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An exploratory study of Sentinel-1 SAR for rapid urban flood mapping on Google Earth Engine
International Journal of Applied Earth Observation and Geoinformation ( IF 7.5 ) Pub Date : 2022-09-09 , DOI: 10.1016/j.jag.2022.103002
Md Tazmul Islam, Qingmin Meng

Real-time, near-real-time, and accurate flood extent information is critical for emergency response during disaster events such as floods. Accurate extents are critical for disaster management and relief efforts. Despite multiple efforts, there are still many challenges in automated processing of Sentinel-1 SAR to generate reliable inundation maps. The major advantage of SAR compared to optical imagery is its data collection capability despite any weather conditions even thick cloud situation. Currently, there is a knowledge gap of employing different polarization combinations of SAR imagery for flooding research. First, ten different combinations of the two original VH and VV polarizations are designed for rapid and accurate urban flood mapping. To examine the significant potentials of the polarization combinations for flood mapping, four flood mapping methods namely threshold, change detection, unsupervised and supervised classification, in combination with a zero-depth flood method, are designed and used to map flood extents. Among different polarization combinations, the multiplication, squared multiplication, addition, and squared addition combinations have resulted in good results for flood extent mapping. In addition, a flood depth estimation approach has been used to address the overestimation of urban flooded areas. In all four methods, the deduction of overestimated flooded areas using the threshold of zero flood depth has improved the overall accuracy on average 7 % for all methods. The results show that all four methods implemented on Google Earth Engine are good using different combinations to identify flooded areas but change detection method requires little user involvement, and this can be applied to new study areas without estimating flooding depth for the affected areas. Whereas the supervised classification will need more user’s involvement to collect sample points. Among all the combinations, squared addition of polarizations has been consistently performed well for all methods. All the analysis has been done on the Google Earth Engine platform, and this strategy can be used to map flood in any urban environment. The finding of this study will enhance local governments and federal agencies rapid assessment of flooding disasters and making accurate decisions.



中文翻译:

Sentinel-1 SAR 在 Google 地球引擎上用于快速城市洪水测绘的探索性研究

实时、近实时和准确的洪水范围信息对于洪水等灾害事件期间的应急响应至关重要。准确的范围对于灾害管理和救援工作至关重要。尽管付出了很多努力,但在自动处理 Sentinel-1 SAR 以生成可靠的淹没图方面仍然存在许多挑战。与光学图像相比,SAR 的主要优势在于其数据收集能力,即使在任何天气条件下,甚至是厚云情况下。目前,在洪水研究中使用 SAR 图像的不同偏振组合存在知识空白。首先,设计了两种原始 VH 和 VV 偏振的十种不同组合,用于快速准确的城市洪水测绘。为了检查极化组合在洪水测绘中的重要潜力,设计了阈值、变化检测、无监督和有监督分类四种洪水绘图方法,并结合零深度洪水方法绘制洪水范围。在不同的极化组合中,乘法、平方乘法、加法和平方加法组合在洪水范围制图上取得了很好的效果。此外,洪水深度估计方法已被用于解决对城市洪水区域的高估问题。在所有四种方法中,使用零洪水深度阈值扣除高估的洪水区域,所有方法的总体准确度平均提高了 7%。结果表明,在谷歌地球引擎上实施的所有四种方法都可以很好地使用不同的组合来识别洪水区域,但变化检测方法几乎不需要用户参与,这可以应用于新的研究区域,而无需估计受影响区域的洪水深度。而监督分类将需要更多用户的参与来收集样本点。在所有组合中,极化的平方相加在所有方法中一直表现良好。所有分析均在 Google Earth Engine 平台上完成,该策略可用于绘制任何城市环境中的洪水地图。这项研究的结果将加强地方政府和联邦机构对洪水灾害的快速评估并做出准确的决策。这可以应用于新的研究区域,而无需估计受影响区域的洪水深度。而监督分类将需要更多用户的参与来收集样本点。在所有组合中,极化的平方相加在所有方法中一直表现良好。所有分析均在 Google Earth Engine 平台上完成,该策略可用于绘制任何城市环境中的洪水地图。这项研究的结果将加强地方政府和联邦机构对洪水灾害的快速评估并做出准确的决策。这可以应用于新的研究区域,而无需估计受影响区域的洪水深度。而监督分类将需要更多用户的参与来收集样本点。在所有组合中,极化的平方相加在所有方法中一直表现良好。所有分析均在 Google Earth Engine 平台上完成,该策略可用于绘制任何城市环境中的洪水地图。这项研究的结果将加强地方政府和联邦机构对洪水灾害的快速评估并做出准确的决策。对于所有方法,极化的平方加法一直表现良好。所有分析均在 Google Earth Engine 平台上完成,该策略可用于绘制任何城市环境中的洪水地图。这项研究的结果将加强地方政府和联邦机构对洪水灾害的快速评估并做出准确的决策。对于所有方法,极化的平方加法一直表现良好。所有分析均在 Google Earth Engine 平台上完成,该策略可用于绘制任何城市环境中的洪水地图。这项研究的结果将加强地方政府和联邦机构对洪水灾害的快速评估并做出准确的决策。

更新日期:2022-09-10
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