Novel approach to large‐scale monitoring of submerged aquatic vegetation: A nationwide example from Sweden. (20th August 2021)

• Record Type: Journal Article
• Authors: Huber, Silvia; Hansen, Lars B.; Nielsen, Lisbeth T.; Rasmussen, Mikkel L.; Sølvsteen, Jonas; Berglund, Johnny; Paz von Friesen, Carlos; Danbolt, Magnus; Envall, Mats; Infantes, Eduardo; Moksnes, Per
• Is Part Of: Integrated environmental assessment and management. Volume 18:Number 4(2022)
• Page Start: 909
• Page End: 920
• Publication Date: 2021-08-20
• Subjects: Ecological status -- Environmental monitoring -- Machine learning -- Sentinel‐2; Environmental management -- Periodicals; Pollution -- Periodicals; Environmental toxicology -- Periodicals; Environmental risk assessment -- Periodicals; Environmental impact analysis -- Periodicals; 628
• DOI: 10.1002/ieam.4493
• Languages: English
• ISSNs: 1551-3777

Abstract

Abstract: 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 km 2 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). Key Points: Submerged aquatic vegetation (SAV) provides critical ecosystem functions and is an important biological indicator of ecological status of coastal environments. At smaller scales, various methods exist to map and monitor SAV distribution, but regular and coherent information at a large scale required for reporting related to environmental policies is not yet in place. The first high‐resolution SAV distribution map covering the entire shallow Swedish coast revealed the potential to combine Copernicus Sentinel‐2 satellite imagery, machine learning, and cloud technology. Integrating new technologies into cloud‐based applications allows us to gain up‐to‐date knowledge of SAV abundance and growth dynamics, which is critical to assess the impacts of management and conservation efforts, and monitor overall marine health regularly and at large scale.