On the development of a digital elevation model over South Africa using ground and satellite data
Bibliographic record
Abstract
A digital elevation model (DEM) represents the bare land surface of the Earth. DEMs are used in a wide range of applications, including geological studies, geomorphology, water resources and hydrology, evaluation of natural hazards, and vegetation surveys. In recent years, DEMs have increasingly been used in geographic information systems (GIS), mainly due to the availability of free satellite-based DEMs, some with global coverage. The satellite-based DEMs over South Africa provide topographic surface representation but are associated with errors, and in recent decades there have been significant efforts to improve accuracy. In South Africa, the ground levelling (trigonometrical beacon) data is more capable of representing the terrain heights accurately. However, the data points are farther apart, which makes it difficult for accurate continuous terrain representation. In this research, contributions are made towards the development of an accurate digital elevation model from ground and satellite data over South Africa. This is achieved by preparing satellite-based DEMs (AW3D30, SRTM, ASTER, TanDEM-X, and MERIT), assessing the quality of the satellite-based DEMs, selecting candidate DEMs for fusion, modelling candidate DEM errors, and fusing DEMs. The aerial-based DEM from LiDAR is also applied in the assessment of the quality of satellite-based DEMs, although this was only possible in selected areas due to a lack of LiDAR data covering the whole of South Africa. Following removal of outliers from each DEM, a different number of ground levelling data is used in the assessment of the DEMs (26364, 25728, 23773, 25967 and 24485) ground levelling points for AW3D30, SRTM, ASTER, TanDEM-X and MERIT, respectively. The vertical quality assessment results indicate that the standard deviations of the differences between ground levelling and DEMs heights are ±5.09, ±7.03, ±9.20, ±4.99 and ±8.36 m for AW3D30, SRTM, ASTER, TanDEM-X and MERIT, respectively. In general, the vertical accuracies of the satellite-based DEMs are relatively lower in higher areas than in low areas. The results of height differences between satellite-based and LiDAR DEMs heights in different geomorphological ranges indicate that the AW3D30 and TanDEM-X are better candidate DEMs for generating a new DEM over South Africa. Applying a combination of linear regression, multiple regression, and adaptive terrain-dependent methods to these DEMs, their vertical accuracies improved. The standard deviations of the differences between ground levelling and the improved DEMs at 8,657 points over South Africa decreased from ±5.745 to ±4.995 m for AW3D30 and ±5.073 to ±4.582 m for TanDEM-X. A fused DEM was developed from improved AW3D30 and TanDEM-X DEMs using a combination of different fusion methods (linear combination, weighted averaging, and simple averaging) over South Africa. The fused DEM was assessed using 8,657 ground levelling points over South Africa. The standard deviation of the height differences between ground levelling and the fused DEM is ±4.290 m, indicating the superiority of the fused DEM over all the satellite-based DEMs used in this study. The fused DEM can be applied in areas with a slope less than 20° where an accuracy of less than 4.3 m is achievable. In the steepest areas, it can still achieve better vertical accuracies compared to other satellite-based DEMs tested.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".