Presentation Type
Presentation
Faculty Mentor’s Full Name
Cara Nelson
Faculty Mentor’s Department
Ecosystem and Conservation Sciences
Abstract / Artist's Statement
When implemented correctly, monitoring plays a crucial role in ecological restoration. A well-designed monitoring program can determine whether a project was implemented as planned and whether objectives are being met, as well as build public support and improve project efficiency through adaptive management. Despite these and other valuable outcomes, monitoring is rarely implemented to the necessary extent, especially with respect to changes in vegetation. One reason for this is that conventional approaches to vegetation monitoring are time-consuming and achieving necessary levels of precision can be expensive. Qualitative and remote sensing methods have been proposed to be more cost-effective. For example, managers are using a rapid monitoring method, the Qualitative Rapid Assessment (QRA), to assess river and floodplain responses to restoration treatments on the Clark Fork River in Montana, to reduce costs of data collection. Remote sensing using UAV (unmanned aerial vehicle) technology is also becoming a common method to monitor vegetation due to the ability to provide high resolution imagery for large areas quickly with minimal labor costs. However, how well qualitative and remote sensing data compare to on-the-ground quantitative data needs to be further investigated. Our proposed project aims to compare on-the-ground quantitative data to data collected using the QRA and interpreted from high resolution imagery for monitoring vegetation cover. Specifically, we will compare mean percent cover and calculate and compare measurements of observer error, margin of error achieved, and required sample sizes among the three types of monitoring for three metrics: woody vegetation cover on streambanks, woody vegetation cover in the floodplain, and herbaceous vegetation cover. Based on our findings, we will recommend modifications to QRA and remote sensing protocols and sampling design to improve both precision and efficiency. Our findings will contribute to the effective implementation of monitoring at the largest superfund complex in the United States – the Clark Fork River Superfund Complex – and to improving monitoring methods for use in other restoration projects.
Category
Physical Sciences
Comparing the efficacy of three riparian vegetation monitoring methods – qualitative, quantitative, and remote sensing
When implemented correctly, monitoring plays a crucial role in ecological restoration. A well-designed monitoring program can determine whether a project was implemented as planned and whether objectives are being met, as well as build public support and improve project efficiency through adaptive management. Despite these and other valuable outcomes, monitoring is rarely implemented to the necessary extent, especially with respect to changes in vegetation. One reason for this is that conventional approaches to vegetation monitoring are time-consuming and achieving necessary levels of precision can be expensive. Qualitative and remote sensing methods have been proposed to be more cost-effective. For example, managers are using a rapid monitoring method, the Qualitative Rapid Assessment (QRA), to assess river and floodplain responses to restoration treatments on the Clark Fork River in Montana, to reduce costs of data collection. Remote sensing using UAV (unmanned aerial vehicle) technology is also becoming a common method to monitor vegetation due to the ability to provide high resolution imagery for large areas quickly with minimal labor costs. However, how well qualitative and remote sensing data compare to on-the-ground quantitative data needs to be further investigated. Our proposed project aims to compare on-the-ground quantitative data to data collected using the QRA and interpreted from high resolution imagery for monitoring vegetation cover. Specifically, we will compare mean percent cover and calculate and compare measurements of observer error, margin of error achieved, and required sample sizes among the three types of monitoring for three metrics: woody vegetation cover on streambanks, woody vegetation cover in the floodplain, and herbaceous vegetation cover. Based on our findings, we will recommend modifications to QRA and remote sensing protocols and sampling design to improve both precision and efficiency. Our findings will contribute to the effective implementation of monitoring at the largest superfund complex in the United States – the Clark Fork River Superfund Complex – and to improving monitoring methods for use in other restoration projects.