A method for vertical adjustment of digital aerial photogrammetry data by using a high-quality digital terrain model
| Autor: | Petteri Packalen, Ranjith Gopalakrishnan, Daniela Ali-Sisto, Pekka Savolainen, Mikko Kukkonen |
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| Rok vydání: | 2020 |
| Předmět: |
Global and Planetary Change
010504 meteorology & atmospheric sciences Mean squared error Laser scanning 3D reconstruction 0211 other engineering and technologies Point cloud 02 engineering and technology Management Monitoring Policy and Law 01 natural sciences Standard deviation Photogrammetry Vertical direction Computers in Earth Sciences Digital elevation model 021101 geological & geomatics engineering 0105 earth and related environmental sciences Earth-Surface Processes Mathematics Remote sensing |
| Zdroj: | International Journal of Applied Earth Observation and Geoinformation. 84:101954 |
| ISSN: | 1569-8432 |
| Popis: | The accuracy of vertical position information can be degraded by various sources of error in digital aerial photogrammetry (DAP) based point clouds. To address this issue, we propose a relatively straightforward method for automated correction of such point clouds. This method can be used in conjunction with any 3D reconstruction method in which a point cloud is generated from a pair of aerial images. The crux of the method involves separately co-registering each DAP point cloud (formed by the overlap of two or more images) to a common airborne laser scanning (ALS) based digital terrain model. The proposed method has the following essential steps: (1) Ground surface patches are identified in the normalized DAP point clouds by selecting areas in which standard deviation of vertical height is low, (2) height differences between the DAP and ALS point clouds are calculated at these patches, and (3) a correction surface is interpolated from these height differences and is then used to rectify the entire DAP point cloud. The performance of the proposed method is verified using plot data (n = 250) from a forested study area in Eastern Finland. We observed that DAP data from the area corrected using our proposed method resulted in significant increases in prediction accuracy of key forest variables. Specifically, the root mean squared error (RMSE) values for dominant height predictions decreased by up to 23.2%, while the associated model R2 values increased by 16.9%. As for stem volume, RMSEs dropped by 20.6%, while the model R2 improved by 14.6%, respectively. Hence, prediction accuracies were almost as good as with ALS data. The results suggest that vertically misaligned DAP data, if rectified using an algorithm such as the one presented here, could deliver near ALS data quality at a fraction of the cost. |
| Databáze: | OpenAIRE |
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