Using Landsat Time-Series to Monitor and Inform Seagrass Dynamics: A Case Study in the Tabusintac Estuary, New Brunswick, Canada. Issue 1 (2nd January 2021)

• Record Type: Journal Article
• Authors: Leblanc, Melanie-Louise; LaRocque, Armand; Leblon, Brigitte; Hanson, Al; Humphries, Murray M.
• Is Part Of: Canadian journal of remote sensing. Volume 47:Issue 1(2021)
• Page Start: 65
• Page End: 82
• Publication Date: 2021-01-02
• Subjects: Remote sensing -- Periodicals; 621.367805
• DOI: 10.1080/07038992.2021.1893672
• Languages: English
• ISSNs: 0703-8992

Abstract

Abstract: The recent world-wide loss of seagrasses, which are critical components of coastal ecosystems, has ignited an effort among scientists and resource managers to develop effective monitoring tools. Although Landsat time-series is considered one of the most cost-effective options for monitoring landscapes, its application to monitor seagrasses remains scarce due to many factors including difficulties obtaining accurate ground-truth data and perceived limitations in mapping nearshore marine ecosystems. Here, we report on the use of archived Landsat multispectral imagery and the automatic adaptive signature generalization (AASG) to evaluate eelgrass ( Zostera marina ) distribution and abundance between 1984 and 2017, in an estuary located in northeastern New Brunswick, Canada. The AASG algorithm, a novel cost-efficient approach for satellite imagery time-series analysis that requires limited ground truth data, was used to produce fourteen maps, four of which had accuracies ranging from 75 to 85%. The results indicated that eelgrass meadows near the beach barrier were highly dynamic, exhibiting high abundance fluctuations between years and a conversion of dense eelgrass to medium-low eelgrass near the main coastline. This study demonstrates the feasibility of using the AASG algorithm to map seagrass and advantages of including satellite time-series in monitoring programmes to investigate seagrass dynamics and long-term trends.