During flooding, operational tools for mapping flood extent and depth of water in flood-prone areas are required by those planning emergency response, including UK statutory agencies such as the Environment Agency. Satellite data have become a source of information to map and monitor floods, but many of the methods developed to process the data are unsuitable for accurate, near real-time production of flood information products. This paper describes a new semi-automated methodology developed to provide operational mapping of flood extent and flood depth using satellite synthetic aperture radar (SAR) data combined with light detection and ranging (LiDAR) elevation data. In this method, an analyst uses the flood boundary derived from 8 m spatial resolution satellite SAR data to estimate the flood surface elevation at points around a flooded area using a digital terrain model derived from LiDAR data. This method is compared to a simple satellite ‘SAR-only’ method for generating flood extent and alternative, automated methods of generating flood extent and depth that also used SAR and LiDAR. TerraSAR-X and SPOT 5 data were used from an area including the UK Somerset Levels which suffered a major flood event in February 2014. The new semi-automated method produced similar overall accuracy to the SAR-only method ( Po = 95.8% and Po = 95.3%, respectively), but was more accurate at mapping flood extent where large vegetation or other objects appeared in the satellite SAR data. The automated methods were relatively inaccurate (overall accuracy ranged from Po = 83.4% to Po = 88.8%), but were used to identify where further work on improving the semi-automated-elevation method could be carried out. In addition to the flood extent information provided by the semi-automated-elevation method, flood surface elevation data were produced that could be used to estimated flood depths and flood volumes. The accuracy of the flood elevation surface was tested using LiDAR data acquired of the water surface during the flooding (root mean square error = 0.152 m). The paper discusses progress towards operational flood monitoring using SAR and LiDAR remote sensing products.

Monitoring flood defence condition is a critical part of the effective management of flood defence infrastructure. In this study, the use of remotely sensed data to identify indicators of weakness, deterioration, and damage is explored. Such indicators are often tell‐tale signs of processes that can lead to flood defence failure and breaching. Sources of data and the techniques to analyse those data are discussed and a framework is developed that links data sources to indicators of flood defence performance. A range of examples are presented that highlight both the value and limitations of bringing together multiple sources of data such as Light Detection and Ranging and historical mapping to supplement visual inspection assessments. The paper concludes that nationally available sources of remotely sensed data can be used to supplement existing visual condition inspection procedures to help prioritise and programme maintenance and repair work.

No abstract