Graduate Thesis Or Dissertation
 

Characterizing an Annual Grass Invasion and Its Link to Environmental and Disturbance Factors Using Remote Sensing: New Tools and Applications

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  • The spread of nonnative species across the globe has contributed to biodiversity loss and changes in ecosystem structure and function. Monitoring the introduction, naturalization, and spread of introduced species is critical in abating negative impacts wrought by species invasions. However, providing basic information concerning the presence or spread of many introduced species is often only considered once the invasion is already at an advanced stage, resulting in economic or ecological impacts. To better assess the present and future effects of and risk from introduced species, a clear understanding of invasive species populations' spatial and temporal patterns is needed. In some cases, remote sensing can serve as a useful information source that may be leveraged to characterize and monitor the invasion of nonnative species. This dissertation utilizes remote sensing and other geospatial data sources to better understand a nonnative annual grass (Ventenata dubia) invasion in the northwestern United States. Each research chapter builds a different facet of our understanding of this invasion by connecting land-surface processes, environmental conditions, and landscape disturbances. These three different topics help to describe the current state of the invasion, how it progressed to this state, and what this may mean for the future. The first research-chapter adapts image fusion methods to a cloud-computing environment in an effort to improve the spatial and temporal resolution of estimates of land surface phenology. The research focused on whether these methods would enable the estimation of phenology in heterogeneous landscapes that have historically been difficult to characterize. This chapter showed that high-quality image fusion results are possible with less processing time when image fusion is conducted in a cloud-computing environment. Further, this chapter showed that phenology estimated from these data can capture patterns occurring in grassland, shrubland, and open forest land cover types. The second research-chapter leverages the improved land surface phenology estimates from the first research-chapter to model the present distribution of the invasive annual grass species Ventenata dubia in the Blue Mountains Ecoregion of the interior Pacific Northwest. The results from this chapter suggest that both phenological and environmental information are needed to best detect populations of ventenata. The model based on phenological and environmental information predicted that ventenata was present in 7.8% of the Blue Mountains Ecoregion in 2017. The third research-chapter uses the information gained from the proceeding chapters to examine the change occurring over a decade of invasion by applying the model developed in the second research-chapter to the image archive and examining the invasion progression. Spatial and temporal patterns of the invasion were characterized by their association with the biophysical environment and the effect of wildfire on ventenata occurence was investigated. This analysis revealed that ventenata may have been introduced to lower shrubland ecosystems but has since transitioned to higher elevation dry conifer forests and areas with abundant ecotone. Furthermore, this chapter shows that wildfire occurrence and severity was associated with an increased probability of invasion in some parts of the interior Pacific Northwest. Although this research is focused on a specific annual grass species (Ventenata dubia), insights gained from this investigation are applicable to other invasive annual grasses. This research contributes to the scientific advancement in the study of exotic plant invasion and provides useful baseline ecological information that can be employed to inform both policy and management. Additionally, the methods developed for cloud-computing-based image fusion offer a useful tool to the remote sensing community that has the flexibility to be utilized for many applications.
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  • The primary funding for this research was provided by the Joint Fire Science Program (Proposal ID: 16-1-01-21) through a Joint Venture Agreement between the USFS Pacific Northwest Research Station and Oregon State University. Additional financial support for my program was provided by the Department of Forest Engineering, Resources and Management, the Department of Forest Ecosystems and Society, and the College of Earth, Ocean, and Atmospheric Sciences.
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  • Pending Publication
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  • 2021-06-11 to 2021-06-15

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