Antarctic Supraglacial Lake Identification Using Landsat-8 Image Classification
Antarctic Supraglacial Lake Identification Using Landsat-8 Image Classification
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DOI：
10.3390/rs12081327
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发表时间：
2020-04
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通讯作者：
A. Halberstadt;C. Gleason;M. Moussavi;A. Pope;L. Trusel;R. DeConto
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作者：
A. Halberstadt;C. Gleason;M. Moussavi;A. Pope;L. Trusel;R. DeConto
南极冰盖边缘的冰架上产生的表面融水可以驱动冰架崩溃，导致冰盖质量损失，并导致全球海平面上升。定量评估冰上湖的演变需要了解南极表面融水对冰盖和冰架稳定性的影响。云计算平台使得所需的遥感分析在计算上变得微不足道，但尚未对泛南极湖泊测绘的图像处理技术进行仔细评估。这项工作铺平了道路，自动化湖泊识别在整个大陆尺度的卫星观测记录，通过彻底的方法分析。我们部署了一套不同的训练监督分类器来映射和量化多光谱Landsat-8场景中的冰上湖泊区域，使用通过手动解释k均值聚类结果生成的训练数据。使用训练数据集获得最佳结果，这些数据集包括来自多个区域的光谱多样的无监督聚类，并且包括岩石和云阴影类。我们成功地将经过训练的监督分类器应用于两个冰架，这些冰架具有高于20°阈值太阳仰角的不同冰上湖泊特征，与手动生成的验证数据集相比，分类准确率超过90%。我们训练的分类器的应用产生了湖泊演变的季节性模式。云阴影区阻碍了我们的分类器的大规模应用，在以前的工作。我们的研究结果表明，在部署“现成的”算法在南极洲湖泊测绘之前，需要谨慎，并建议仔细审查训练数据和所需的输出类是必不可少的准确结果。我们的监督分类技术提供了一种替代和独立的湖泊识别方法，为整个大陆的冰上湖泊测绘产品的开发提供信息。
Surface meltwater generated on ice shelves fringing the Antarctic Ice Sheet can drive ice-shelf collapse, leading to ice sheet mass loss and contributing to global sea level rise. A quantitative assessment of supraglacial lake evolution is required to understand the influence of Antarctic surface meltwater on ice-sheet and ice-shelf stability. Cloud computing platforms have made the required remote sensing analysis computationally trivial, yet a careful evaluation of image processing techniques for pan-Antarctic lake mapping has yet to be performed. This work paves the way for automating lake identification at a continental scale throughout the satellite observational record via a thorough methodological analysis. We deploy a suite of different trained supervised classifiers to map and quantify supraglacial lake areas from multispectral Landsat-8 scenes, using training data generated via manual interpretation of the results from k-means clustering. Best results are obtained using training datasets that comprise spectrally diverse unsupervised clusters from multiple regions and that include rock and cloud shadow classes. We successfully apply our trained supervised classifiers across two ice shelves with different supraglacial lake characteristics above a threshold sun elevation of 20°, achieving classification accuracies of over 90% when compared to manually generated validation datasets. The application of our trained classifiers produces a seasonal pattern of lake evolution. Cloud shadowed areas hinder large-scale application of our classifiers, as in previous work. Our results show that caution is required before deploying ‘off the shelf’ algorithms for lake mapping in Antarctica, and suggest that careful scrutiny of training data and desired output classes is essential for accurate results. Our supervised classification technique provides an alternative and independent method of lake identification to inform the development of a continent-wide supraglacial lake mapping product.