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Smoothing/filtering LiDAR digital surface models. Experiments with loess regression and discrete wavelets

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Abstract

This paper reports on the smoothing/filtering analysis of a digital surface model (DSM) derived from LiDAR altimetry for part of the River Coquet, Northumberland, UK using loess regression and the 2D discrete wavelet transform (DWT) implemented in the S-PLUS and R statistical packages. The chosen method of analysis employs a simple method to generate ‘noise’ which is then added to a smooth sample of LiDAR data; loess regression and wavelet methods are then used to smooth/filter this data and compare with the original ‘smooth’ sample in terms of RMSE. Various combinations of functions and parameters were chosen for both methods. Although wavelet analysis was effective in filtering the noise from the data, loess regression employing a quadratic parametric function produced the lowest RMSE and was the most effective.

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Notes

  1. known also as a laser altimeter (Dubayah et al. 2000)

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Acknowledgements

The LiDAR dataset was kindly supplied by the National Centre for Environmental Data and Surveillance, Environment Agency, Bath. We are grateful to Andrew Large and Malcolm Newson of the University of Newcastle, and Ian Fuller of Massey University New Zealand for collaboration on an earlier stage of this research. Thanks are also extended to Professor Michael Goodchild for permission for the use of his image in Fig. 2, the Cartography Unit at the Department of Geography University of Leicester for preparing Figs. 2 and 3, and the anonymous referees for some useful suggestions that improved the paper.

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Authors and Affiliations

  1. Department of Geography, University of Leicester, Leicester, LE17RH, UK

    Nicholas J. Tate & Claire H. Jarvis

  2. School of Computing, University of Glamorgan, Pontypridd, Wales, CF37 1DL, UK

    Chris Brunsdon

  3. National Centre for Geocomputation, National University of Ireland, Maynooth, County Kildare, Ireland

    Martin Charlton & A. Stewart Fotheringham

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  1. Nicholas J. Tate
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  2. Chris Brunsdon
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  3. Martin Charlton
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Correspondence to Nicholas J. Tate.

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Tate, N.J., Brunsdon, C., Charlton, M. et al. Smoothing/filtering LiDAR digital surface models. Experiments with loess regression and discrete wavelets. J Geograph Syst 7, 273–290 (2005). https://doi.org/10.1007/s10109-005-0007-4

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