Mapping hurricane damage: A comparative analysis of satellite monitoring methods
Mapping hurricane damage: A comparative analysis of satellite monitoring methods
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绘制飓风损害图：卫星监测方法的比较分析
DOI：
10.1016/j.jag.2020.102134
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发表时间：
2020
影响因子：
7.5
通讯作者：
Muller-Karger, Frank E.
中科院分区：
文献类型：
--
作者：
McCarthy, Matthew J.;Jessen, Brita;Barry, Michael J.;Figueroa, Marissa;McIntosh, Jessica;Murray, Tylar;Schmid, Jill;Muller-Karger, Frank E.
湿地是地球上第二大最有价值的自然资源，但自1900年以来已经减少了约70%。通过建立避难所，尽量减少人为影响，恢复和保护工作在某些地区取得了成功。然而，这些地区仍然容易受到自然灾害造成的湿地破坏。2017年9月11日，飓风厄玛（Irma）以三级飓风的强度在美国西南部佛罗里达登陆，这类强风暴可能导致红树林和其他湿地栖息地遭到破坏。商业卫星提供的多谱段图像提供了一种手段，可用于评估大面积区域内不同湿地生境类型受到损害的程度，其空间分辨率高（2百万像素或更高）。使用这些图像带来了许多挑战，包括对湿地和非湿地植被进行一致和准确的分类。机器学习方法已经在小空间尺度上展示了高精度的映射能力，但需要大量强大的训练数据。与此同时，以更高分辨率绘制更大区域的雄心勃勃的努力可能会使用数十万张图像，因此会遇到大数据处理的挑战。大规模的工作面临着采用传统映射方法的困境，这些方法可能适合大数据分析，但可能导致精度低于新方法，或者转向机器学习方法，这需要强大的训练数据。考虑到这些因素，我们描述了传统决策树（DT）方法的一个版本，并比较了两种常见的机器学习方法，使用2018年11月12日收集的WorldView-2图像来获得土地覆盖类别，包括飓风伊尔玛影响该地区后的一个生长季节。具体来说，我们比较了支持向量机[SVM]和神经网络[NN]方法，并使用在强大的现场活动中收集的单独的地面实况数据集进行训练和验证。总体准确度仅略有不同（NN为85%，DT和SVM为83%），但DT更准确地识别了健康的红树林（91%对88% NN和86% SVM），NN更准确地识别了退化的红树林（62%对57% NN和38% DT）。这些结果，再加上各自的培训要求，与大规模高分辨率沿海生境绘图的前进方向的影响。
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.
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飓风艾尔玛对圣马丁（加勒比海）人类退化的红树林造成损害
影响因子：
4.6
作者：
R. Walcker;C. Laplanche;M. Herteman;L. Lambs;F. Fromard
通讯作者：
F. Fromard
2000 年代大气甲烷稳定：自然湿地排放的关键作用
影响因子：
6.3
作者：
I. Pison;B. Ringeval;P. Bousquet;C. Prigent;F. Papa
通讯作者：
F. Papa
影响因子：
5.2
作者：
A. Turner;D. Jacob;J. Benmergui;S. Wofsy;J. Maasakkers;A. Butz;O. Hasekamp;S. Biraud
通讯作者：
A. Turner;D. Jacob;J. Benmergui;S. Wofsy;J. Maasakkers;A. Butz;O. Hasekamp;S. Biraud
影响因子：
2.7
作者：
Radabaugh, Kara R.;Moyer, Ryan P.;Khan, Nicole S.
通讯作者：
Khan, Nicole S.