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.

中文翻译：

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使用随机森林、核逻辑回归和朴素贝叶斯树模型对阿联酋北部 1990 年至 2019 年红树林变化的时空制图和监测

红树林正在充当阿拉伯联合酋长国沿海城市的绿肺，为野生动物提供栖息地，将蓝碳储存在沉积物中并保护海岸线。因此，保护​​和更好地了解红树林生态环境的第一步是绘制和监测多个空间尺度的红树林范围。本研究旨在开发一种新的低成本遥感方法，用于时空制图和监测阿拉伯联合酋长国北部 (NUAE) 的红树林范围。该方法是基于使用多时态 Landsat 图像的随机森林 (RF)、核逻辑回归 (KLR) 和朴素贝叶斯树 (NBT) 机器学习算法开发的。我们的准确度指标结果包括准确度、精确度、召回率、F1 分数显示，RF 的 F1 分数超过 0.90，优于 KLR 和 NB。每对制作的红树林地图（1990-2000、2000-2010、2010-2019 和 1990-2019）都被用于图像差异算法（ID），通过应用从 +1 到 -1 的阈值范围来监测红树林的范围。我们的结果对生态和研究界非常重要。本研究中提供的新地图将为沿海管理组织提供很好的参考和有用的资源。