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首页> 外文期刊>Journal of the American Water Resources Association >INTERCOMPARISON OF SATELLITE REMOTE SENSING-BASED FLOOD INUNDATION MAPPING TECHNIQUES
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INTERCOMPARISON OF SATELLITE REMOTE SENSING-BASED FLOOD INUNDATION MAPPING TECHNIQUES

机译：基于卫星遥感的洪水淹没映射技术的比较

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摘要

The objective of this study was to determine the accuracy of five different digital image processing techniques to map flood inundation extent with Landsat 8-Operational Land Imager satellite imagery. The May 2016 flooding event in the Hempstead region of the Brazos River, Texas is used as a case study for this first comprehensive comparison of classification techniques of its kind. Five flood water classification techniques (i.e., supervised classification, unsupervised classification, delta-cue change detection, Normalized Difference Water Index [NDWI], modified NDWI [MNDWI]) were implemented to characterize flooded regions. To identify flood water obscured by cloud cover, a digital elevation model (DEM)-based approach was employed. Classified floods were compared using an Advanced Fitness Index to a reference flood map created based on manual digitization, as well as other data sources, using the same satellite image. Supervised classification yielded the highest accuracy of 86.4%, while unsupervised, MNDWI, and NDWI closely followed at 79.6%, 77.3%, and 77.1%, respectively. Delta-cue change detection yielded the lowest accuracy with 70.1%. Thus, supervised classification is recommended for flood water classification and inundation map generation under these settings. The DEM-based approach used to identify cloud-obscured flood water pixels was found reliable and easy to apply. It is therefore recommended for regions with relatively flat topography.
机译：这项研究的目的是确定使用Landsat 8-Operational Land Imager卫星图像绘制洪水淹没程度的五种不同数字图像处理技术的准确性。 2016年5月在得克萨斯州布拉索斯河亨普斯特德地区发生的洪灾事件被用作案例研究，以进行此类分类技术的首次全面比较。实施了五种洪水分类技术（即监督分类，非监督分类，三角洲变化检测，归一化差水指数[NDWI]，修改后的NDWI [MNDWI]）来表征洪水区域。为了识别被云层遮盖的洪水，采用了基于数字高程模型（DEM）的方法。使用高级适应性指数将分类洪水与使用相同卫星图像基于手动数字化以及其他数据源创建的参考洪水地图进行了比较。监督分类产生的最高准确度为86.4％，而无监督分类的MNDWI和NDWI紧随其后，分别为79.6％，77.3％和77.1％。 Delta-cue变化检测产生的最低准确性为70.1％。因此，在这些设置下，建议对洪水分类和淹没图生成进行监督分类。发现用于识别云遮盖的洪水像素的基于DEM的方法可靠且易于应用。因此，建议将其用于地形相对平坦的区域。

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