Automated stereo-photogrammetric DEM generation at high latitudes: Surface Extraction with TIN-based Search-space Minimization (SETSM) validation and demonstration over glaciated regions

Abstract

Digital elevation models (DEMs) are critical to a wide range of geoscience investigations. High-latitude and polar regions are particularly challenging for automated, stereo-photogrammetric DEM extraction due to the abundance of surfaces that are low-contrast and repetitively textured, such as snow and shadowed terrain, and have discontinuities such as in crevasse fields, glacier calving faces or iceberg edges. Sub-meter, stereo-mode satellite imagery of high geometric and radiometric quality is becoming increasingly accessible, offering the potential for dramatically increasing the spatial coverage and quality of high-latitude DEMs. Here we demonstrate and validate automated DEMs generated from the Surface Extraction with Triangulated Irregular Network-based Search-space Minimization (SETSM) algorithm designed for these challenging terrains using only the satellite rational polynomial coefficients (RPCs). Comparison between 2-m DEMs created from WorldView image pairs and low-altitude LiDAR point clouds in west Greenland give DEM biases of less than 5 m, which is the maximum systematic RPC error. Co-registration with the LiDAR data reduces the DEM RMS error to ~20 cm, which is comparable to the uncertainty of the LiDAR data. We demonstrate SETSMs automatic RPC refinement and bias reduction by successfully extracting a high-quality DEM from Pleiades stereo pair images with large RPC errors. Finally, we provide examples of SETSM DEMs that demonstrate their utility for a range of applications of interest to polar scientists.