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

Cobby, David M.; Mason, David C.; Davenport, I. J.

doi:10.1016/s0924-2716(01)00039-9

Cited by 237 publications

(185 citation statements)

References 30 publications

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“…This effect can be largely eliminated by thinning height samples in overlap regions prior to segmentation depending on their distance from their swath centre, to give the same sample density in the overlaps as at the swath centres (Cobby et al 2001). This effectively removes overlap regions.…”

Section: Resultsmentioning

confidence: 99%

“…A linear relationship was found to hold between manually surveyed crop heights and LiDAR-measured height standard deviations (s D ). Cobby et al (2001) combined these data with LiDAR and survey data from a further 37 fields from the second test area near Shrewsbury, and found that the best fit to the combined dataset had a logarithmic form:…”

Section: Height Measurement Using Lidarmentioning

confidence: 99%

“…A range image segmentation system for LiDAR data has been developed aimed at a number of environmental applications (Mason et al 1999, Cobby et al 2001. Its objectives are to convert the input height image into output raster images of surface topography and vegetation height at each point, and to attach a region label to each pixel.…”

Section: Image Segmentationmentioning

confidence: 99%

“…Details of the segmentation algorithm are given in Cobby et al (2001), where it is used to provide surface topography and vegetation height maps for calibration and validation of a 2D river flood flow model. A summary of the main features is given below.…”

Section: Image Segmentationmentioning

confidence: 99%

“…The constant 0.1 in equation (2) was estimated by fitting predicted ground heights to their measured equivalents at a set of ground control points (Cobby et al 2001). In regions with large average vegetation height, equation (1) overestimates vegetation height and a different approach is used.…”

Section: Topographic and Vegetation Height Extraction For Each Regionmentioning

confidence: 99%

See 4 more Smart Citations

Measurement of habitat predictor variables for organism-habitat models using remote sensing and image segmentation

Mason

Anderson

Bradbury

et al. 2003

International Journal of Remote Sensing

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Abstract. Robust predictive models of the effects of habitat change on species abundance over large geographical areas are a fundamental gap in our understanding of population distributions, yet are urgently required by conservation practitioners. Predictive models based on underpinning relationships between environmental predictors and the individual organism are likely to require measurement of spatially fine-grained predictor variables. Further, models must show spatial generality if they are to be used to predict the consequences of habitat change over large geographical areas.Remote sensing techniques using airborne scanning laser altimetry (LiDAR) and high resolution multi-spectral imagery allow spatially fine-grained predictor variables to be measured over large geographical areas and thus facilitate testing of the spatial generality of organism-habitat models. These techniques are considered using the skylark as an example species. A range image segmentation system for LiDAR data is described which allows measurement of skylark habitat predictor variables such as within-field vegetation height, boundary height and shape for individual fields within the LiDAR image. Additional variables such as field vegetation type and fractional vegetation ground cover may be obtained from coregistered multi-spectral data. These techniques could have wide application in testing the generality of relationships between populations and habitats, and in ecological monitoring of change in habitat structures and the associated effects on wildlife, over large geographical areas.

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Section: Resultsmentioning

confidence: 99%

Section: Height Measurement Using Lidarmentioning

confidence: 99%

Section: Image Segmentationmentioning

confidence: 99%

Section: Image Segmentationmentioning

confidence: 99%

Section: Topographic and Vegetation Height Extraction For Each Regionmentioning

confidence: 99%

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Measurement of habitat predictor variables for organism-habitat models using remote sensing and image segmentation

Mason

Anderson

Bradbury

et al. 2003

International Journal of Remote Sensing

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Flood Inundation Modelling Using LiDAR and SAR Data

Bates

Horritt

Cobby

et al. 2004

Spatial Modelling of the Terrestrial Environment

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Flood Routing and Inundation Prediction

Bates

2005

Encyclopedia of Hydrological Sciences

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This contribution considers the methods available to hydrologists to simulate flow routing and inundation in natural channels. Whilst the fluid dynamics of free surface flow in rivers and over floodplains may be complex, the methods available to treat such problems range widely in complexity from simple empirical models to full solutions of three‐dimensional Navier–Stokes equations with sophisticated representations of flow turbulence. These methods are reviewed in detail, followed by a consideration of their requirements for topography, friction, initial condition, boundary condition, and validation data. In particular, the contribution discusses how these needs are being met increasingly using remote sensing techniques. Lastly, the contribution considers techniques to evaluate uncertainties in flow modeling.

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

Cited by 237 publications

References 30 publications

Measurement of habitat predictor variables for organism-habitat models using remote sensing and image segmentation

Measurement of habitat predictor variables for organism-habitat models using remote sensing and image segmentation

Flood Inundation Modelling Using LiDAR and SAR Data

Flood Routing and Inundation Prediction

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