ABSTRACT

One major characteristic of floods is flood extent. Information on this characteristic is indispensable for flood monitoring. Recently, synthetic aperture radar (SAR) data have been increasing in quality and quantity. This allows more flood studies conducted over large areas regardless of cloud and weather conditions and provides advantages including clear surface water classification based on SAR scattering mechanisms for low values (open water) and high values (inundated vegetation, etc.). However, challenges remain due to sources of uncertainties, such as atmospheric disturbances and vegetation masking parts of water surfaces. Therefore, in this study, we aim to optimize flood mapping processes on flooded vegetation that generated high-value pixels based on a SAR scattering mechanism called double bounce that classifies vegetative flooded water in L-band SAR images. This optimization is nearly impossible using Sentinel-1 scenes. Backscattering of time-series Sentinel-1 and ALOS-2 images acquired for the 2018 and 2019 flood season was analysed, thresholded and hybridized for flood mapping of a study site in the Tam Nong district of the Dong Thap Province of Vietnam. We found that the accuracy of SAR flood maps was improved compared to ground truth data when the SAR-extracted vegetative-flooded plains were considered flooded.

摘要

洪水的一大特点是洪水泛滥。有关此特性的信息对于洪水监控是必不可少的。最近，合成孔径雷达（SAR）数据的质量和数量一直在增加。这样一来，无论云还是天气情况如何，都可以在大范围内进行更多的洪水研究，并具有多种优势，包括基于SAR散射机制的低水位（裸露水）和高水位（淹没的植被等）的清晰地表水分类。然而，由于不确定因素的影响，挑战仍然存在，例如大气干扰和植被掩盖了部分水面。因此，在这项研究中 我们旨在基于称为双反射的SAR散射机制（对L波段SAR图像中的植物性洪水进行分类）来优化生成高值像素的淹没植被的洪水映射过程。使用Sentinel-1场景几乎不可能实现这种优化。分析，确定阈值并混合了2018年和2019年汛期的Sentinel-1和ALOS-2时间序列图像的反向散射，以绘制越南同塔省Tam Nong区的研究地点的洪水图。我们发现，当SAR提取的植物淹没平原被淹没时，与地面真实数据相比，SAR淹没地图的准确性有所提高。分析，确定阈值并混合了2018年和2019年汛期的Sentinel-1和ALOS-2时间序列图像的反向散射，以绘制越南同塔省Tam Nong区的研究地点的洪水图。我们发现，当SAR提取的植物淹没平原被淹没时，与地面真实数据相比，SAR淹没地图的准确性有所提高。分析，确定阈值并混合了2018年和2019年汛期的Sentinel-1和ALOS-2时间序列图像的反向散射，以绘制越南同塔省Tam Nong区的研究地点的洪水图。我们发现，当SAR提取的植物淹没平原被淹没时，与地面真实数据相比，SAR淹没地图的准确性有所提高。