大规模监测淹没水生植被的新方法：瑞典的全国性例子,Integrated Environmental Assessment and Management

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大规模监测淹没水生植被的新方法：瑞典的全国性例子
Integrated Environmental Assessment and Management ( IF 3.0 ) Pub Date : 2021-07-16 , DOI: 10.1002/ieam.4493
Silvia Huber ₁ , Lars B Hansen ₁ , Lisbeth T Nielsen ₁ , Mikkel L Rasmussen ₁ , Jonas Sølvsteen ₁ , Johnny Berglund ₂ , Carlos Paz von Friesen ₂ , Magnus Danbolt ₃ , Mats Envall ₄ , Eduardo Infantes ₄ , Per Moksnes ₄

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根据欧盟栖息地指令、水框架指令和海洋战略框架指令，成员国需要绘制、监测和评估不同海洋栖息地和生境的质量和面积分布变化，以更有效地保护海洋环境。淹没的水生植被 (SAV) 是沿海生态系统生态状况的关键指标，因此被广泛用于与这些指令相关的报告。由于使用传统小规模方法进行大规模监测的挑战，瑞典缺乏对 SAV 区域分布的环境监测。为了弥补这一差距，我们在 2020 年启动了一个项目，将 Copernicus Sentinel-2 卫星图像、新颖的机器学习 (ML) 技术、基于云的 Web 应用程序中的高级数据处理，使用户能够创建最新的 SAV 分类。同时，该方法被用于推导瑞典整个海岸线的第一张高分辨率 SAV 地图，面积为 1550 平方公里²被映射为 SAV。使用来自瑞典海岸三个不同地区的独立现场数据对分类准确性进行的定量评估表明，在较浅区域，特别是在水透明度高的地方（每个区域的平均总准确度为 0.60-0.77），准确度相对较高。然而，该分类遗漏了大部分生长在较深水域的植被（平均 31%–50%），并且在植被破碎或混合且水质较差的地区表现不佳，这些挑战应在制定制图方法时应对融入欧盟指令等监测框架。在本文中，2022 年整合环境评估管理；18:909–920。© 2021 作者。由 Wiley Periodicals LLC 代表环境毒理学与化学学会 (SETAC) 出版的综合环境评估和管理。

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Novel approach to large-scale monitoring of submerged aquatic vegetation: A nationwide example from Sweden

According to the EU Habitats directive, the Water Framework Directive, and the Marine Strategy Framework Directive, member states are required to map, monitor, and evaluate changes in quality and areal distribution of different marine habitats and biotopes to protect the marine environment more effectively. Submerged aquatic vegetation (SAV) is a key indicator of the ecological status of coastal ecosystems and is therefore widely used in reporting related to these directives. Environmental monitoring of the areal distribution of SAV is lacking in Sweden due to the challenges of large-scale monitoring using traditional small-scale methods. To address this gap, in 2020, we embarked on a project to combine Copernicus Sentinel-2 satellite imagery, novel machine learning (ML) techniques, and advanced data processing in a cloud-based web application that enables users to create up-to-date SAV classifications. At the same time, the approach was used to derive the first high-resolution SAV map for the entire coastline of Sweden, where an area of 1550 km² was mapped as SAV. Quantitative evaluation of the accuracy of the classification using independent field data from three different regions along the Swedish coast demonstrated relative high accuracy within shallower areas, particularly where water transparency was high (average total accuracy per region 0.60–0.77). However, the classification missed large proportions of vegetation growing in deeper water (on average 31%–50%) and performed poorly in areas with fragmented or mixed vegetation and poor water quality, challenges that should be addressed in the development of the mapping methods towards integration into monitoring frameworks such as the EU directives. In this article, we present the results of the first satellite-derived SAV classification for the entire Swedish coast and show the implementation of a cloud-based SAV mapping application (prototype) developed within the frame of the project. Integr Environ Assess Manag 2022;18:909–920. © 2021 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

更新日期：2021-07-16

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