Volume 7, Issue 1, June 2019, Page: 5-12
Spatial Enhancement of DEM Using Interpolation Methods: A Case Study of Kuwait’s Coastal Zones
Nawaf Al-Mutairi, Environmental Sciences Department, Faculty of Science, Damietta University, New Damietta, Egypt
Mohammad Alsahli, Department of Geography, Social Sciences College, Kuwait University, Shuwaikh, Kuwait
Mahmoud Ibrahim, Environmental Sciences Department, Faculty of Science, Damietta University, New Damietta, Egypt
Rasha Abou Samra, Environmental Sciences Department, Faculty of Science, Damietta University, New Damietta, Egypt
Maie El-Gammal, Environmental Sciences Department, Faculty of Science, Damietta University, New Damietta, Egypt
Received: Aug. 7, 2019;       Accepted: Sep. 4, 2019;       Published: Sep. 19, 2019
DOI: 10.11648/j.ajrs.20190701.12      View  60      Downloads  14
Abstract
Digital elevation models (DEMs) are essential tools utilized in several branches of science, including environmental, geological, and geospatial studies. Unfortunately, high-accuracy DEM data such as LiDAR are not publicly available, and the coverage is limited. Therefore, the use of alternative methods, such as interpolation techniques (i.e., kriging, inverse distance weighting, radial basis functions), is greatly advantageous for the production of enhanced DEMs. The results of this study show that interpolated DEMs had minimal errors (RMSE = 1.44) with an increase of about 28% from the original DEM. However, the spatial resolution of interpolated DEM data was enhanced significantly by 83%. The deterministic interpolation methods provided more accurate estimations for producing DEMs in the coastal zones of Kuwait than geostatistical interpolation methods. The reference elevation data were collected using GPS and accurate topographic maps (1:25,000), and elevation points from the interpolated DEM were matched significantly (R2 = 0.88; R2 = 94, respectively). Given the lack of accurate DEM data, the interpolated DEM produced in this study are held in high regard and highly recommended for use in the coastal zone of Kuwait.
Keywords
Digital Elevation Model, Sea Level Rise, Coastal Zone, Interpolation, GIS
To cite this article
Nawaf Al-Mutairi, Mohammad Alsahli, Mahmoud Ibrahim, Rasha Abou Samra, Maie El-Gammal, Spatial Enhancement of DEM Using Interpolation Methods: A Case Study of Kuwait’s Coastal Zones, American Journal of Remote Sensing. Vol. 7, No. 1, 2019, pp. 5-12. doi: 10.11648/j.ajrs.20190701.12
Copyright
Copyright © 2019 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Reference
[1]
Li, X., Shen, H., Feng, R., Li, J., & Zhang, L. DEM generation from contours and a low-resolution DEM. ISPRS Journal of Photogrammetry and Remote Sensing, 2017, 134, 135–147.
[2]
Pohjola, J., Turunen, J., & Lipping, T. Creating high-resolution digital elevation model using thin plate spline interpolation and Monte Carlo simulation. Working Report 2009-56. 2009, Pori, Finland: Tampere University of Technology.
[3]
Yue, T.-X., Song, D.-J., Du, Z.-P., & Wang, W. High-accuracy surface modelling and its application to DEM generation. International Journal of Remote Sensing, 2010, 31 (8), 2205–2226.
[4]
Nelson, A., Reuter, H. I., & Gessler, P. DEM production methods and sources. Developments in Soil Science, 2009, 33, 65–85.
[5]
Kobrick, M. On the Toes of Giants- How SRTM was Born. Photogrammetric Engineering and Remote Sensing, 2006, 72 (3), 206–210.
[6]
Carter, M., Shepherd, J. M., Burian, S., & Jeyachandran, I. Integration of lidar data into a coupled mesoscale–land surface model: A theoretical assessment of sensitivity of urban–coastal mesoscale circulations to urban canopy parameters. Journal of Atmospheric and Oceanic Technology, 2012, 29 (3): 328–346.
[7]
Elkhrachy, I. Vertical accuracy assessment for SRTM and ASTER Digital Elevation Models: A case study of Najran city, Saudi Arabia. Ain Shams Engineering Journal. 2017. (article in press).
[8]
Chaplot, V., Darboux, F., Bourennane, H., Leguédois, S., Silvera, N., & Phachomphon, K. Accuracy of interpolation techniques for the derivation of digital elevation models in relation to landform types and data density. Geomorphology, 2006, 77 (1-2), 126–141.
[9]
Liu, L., Lin, Y., Liu, J., Wang, L., Wang, D., Shui, T., et al. Analysis of local-scale urban heat island characteristics using an integrated method of mobile measurement and GIS-based spatial interpolation. Building and Environment, 2017, 117, 191–207.
[10]
ESRI. How IDW works. 2017a. http://desktop.arcgis.com/en/arcmap/10.3/tools/3d-analyst-toolbox/how-idw-works.htm. Accessed 12 May 2017.
[11]
Alsahli, M. M. M., & Alhasem, A. M. Vulnerability of Kuwait coast to sea level rise. Geografisk Tidsskrift-Danish Journal of Geography, 2016, 116 (1), 56–70.
[12]
Nas, B., Karabork, H., Ekercin, S., & Berktay, A.. Assessing water quality in the Beysehir Lake (Turkey) by the application of GIS, geostatistics and remote sensing. In Proceedings of the 12th World Lake Conference, 2007, Taal (pp. 639–646). Jaipur, India.
[13]
Al-Sarawi, M. A. Surface geomorphology of Kuwait. GeoJournal, 1995, 35 (4), 493–503.
[14]
Varga, M., & Bašić, T. Accuracy validation and comparison of global digital elevation models over Croatia. International journal of remote sensing, 2015, 36 (1), 170–189.
[15]
Al-Sulaimi, J., & Mukhopadhyay, A. An overview of the surface and near-surface geology, geomorphology and natural resources of Kuwait. Earth-Science Reviews, 2000, 50 (3), 227–267.
[16]
USGS. Routine ASTER global digital elevation model. 2016. https://lpdaac.usgs.gov/dataset_discovery/aster/aster_products_table/astgtm. Accessed 21 December 2017.
[17]
Tobler, W. R.. A computer movie simulating urban growth in the Detroit region. Economic Geography, 1970, 46 (sup1), 234–240.
[18]
Bhunia, G. S., Shit, P. K., & Maiti, R. Comparison of GIS-based interpolation methods for spatial distribution of soil organic carbon (SOC). Journal of the Saudi Society of Agricultural Sciences, 2016, 17 (2), 114–126 https://doi.org/10.1016/j.jssas.2016.02.001
[19]
ESRI. Deterministic methods for spatial interpolation. 2017b. http://pro.arcgis.com/en/pro-app/help/analysis/geostatistical-analyst/deterministic-methods-for-spatial-interpolation.htm. Accessed 29 November 2017.
[20]
Aguilar, F. J., Agüera, F., Aguilar, M. A., & Carvajal, F. Effects of terrain morphology, sampling density, and interpolation methods on grid DEM accuracy. Photogrammetric Engineering & Remote Sensing, 2005, 71 (7), 805–816.
[21]
Li, Y., Shi, Z., Wu, C.-F., Li, H. Y., & Li, F. Improved prediction and reduction of sampling density for soil salinity by different geostatistical methods. Agricultural Sciences in China, 2007, 6 (7), 832–841.
[22]
Isaaks, E. H., & Srivastava, R. M. Applied geostatistics. (1989). New York: Oxford University Press.
[23]
Cuartero, A., Polo, M. E., Rodriguez, P. G., Felicisimo, A. M., & Ruiz-Cuetos, J. C. The use of spherical statistics to analyze digital elevation models: An example from LiDAR and ASTER GDEM. IEEE Geoscience and Remote Sensing Letters, 2014, 11 (7), 1200–1204.
[24]
Becek, K. Assessing global digital elevation models using the runway method: The advanced spaceborne thermal emission and reflection radiometer versus the shuttle radar topography mission case. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52 (8), 4823–4831.
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