Extracting Ground-Level DEM From SRTM DEM in Forest Environments Based on Mathematical Morphology

Abstract

The demands for bare-earth or ground-level digital elevation models (DEMs) are significant for various environmental and ecological studies. As one of the most widely used global DEMs, the Shuttle Radar Topography Mission (SRTM) DEM is available to the public. However, the SRTM DEM is not a bare-earth DEM because it includes man-made structures and vegetation. The objective of this paper is to develop a mathematical morphology-based approach to generate ground-level DEM (GLD) from the SRTM DEM in forest environments. The proposed algorithm is implemented as follows. First, an initial GLD is derived from the SRTM DEM by using morphological operations with a single structuring element. Second, homogeneous forest patches are generated by applying watershed segmentation to pseudocanopy height (PCH) that is obtained by subtracting the initial GLD from the SRTM DEM. Based on segmented patches, a refined GLD is derived by using morphological operations with adaptive structuring elements of different sizes. Third, PCH is updated with the refined GLD and then resegmented into forest patches, from which a final GLD is obtained by subtracting the updated mean PCH from the SRTM DEM. Finally, bare-earth DEMs from the National Elevation Data Set (NED) are used to validate the extracted GLD. The results show that the root mean square error of the final GLD compared with the NED is significantly reduced for two study sites. This type of GLD would be applicable to large-scale environmental studies where accurate topographical information is not available.