Cost-Efficient Digital Elevation Model (DEM) Acquisition on Flume Tank Morphodynamic Observation

Authors

  • Octria Adi Prasojo Geology Study Program, FMIPA, Universitas Indonesia, Depok City, West Java, Indonesia
  • Adlirrahman Aufar Geology Study Program, FMIPA, Universitas Indonesia, Depok City, West Java, Indonesia
  • Reza Syahputra Geology Study Program, FMIPA, Universitas Indonesia, Depok City, West Java, Indonesia

DOI:

https://doi.org/10.25299/jgeet.2024.9.04.2311

Keywords:

flume tank, digital elevation model, laser distance meter, quantitative sedimentology

Abstract

Digital Elevation Model (DEM) is well known for providing solutions to the theoretical and application-related problems around geosciences. The use of DEM in flume tank experiment is getting more common nowadays. Flume tank itself is built to simulate the landscape and stratigraphy at laboratory scale. This physical experiment may have tremendous impacts on understanding the sedimentation process in a laboratory-scaled experiment. Normally, the morphodynamic behaviour of a laboratory-scaled deposit in the flume tank experiment would be observed through its digital elevation model. In this paper, a novel method in constructing cost-efficient digital elevation model was presented. By using this inexpensive tool to create a digital elevation model in a flume tank experiment setup, some challenges and benefits will follow this method. Some challenges including tool’s resolution and time consuming could be diminished in the near future by creating automated motor system to move the laser distance meter sequentially. Automated and integrated system from the LDM to the processing software could also reduce the time consumption. In the other hand, some benefits including financial benefit, reliability in a sedimentary structure scale, and also the practicality to be applied in any flume tank system available in Indonesia. Nevertheless, this method had been tested and some reliable results from the previous studies in Quantitative Sedimentology Laboratory, Universitas Indonesia was presented in this paper. Hopefully, some major improvements could be done to get more accurate and detail digital elevation model in the near future.

Downloads

Download data is not yet available.

References

Favalli, M., Fornaciai, A., Isola, I., Tarquini, S., Nannipieri, L., 2012, Multiview 3D reconstruction in geosciences, Comput. Geosci., 44, pp. 168-176.

Hopkinson, C., Hayashi, M., Peddle, D., 2009, Comparing alpine watershed attributes from LiDAR, photogrammetric, and contour-based digital elevation models Hydrol. Processes, 23 (3), pp. 451-463.

Hutchinson, M.F., Gallant, J.C., 2000, Digital elevation models and representation of terrain shape J.P. Wilson, J.C. Gallant (Eds.), Terrain Analysis: Principles and Applications, Wiley, New York, pp. 29-50.

Kleinhans, M.G., Dijk, W.M. Van, Lageweg, W.I. Van De, Hoyal, D.C.J.D., Markies, H., Maarseveen, M. Van, Roosendaal, C., Weesep, W. Van, Breemen, D. Van, Hoendervoogt, R., Cheshier, N., 2014. Earth-Science Reviews Quantifiable effectiveness of experimental scaling of river- and delta morphodynamics and stratigraphy. Earth Sci. Rev. 133, 43–61.

Le Coz, M., Delclaux, F., Genthon,1 P., Favreau G., 2009, Assessment of Digital Elevation Mo1del (DEM) aggregation methods for hydrological modeling: L1ake Chad basin, Africa, Comput. Geosci., 35 (8), pp. 1661-1670

Li, X., Fu, W., Shen, H., Huang, C., Zhang L., 2017, Monitoring snow cover variability (2000–2014) in the Hengduan Mountains based on cloud-removed MODIS products with an adaptive spatio-temporal weighted method, Journal of Hydrology 551.

Oky, P., Ardiansyah, D., Yokoyama, R., 2017, DEM generation method from contour lines based on the steepest slope segment chain and a monotone interpolation function, ISPRS Journal of Photogrammetry and Remote Sensing 134.

Paola C., Straub, K., Mohrig, D., Reinhardt, L., 2009, The “unreasonable effectiveness” of stratigraphic and geomorphic experiments, Earth-Science Reviews 97, 1-43.

Rishikeshan, C.A., Katiyar, S.K., Mahesh, V.N.V, 2014, Detailed evaluation of DEM interpolation methods in GIS using DGPS data, In: the 6th International Conference on Computational Intelligence and mmunication Networks (CICN), Bhopal, India, pp. 666–671.

Reynolds, O., 1887. On certain laws relating to the regime of rivers and estuaries and on the possibility of experiments on a small scale. Br. Assoc. Rep. Lond. 555–562.

Shen, H., Meng, X., Zhang L., 2016, An integrated framework for the spatio-temporal-spectral fusion of remote sensing images, IEEE Trans. Geosci. Remote Sens., 54 (12), pp. 7135-7148.

Tsai, F., Hwang, J.H., Chen, L.C., Lin, T.H., 2010, Post-disaster assessment of landslides in southern Taiwan after 2009 Typhoon Morakot using remote sensing and spatial analysis Natural Hazards Earth Syst. Sci., 10 (10), pp. 2179-2190.

Yang, L., Meng, X., Zhang, X., 2011, SRTM DEM and its application advances, Int. J. Remote Sens., 32 (14), pp. 3875-3896.

Downloads

Published

2024-12-06 — Updated on 2024-12-27

Versions