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TitleTesting urban flood mapping approaches from satellite and in-situ data collected during 2017 and 2019 events in eastern Canada
AuthorOlthof, I; Svacina, N
SourceFlood mapping in urban and vegetated areas; Remote Sensing vol. 12, issue 19, 3141, 2020 p. 1-27, Open
Access logo Open Access
Alt SeriesNatural Resources Canada, Contribution Series 20200458
Mediapaper; on-line; digital
File formatpdf; html
ProvinceOntario; Quebec
AreaOttawa River; St-Lawrence River; Canada
Lat/Long WENS -76.1667 -72.5000 47.5000 45.3333
SubjectsScience and Technology; optical properties; radar imagery; radar methods; Radar
Illustrationslocation maps; tables; graphs; diagrams
ProgramCanada Centre for Remote Sensing Optical methods and applications
Released2020 09 24
AbstractThe increasing frequency of flooding worldwide has driven research to improve near real-time flood mapping from remote-sensing data. Improved automation and processing speed to map both open water and vegetated area flooding have resulted from these research efforts. Despite these achievements, flood mapping in urban areas where a significant number of overall impacts are felt remains a challenge. Near real-time data availability, shadowing caused by manmade infrastructure, spatial resolution, and cloud cover inhibiting optical transmission, are all factors that complicate detailed urban flood mapping needed to inform response efforts. This paper uses numerous data sources collected during two major flood events that impacted the same region of Eastern Canada in 2017 and 2019 to test different urban flood mapping approaches presented as case studies in three separate urban boroughs. Cloud-free high-resolution 3 m PlanetLab optical data acquired near peak-flood in 2019 were used to generate a maximum flood extent product for that year. Approaches using new Lidar Digital Elevation Models (DEM)s and water height estimated from nineteen RADARSAT-2 flood maps, point-based flood perimeter observations from citizen geographic information, and simulated traffic camera or other urban sensor network data were tested and verified using independent data. Coherent change detection (CCD) using multi-temporal Interferometric Wide (IW) Sentinel-1 data was also tested. Results indicate that while clear-sky high-resolution optical imagery represents the current gold standard, its availability is not guaranteed due to timely coverage and cloud cover. Water height estimated from 8 to 12.5 m resolution RADARSAT-2 flood perimeters were not sufficiently accurate to flood adjacent urban areas using a Lidar DEM in near real-time, but all nineteen scenes combined captured boroughs that flooded at least once in both flood years. CCD identified flooded boroughs and roughly captured their flood extents, but lacked timeliness and sufficient detail to inform street-level decision-making in near real-time. Point-based flood perimeter observation, whether from in-situ sensors or high-resolution optical satellites combined with Lidar DEMs, can generate accurate full flood extents under certain conditions. Observed point-based flood perimeters on manmade features with low topographic variation produced the most accurate flood extents due to reliable water height estimation from these points.
Summary(Plain Language Summary, not published)
The Emergency Geomatics Services (EGS) at NRCan are responsible for near real-time flood mapping in Canada in order to provide current awareness of a flood disaster as it evolves to emergency personnel including the military. This information can be used to help responders develop strategies to minimize impacts of flooding by e.g. building sandbank barriers, or to prioritize intervention in areas that are most vulnerable, e.g. senior residences. Current EGS flood mapping tools rely on satellite data and are effective in providing accurate flood extents outside of urban areas due to limitations of satellite imagery caused by cloud cover, shadow and obstructions in urban settings. Unfortunately, urban areas are where this information is often most needed. This paper describes and tests a number of approaches to improve urban flood maps using a variety of data potentially available to EGS during a flood crisis. The most promising approaches leverage an important government investment in the acquisition of detailed topographic maps over urban areas to predict where flooding will occur. Ultimately, new methods will be combined with existing methods to generate reliable near real-time flood maps in rural, suburban and urban settings.

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