Evaluating Seagrass Meadow Dynamics by Integrating Field-Based and Remote Sensing Techniques

Abstract

Marine phanerogams are considered biological sentinels or indicators since any modification in seagrass meadow distribution and coverage signals negative changes in the marine environment. In recent decades, seagrass meadows have undergone global losses at accelerating rates, and almost one-third of their coverage has disappeared globally. This study focused on the dynamics of seagrass meadows in the northern Adriatic Sea, which is one of the most anthropogenically affected areas in the Mediterranean Sea. Seagrass distribution data and remote sensing products were utilized to identify the stable and dynamic parts of the seagrass ecosystem. Different seagrass species could not be distinguished with the Sentinel-2 (BOA) satellite image. However, results revealed a generally stable seagrass meadow (283.5 Ha) but, on the other hand, a stochastic behavior in seagrass meadow retraction (90.8 Ha) linked to local environmental processes associated with anthropogenic activities or climate change. If systemized, this proposed approach to monitoring seagrass meadow dynamics could be developed as a spatial decision support system for the entire Mediterranean basin. Such a tool could serve as a key element for decision makers in marine protected areas and would potentially support more effective conservation and management actions in these highly productive and important environments.

Monitoring the Spatial and Interannual Dynamic of Zostera noltei

“…Sentinel-2 data have been widely used to map different habitats and several studies have demonstrated their reliability in mapping and monitoring changes in marine biocenoses such as coral (Hedley et al 2012 ), mangrove (Pham et al 2019b , 2020 ) and seagrass beds Topouzelis et al 2016 ; Traganos and Reinartz 2018b ; Ha et al 2020 , 2021 ; Wicaksono et al 2021 ; Nur et al 2021 ; Ivajnšič et al 2022 ; Hartoni et al 2022 ). Many authors have used vegetation indices as the input for RF classification to map plant communities in terrestrial wetlands (Fletcher 2016 ) and coastal wetlands (Zoffoli et al 2020 ; Martínez Prentice et al 2021 ; Benmokhtar et al 2021 ), and compared to many machine learning algorithms and support vector machine techniques, the RF algorithm has produced promising results in terms of classifying seagrass (Zhang et al 2013 ; Traganos and Reinartz 2018b ; Ha et al 2020 ).…”

“…High-resolution sensors like SPOT-7 (1.5 m pixels) and Pléiades 1 A and Pléiades 1B satellites, which deliver 0.5 m imagery products with a 20 km swath, or WorldView with almost 0.3-0.4 m pixels, can counteract 2018, 08.20.2019, 09.28.2020 the limitations of Sentinel-2 (10 m pixels) for detecting smaller patches of seagrass and limiting the confusion of mixed pixels, but this requires funding. Sentinel-2 data have been widely used to map different habitats and several studies have demonstrated their reliability in mapping and monitoring changes in marine biocenoses such as coral (Hedley et al 2012), mangrove (Pham et al 2019b(Pham et al , 2020 and seagrass beds Topouzelis et al 2016;Traganos and Reinartz 2018b;Ha et al 2020Ha et al , 2021Wicaksono et al 2021;Nur et al 2021;Ivajnšič et al 2022;Hartoni et al 2022). Many authors have used vegetation indices as the input for RF classification to map plant communities in terrestrial wetlands (Fletcher 2016) and coastal wetlands (Zoffoli et al 2020;Martínez Prentice et al 2021;Benmokhtar et al 2021), and compared to many machine learning algorithms and support vector machine techniques, the RF algorithm has produced promising results in terms of classifying seagrass (Zhang et al 2013;Traganos and Reinartz 2018b;Ha et al 2020).…”

Assessing Seasonal and Inter-Annual Changes in the Total Cover of Submerged Aquatic Vegetation Using Sentinel-2 Imagery

“…Remote sensing has emerged as an invaluable tool for studying large-scale SAV patterns and is widely used for mapping, monitoring and modeling shallow marine ecosystems [9]. While the majority of remote sensing studies aim at assessing the SAV distribution or abundance patterns at a single point in time [10][11][12][13][14][15], there are a limited number of studies dealing with temporal changes over a period of time [16][17][18]. This is mostly conditioned on the deficiency of long-term data series [18].…”

“…While the majority of remote sensing studies aim at assessing the SAV distribution or abundance patterns at a single point in time [10][11][12][13][14][15], there are a limited number of studies dealing with temporal changes over a period of time [16][17][18]. This is mostly conditioned on the deficiency of long-term data series [18]. At the same time, only regular mapping over successive time periods allows for a quantitative assessment of the SAV loss, deterioration or recolonization extent.…”

On the possibility to map submerged aquatic vegetation cover with Sentinel-2 in low-transparency waters

“…The ability of S2 for benthic classification and quantification has mostly been investigated in clear ocean waters 16 , 18 – 22 Recently, there has been increasing interest to use the sensor also in optically complex, temperate water bodies. For example, S2 imagery was used for the vegetation presence and absence prediction in optically complex waters of the Atlantic Canada 23 .…”