Mapping coastal marine ecosystems of the National Park of Banc d’Arguin (PNBA) in Mauritania using Sentinel-2 imagery

Pottier, Auréa; Catry, Teresa; Trégarot, Ewan; Maréchal, Jean‐Philippe; Fayad, V.; David, Gilbert; Cheikh, M. Sidi; Failler, Pierre

doi:10.1016/j.jag.2021.102419

Cited by 8 publications

(5 citation statements)

References 83 publications

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“…73% when classifying seven distinct intertidal habitat types. These indicators suggest a high quality classification, comparable to other similar studies for intertidal habitat mapping (e.g., [29,43,[103][104][105][106]).…”

Section: Classification Performancesupporting

confidence: 84%

“…Previous studies successfully classified a higher number of intertidal sediment classes, but these often involved the use of higher resolution imagery [31,103,108], or their workflows were fully optimized to be focused only on sediment mapping, using SAR [32,109] or DSLR cameras mounted on unmanned aerial vehicles [103]. The use of higher resolution satellite data may help to increase the number of sediment classes, but these products often have much less radiometric resolution or are of limited access or may come at very high monetary and computational costs (see [29]). In addition, the exclusive focus on sediment types will likely disregard other important intertidal habitats.…”

Section: Classification Performancementioning

confidence: 99%

“…Applying remote sensing to ecology has become a popular and efficient way to study and characterize the vast and often disjunct intertidal areas that would otherwise be extremely difficult to study and to allow the development of reliable maps of habitats with reduced costs and effort. There are several recent published records aiming at mapping the distribution of intertidal vegetation (e.g., [27][28][29], sediment types (e.g., [30][31][32], and benthic invertebrate beds [33][34][35]. Some studies have also focused on mapping intertidal areas as habitats for shorebirds, but most were solely focused on intertidal vegetation (e.g., [36,37] or were dedicated to breeding areas [35,[38][39][40].…”

Section: Introductionmentioning

confidence: 99%

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Combining Multispectral and Radar Imagery with Machine Learning Techniques to Map Intertidal Habitats for Migratory Shorebirds

et al. 2022

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Migratory shorebirds are notable consumers of benthic invertebrates on intertidal sediments. The distribution and abundance of shorebirds will strongly depend on their prey and on landscape and sediment features such as mud and surface water content, topography, and the presence of ecosystem engineers. An understanding of shorebird distribution and ecology thus requires knowledge of the various habitat types which may be distinguished in intertidal areas. Here, we combine Sentinel-1 and Sentinel-2 imagery and a digital elevation model (DEM), using machine learning techniques to map intertidal habitat types of importance to migratory shorebirds and their benthic prey. We do this on the third most important non-breeding area for migratory shorebirds in the East Atlantic Flyway, in the Bijagós Archipelago in West Africa. Using pixel-level random forests, we successfully mapped rocks, shell beds, and macroalgae and distinguished between areas of bare sediment and areas occupied by fiddler crabs, an ecosystem engineer that promotes significant bioturbation on intertidal flats. We also classified two sediment types (sandy and mixed) within the bare sediment and fiddler crab areas, according to their mud content. The overall classification accuracy was 82%, and the Kappa Coefficient was 73%. The most important predictors were elevation, the Sentinel-2-derived water and moisture indexes, and Sentinel-1 VH band. The association of Sentinel-2 with Sentinel-1 and a DEM produced the best results compared to the models without these variables. This map provides an overall picture of the composition of the intertidal habitats in a site of international importance for migratory shorebirds. Most of the intertidal flats of the Bijagós Archipelago are covered by bare sandy sediments (59%), and ca. 22% is occupied by fiddler crabs. This likely has significant implications for the spatial arrangement of the shorebird and benthic invertebrate communities due to the ecosystem engineering by the fiddler crabs, which promotes two vastly different intertidal species assemblages. This large-scale mapping provides an important product for the future monitoring of this high biodiversity area, particularly for ecological research related to the distribution and feeding ecology of the shorebirds and their prey. Such information is key from a conservation and management perspective. By delivering a successful and comprehensive mapping workflow, we contribute to the filling of the current knowledge gap on the application of remote sensing and machine learning techniques within intertidal areas, which are among the most challenging environments to map using remote sensing techniques.

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Section: Classification Performancesupporting

confidence: 84%

Section: Classification Performancementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Combining Multispectral and Radar Imagery with Machine Learning Techniques to Map Intertidal Habitats for Migratory Shorebirds

et al. 2022

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“…Nonetheless, marine regions of West Africa, a major upwelling zone (i.e., the Canary current) with rich coastal ecosystems including seagrass meadows, mangroves and tidal flats, are recognized as hosting globally important populations of coastal birds, marine mammals (e.g., Atlantic humpback dolphins and West African manatee), and marine turtles (e.g. Barnett et al, 2004;Cunha and Araujo, 2009;Campredon and Catry, 2016;Pottier et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Green Turtles Highlight Connectivity Across a Regional Marine Protected Area Network in West Africa

Patrício

Beal²,

Barbosa³

et al. 2022

Front. Mar. Sci.

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Networks of marine protected areas (MPAs) are invaluable for the protection of species with high dispersal capacity, yet connectivity within networks is poorly understood. We demonstrate the connectivity within the regional MPA network in West Africa (RAMPAO), mediated by the largest green turtle population in the eastern Atlantic Ocean. We equipped with satellite tags 45 female green turtles nesting in the Bijagós Archipelago, Guinea-Bissau, and tracked them during internesting, migration, and foraging to quantify the degree of coverage the RAMPAO network provides during each of these critical periods. During the internesting period, turtles were largely concentrated around the nesting islands, with a mean of 94.8% (SD 0.1%, range: 46% - 100%, n = 40 turtles) of tracking positions falling within MPA limits. Among the 35 turtles successfully tracked into the foraging period, we identified variable migratory strategies, with 12 turtles remaining near-resident at distances of 40-90 km from breeding sites, 10 turtles migrating 300-400 km to The Gambia and Senegal, and 13 turtles traveling >1000 km to northern Mauritania. Of the 35 foraging turtles, 26 used MPAs, with a mean of 78.0% (SD 34.8%, range: 3.7% - 100%) of their tracking positions falling within the limits of RAMPAO MPAs, across Guinea-Bissau, Senegal and Mauritania. Migration corridors with high concentrations of passing turtles were mostly located nearshore, and 21% of these high passage areas fell within the MPA network. Overall, we found that this population connects five RAMPAO MPAs, yet some foraging sites (e.g., in the Bijagós) and important migration areas (e.g., Cap-Vert peninsula) described here are currently unprotected. These results are relevant to any considerations of MPA extension or establishment within the regional network, which would contribute towards meeting the Convention on Biological Diversity targets for national marine protected area estate coverage. By documenting biological connectivity across RAMPAO, this study represents an important example of the relevance of international protected area networks for green turtle conservation and for wider conservation action at a regional scale.

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“…In this study, the algorithm used for classification was support vector machine (SVM), which was used in seagrass mapping and reported to be sufficiently accurate ( Pottier et al, 2021 ). SVM is not sensitive to training data size and does not assume the probability distribution of the data ( Mountrakis, Im & Ogole, 2011 ).…”

Section: Methodsmentioning

confidence: 99%

Species level mapping of a seagrass bed using an unmanned aerial vehicle and deep learning technique

Tahara

Sudo

Yamakita

et al. 2022

PeerJ

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Background Seagrass beds are essential habitats in coastal ecosystems, providing valuable ecosystem services, but are threatened by various climate change and human activities. Seagrass monitoring by remote sensing have been conducted over past decades using satellite and aerial images, which have low resolution to analyze changes in the composition of different seagrass species in the meadows. Recently, unmanned aerial vehicles (UAVs) have allowed us to obtain much higher resolution images, which is promising in observing fine-scale changes in seagrass species composition. Furthermore, image processing techniques based on deep learning can be applied to the discrimination of seagrass species that were difficult based only on color variation. In this study, we conducted mapping of a multispecific seagrass bed in Saroma-ko Lagoon, Hokkaido, Japan, and compared the accuracy of the three discrimination methods of seagrass bed areas and species composition, i.e., pixel-based classification, object-based classification, and the application of deep neural network. Methods We set five benthic classes, two seagrass species (Zostera marina and Z. japonica), brown and green macroalgae, and no vegetation for creating a benthic cover map. High-resolution images by UAV photography enabled us to produce a map at fine scales (<1 cm resolution). Results The application of a deep neural network successfully classified the two seagrass species. The accuracy of seagrass bed classification was the highest (82%) when the deep neural network was applied. Conclusion Our results highlighted that a combination of UAV mapping and deep learning could help monitor the spatial extent of seagrass beds and classify their species composition at very fine scales.

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Mapping coastal marine ecosystems of the National Park of Banc d’Arguin (PNBA) in Mauritania using Sentinel-2 imagery

Cited by 8 publications

References 83 publications

Combining Multispectral and Radar Imagery with Machine Learning Techniques to Map Intertidal Habitats for Migratory Shorebirds

Combining Multispectral and Radar Imagery with Machine Learning Techniques to Map Intertidal Habitats for Migratory Shorebirds

Green Turtles Highlight Connectivity Across a Regional Marine Protected Area Network in West Africa

Species level mapping of a seagrass bed using an unmanned aerial vehicle and deep learning technique

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