Three-Dimensional Surface Area Estimation from UAV-Derived Digital Surface Models: A Multi-Scale Benchmark of Discretization Methods and a Sector-Adaptive Jenness Variant
Öz
Three-dimensional (3D) surface area is an important descriptor in geomorphology and terrain-roughness analysis, but it depends on elevation-model resolution and surface discretization. This study combines a real Unmanned Aerial Vehicle (UAV) application with a controlled multi-scale benchmark to examine how 3D surface area and the 3D/2D ratio vary with resolution, how discretization methods perform, and when a sector-adaptive Jenness variant contributes. A high-resolution Digital Surface Model (DSM) of an approximately 2 km² area in Ürgüp-Nevşehir, Türkiye, was produced using Structure-from-Motion (SfM) photogrammetry. With classical Jenness, the real-DSM 3D/2D ratio decreased from 1.243 at the native 0.06095 m grid to 1.025 at 50 m. The sector-adaptive variant produced lower estimates at higher computational cost. The synthetic benchmark used 24 surfaces, 9 ground-sampling distances and 4 methods, yielding 88 reference comparisons. Classical Jenness gave the lowest error in 77 comparisons. The proposed variant reduced error on composite surfaces but degraded on sharp morphologies; it is therefore specialized, not a general alternative.
Anahtar Kelimeler
UAV, Digital surface model, 3D surface area, Jenness algorithm, Terrain roughness
Kaynakça
- Bielski, C., López-Vázquez, C., Grohmann, C. H., Guth, P. L., Hawker, L., Gesch, D., Trevisani, S., Herrera-Cruz, V., Riazanoff, S., Corseaux, A., Reuter, H. I., & Strobl, P. (2024). Novel approach for ranking DEMs: Copernicus DEM improves one arc second open global topography. IEEE Transactions on Geoscience and Remote Sensing, 62, Article 4503922. https://doi.org/10.1109/TGRS.2024.3368015
- Colomina, I., & Molina, P. (2014). Unmanned aerial systems for photogrammetry and remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 92, 79–97. https://doi.org/10.1016/j.isprsjprs.2014.02.013
- Dai, A., Gupta, S., & Gao, G. (2024). Neural elevation models for terrain mapping and path planning. arXiv. https://doi.org/10.48550/arXiv.2405.15227
- Eltner, A., Kaiser, A., Castillo, C., Rock, G., Neugirg, F., & Abellán, A. (2016). Image-based surface reconstruction in geomorphometry: Merits, limits and developments. Earth Surface Dynamics, 4(2), 359–389. https://doi.org/10.5194/esurf-4-359-2016
- Fan, L. (2022, August 15–18). Comparisons of five indices for estimating local terrain surface roughness using LiDAR point clouds [Conference presentation]. 29th International Conference on Geoinformatics, Beijing, China.
- Fan, L., & Zhao, Y. (2024). Comparing roughness maps generated by five typical roughness descriptors for LiDAR-derived digital elevation models. AIMS Geosciences, 10(2), 228–241. https://doi.org/10.3934/geosci.2024013
- Feng, H., Xu, X., & De Floriani, L. (2024, June 19–21). ImplicitTerrain: A continuous surface model for terrain data analysis [Conference presentation]. IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshop, Seattle WA, USA.
- Fonstad, M. A., Dietrich, J. T., Courville, B. C., Jensen, J. L., & Carbonneau, P. E. (2013). Topographic structure from motion: A new development in photogrammetric measurement. Earth Surface Processes and Landforms, 38(4), 421–430. https://doi.org/10.1002/esp.3366
- Grohmann, C. H., Smith, M. J., & Riccomini, C. (2011). Multiscale analysis of topographic surface roughness in the Midland Valley, Scotland. IEEE Transactions on Geoscience and Remote Sensing, 49(4), 1200–1213. https://doi.org/10.1109/TGRS.2010.2053546
- Hengl, T., & Reuter, H. I. (Eds.). (2009). Geomorphometry: Concepts, software, applications (Developments in Soil Science, Vol. 33). Elsevier.
