How can Sentinel‐2 contribute to seagrass mapping in shallow, turbid Baltic Sea waters?

Abstract

Seagrass meadows are one of the most important benthic habitats in the Baltic Sea. Nevertheless, spatially continuous mapping data of Zostera marina, the predominant seagrass species in the Baltic Sea, are lacking in the shallow coastal waters. Sentinel‐2 turned out to be valuable for mapping coastal benthic habitats in clear waters, whereas knowledge in turbid waters is rare. Here, we transfer a clear water mapping approach to turbid waters to assess how Sentinel‐2 can contribute to seagrass mapping in the Western Baltic Sea. Sentinel‐2 data were atmospherically corrected using ACOLITE and subsequently corrected for water column effects. To generate a data basis for training and validating random forest classification models, we developed an upscaling approach using video transect data and aerial imagery. We were able to map five coastal benthic habitats: bare sand (25 km²), sand dominated (16 km²), seagrass dominated (7 km²), dense seagrass (25 km²) and mixed substrates with red/ brown algae (3.5 km²) in a study area along the northern German coastline. Validation with independent data pointed out that water column correction does not significantly improve classification results compared to solely atmospherically corrected data (balanced overall accuracies ~0.92). Within optically shallow waters (0–4 m), per class and overall balanced accuracies (>0.82) differed marginally depending on the water depth. Overall balanced accuracy became worse (<0.8) approaching the border to optically deep water (~ 5 m). The spatial resolution of Sentinel‐2 (10–20 m) allowed delineating detailed spatial patterns of seagrass habitats, which may serve as a basis to retrieve spatially continuous data for ecologically relevant metrics such as patchiness. Thus, Sentinel‐2 can contribute unprecedented information for seagrass mapping between 0 and around 5 m water depths in the Western Baltic Sea.

Human-Induced Hydrological Connectivity: Impacts of Footpaths on Beach Wrack Transport in a Frequently Visited Baltic Coastal Wetland

“…Although storm Eunice in February lasted longer, the beach wrack area did not increase and no fresh sea grass leaves were visible. One seagrass shoot litters 6-30 leaves per year (Mateo et al, 2006;Weinberger et al, 2021) and the seagrass meadows offshore (see Kuhwald et al, 2021) provide a large source. Still, it seems as if most cast material was already transported onshore by storm Malik, thus beach wrack accumulation might depend more on timing and source availability than on storm strength.…”

“…The composition of beach wrack is geographically highly diverse and overall the contribution of ephemeral and nutrient-opportunistic seaweeds increased along Schleswig-Holstein's Baltic Sea coast (Weinberger et al, 2021). However, beach wrack at the study site Stein is dominated by seagrass (relative contribution of 89-95%, Weinberger et al, 2021), probably due to the large seagrass meadows north-west off the coast (Kuhwald et al, 2021; see Figure 1B). Aquatic vegetation is dominated by Zostera marina (Schubert et al, 2015) and to a lesser degree by Fucus vesiculosus and Fucus evanescens (Rohde et al, 2008).…”

Developing seagrass index for long term monitoring of Zostera japonica seagrass bed: a case study in Yellow River Delta, China

“…As Z. japonica and other types showed distinct SGI values, the unsupervised extraction using multi-class Otsu algorithm achieved good accuracy. Most previous studies utilized supervised classification algorithms for seagrass mapping (Coffer et al, 2020;Kuhwald et al, 2021;Fernandes et al, 2022;Lebrasse et al, 2022). Sample collection is the most time-consuming and labor-intensive task in supervised classifications.…”

“…As reviewed by Hossain et al (2015) and Veettil et al, (2020), seagrass mapping approaches should consider habitat conditions including water depth (exposed intertidal, submerged intertidal, shallow subtidal or deep subtidal) and water clarity (clear or turbid). For submerged seagrasses in subtidal area (e.g., water depth > 2 m), water depth corrections are usually required to reduce the effects of water attenuation and retrieve bottom reflectance, based on which seagrasses are identified (Kuhwald et al, 2021;Tu et al, 2021). For intertidal seagrasses which are exposed during low tide, surface reflectance-based vegetation indices, such as Normalized Differential Vegetation Index (NDVI) have been adopted to delineate seagrass extents (Valle et al, 2015;Zoffoli et al, 2020;Zoffoli et al, 2021).…”

“…For intertidal seagrasses such as Zostera noltei or Zostera marina, some researchers have investigated monitoring long-term variations of seagrass extents based on satellite imagery acquired on multiple years (Calleja et al, 2017;Zoffoli et al, 2020;Zoffoli et al, 2021). These studies generally selected single-date cloud-clear image acquired at low tide and identified seagrasses using supervised classification (Coffer et al, 2020;Kuhwald et al, 2021;Carpenter et al, 2022;Lebrasse et al, 2022) or unsupervised classification (Barrell and Grant, 2015;St-Pierre and Gagnon, 2020;. For example, Calleja et al (2017) selected 10 cloudless Landsat images with lowest tidal level for each summer during 1984-2015 and developed rule-based thresholds to identify Zostera noltei in Bay of Santander, Spain.…”