Mapping and Quantification of the Dwarf Eelgrass Zostera noltei Using a Random Forest Algorithm on a SPOT 7 Satellite Image

Abstract

The dwarf eelgrass Zostera noltei Hornemann (Z. noltei) 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 Z. noltei 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 (Z. noltei-associated macroalgae). The use of SPOT 7 satellite images seems to be satisfactory for long-term monitoring of Z. noltei 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.

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 ). For example, to monitor the dynamics of the Posidonia oceanica (L.) Delile meadows and Cymodocea nodosa (Ucria) Ascherson meadows in the Eastern Mediterranean, Traganos and Reinartz ( 2018a ) used the random forest algorithm and support vector machines for the RapidEye time series, after adjusting the atmospheric and analytical water column.…”

“…Quantitative information on density and biomass cannot be achieved using the classification results alone (Price et al 2022 ). The empirical relationship between field data and vegetation indices revealed by high hyperspectral or multispectral satellite imagery can provide biomass and density maps (Barillé et al 2010 ; Bargain et al 2013 ; Roelfsema et al 2014 ; Benmokhtar et al 2021 ), and the biomass of the Z. noltei beds in Merja Zerga lagoon was estimated using the quantitative experimental NDVI – Z. noltei biomass relationship created by Barillé et al ( 2010 ), whereby biomass = 610.61 (NDVI)^1.88 (n = 31, r2 = 0.97). The NDVI–biomass quantitative model applied to satellite image data is useful as a non-destructive method for estimating seagrass and microphytobenthos biomass (Zoffoli et al 2020 , 2021 ), and when we applied this method to the Sentinel-2 data, we found that Z. noltei biomass reached a maximum of 231 g DW/m² in 2020.…”

“…Two classification methods were used to map the Z. noltei beds in the Merja Zerga lagoon: a supervised classification approach was used with the Sentinel-2 images, with a random forest (RF) algorithm (this approach was inspired from Benmokhtar et al 2021 study) and an object-based classification (OBIA) carried out on the mosaics (Fig. 3 ).The Sentinel-2 images used were first atmospherically corrected using the Dark Object Subtraction 1 (DOS1) available in the Semi-Automatic Classification Plugin version 7.9.10 Matera in QGIS software v. 3.16.4.…”

“…This ecosystem mapping now requires updating with an approach that can be easily implemented for long-term monitoring. Among all studies monitoring Z. noltei beds in Morocco using remote sensing, Benmokhtar et al ( 2021 ) was the first attempt of its kind to map these beds and estimate their biomass in Merja Zerga lagoon using a random forest algorithm on a SPOT 7 satellite image. For this study, an expansion of Benmokhtar et al ( 2021 ) study, the aim was first to assess the consistency of Sentinel-2 images and random forest classification in mapping Z. noltei beds in Merja Zerga lagoon, and thus estimating its aboveground biomass (referred to simply as ‘biomass’ in the rest of the article).…”

“…Among all studies monitoring Z. noltei beds in Morocco using remote sensing, Benmokhtar et al ( 2021 ) was the first attempt of its kind to map these beds and estimate their biomass in Merja Zerga lagoon using a random forest algorithm on a SPOT 7 satellite image. For this study, an expansion of Benmokhtar et al ( 2021 ) study, the aim was first to assess the consistency of Sentinel-2 images and random forest classification in mapping Z. noltei beds in Merja Zerga lagoon, and thus estimating its aboveground biomass (referred to simply as ‘biomass’ in the rest of the article). Its second objective was to detect annual changes in the extent of the Z. noltei in Merja Zerga lagoon using another approach - an object-based classification method (OBIA) - applied to two orthophoto mosaics.…”