UC San Diego

Mangrove forests are critical to mitigating climate change and provide many essential benefits to their ecosystems and local environments but are under threat due to deforestation. However, monitoring mangroves through remote sensing can help pinpoint and alleviate the causes of their deforestation. Machine learning can be used with remotely sensed low-resolution satellite or high-resolution aerial imagery to automatically create mangrove extent maps with higher accuracy and frequency than previously possible. This study explores and offers recommendations for two practical scenarios. In the first practical scenario, where only low-resolution hyperspectral satellite imagery is acquired, we implemented several classical machine learning models and applied these results to data acquired in the Clarendon parish of Jamaica. We found that utilizing extensive feature engineering and hyperspectral bands can result in strong performance for mangrove extent classification, with an accuracy of 93% for our extremely randomized trees model. In the second practical scenario, we explored when there is full coverage of both low-resolution satellite and high-resolution aerial imagery over a survey area. We created a hybrid model which fuses low-resolution pixels and high-resolution imagery, achieving an accuracy of 97% when applied to a dataset based in Baja California Sur, Mexico, offering another high-performance method to automatically create mangrove extent maps if both high- and low-resolution imagery is available. Overall, the methods tested over these two scenarios provide stakeholders flexibility in data and methods used to achieve accurate, automatic mangrove extent measurement, enabling more frequent mangrove monitoring and further enabling the protection of these important ecosystems.