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Flood mapping through principal component analysis of multitemporal satellite imagery considering the alteration of water spectral properties due to turbidity conditions

机译：通过考虑浊度条件引起的水频谱特性变化的多时相卫星影像主成分分析进行洪水制图

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ABSTRACT We propose a methodology for flood mapping by remote sensing considering the alteration of the spectral response of water due to turbidity conditions using principal component analysis (PCA) of multitemporal satellite imagery limited to the visible and the near IR region of the electromagnetic spectrum. Crosta technique is applied to the resulting load matrix to select the PCs of interest, and to interpret the nature of the changes. Finally, it is proposed the use of linear unmixing, using the selected PCs as input, to classify the categories required for flood mapping. The proposed methodology is applied to mapping the flood areas after the tropical storm Manuel during September 2013 in Acapulco, Mexico. The proposed procedure is a more robust alternative to quotients or index-orientated approaches based on fixed spectral response of the water, e.g. the Normalized Difference Water Index. This approach can be useful to authorities, civil protection and other organizations dedicated to risk management during natural contingences to assess quickly the dimension of affected areas, without the expensive and complicated mobilization of recourses to the site, and to give a more efficient response.
机译：摘要我们提出了一种通过遥感进行洪水映射的方法，其中考虑了使用浊度条件引起的水频谱响应的变化，该方法使用多时相卫星图像的主成分分析（PCA）限于电磁波谱的可见和近红外区域。 Crosta技术应用于所得的负载矩阵，以选择目标PC，并解释变化的性质。最后，建议使用线性分解，使用选定的PC作为输入，对洪水映射所需的类别进行分类。拟议的方法适用于2013年9月在墨西哥阿卡普尔科发生热带风暴曼努埃尔之后的洪水区域地图。所提出的程序是基于水的固定光谱响应（例如水）的商或指数导向方法的更鲁棒的替代方案。归一化差异水指数。这种方法对于在自然灾害期间致力于风险管理的当局，民防部门和其他组织有用，可以快速评估受灾地区的规模，而无需动用昂贵且复杂的资源去现场，并做出更有效的响应。

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