Pedzisai Ezra, Mutanga Onisimo, Odindi John, Bangira Tsitsi
Discipline of Geography, School of Agricultural, Earth and Environmental Sciences Private Bag X01, Pietermaritzburg 3201, South Africa.
Heliyon. 2023 Feb 18;9(3):e13332. doi: 10.1016/j.heliyon.2023.e13332. eCollection 2023 Mar.
Flood disasters destroy infrastructure, disrupt ecosystem processes, adversely affect social and economic activities and cause human fatalities. As such, flood extent mapping (FEM) is critical to mitigate these impacts. Specifically, FEM is essential to mitigate adverse impacts through early warning, efficient response during evacuation, search, rescue and recovery. Furthermore, accurate FEM is crucial for policy formulation, planning and management, rehabilitation, and promoting community resilience for sustainable occupation and use of floodplains. Recently, remote sensing has become valuable in flood studies. However, whereas free passive remote sensing images have been common input into predictive models, damage assessment and FEM, their utility is constrained by clouds during flooding events. Conversely, microwave-based data is unconstrained by clouds, hence is important for FEM. Hence, to increase the reliability and accuracy of FEM using Sentinel-1 radar data, we propose a three-step process that builds an ensemble of scenarios pyramid (ESP) based on change detection and thresholding technique. We deployed the ESP technique and tested it on a use-case based on two, five and 10 images. The use-case calculated three co-polarized Vertical-Vertical (VV) and three cross-polarized Vertical-Horizontal (VH) normalized difference flood index scenarios to form six binary classified FEMs at the base. We ensembled the base scenarios to three dual-polarized centre FEMs, and likewise the centre scenarios to a final pinnacle flood extent map. The base, centre and pinnacle scenarios were validated using six binary classification performance metrics. The results show that the ESP increased the base-to-pinnacle minimum classification performance metrics with overall accuracy, Cohen's Kappa, intersect over union, recall, F1-score, and Matthews Correlation coefficient of 93.204%, 0.864, 0.865, 0.870, 0.927, and 0.871 respectively. The study also established that the VV channels were superior in FEM than VH at the ESP base. Overall, this study demonstrates the efficacy of the ESP for operational flood disaster management.
洪水灾害会破坏基础设施，扰乱生态系统进程，对社会和经济活动产生不利影响，并导致人员伤亡。因此，洪水范围测绘（FEM）对于减轻这些影响至关重要。具体而言，FEM对于通过预警、疏散、搜索、救援和恢复期间的高效应对来减轻不利影响至关重要。此外，准确的FEM对于政策制定、规划与管理、恢复以及提高社区抵御能力以实现洪泛区的可持续占用和利用也至关重要。最近，遥感技术在洪水研究中变得很有价值。然而，尽管免费的被动遥感图像一直是预测模型、损害评估和FEM的常见输入数据，但它们在洪水事件期间的效用受到云层的限制。相反，基于微波的数据不受云层限制，因此对于FEM很重要。因此，为了提高使用哨兵-1雷达数据进行FEM的可靠性和准确性，我们提出了一个三步流程，该流程基于变化检测和阈值技术构建情景金字塔集合（ESP）。我们部署了ESP技术，并基于两张、五张和十张图像的用例对其进行了测试。该用例计算了三个同极化垂直-垂直（VV）和三个交叉极化垂直-水平（VH）归一化差异洪水指数情景，以在底部形成六个二分类FEM。我们将底部情景合并为三个双极化中心FEM，同样地，将中心情景合并为最终的顶峰洪水范围图。使用六个二分类性能指标对底部、中心和顶峰情景进行了验证。结果表明，ESP提高了从底部到顶峰的最低分类性能指标，总体准确率、科恩卡方系数、交并比、召回率、F1分数和马修斯相关系数分别为93.204%、0.864、0.865、0.870、0.927和0.871。该研究还确定，在ESP底部，VV通道在FEM方面优于VH通道。总体而言，本研究证明了ESP在洪水灾害业务管理中的有效性。
