David C. Mason

Image processing of airborne scanning laser altimetry data for improved river flood modelling

Airborne scanning laser altimetry (LiDAR) is an important new data source for environmental applications, being able to map topographic height, and the height of surface objects, to high vertical and horizontal accuracy over large areas. This paper describes a range image segmentation system for data from a LiDAR measuring either time of last significant return, or measuring time of both first and last returns. We focus on the application of the segmenter to improving the data required by 2D hydraulic flood models, i.e. maps of topographic height which provide model bathymetry, and vegetation height, which could be converted to distributed floodplain friction coefficients. In addition, the location of river channels and a suitable height contour are used to define the extent of the model domain. An advantage of segmentation is that it allows different topographic and vegetation height extraction algorithms to be used in regions of different cover type. LiDAR data for a reach of the River Severn, UK, is presented. Short vegetation heights (grass and cereal crops) are predicted with a rms error of 14 cm. The topography underlying such cover differs from manually measured spot heights by 17 cm (rms error). The topographic accuracy decreases in the presence of a densely wooded slope. Errors in the vegetation height map, apparent at the overlap regions of adjacent swaths, are reduced by the removal of heights measured at large scan angles.

Flood Detection in Urban Areas Using TerraSAR-X

Flooding is a major hazard in both rural and urban areas worldwide, but it is in urban areas that the impacts are most severe. An investigation of the ability of high-resolution TerraSAR-X synthetic aperture radar (SAR) data to detect flooded regions in urban areas is described. The study uses a TerraSAR-X image of a one-in-150-year flood near Tewkesbury, U.K., in 2007, for which contemporaneous aerial photography exists for validation. The German Aerospace Center (DLR) SAR end-to-end simulator (SETES) was used in conjunction with airborne scanning laser altimetry (LiDAR) data to estimate regions of the image in which water would not be visible due to shadow or layover caused by buildings and taller vegetation. A semiautomatic algorithm for the detection of floodwater in urban areas is described, together with its validation using aerial photographs. Of the urban water pixels that are visible to TerraSAR-X, 76% were correctly detected, with an associated false positive rate of 25%. If all the urban water pixels were considered, including those in shadow and layover regions, these figures fell to 58% and 19%, respectively. The algorithm is aimed at producing urban flood extents with which to calibrate and validate urban flood inundation models, and these findings indicate that TerraSAR-X is capable of providing useful data for this purpose.

Integrating remote sensing observations of flood hydrology and hydraulic modelling

The further development of two-dimensional finite element models of river flood flow is currently constrained by a lack of data for rigorous parameterization and validation. Remote sensing techniques have the potential to overcome a number of these constraints thereby allowing a research design for model development. This is illustrated with reference to a case study of a two-dimensional finite element model applied to the Missouri River, Nebraska and compared with a synchronous Landsat TM image of flood inundation extent. The case study allows research needs for the integration of hydraulic modelling and remote sensing to be defined.

Accurate and efficient determination of the shoreline in ERS-1 SAR images

Extraction of the shoreline in SAR images is a difficult task to perform using simple image processing operations such as grey-value thresholding, due to the presence of speckle and because the signal returned from the sea surface may be similar to that from the land. A semiautomatic method for detecting the shoreline accurately and efficiently in ERS-1 SAR images is presented. This is aimed primarily at a particular application, namely the construction of a digital elevation model of an intertidal zone using SAR images and hydrodynamic model output, but could be carried over to other applications. A coarse-fine resolution processing approach is employed, in which sea regions are first detected as regions of low edge density in a low resolution image, then image areas near the shoreline are subjected to more elaborate processing at high resolution using an active contour model. Over 90% of the shoreline detected by the automatic delineation process appear visually correct.

A Change Detection Approach to Flood Mapping in Urban Areas Using TerraSAR-X

Very high resolution synthetic aperture radar (SAR) sensors represent an alternative to aerial photography for delineating floods in built-up environments where flood risk is highest. However, even with currently available SAR image resolutions of 3 m and higher, signal returns from man-made structures hamper the accurate mapping of flooded areas. Enhanced image processing algorithms and a better exploitation of image archives are required to facilitate the use of microwave remote-sensing data for monitoring flood dynamics in urban areas. In this paper, a hybrid methodology combining backscatter thresholding, region growing, and change detection (CD) is introduced as an approach enabling the automated, objective, and reliable flood extent extraction from very high resolution urban SAR images. The method is based on the calibration of a statistical distribution of “open water” backscatter values from images of floods. Images acquired during dry conditions enable the identification of areas that are not “visible” to the sensor (i.e., regions affected by “shadow”) and that systematically behave as specular reflectors (e.g., smooth tarmac, permanent water bodies). CD with respect to a reference image thereby reduces overdetection of inundated areas. A case study of the July 2007 Severn River flood (UK) observed by airborne photography and the very high resolution SAR sensor on board TerraSAR-X highlights advantages and limitations of the method. Even though the proposed fully automated SAR-based flood-mapping technique overcomes some limitations of previous methods, further technological and methodological improvements are necessary for SAR-based flood detection in urban areas to match the mapping capability of high-quality aerial photography.

Waterline mapping in flooded vegetation from airborne SAR imagery

Multifrequency, polarimetric airborne synthetic aperture radar (SAR) survey of a salt marsh on the east coast of the UK is used to investigate the radar backscattering properties of emergent salt marsh vegetation. Two characteristics of flooded vegetation are observed: backscatter enhanced by approximately 1.2 dB at C-band, and 180° HH-VV phase differences at L-band. Both are indicative of a double bounce backscattering mechanism between the horizontal water surface and upright emergent vegetation. The mapping of inundated vegetation is demonstrated for both these signatures, using a statistical active contour model for the C-band enhanced backscatter, and median filtering and thresholding for the L-band HH-VV phase difference. The two techniques are validated against the waterline derived from tidal elevation measured at the time of overpass intersected with an intertidal DEM derived from airborne laser altimetry. The inclusion of flooded vegetation is found to reduce errors in waterline location by a factor of approximately 2, equivalent to a reduction in waterline location error from 120 to 70 m. The DEM is also used to derive SAR waterline heights, which are observed to underpredict the tidal elevation due to the effects of vegetation. The underprediction can be corrected for vegetation effects using canopy height maps derived from the laser altimetry. This third technique is found to improve the systematic error in waterline heights from 20 to 4 cm, but little improvement in random error is evident, chiefly due to significant noise in the vegetation height map.

Improving River Flood Extent Delineation From Synthetic Aperture Radar Using Airborne Laser Altimetry

Flood extent maps that are derived from synthetic aperture radar (SAR) images provide spatially distributed data for validating hydraulic models of river flood flow. The accuracy of such maps is reduced by a number of factors, including variation in backscatter from the different land cover types that are adjacent to the flood, changes in returns from the water surface that are caused by different meteorological conditions, and the presence of emergent vegetation. This paper describes how improved accuracy can be achieved by modifying an existing flood extent delineation algorithm to use airborne laser altimetry [light detection and ranging (lidar)] as well as SAR data. The lidar data provide an additional constraint that water line heights should vary smoothly along the flooded reach. The method was tested on a SAR image of a flood for which contemporaneous aerial photography existed, together with lidar data of the un flooded reach. The water line heights of the SAR flood extent that was conditioned on both SAR and lidar data matched the corresponding heights from the aerial photograph water line significantly more closely than those from the SAR flood extent that was conditioned only on SAR data. For water line heights in areas of low slope and vegetation, the root-mean-square error on the height differences reduced from 221.1 cm for the latter case to 55.5 cm for the former.

Construction of an inter‐tidal digital elevation model by the ‘Water‐Line’ Method

Progress in the development of a method for constructing a Digital Elevation Model (DEM) of an inter-tidal zone using remote sensing and hydrodynamic modelling is described. The method allows the construction of an inter-tidal DEM over large areas relatively quickly as well as frequent subsequent monitoring of the DEM to detect changes, and is particularly suited to SAR satellite data because of the all-weather day-night capability of SAR. The resulting DEM may be used for the development of improved hydrodynamic models, and for studying sediment mass transport in the inter-tidal zone.

Segmentation of remotely-sensed images by a split-and-merge process+

This paper describes the application of an image segmentation technique to remotely-sensed terrain images used for environmental monitoring. The segmentation is a preprocessing operation which is a...

Application of airborne scanning laser altimetry to the study of tidal channel geomorphology

Abstract Tidal channels play a fundamental role in the hydrodynamic and morphological processes operating within a tidal basin. Conventional means of mapping intertidal zones are either too cumbersome or do not provide sufficient information for accurate quantitative measurements of the channels. This paper describes the use of airborne scanning altimetry (light detection and ranging—LiDAR) for studying tidal channel geomorphology. The main emphasis is on developing a technique for extracting tidal channels from LiDAR data. Some common existing techniques of fluvial channel extraction are evaluated with LiDAR data of tidal basins and found moderate in performance. A semiautomatic approach is proposed which has a performance better than that of existing techniques. This approach is realised using an adaptive height thresholding technique to locate channel fragments. A channel joining mechanism then connects the channel fragments using a weighted distance transform. The paper also describes briefly an automatic procedure to carry out measurements on the basin image and derive geomorphological parameters such as drainage density. The results obtained illustrate differences in the geomorphology of tidal and terrestrial basins.