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Mapping seagrass meadows and assessing blue carbon stocks using Sentinel-2 satellite imagery: A case study in the Canary Islands, Spain
* 1 , 2 , 1 , 1
1  Departamento de Física, Universidad de La Laguna, 38200 San Cristóbal de La Laguna, Spain
2  Instituto Español de Oceanografía-CSIC, The Canary Islands, Spain
Academic Editor: Riccardo Buccolieri


Seagrass meadows, recognised as powerful carbon sinks and crucial players in climate change mitigation, face significant threats from global warming and anthropogenic activities. Traditional in situ monitoring methods, while accurate, are often expensive, time-consuming, and constrained by the extent of coverage. Thus, this research aims to evaluate the potential of freely accessible Sentinel-2 satellite imagery in mapping and monitoring Cymodocea nodosa seagrass meadows in El Médano (Tenerife, Canary Islands), contributing to ecosystem conservation efforts. The study employed an image from October 27, 2022 processed at Level-1C. This leads to significant challenges due to the optical signal’s attenuation caused by the atmosphere and the water column. The atmospheric correction was addressed by employing the Sen2Cor tool within the Sentinel Application Platform (SNAP). For the water column effect, Lyzenga’s method was used. This method is based on Beer-Lambert’s absorption law, which specifies a log-linear relationship between reflectance values and water depth. Following these corrections, supervised classifications were conducted using the Random Forest, K-Nearest Neighbors (KNN), and KDTree-KNN algorithms, supplemented by unsupervised classifications and in situ data. The blue carbon sequestered by the C. nodosa in the study area was also computed using the InVEST (Integrated Valuation of Ecosystem Services and Tradeoffs) software. The Random Forest classifiers produced the highest F1-scores, ranging between 0.96 and 0.99. Results revealed an average area of 237±5 ha occupied by the C. nodosa in the study region, which translates to an average sequestration of 111,349±2,330 Mg of CO2. Even at the lowest sequestration level, the seagrass meadows of this study area have the potential to offset the CO2 emissions produced by the industrial combustion plants sector across the Canary Islands. This research represents a significant step in protecting and comprehending these invaluable ecosystems. It effectively underscores the potential of Sentinel-2 satellite data for mapping seagrass meadows and emphasises their crucial role in achieving net zero carbon emissions on our planet.

Keywords: Cymodocea nodosa; machine learning algorithms; classification; ecosystem services, InVEST.
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