Hein. Analysis of Eelgrass in Padilla Bay, Washington Using an on Using an Uncrewed Aerial System

Hein, Hannah. 2022. “Analysis of Eelgrass in Padilla Bay, Washington Using an Uncrewed Aerial System” (2022). WWU Graduate School Collection . 1100.

Abstract
Eelgrass is an important part of coastal environments in the Pacific Northwest as it provides crucial habitat and moderates storm surge. Padilla Bay, Washington is home to one of the largest eelgrass meadows in North America and contains the native Zostera marina and the non-native Z. japonica . The relationship between these two species is of interest due to the influx of Z. japonica in previously unvegetated areas. I used an uncrewed aerial system (UAS) and a multispectral camera to study species dynamics. I compared the ability of values derived from the band data, two vegetation indices, and a principal components analysis (PCA) to predict eelgrass cover using pixel- and object-based methods. In the pixel-based analysis, using a red and red edge band in a multiple linear regression was the best way to estimate overall percent cover for the full season (R2 = 0.79). Regressions used to predict Z. marina (R2=0.55) and Z. japonica (R2=0.32) cover individually performed poorly. In the object-based analysis, using band means and standard deviations in a support vector machine (SVM) classification resulted in an overall accuracy of 78.3%. This method performed the best at classifying segments based on dominant species, with user’s accuracies for Z. marina and Z. japonica of 80% and 100%, respectively. PCA-informed segmentation and classification also performed well, with an overall accuracy of 70.6%. Conducting object-based image segmentation with the Micasense Dual Camera System and SVM classification may be a promising method for identifying spectral differences between Z. marina and Z. japonica .