On the development of a digital elevation model over South Africa using ground and satellite data
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».