Abstract
Mapping the historical occurrence of flood water in time and space provides information that can be used to help mitigate damage from future flood events. In Canada, flood mapping has been performed mainly from RADARSAT imagery in near real-time to enhance situational awareness during an emergency, and more recently from Landsat to examine historical surface water dynamics from the mid-1980s to present. Here, we seek to integrate the two data sources for both operational and historical flood mapping. A main challenge of a multi-sensor approach is ensuring consistency between surface water mapped from sensors that fundamentally interact with the target differently, particularly in areas of flooded vegetation. In addition, automation of workflows that previously relied on manual interpretation is increasingly needed due to large data volumes contained within satellite image archives. Despite differences between data received from both sensors, common approaches to surface water and flooded vegetation mapping including multi-channel classification and region growing can be applied with sensor-specific adaptations for each. Historical open water maps from 202 Landsat scenes spanning the years 1985–2016 generated previously were enhanced to improve flooded vegetation mapping along the Saint John River in New Brunswick, Canada. Open water and flooded vegetation maps were created over the same region from 181 RADARSAT 1 and 2 scenes acquired between 2003–2016. Comparisons of maps from different sensors and hydrometric data were performed to examine consistency and robustness of products derived from different sensors. Simulations reveal that the methodology used to map open water from dual-pol RADARSAT 2 is insensitive to up to about 20% training error. Landsat depicts open water inundation well, while flooded vegetation can be reliably mapped in leaf-off conditions. RADARSAT mapped approximately 8% less open water area than Landsat and 0.5% more flooded vegetation, while the combined area of open water and flooded vegetation agreed to within 0.2% between sensors. Derived historical products depicting inundation frequency and trends were also generated from each sensor’s time-series of surface water maps and compared.
Highlights
Water is both a vital resource and a hazard during flooding whose distribution simultaneously influences and is a result of land use and climate
For the three types of labelling error introduced into training data, kappa was over 0.84 at 0% training error, indicating excellent agreement due to good classification performance and a RADARSAT image representative of baseline water extents mapped by NHN
Inundation frequency has routinely been mapped from historical Landsat data, but not from RADARSAT imagery until now
Summary
Water is both a vital resource and a hazard during flooding whose distribution simultaneously influences and is a result of land use and climate. At the ends of the location-frequency spectrum are permanently inundated water bodies and permanently dry land that is never inundated. Between these two extremes are areas where surface water is ephemeral. The timing of ephemeral water is often seasonal as in the case of springtime flooding due to snowmelt at high latitudes or monsoon in the tropics, while its occurrence is becoming more difficult to predict due to increasingly frequent extreme weather events [1]. Floodplain characterization that provides knowledge of the location and frequency of flooding caused by both seasonal and extreme weather events is critical for public safety, as well as land use planning, land valuation and insurance
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