Wetlands are the second-most valuable natural resource on Earth but have declined by approximately 70 % since 1900. Restoration and conservation efforts have succeeded in some areas through establishment of refuges where anthropogenic impacts are minimized. However, these areas are still prone to wetland damage caused by natural disasters. Severe storms such as Hurricane Irma, which made landfall as a Category 3 hurricane in southwest Florida (USA) on September 11, 2017, can cause the destruction of mangroves and other wetland habitat. Multispectral images from commercial satellites provide a means to assess the extent of the damage to different wetland habitat types with high spatial resolution (2 m pixels or finer) over large areas. Using such images presents a number of challenges, including deriving consistent and accurate classification of wetland and non-wetland vegetation. Machine learning methods have demonstrated high-accuracy mapping capabilities on small spatial scales, but require a large amount of robust training data. Meanwhile, ambitious efforts to map larger areas at finer resolutions may use hundreds of thousands of images, and therefore encounter Big-Data processing challenges. Large-scale efforts face the dilemma of adopting traditional mapping methods that may lend themselves to Big Data analytics but may result in accuracies that are inferior to new methods, or move to machine learning methods, which require robust training data. Given these considerations, we describe a version of the traditional Decision Tree (DT) approach and compare two common machine learning methods to derive land cover classes using a WorldView-2 image collected on November 12, 2018 to include one growing season after Hurricane Irma affected this area. Specifically, we compared the Support Vector Machine [SVM] and Neural Network [NN] methods, trained and validated with separate ground-truth datasets collected during a robust field campaign. Overall accuracies were only marginally different (85 % NN vs 83 % each DT and SVM), but healthy mangroves were more accurately identified with the DT (91 % vs 88 % NN and 86 % SVM), and degraded mangroves were more accurately identified with NN (62 % vs 57 % NN and 38 % DT). These results, combined with their respective training requirements, have implications for the direction with which large-scale high-resolution mapping of coastal habitats proceeds.

湿地是地球上第二宝贵的自然资源，但自1900年以来已减少了约70％。通过建立避难所，使人为影响降到最低，某些地区的恢复和保护工作取得了成功。但是，这些地区仍然容易遭受自然灾害造成的湿地破坏。2017年9月11日，飓风艾尔玛（Irma）等严重风暴在美国佛罗里达西南部登陆，成为三级飓风，可能导致红树林和其他湿地栖息地遭到破坏。来自商业卫星的多光谱图像提供了一种在大面积上以高空间分辨率（2 m像素或更精细）评估对不同湿地生境类型的破坏程度的方法。使用此类图片会带来很多挑战，包括对湿地和非湿地植被进行一致而准确的分类。机器学习方法已经证明了在小空间尺度上的高精度映射功能，但是需要大量可靠的训练数据。同时，以更大的分辨率绘制较大区域的雄心勃勃的努力可能会使用成千上万张图像，因此会遇到大数据处理的挑战。大规模的工作面临采用传统映射方法的困境，这些方法可能适合大数据分析，但可能导致其准确性不如新方法，或者转向需要强大训练数据的机器学习方法。考虑到这些因素，我们描述了传统决策树（DT）方法的一种版本，并使用2018年11月12日收集的WorldView-2图像比较了两种常见的机器学习方法以得出土地覆盖类别，以包括飓风``艾尔玛''（Irma）影响该地区之后的一个生长季节。具体来说，我们比较了支持向量机[SVM]和神经网络[NN]的方法，这些方法是通过在强大的野战期间收集的单独的真实数据集进行训练和验证的。总体精度仅略有不同（DT和SVM分别为85％NN和83％），但使用DT可以更准确地识别出健康的红树林（91％NN和88％NN和86％SVM），而退化的红树林则可以更准确地识别。 NN（62％vs 57％NN和38％DT）。这些结果，加上各自的培训要求，