From Geo Week News by Eric van Rees.
Presenting the risks of incomplete data
This particular project aims to improve resilience and emergency preparedness against coastal flooding in urban centers. Accurately predicting flood behavior is complicated, especially when not accounting for underground spaces. By presenting the research, UrbanArk demonstrates why incomplete data in high-risk areas poses risks to the population.
Research by Dr. Aaron Miller, a Research Assistant at Queens University Belfast, focuses on the acquisition and analysis of static and mobile lidar data. Working as part of the UrbanArk project, Dr. Miller captured both above and below-ground data in areas of Belfast using a handheld SLAM scanner from UK tech firm GeoSLAM. He further used a terrestrial laser scanner, the FARO Focus, to complement the data. Static scanning was used to compare against the GeoSLAM data, in terms of accuracy, resolution and efficiency. The survey of the study area was completed using the GeoSLAM as the only form of lidar data collection. Due to its considerable amount of subsurface space and proximity to the coast, Belfast proved to be an ideal candidate for the research.
Underground lidar data and flood behavior
Dr. Miller explains that there is little information available regarding underground spaces within the urban environment and that these spaces are priority risk areas in a flood scenario, so knowing their location and ideally their use, would inform emergency services of which spaces may need evacuating first. Furthermore, in a flood scenario, underground spaces act as sinks for floodwater, which could divert the flow of water throughout the streetscape and change the rate of inundation at the street scale.
He continues: “In Belfast for example, Victoria Square shopping center has a two-story underground carpark: this would have major implications on flood dynamics in the surrounding areas. By surveying these spaces and gathering volumetric data, it is possible to include some information regarding these spaces in flood models, therefore providing a more realistic depiction of future flood scenarios.”
In addition to this, lidar data is used as the basis for digital surface models: “The higher the resolution of the lidar dataset, the less estimation is involved in the production of a DSM. The GeoSLAM allows for the collection of lidar data that is dense enough to include street-level features, such as roadside curbs, which are proven to impact flood simulations at the street scale. A more accurate DSM lends itself to the production of more accurate flood models.”
For the complete article on Understanding Flood Behavior CLICK HERE.
