Future Work

The models developed in this study can be improved by increasing the number of trainable samples. This can be attainable given standardized biomass density sampling techniques.

Expandable datasets can be of interdisciplinary interest. Additionally, the random forest biomass density model can be enhanced by using an exhaustive hyperparameter optimization algorithm. However, this is computationally expensive since all combinations of potential hyperparameters are used for evaluating the model. Finally, the ability of the RF model to identify zero biomass

46

locations should be further investigated. Such a model can be used to delineate vegetated from non-vegetated areas which is crucial for modeling the evolution of coastal wetlands.

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