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Mangrove forests are acting as a green lung for the coastal cities of the United Arab Emirates, providing a habitat for wildlife, storing blue carbon in sediment and protecting shoreline. Thus, the first step toward conservation and a better understanding of the ecological setting of mangroves is mapping and monitoring mangrove extent over multiple spatial scales. This study aims to develop a novel low-cost remote sensing approach for spatiotemporal mapping and monitoring mangrove forest extent in the northern part of the United Arab Emirates (NUAE). The approach was developed based on random forest (RF), Kernel logistic regression (KLR), and Naive Bayes Tree (NBT) machine learning algorithms which use multitemporal Landsat images. Our results of accuracy metrics include accuracy, precision, recall, F1 score revealed that RF outperformed the KLR and NB with an F1 score of more than 0.90. Each pair of produced mangrove maps (1990-2000, 2000-2010, 2010-2019 and 1990-2019) was used to image difference algorithm (ID) to monitor mangrove extent by applying a threshold ranges from +1 to -1. Our results are of great importance to the ecological and research community. The new maps presented in this study will be a good reference and a useful source for the coastal management organization.

frontiers in environmental science

Spatiotemporal Mapping and Monitoring of Mangrove Forests Changes From 1990 to 2019 in the Northern Emirates, UAE Using Random Forest, Kernel Logistic Regression and Naive Bayes Tree Models