UAS Photogrammetry for Precise Digital Elevation Models of Complex Topography: A Strategy Guide

Research output: Contribution to journalResearch articleContributedpeer-review



Abstract. The presented research investigates different strategies to acquire high-precision digital elevation models (DEMs) of complex and inaccessible terrain using Structure-from-Motion and Multi-View Stereo applied to data of an unoccupied aerial system (UAS) equipped with real-time-kinematic (RTK)-GNSS. The survey scenarios are taken from real-life situations and thus, in comparison to many previous studies, provide information on how to operate under challenging conditions in difficult terrain. Among others, the study examines the influence of different flight configurations (parallel axes and cross-grid), flight altitudes (relative to ellipsoid or terrain) and associated variations in ground sampling distance, image orientations (nadir and oblique), advanced camera self-calibration techniques and georeferencing strategies in image block processing (direct and integrated) on the overall accuracy of the resulting DEMs. Random and systematic errors, including spatial patterns such as doming and bowling, are quantified using check points and differences between DEM calculations and independently acquired surface data from laser scans. This comprehensive analysis contributes valuable insights for UAS-based analysis of complex terrain with improved accuracy in DEM generation and subsequent applications like change detection.


Original languageEnglish
Pages (from-to)57–64
Number of pages8
JournalISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Publication statusPublished - 10 Jun 2024


Title2024 ISPRS Technical Commission II (TCII) Symposium
SubtitleThe role of photogrammetry for a sustainable world
Duration11 - 14 June 2024
Degree of recognitionInternational event
LocationFlamingo Las Vegas Hotel
CityLas Vegas
CountryUnited States of America

External IDs

ORCID /0000-0003-2169-8762/work/161890858
ORCID /0009-0009-6596-8577/work/162842312
Mendeley c88ade49-787b-3f0e-a51f-c419cfba8253