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.

摘要

海藻是沿海生态系统的重要组成部分，最近在全球范围内遭受损失，这激发了科学家和资源管理人员努力开发有效的监测工具。尽管Landsat时间序列被认为是监测景观的最具成本效益的选择之一，但由于许多因素，包括获取准确的地面真相数据的困难和在绘制近海海洋生态系统中的局限性，其在监测海草方面的应用仍然很少。在这里，我们报告了使用存档的Landsat多光谱图像和自动自适应特征综合（AASG）来评估鳗草（Zostera marina）的情况。1984年至2017年之间的分布和丰度，位于加拿大新不伦瑞克省东北部的一个河口。AASG算法是一种用于卫星图像时间序列分析的经济高效的新方法，需要有限的地面真实数据，可用于生成十四张地图，其中四张地图的准确度在75％到85％之间。结果表明，靠近海滩屏障的鳗草草甸具有很高的动态性，多年间表现出高丰度波动，并且在主要海岸线附近从密集的鳗草转化为中低等的鳗草。这项研究证明了使用AASG算法绘制海草的可行性以及将卫星时间序列包括在监测程序中以调查海草动态和长期趋势的优势。