Share:


A comparative analysis of different DEM interpolation methods in GIS: case study of Rahovec, Kosovo

    Besim Ajvazi Affiliation
    ; Kornél Czimber Affiliation

Abstract

Geographic Information System (GIS) uses geospatial databases as a model of the real world. Since we are speaking of the real world this entails that in many cases the information about the Earth’s surface is highly important. Therefore, the generation of a surface model is significant. Basically, the quality of the Digital Elevation Model (DEM) depends on the source data or techniques used to obtain them. However, different spatial interpolation methods used for the same data may provide different results. This paper compares the accuracy of different spatial interpolation methods such as IDW, Kriging, Natural Neighbor and Spline. Since interpolation is essential in DEM generation, then is important to do a comparative analysis of such methods to find out which one provides more accurate results. The DEM data set used is from an aero photogrammetric surveying. According to this data set, three scenarios are performed for each of the methods. Selected random control points are derived from the base data set. The first example includes 10% of randomly selected control points, the second example includes 20%, and the third example includes 30%. The Mean Absolute Error (MAE) and the Root Mean Square Error (RMSE) are calculated. We find out that results do not have much difference; however, the most accurate results are derived from the Spline and Kriging interpolation methods.

Keyword : GIS, DEM, Spatial Interpolation, IDW, Kriging, Natural Neighbor, Spline

How to Cite
Ajvazi, B., & Czimber, K. (2019). A comparative analysis of different DEM interpolation methods in GIS: case study of Rahovec, Kosovo. Geodesy and Cartography, 45(1), 43-48. https://doi.org/10.3846/gac.2019.7921
Published in Issue
Apr 17, 2019
Abstract Views
2177
PDF Downloads
1687
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

References

Arun, P. V. (2013). A comparative analysis of different DEM interpolation methods. The Egyptian Journal of Remote Sensing and Space Sciences, 16, 133-139. https://doi.org/10.1016/j.ejrs.2013.09.001

Burrough, P. A., & McDonnell, R. A. (1998). Principles of geographic information systems (pp. 333-335). New York: Oxford University Press.

Childs, C. (2004). Interpolating surfaces in ArcGIS spatial analyst. In ArcUser, ESRI (pp. 32-35). California.

Garnero, G., & Godone, D. (2013). Comparisons between different interpolation techniques. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XL-5/W3, 2013, 139-144.

Johnston, K., VerHoef, J., Krivoruchko, K., & Neil, L. (2001). Using ArcGIS™ Geostatistical Analyst. ESRI™, US.

Kravchenko, A. N., & Bullock, D. G. (1999). A comparative study of interpolation methods for mapping soil properties. Agronomy Journal, 91, 393-400. https://doi.org/10.2134/agronj1999.00021962009100030007x

Ledoux, H., & Gold, C. (2005). An efficient natural neighbour interpolation algorithm for geoscientific modelling. In P. F. Fisher (Ed.), Developments in spatial data handling (pp. 97-108). Springer, Heidelberg. https://doi.org/10.1007/3-540-26772-7_8

Ledoux, H., & Gold, C. M. (2004). An efficient natural neighbour interpolation algorithm for geoscientific modelling. In P. Fisher (Ed.), Developments in spatial data handling – 11th International Symposium on Spatial Data Handling (pp. 97-108). Springer.

Mitas, L., & Mitasova, H. (1999). Spatial interpolation. Geographical information systems: principles, techniques, management and application (pp. 481-492). John Wiley & Sons.

Robinson, T. P., & Metternicht, G. (2005). Testing the performance of spatial interpolation techniques for mapping soil properties. Computers and Electronics in Agriculture, 50, 97-108. https://doi.org/10.1016/j.compag.2005.07.003

Samsuzana, A. A. (2008). Development of digital elevation models (DEMs) for agricultural applications. Graduate Theses and Dissertations. Paper 11017.

Tan, Q., & Xu, X. (2014). Comparative analysis of spatial interpolation methods: an experimental study. Sensors & Transducers, 165(2), 155-163.

Webster, R., & Oliver, M. A. (2001). Geostatistics for environmental scientists. John Wiley and Sons, Brisbane, Australia.