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

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.