Flood sequence mapping with multimodal remote sensing under the influence of dense vegetation

ABSTRACT

Remote sensing (RS) imagery is becoming increasingly popular in surface water extent extraction thanks to the increasing availability of RS data and advancements in image processing algorithms, software, and hardware. Many studies proved that RS imagery can work independently or along with other approaches to identify flood extent. However, due to the insufficiency in the number of images from single-sourced RS and independent references for validation, most studies just depicted the inundation status near the peak of the inundation. The potential of those images to document flood events at different stages (e.g. rising and receding stages) has not been well investigated. To close that gap, this study investigated the efficacy of RS-based multi-spatiotemporal flood inundation mapping using multimodal RS images captured on different dates to describe the entire flooding process. Additionally, a Quantile-based Filling & Refining (QFR) workflow was proposed to resolve the blocking effects of dense vegetation in study areas. We tested the multimodal flood mapping workflow plus the QFR correction in four lock and dam sites on the Mississippi River by comparing the RS-based flood maps with HEC-RAS simulations. Our results demonstrated the usefulness of multimodal RS images in describing flooding events at different stages and showcased the potential of those images to serve as a reliable reference source in data-scarce areas. In addition, results showed that the standard water extraction plus post-processing will not guarantee accurate flood maps in densely vegetated areas. In contrast, map processed with QFR were noticeably more consistent with HEC-RAS maps, especially for flood maps generated with PlanetScope images, for which the median accuracy has been improved from below 0.5 to above 0.94 after the QFR postprocessing. Thanks to the simple structure, the proposed multimodal RS flood mapping workflow plus QFR correction procedures can be fully automated and can thus benefit near-real-time applications.

KEYWORDS:

• multimodal remote sensing
• flood mapping
• quantile-based correction
• geo-topo analysis
• image fusion
• Google Earth Engine

Acknowledgements

We would like to acknowledge the National Weather Service North Central River Forecast Center for providing streamflow measurements and Iowa Flood Center for providing HEC-RAS flood maps for comparison and providing access to PlanetScope imagery.

Data Availability statement

Research data, as described in this document, will be made available to qualified and legitimate individuals or organizations upon reasonable request. Requests for access to the data should be directed to the corresponding author.

Disclosure statement

No potential conflict of interest was reported by the author(s).