In the last year, and as a result of the imbalance caused by recurrent eutrophication events in the Mar Menor, there has been an increase in the growth of macroalgae in the lagoon, which has significantly harmed fishing activities by clogging the nets. In this regard, a study has recently been published in the journal Remote Sensing. This study analyses the macroalgae bloom that occurred in the lagoon during the spring-summer of 2022. A set of machine learning techniques is applied to images obtained by the Sentinel-2 satellite in order to obtain indicators of the presence of macroalgae at specific locations in the lagoon. This is supported by in situ observations of the blooming process in different areas of the Mar Menor. Our methodology successfully identifies macroalgal bloom locations (accuracy above 98% and Matthew correlation coefficients above 78% in all cases), and provides a probabilistic approach to understand the probability of occurrence of this event at specific pixels. The analysis also identifies key parameters that contribute to the classification of pixels as algae, which could be used to develop future algorithms for detecting macroalgal blooms. This information can be used by environmental managers to implement early warning and mitigation strategies to prevent deterioration of water quality in coastal areas. The usefulness of satellite observations for ecological and crisis management at local and regional scales is also highlighted in this work.
Reference: Medina-López, E., Navarro, G., Santos-Echeandía, J., Bernárdez, P., Caballero, I., 2023. Machine Learning for Detection of Macroalgal Blooms in the Mar Menor Coastal Lagoon Using Sentinel-2. Remote Sensing 15, 1208. More information available at: https://www.mdpi.com/2072-4292/15/5/1208