Mapping and Quantification of the Dwarf Eelgrass <i>Zostera noltei</i> Using a Random Forest Algorithm on a SPOT 7 Satellite Image
The dwarf eelgrass <i>Zostera noltei</i> Hornemann (<i>Z. noltei</i>) is the most dominant seagrass in semi-enclosed coastal systems of the Atlantic coast of Morocco. The species is experiencing a worldwide decline and monitoring the extent of its meadows would be a useful ap...
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MDPI AG
2021-05-01
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| Series: | ISPRS International Journal of Geo-Information |
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| Online Access: | https://www.mdpi.com/2220-9964/10/5/313 |
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| author | Salma Benmokhtar Marc Robin Mohamed Maanan Hocein Bazairi |
| author_facet | Salma Benmokhtar Marc Robin Mohamed Maanan Hocein Bazairi |
| author_sort | Salma Benmokhtar |
| collection | DOAJ |
| description | The dwarf eelgrass <i>Zostera noltei</i> Hornemann (<i>Z. noltei</i>) is the most dominant seagrass in semi-enclosed coastal systems of the Atlantic coast of Morocco. The species is experiencing a worldwide decline and monitoring the extent of its meadows would be a useful approach to estimate the impacts of natural and anthropogenic stressors. Here, we aimed to map the <i>Z. noltei</i> meadows in the Merja Zerga coastal lagoon (Atlantic coast of Morocco) using remote sensing. We used a random forest algorithm combined with field data to classify a SPOT 7 satellite image. Despite the difficulties related to the non-synchronization of the satellite images with the high tide coefficient, our results revealed, with an accuracy of 95%, that dwarf eelgrass beds can be discriminated successfully from other habitats in the lagoon. The estimated area was 160.76 ha when considering mixed beds (<i>Z. noltei</i>-associated macroalgae). The use of SPOT 7 satellite images seems to be satisfactory for long-term monitoring of <i>Z. noltei</i> meadows in the Merja Zerga lagoon and for biomass estimation using an NDVI–biomass quantitative relationship. Nevertheless, using this method of biomass estimation for dwarf eelgrass meadows could be unsuccessful when it comes to areas where the NDVI is saturated due to the stacking of many layers. |
| first_indexed | 2024-03-10T11:37:37Z |
| format | Article |
| id | doaj.art-82495dd577d44df5b910d0ae6ab34276 |
| institution | Directory Open Access Journal |
| issn | 2220-9964 |
| language | English |
| last_indexed | 2024-03-10T11:37:37Z |
| publishDate | 2021-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj.art-82495dd577d44df5b910d0ae6ab342762023-11-21T18:43:08ZengMDPI AGISPRS International Journal of Geo-Information2220-99642021-05-0110531310.3390/ijgi10050313Mapping and Quantification of the Dwarf Eelgrass <i>Zostera noltei</i> Using a Random Forest Algorithm on a SPOT 7 Satellite ImageSalma Benmokhtar0Marc Robin1Mohamed Maanan2Hocein Bazairi3BioBio Research Center, BioEcoGen Laboratory, Faculty of Sciences, Mohammed V University in Rabat, Rabat 1014, MoroccoLETG UMR CNRS 6554, University of Nantes, CEDEX 3, 44312 Nantes, FranceLETG UMR CNRS 6554, University of Nantes, CEDEX 3, 44312 Nantes, FranceBioBio Research Center, BioEcoGen Laboratory, Faculty of Sciences, Mohammed V University in Rabat, Rabat 1014, MoroccoThe dwarf eelgrass <i>Zostera noltei</i> Hornemann (<i>Z. noltei</i>) is the most dominant seagrass in semi-enclosed coastal systems of the Atlantic coast of Morocco. The species is experiencing a worldwide decline and monitoring the extent of its meadows would be a useful approach to estimate the impacts of natural and anthropogenic stressors. Here, we aimed to map the <i>Z. noltei</i> meadows in the Merja Zerga coastal lagoon (Atlantic coast of Morocco) using remote sensing. We used a random forest algorithm combined with field data to classify a SPOT 7 satellite image. Despite the difficulties related to the non-synchronization of the satellite images with the high tide coefficient, our results revealed, with an accuracy of 95%, that dwarf eelgrass beds can be discriminated successfully from other habitats in the lagoon. The estimated area was 160.76 ha when considering mixed beds (<i>Z. noltei</i>-associated macroalgae). The use of SPOT 7 satellite images seems to be satisfactory for long-term monitoring of <i>Z. noltei</i> meadows in the Merja Zerga lagoon and for biomass estimation using an NDVI–biomass quantitative relationship. Nevertheless, using this method of biomass estimation for dwarf eelgrass meadows could be unsuccessful when it comes to areas where the NDVI is saturated due to the stacking of many layers.https://www.mdpi.com/2220-9964/10/5/313seagrassremote sensingrandom forestvegetation indicesmachine learning classification |
| spellingShingle | Salma Benmokhtar Marc Robin Mohamed Maanan Hocein Bazairi Mapping and Quantification of the Dwarf Eelgrass <i>Zostera noltei</i> Using a Random Forest Algorithm on a SPOT 7 Satellite Image ISPRS International Journal of Geo-Information seagrass remote sensing random forest vegetation indices machine learning classification |
| title | Mapping and Quantification of the Dwarf Eelgrass <i>Zostera noltei</i> Using a Random Forest Algorithm on a SPOT 7 Satellite Image |
| title_full | Mapping and Quantification of the Dwarf Eelgrass <i>Zostera noltei</i> Using a Random Forest Algorithm on a SPOT 7 Satellite Image |
| title_fullStr | Mapping and Quantification of the Dwarf Eelgrass <i>Zostera noltei</i> Using a Random Forest Algorithm on a SPOT 7 Satellite Image |
| title_full_unstemmed | Mapping and Quantification of the Dwarf Eelgrass <i>Zostera noltei</i> Using a Random Forest Algorithm on a SPOT 7 Satellite Image |
| title_short | Mapping and Quantification of the Dwarf Eelgrass <i>Zostera noltei</i> Using a Random Forest Algorithm on a SPOT 7 Satellite Image |
| title_sort | mapping and quantification of the dwarf eelgrass i zostera noltei i using a random forest algorithm on a spot 7 satellite image |
| topic | seagrass remote sensing random forest vegetation indices machine learning classification |
| url | https://www.mdpi.com/2220-9964/10/5/313 |
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