Digital Terrain Models Generated with Low-Cost UAV Photogrammetry: Methodology and Accuracy

• Published in: ISPRS International Journal of Geo-Information
• Main Authors: Sergio Iván Jiménez-Jiménez, Waldo Ojeda-Bustamante, Mariana de Jesús Marcial-Pablo, Juan Enciso
• Format: Article
• Language: English
• Published: MDPI AG 2021-04-01
• Subjects: SfM data mapping; ground control point; flight plan; ground filtering; DTM; autonomous systems
• Online Access: https://www.mdpi.com/2220-9964/10/5/285

Digital terrain model (DTM) generation is essential to recreating terrain morphology once the external elements are removed. Traditional survey methods are still used to collect accurate geographic data on the land surface. Given the emergence of unmanned aerial vehicles (UAVs) equipped with low-cost digital cameras and better photogrammetric methods for digital mapping, efficient approaches are necessary to allow rapid land surveys with high accuracy. This paper provides a review, complemented with the authors’ experience, regarding the UAV photogrammetric process and field survey parameters for DTM generation using popular commercial photogrammetric software to process images obtained with fixed-wing or multicopter UAVs. We analyzed the quality and accuracy of the DTMs based on four categories: (i) the UAV system (UAV platforms and camera); (ii) flight planning and image acquisition (flight altitude, image overlap, UAV speed, orientation of the flight line, camera configuration, and georeferencing); (iii) photogrammetric DTM generation (software, image alignment, dense point cloud generation, and ground filtering); (iv) geomorphology and land use/cover. For flat terrain, UAV photogrammetry provided a horizontal root mean square error (RMSE) between 1 to 3 × the ground sample distance (GSD) and a vertical RMSE between 1 to 4.5 × GSD, and, for complex topography, a horizontal RMSE between 1 to 7 × GSD and a vertical RMSE between 1.5 to 5 × GSD. Finally, we stress that UAV photogrammetry can provide DTMs with high accuracy when the photogrammetric process variables are optimized.