The Day Roads Became Rivers: A GIS-Based Assessment of Flash Floods in Worcester

In the last 4 years Worcester, UK has been hit by several intense convective rainstorms, which caused flash floods outside of existing surface drainage networks. This paper addresses two questions related to such events: Firstly to what extent can the occurrence of flash flood flow accumulation can be determined using only commonly available data and tools, assuming the rainfall events caused mainly surface runoff due to their tropical intensity and the relatively impermeable urban catchment surface? Secondly, are the flood in-cidents in Worcester aggravated by roads serving as preferential flow paths under these conditions? The as-sessment results indicated that roads do not have an influence on the flow path of flash flood rainfall in Worcester. Flow accumulation calculated with a 10m DEM, corresponds well with reported flood incidents. This basic assessment method can be used to inform the implementation of non structural flood mitigation and public awareness.

Mapping of Submerged Aquatic Vegetation in Rivers From Very High Resolution Image Data, Using Object Based Image Analysis Combined with Expert Knowledge

The use of remote sensing for monitoring of submerged aquatic vegetation (SAV) in fluvial environments has been limited by the spatial and spectral resolution of available image data. The absorption of light in water also complicates the use of common image analysis methods. This paper presents the results of a study that uses very high resolution (VHR) image data, collected with a Near Infrared sensitive DSLR camera, to map the distribution of SAV species for three sites along the Desselse Nete, a lowland river in Flanders, Belgium. Plant species, including Ranunculus aquatilis L., Callitriche obtusangula Le Gall, Potamogeton natans L., Sparganium emersum L. and Potamogeton crispus L., were classified from the data using Object-Based Image Analysis (OBIA) and expert knowledge. A classification rule set based on a combination of both spectral and structural image variation (e.g. texture and shape) was developed for images from two sites. A comparison of the classifications with manually delineated ground truth maps resulted for both sites in 61% overall accuracy. Application of the rule set to a third validation image, resulted in 53% overall accuracy. These consistent results show promise for species level mapping in such biodiverse environments, but also prompt a discussion on assessment of classification accuracy.