Producing digital elevation models with uncertainty estimates using a multi-scale Kalman filter

John C. Gallant 1 and Michael F. Hutchinson 2
1 CSIRO Land and Water
Clunies Ross St, Acton ACT 2602, Australia
Tel.: +612 6246 5734; Fax: +612 6246 5965
John.Gallant@csiro.au
2 Centre for Resource and Environmental Studies
Australian National University, Acton ACT 2602, Australia
Tel: +612 6125 4783
Michael.Hutchinson@anu.edu.au

Abstract
The Shuttle Radar Topographic Mission (SRTM) digital elevation data provides near-global coverage at about 90 m resolution and in much of the world is now the best available topographic data. Its application for quantitative  analysis is limited by random noise and systematic offsets due to vegetation. This paper describes a multiscale Kalman smoothing algorithm for removing vegetation effects and  smoothing random variations. The algorithm assimilates dense SRTM data, a vegetation mask and sparser but more accurate ICESat satellite laser altimetry data  to produce improved estimates of ground height. The method is found to be effective provided the vegetation mask accurately reflects the location of vegetation-induced offsets in the SRTM data. The method also produces estimates of uncertainty in the elevations, facilitating the use of methods for propagating error through derived terrain attributes.

Keywords: multiscale smoothing, digital elevation model, SRTM, uncertainty estimates

In: Caetano, M. and Painho, M. (eds). Proceedings of the 7th International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences, 5 – 7 July 2006, Lisboa, Instituto Geográfico Português

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