Abstract

Coastal salt marshes sequester large quantities of “blue carbon” in plant biomass and sediments, and provide numerous other valuable ecosystem functions and services. However, these ecosystems are increasingly threatened by external stressors, including rising sea levels and a changing climate, which have resulted in large losses of tidal marsh habitat. Measuring plant biomass is critical for understanding how carbon storage may be affected as stressors continue to cause marsh losses, and for improving conservation and management efforts. A number of studies have quantified aboveground biomass (AGB) in salt marshes using remote sensing techniques, and with the development of high resolution sensors there is excellent potential to improve estimates over large scales. However, few studies have evaluated how variability in spatial resolution and viewing angle across platforms impacts AGB estimates, despite the large range of potential imaging systems available. Using 3 cm and 6 cm resolution nadir hyperspectral drone imagery, and 0.5-3 cm oblique imagery collected from a ground-based camera at three viewing angles from two different-aged barrier island salt marshes in Virginia, USA, I evaluated the accuracy of regression models predicting S. alterniflora AGB from vegetation indices across resolution and viewing angle. The overall best performing linear regression models were obtained using the 3 cm nadir drone imagery. However, the best 6 cm regression models demonstrated only minor losses in accuracy relative to 3 cm. AGB estimates from obliquely angled imagery were less accurate than either nadir resolution. The most accurate oblique models were obtained at the highest viewing angle, with performance decreasing as the viewing angle became shallower. These results suggest that not all platforms perform similarly within salt marsh ecosystems, and that both spatial resolution and viewing angle must be considered in choice of imaging systems.

Library of Congress Subject Headings

Spartina alterniflora--Remote sensing; Imaging systems--Image quality

Publication Date

8-4-2022

Document Type

Thesis

Student Type

Graduate

Degree Name

Environmental Science (MS)

Department, Program, or Center

Thomas H. Gosnell School of Life Sciences (COS)

Advisor

Anna Christina Tyler

Advisor/Committee Member

Charles M. Bachmann

Advisor/Committee Member

Jan van Aardt

Campus

RIT – Main Campus

Plan Codes

ENVS-MS

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