Branching Algorithm to Identify Bottom Habitat in the Optically Complex Coastal Waters of Atlantic Canada Using Sentinel-2 Satellite Imagery

Wilson, Kristen L.; Wong, Melisa C.; Devred, Émmanuel

doi:10.3389/fenvs.2020.579856

Cited by 16 publications

(11 citation statements)

References 71 publications

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“…Due to a lack of sufficient reference data, we are unable to clearly state whether Sentninel‐2 can spectrally distinguish overgrown stone habitats from seagrass meadows. Sentinel‐2 spectra of brown macroalgae and seagrass meadows appear similar in waters (Wilson et al, 2020). Furthermore, Sentinel‐2’s pixel sizes may capture the narrow, heterogeneous stone habitats close to the shoreline as mixed pixels, which may hamper a further differentiation of the habitats.…”

Section: Discussionmentioning

confidence: 99%

“…These previous studies, however, were conducted in clear waters, where the maximum detection depth was around 20 m. Investigating Sentinel‐2’s capabilities for mapping seagrass also in turbid waters paves the way for global assessments. Wilson et al (2020) and Zoffoli et al (2020) carried out first attempts using Sentinel‐2 for eelgrass and dwarf eelgrass ( Zostera marina, Zostera noltei ). The first successfully distinguished presence and absence of eelgrass in the complex, temperate waters of Atlantic Canada, while the latter assessed its seasonal variability along the European Atlantic coast.…”

Section: Introductionmentioning

confidence: 99%

“…Zoffoli et al (2020) focused on intertidal seagrass and therefore left the influence of the water column on the bottom signal unaddressed. Wilson et al (2020) did not correct for the influence of the water column, although Traganos and Reinartz (2017) reported the correction as beneficial even for clear waters. No study, however, is available for analysing the potential of Sentinel‐2 for mapping seagrass in the turbid waters of the Baltic Sea (Secchi disc depth between 2 m and >7 m; Stock, 2015), although it is considered as an ideal study area for marine and coastal ecosystem management (Reusch et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

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

Kuhwald

Deimling

Schubert

et al. 2021

Remote Sens Ecol Conserv

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Kuhwald

Deimling

Schubert

et al. 2021

Remote Sens Ecol Conserv

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“…Monitoring and studying seagrass and macrophyte beds extent and location is therefore crucial for evaluating their ecological status and evolution (Kellaris et al, 2019;Veetil et al, 2020). Moreover, they are particularly useful as indicators of environmental quality, supporting decision-makers with issues of protection, restoration and conservation management of these habitats, as well as the management of commercially important macrophytes (Nahirnick et al, 2018;Wilson et al, 2020). Traditionally, monitoring and studying their health needs in situ sampling involving diving, which is time consuming, implying human and logistical resources, and providing only small spatial resolution (Kutser et al, 2006;Beca-Carretero et al, 2020;Veetil et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Using a UAV-Mounted Multispectral Camera for the Monitoring of Marine Macrophytes

et al. 2021

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Marine macrophytes constitute one of the most productive ecosystems on the planet, as well as one of the most threatened by anthropogenic activities and climate change. Their monitoring is therefore essential, which has experienced a fast methodological evolution in recent years, from traditional in situ sampling to the use of satellite remote sensing, and subsequently by sensors mounted on unmanned aerial vehicles (UAV). This study aims to advance the monitoring of these ecosystems through the use of a UAV equipped with a 10-band multispectral camera, using different algorithms [i.e., maximum likelihood classifier (MLC), minimum distance classifier (MDC), and spectral angle classifier (SAC)], and using the Bay of Cádiz Natural Park (southern Spain) as a case of study. The results obtained with MLC confirm the suitability of this technique for detecting and differentiating seagrass meadows in a range of 0–2 m depth and the efficiency of this tool for studying and monitoring marine macrophytes in coastal areas. We inferred the existence of a cover of 25452 m2 of Cymodocea nodosa, and macroalgae species such as Caulerpa prolifera, covering 22172 m2 of Santibañez (inner Bay of Cádiz).

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“…Based on spectral characteristics, it can be found that the most obvious characteristic of aquatic plants is the steep slope effect in the near-infrared band, and the steep slope effect of aquatic plants with higher enrichment is more obvious [11]. Many remote sensing index methods have been proposed by scholars around the world, including Normalized Difference Vegetation Index (NDVI) [12][13][14], Floating Algae Index (FAI) [15,16], Normalized Difference Water Index (NDWI) [17,18], Enhanced Vegetation Index (EVI) [19], and Normalized Difference Index of Cyanobacteria Bloom (NDICB) [20]. Among them, NDVI is the most widely used remote sensing index method.…”

Section: Introductionmentioning

confidence: 99%

Spatial Differentiation Analysis of Water Quality in Dianchi Lake Based on GF-5 NDVI Characteristic Optimization

Lin

Gan

Yuan

et al. 2021

Journal of Spectroscopy

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Remote sensing monitoring of aquatic vegetation is critical to the water quality evaluation of plateau lakes. To obtain a clear understanding of the water environment status of Dianchi Lake, a GF-5 hyperspectral characteristics-based optimal NDVI approach was employed to quantify the aquatic vegetation cover and analyze water quality. By characteristic bands recognition, the optimal NDVI was obtained; the spatial distribution of aquatic plants and water quality in Dianchi Lake were then analyzed. Results showed the following: (1) For Caohai, the optimal NDVI value was calculated by B86 in the red band range and B151 in the near-infrared band range, which achieve the best spectral response. For Waihai, the respective bands were B86 in the red band range and B99 in the near-infrared band range. (2) We also found significant regional differences in aquatic plants distribution for the study area. Caohai was dominated by aquatic plants and high-quality water areas only occurred in the northern tip. While the situation for Waihai was much optimistic, areas with poor water quality were mainly found in the north and south parts. Water quality also showed a descending trend from the lakeside zone to the lake center. (3) By comparing to previous studies, we concluded that policy interventions and water protection measures carried out by the government during the past years are extremely effective. The optimal NDVI method provides a reliable evaluation and is potentially transferable to other plateau lake areas as a robust approach for the rapid assessment of water quality.

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Branching Algorithm to Identify Bottom Habitat in the Optically Complex Coastal Waters of Atlantic Canada Using Sentinel-2 Satellite Imagery

Cited by 16 publications

References 71 publications

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

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

Using a UAV-Mounted Multispectral Camera for the Monitoring of Marine Macrophytes

Spatial Differentiation Analysis of Water Quality in Dianchi Lake Based on GF-5 NDVI Characteristic Optimization

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