recovery produced strong accuracy values when averaged across all fires and more tempered results when applied to new fires not included in model development. Spatially continuous analyses are important because they can account for variability in post-fire recovery of Great Basin shrublands. While such analyses have previously been hampered by data limitations, our results suggest that advances in data availabilities and cloud computing resources may be increasing opportunities for adopting spatial approaches for providing ecological insight and to inform post-fire management decision-making.