The tree-canopy effect in gravity forward modelling

SUMMARY High resolution and accurate digital terrain models (DTMs) are frequently used as input data sets to define the topographic masses in gravity forward modelling, for example, for terrain corrections in the context of regional gravity modelling. However, over vegetated areas such as forests and scrublands, the radar- and image-based digital elevation models (DEMs) may contain a tree bias, and therefore do not represent the bare-ground surface. The presence of vegetation-induced signals in DEMs, denoted here the tree-canopy effect, will introduce errors in the gravity forward modelling. In this study, the role of the tree-canopy effect in gravity forward modelling calculations is numerically investigated. First, spectral forward modelling techniques were applied to analyse a global tree-canopy bias model with a horizontal resolution of 1 km x 1 km and to quantify its effect on global gravity forward modelling results. We demonstrate that tree-canopy signals in the DEM produce a positive bias in the topographic gravitational field over vegetated areas, with values ranging from 0 to ∼2.7 mGal for gravity disturbances. Second, the role of the tree-canopy effect in high-frequency gravity forward modelling is studied using well-known residual terrain modelling (RTM) techniques. As DEM data sets, we used the 3″ SRTM (Shuttle Radar Topography Mission Digital 9 m Elevation Database) V4.1 (containing vegetation biases) and the 3″ MERIT-DEM (Multi-Error-Removed Improved-Terrain Digital elevation model) as a representation of the bare-ground elevations. Using Tasmania and the Amazon rainforest regions as test areas with significant tree-canopy signals we show that the tree-height effect on RTM calculations is of high-frequency nature, with rather small signals which reach in extreme cases amplitudes of ∼1–2 mGal occurring at forest boundaries. Third, using ground gravity observations, validation experiments were performed over the Australian Alps, Tasmania and the Canadian Rocky Mountains. All validation experiments show that the bare-ground elevation model MERIT-DEM performs better than SRTM V4.1 in terms of reduction of the discrepancies between modelled and observed gravity values. As a general conclusion, bare-ground DEM models should be preferred in any gravity forward modelling application to avoid or reduce the tree-canopy effect.