Indexed By
Article Tools
Email this article (Login required)
Email the author (Login required)
About The Authors

Atriyon Julzarika
Indonesian National Institute of Aeronautics and Space (LAPAN)

Remote Sensing Applications Center, LAPAN

Trias Aditya
Indonesian National Institute of Aeronautics and Space (LAPAN)

S Subaryono
Indonesian National Institute of Aeronautics and Space (LAPAN)

H Harintaka
Indonesian National Institute of Aeronautics and Space (LAPAN)

Ratna Sari Dewi
Indonesian Geospatial Information Agency (BIG), Jalan Raya Jakarta - Bogor km 46, Cibinong 16911

Luki Subehi
Indonesian Institute of Science (LIPI), Jl. Jend. Gatot Subroto 10, Jakarta 12710


Information for Author
Visitor Statistic

Integration of the latest Digital Terrain Model (DTM) with Synthetic Aperture Radar (SAR) Bathymetry

Atriyon Julzarika, Trias Aditya, S Subaryono, H Harintaka, Ratna Sari Dewi, Luki Subehi
  J. Degrade. Min. Land Manage. , pp. 2759-2768  
Viewed : 50 times


Topography and bathymetry integration is one of the essential things in providing height data. So far, the topography and bathymetry problems are the lack of height data availability, not up to date, and low vertical accuracy. The latest DTM is one of the topography data with up to date elevation with a spatial resolution of 5 m. Bathymetry extracted from SAR images. It is an alternative depth data for ocean bathymetry and inland water bathymetry. Topography and bathymetry integration is required to obtain comprehensive height data. This study aimed to integrate the latest DTM with SAR bathymetry. The method used in this integration was DEM integration. The method combined the latest DTM data with SAR bathymetry based on the correlation of the two data's standard deviation. The integration of the latest DTM with SAR bathymetry needs to consider differences in height reference fields. Two integration studies were conducted in this research-the latest DTM integration with ocean bathymetry for Rote Island. Then the integration of the latest DTM with inland water bathymetry in Lake Singkarak. The result of the integration is necessary to check the surface by generating longitudinal and cross-section profiles. Integrating the latest DTM and SAR bathymetry can be used for various mapping surveys on lands and waters.


DEM integration; DTM; Lake Singkarak; Rote island; SAR bathymetry

Full Text:



Aji, A., Wang, F., Vo, H., Lee, R., Liu, Q., Zhang, X., Saltz, J. and Hadoop., 2013. GIS: A high performance spatial data warehousing system over mapreduce. Proceedings of the VLDB Endowmen 6: 1009–1020.

Allouis, T., Bailly, J.S. and Feurer, D. 2015. Assessing water surface effects on LiDAR bathymetry measurements in very shallow rivers: theoretical study. UMR Territoires Environnement Télédétection et Information Spatiale (TETIS).

Alpers, W. and Hennings, I. 1984. A theory of the imaging mechanism of underwater bottom topography by real and synthetic aperture radar. Journal of Geophysical Research: Oceans 89: 10529 - 10546, doi: 10.1029/JC089iC06p10529.

Alpers, W. and Rufenach, C. 1979. The effect of orbital motions on synthetic aperture radar imagery of ocean waves. IEEE Transsactions on Antennas and Propagation 27 (5): 685 - 690, doi: 10.1109/TAP.1979.11421 63.

Arai, Y. 2019. Pre-treatment for preventing degradation of measurement accuracy from speckle noise in speckle interferometry. Measurement 136: 36 – 41, doi: 10.1016/j.measurement.2018.10.046.

ASPRS, 2014. ASPRS Accuracy Standard for Digital Geospatial Data. ASPRS. USA.

Bakon, M., Perissin, D., Lazecky, M. and Papco, J. 2014. Infrastructure Non-linear Deformation Monitoring Via Satellite Radar Interferometry. Procedia Technology 16: 294 - 300, doi: 10.1016/j.protcy.2014.10.095

Baptista, P., Cunha, T.R., Bernardes, C., Gama, C., Ferreira, O. and Dias, A. 2011. A precise and efficient methodology to analyze the shoreline displacement rate. Journal of Coastal Research 27(2): 223 - 232.

BIG. 2019. DEMNAS. Retrieved from

Bigdeli, B., Amini Amirkolaee, H. and Pahlavani, P. 2018. DTM extraction under forest canopy using LiDAR data and a modified invasive weed optimization algorithm. Remote Sensing of Environment 216 (June): 289 – 300, doi: 10.1016/j.rse.2018.06.045.

Bordogna, G., Kliment, T., Frigerio, L., Brivio, P., Crema, A., Stroppiana, D., Boschetti, M. and Sterlacchini, S.A. 2016. Spatial data infrastructure integrating multisource heterogeneous geospatial data and time series: A study case in agriculture. ISPRS International Journal of Geo-Information 5(5): 73, doi: 10.3390/ijgi5050073.

Brusch, S., Held, P., Lehner, S., Rosenthal, W. and Pleskachevsky, A. 2011. Underwater bottom topography in coastal areas from TerraSAR-X data. International Journal of Remote Sensing 32(16): 4527 - 4543. doi: 10.1080/01431 161.2010.48906 3.

Champion, N. and Boldo, D. 2006. A robust algorithm for estimating digital terrain models from digital surface models in dense urban areas. Proceedings ISPRS Commission 3 Symposium, Photogrammetric Computer Vision.

Chang, L., Ku, O. and Hanssen, R. F. 2019. Identification of deformation pattern changes caused by enhanced oil recovery (EOR) using InSAR. International Journal of Remote Sensing 40(4): 1495 – 1505, doi: 10.1080/01431161.2018.1526426.

Costantini, M. 1998. A novel phase unwrapping method based on network programming. IEEE Transactions on Geoscience and Remote Sensing 36(3): 813 – 821, doi: 10.1109/36.673674.

Cuevas-González, M., Crosetto, M., Monserrat, O. and Crippa, B. 2018. Sentinel-1A/B imagery for terrain deformation monitoring: a strategy for Atmospheric Phase Screening (APS) estimation. Procedia Computer Science 138: 388 – 392, doi: 10.1016/j.procs.2018.10.055.

Dias, P., Catalao, J. and Marques, F.O. 2018. Sentinel-1 InSAR data applied to surface deformation in Macaronesia (Canaries and Cape Verde). Procedia Computer Science 138: 382 - 387, doi: 10.1016/j.procs.2018.10.054.

ESA, 2013. Sentinel-1 Handbook. European Space Agency (ESA).

Ferretti, A., Fumagalli, A., Novali, F., Prati, C., Rocca, F. and Rucci, A. 2011. A new algorithm for processing interferometric data-stacks: SqueeSAR. IEEE Transactions on Geoscience and Remote Sensing 49(9): 3460 - 3470, doi: 10.1109/TGRS.2011.2124465.

Ferretti, A., Monti-Guarnieri, A., Prati, C. and Fabio, R. 2007. InSAR Principles: Guidelines for SAR Interferometry Processing and Interpretation. Proceedings of the National Academy of Sciences.

Finkl, C.W., Benedet, L. and Andrews, J.L. 2005. Interpretation of seabed geomorphology based on spatial analysis of high-density airborne laser bathymetry. Journal of Coastal Research 21(3): 501- 514.

Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D. and Moore, R. 2017. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment 202: 18 - 27.

Hengl, T. and Evans, I.S., 2009. Mathematical and digital models of the land surface. In: Hengl, T. and Reuter, H.I. (eds.), Geomorphometry: Concepts, Software, Applications. Elsevier, Amsterdam, pp. 31 - 63.

Hennings, I. 1990. Radar imaging of submarine sand waves in tidal channels. Journal of Geophysical Research 95: 9713–9721.

Hessner, K., Reichert, K. and Rosenthal, W. 1999. Mapping of sea bottom topography in shallow water by using a nautical radar. In: 2nd International Symposium on Operationalization of Remote Sensing, Enschede, 16–20 Aug 1999.

Hirt, C. 2014. Encyclopedia of Geodesy. (November), 0 – 6, doi: 10.1007/978-3-319-02370-0.

Hoja, D. and D'Angelo, P, 2010. Analysis of Dem combination methods using high resolution optical stereo imagery and interferometric SAR Data. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Science, Volume XXXVIII, Part 1, Calgary, Canada.

Hoja, D., Reinartz, P. and Schroeder, M. 2006, Comparison of Dem generation and combination methods using high resolution optical stereo imagery and interferometric SAR Data. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Science Volume XXXVI, Part 1, Paris, France.

Hooper, A., Bekaert, D., Spaans, K. and Arikan, M. 2012. Recent advances in SAR interferometry time series analysis for measuring crustal deformation. Tectonophysics 514 - 517: 1 – 13. doi: 10.1016/j.tecto.2011.10.013.

Horstmann, J., Borge, J., Seemann, J., Carrasco, R. and Lund, B., 2015. Chapter 16 - wind, wave, and current retrieval utilizing X-band marine radars. In: Liu, Yonggang, Kerkering, Heather, Weisberg, Robert H. (Eds.), Coastal Ocean Observing Systems. Academic Press, pp. 281–304.

Hurukawa, N., Wulandari, B.R. and Kasahara, M. 2014. Earthquake history of the Sumatran fault, Indonesia, since 1892, derived from relocation of large earthquakes. Bulletin of the Seismological Society of America 104(4): 1750–1762, doi: 10.1785/0120130201.

IHB, 2006. Technical Aspect of the Law of the Sea (TALOS). UNESCO, IOC, IHO, IAG. International Hydrographic Bureau (IHB). United Nations.

Inglada, J. and Garello, R. 2002. On rewriting the imaging mechanism of underwater bottom topography by synthetic aperture radar as a volterra series expansion. IEEE Journal of Oceanic Engineering 27: 665 – 674, doi: 10.1109/JOE.2002.1040949.

Isardsat. 2017. Base. Retrieved from

Julzarika, A. 2015. Height model integration using Alos PALSAR, X SAR, SRTM C, and ICESAT/GLAS. International Jornal of Remote Sensing and Earth Sciences 12(2): 107 – 11 doi: 10.30536/j.ijreses.2015.v12.a2691.

Julzarika, A. and Djurdjani, D. 2019. DEM classifications: opportunities and potential of its applications. Journal of Degraded and Mining Lands Management 6(4): 1897 - 1905, doi: 10.15243/jdmlm. 2019.064.1897.

Julzarika, A. and Harinta ka. 2019. Utilization of Sentinel Satellite for Vertical Deformation Monitoring in Semangko FAULT-Indonesia, (ACRS), 1–7.

Julzarika, A. and Harintaka. 2020. Indonesian DEMNAS: DSM or DTM? in 2019. IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology (AGERS) (pp. 31–36), doi: 10.1109/AGERS48446.2019.9034351.

Julzarika, A., Anggraini, N., Kayat, and Pertiwi, M. 2019. Land changes detection on Rote Island using harmonic modelling method. Journal of Degraded and Mining Lands Management 6(3): 1719 - 1725, doi: 10.15243/jdmlm. 2019.063.1719.

Kaichang, D., Deren, L. and Deyi, L. 2000. Remote sensing image classification with GIS data based on spatial data mining techniques. Geo-spatial Information Science 3: 30 – 35, doi: 10.1007/BF02829393.

Kienzle, S. 2004. The effect of DEM raster resolution on first order, second order, and compound terrain derivatives. Transactions in GIS 8: 83 - 111.

Krieger, G., Moreira, A., Fiedler, H., Hajnsek, I., Werner, M., Younis, M. and Zink, M. 2007. TanDEM-X: a satellite formation for highresolution SAR interferometry. IEEE Transactions on Geoscience and Remote Sensing 45(11): 3317 - 3341.

Lehner, S., Pleskachevsky, A. and Bruck, M., 2012. High-resolution satellite measurements of coastal wind field and sea state. International Journal of Remote Sensing 33(23): 7337 - 7360.

Li, X.M. and Lehner, S. 2014. Algorithm for sea surface wind retrieval from TerraSAR-X and TanDEM-X data IEEE Transactions on Geoscience and Remote Sensing 52: 2928 – 2939, doi: 10.1109/TGRS.2013.22677 80.

Li, Z., Zhu, Q. and Gold, C., 2005. Digital Terrain Modeling Principles and Methodology. CRC Press. Florida. USA.

Liosis, N., Marpu, P.R., Pavlopoulos, K. and Ouarda, T.B.M.J. 2018. Ground subsidence monitoring with SAR interferometry techniques in the rural area of Al Wagan, UAE. Remote Sensing of Environment 216 (June): 276–288, doi: 10.1016/j.rse.2018.07.001.

Liu, Y., Zhao, C., Zhang, Q. and Yang, C. 2018. Complex surface deformation monitoring and mechanism inversion over Qingxu-Jiaocheng, China with multi-sensor SAR images. Journal of Geodynamics 114(January): 41 – 52, doi: 10.1016/j.jog.2018.01.016.

Lusch, D.P. 1999. Introduction to Microwave Remote Sensing. Center for Remote Sensing. Michigan University. USA.

Maune, D.F. and Nayegandhi, A. 2018. Digital Elevation Model Technologies and Applications: The DEM Users Manual. American Society for Photogrammetry and Remote Sensing.

Mishra, M.K., Ganguly, D., Chauhan, P. and Ajai, 2014. Estimation of coastal bathymetry using RISAT-1 C-band microwave SAR data. IEEE Geoscience and Remote Sensing Letters 11(3): 671 - 675.

Naidoo, L., Mathieu, R., Main, R., Wessels, K. and Asner, G.P. 2016. L-band Synthetic Aperture Radar imagery performs better than optical datasets at retrieving woody fractional cover in deciduous, dry savannahs. International Journal of Applied Earth Observation and Geoinformation 52: 54 – 64, doi: 10.1016/j.jag.2016.05.006.

Pereira, P., Baptista, P., Cunha, T., Silva, P.A., Romao, S. and Lafon, V. 2019. Estimation of the nearshore bathymetry from high temporal resolution Sentinel-1A C-band SAR data - A case study. Remote Sensing of Environment 223: 166 - 178.

Pirotti, F. 2010. Assessing a template matching approach for tree height and position extraction from lidar-derived canopy height models of Pinus pinaster stands. Forests 1(4): 194 – 208, doi: 10.3390/f1040194.

Pleskachevsky, A., Lehner, S., Heege, T. and Mott, C. 2011. Synergy and fusion of optical and synthetic aperture radar satellite data for underwater topography estimation in coastal areas. Ocean Dynamic 61(12): 2099 – 2120, doi: 10.1007/s102-011-0460-1.

Pratomo, D.G., Khomsin, and Putranto, B.F.E. 2019. Analysis of the green light penetration from Airborne LiDARBathymetry in Shallow Water Area. Geomatics International Conference 2019. IOP Conference Series: Earth and Environmental Science 389 012003 IOP Publishing, doi:10.1088/1755-1315/389/1/012003.

Romeiser, R. and Alpers, W. 1997. An improved composite surface model for the radar backscattering cross section of the ocean surface: Model response to surface roughness variations and the radar imaging of underwater bottom. Journal of Geophysical Research 102: 25251 - 25267.

Rucci, A., Ferretti, A., Monti Guarnieri, A. and Rocca, F. 2012. Sentinel 1 SAR interferometry applications: The outlook for sub millimeter measurements. Remote Sensing of Environment 120: 156 – 163, doi: 10.1016/j.rse.2011.09.030.

Serrano-Juan, A., Pujades, E., Vázquez-Suñè, E., Crosetto, M. and Cuevas-González, M. 2017. Leveling vs. InSAR in urban underground construction monitoring: Pros and cons. Case of la sagrera railway station (Barcelona, Spain). Engineering Geology 218: 1 - 11, doi: 10.1016/j.enggeo.2016.12.016.

Shen, S. 2018. Simulation study on detecting shallow bathymetry via wavelength. IOP Conference Series Earth and Environmental Science 170(2): 022,055.

Siermann, J., Harvey, C., Morgan, G. and Heege, T. 2014. Satellite derived bathymetry and digital elevation models (DEM). International Petroleum Technology Concerence-17346, doi: 10.2523/IPTC-17346 –MS.

Smith, W.H.F. and Sandwell, D.T. 1997. Global sea floor topography from satellite altimetry and ship depth soundings. Science 277(5334): 1956 - 1962, doi: 10.1126/scien ce.277.5334.1956.

Tarikhi, P. 2012. Liqui-InSAR; SAR interferometry for aquatic body. International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences XXXIX-B7, doi: 10.5194/isprsarchives-XXXIX-B7-85-2012.

Trisakti and Julzarika, A. 2011. DEM generation from stereo ALOS Prism and its quality improvement. International Journal of Remote Sensing and Earth Sciences 8: 41-48.

Valeriano, D.M., Mello, E.M., Moreira, J.C., Shimabukuro, Y.E., Duarte, V. and Barbosa, C.C., 2004. Monitoring tropical forest from space: The PRODES digital project. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 35: 272 – 274.

Vanicek, P. and Krakiwsky, E. 1986. Geodesy, the Concepts. North-Holland, Amsterdam, NY, Oxford, Tokyo.

Vogelzang J. 1989. The mapping of bottom topography with imaging radar. A comparison of the hydrodynamic modulation in some existing models. International. Journal of Remote Sensing 10(9): 1503 – 1518, doi: 10.1080/01431168908903986.

Wackerman, C., Lyzenga, D., Ericson, E. and Walker, D. 1998. Estimating near-shore bathymetry using SAR. IGARSS 98 Symposium Proceeding IEEE, doi: 10.1109/igars s.1998.69240 7.

Wensink, H. and Alpers, W. 2015. SAR-based bathymetry. Ensyclopedia of Remote Sensing. Springer, doi: 10.1007/978-0-387-36699-9_207.

Wiehle, S. and Lehner, S. 2015. Automated waterline detection in the Wadden Sea using high-resolution TerraSAR-X images. Journal of Sensors Volume 2015, Article ID 450857, doi: 10.1155/2015/45085 7.

Wiehle, S. and Pleskachevsky, A. 2018. Bathymetry derived from Sentinel-1 Synthetic Aperture Radar data. In: 12th European Conference on Synthetic Aperture Radar Electronic Proceedings. Verband der Elektrotechnik Elektronik Informationstechnik e.V. Kartonhulle, Aachen, Germany, 978-3-8007-4636-1, pp. 1489.

Wiehle, S., Pleskachevsky, A. and Gebhardt, C. 2019. Automatic bathymetry retrieval from SAR images. CEAS Space Journal 11: 05 – 114, doi: 10.1007/s12567-018-0234-4.

Wilkinson, L. 2012. The grammar of graphics. In Handbook of Computational Statistics. Springer: New York, NY, USA; pp. 375–414

Wilson, J.P. 2012. Digital terrain modeling. Geomorphology 137: 107–121.

Yang, C., Yu, M., Hu, F., Jiang, Y. and Li, Y. 2017. Utilizing cloud computing to address big geospatial data challenges. Computer, Environment and Urban Systems 61: 120–128.

Young, I.R., Rosenthal, W. and Ziemer, F., 1985. A three-dimensional analysis of marine radar images for the determination of ocean wave directionality and surface currents. Journal of Geophysical Research 90-C1: 1049 - 1059.

Zhang, Y., Zhang, Y., Zhang, Y. and Li, X. 2016. Automatic Extraction of DTM from Low Resolution DSM by two steps semi-global filtering. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences 3(July): 249 – 255, doi: 10.5194/isprs-annals-III-3-249-2016.


  • There are currently no refbacks.

Copyright (c) 2021 Journal of Degraded and Mining Lands Management

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

Indexed By