Kuncoro Teguh Setiawan, Nana Suwargana, Devica Natalia Br. Ginting, Masita Dwi Mandini Manessa, Nanin Anggraini, Syifa Wismayati Adawiah, Atriyon Julzarika, Surahman Surahman, Syamsu Rosid, Agustinus Harsono Supardjo


The scope of this research is the application of the random forest method to SPOT 7 data to produce bathymetry information for shallow waters in Indonesia. The study aimed to analyze the effect of base objects in shallow marine habitats on estimating bathymetry from SPOT 7 satellite imagery. SPOT 7 satellite imagery of the shallow sea waters of Gili Matra, West Nusa Tenggara Province was used in this research. The estimation of bathymetry was carried out using two in-situ depth-data modifications, in the form of a random forest algorithm used both without and with benthic habitats (coral reefs, seagrass, macroalgae, and substrates). For bathymetry estimation from SPOT 7 data, the first modification (without benthic habitats) resulted in a 90.2% coefficient of determination (R2) and 1.57 RMSE, while the second modification (with benthic habitats) resulted in an 85.3% coefficient of determination (R2) and 2.48 RMSE. This research showed that the first modification achieved slightly better results than the second modification; thus, the benthic habitat did not significantly influence bathymetry estimation from SPOT 7 imagery.


bathymetry; random forest; SPOT 7

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Arya, A., Winarso, G., & Santoso, A. I., (2016). Ekstraksi Kedalaman Laut Menggunakan Data SPOT 7 di Teluk Belangbelang Mamuju (Accuracy assessment of satellite derived bathymetry using Lyzenga method and its modification using SPOT 7 data at the Belangbelang Bay Waters, Mamuju). J Ilm Geomatika, 22, 9–19.

Budhiman, S., Winarso, G., & Asriningrum, W. (2013). Pengaruh Pengambilan Training Sample Substrat Dasar Berbeda pada Koreksi Kolom Air Menggunakan Data Penginderaan Jauh. Jurnal Penginderaan Jauh, 10(2), 83–92. LAPAN. Indonesia.

Eugenio, F., Marcello, J., & Martin, J. (2015). High resolution maps of bathymetry and benthic habitats in shallow-water environments using multispectral remote sensing imagery. IEEE Trans Geosci Remote Sens 53, 3539–3549. doi: 10.1109/ TGRS.2014.2377300

Guzinski, R., Spondylis, E., Michalis, M., Tusa, S., Brancato, G., Minno L., & Hansen, L. B. (2016). Exploring the utility of bathymetry maps derived with multispectral satellite observations in the field of underwater archaeology. Open Archaeol 2, 243–263. doi: 10.1515/opar-2016-0018

Hell, B., Broman, B., Jakobsson, L., Jakobsson, M., Magnusson, A., & Wiberg, P. (2012). The use of bathymetric data in society and science: A review from the Baltic Sea. AMBIO 41, 138–150.

Hernandez,, W. & Armstrong R., (2016). Deriving bathymetry from multispectral remote sensing data. J Mar Sci Eng 4, 8. doi: 10.3390/jmse4010008.

IHO (2008). IHO standards for hydrographic surveys (5th ed.). Special Publication No. 44, Monaco.

Jagalingam, P., Akshaya, B. J., & Hegde, A. V. (2016). Bathymetry mapping using Landsat 8 satellite imagery. Procedia Engineering 116 (2015), 560–566.

Jawak, S. D., Vadlamani, S. S. & Luis, A. J. (2015). A synoptic review on deriving bathymetry information using remote sensing technologies: Models, method, and comparisons. Advances in Remote Sensing, 4, 147–162.

Kanno, A., Koibuchi, Y., & Isobe, M. (2011). Shallow water bathymetry from multispectral satellite images: Extensions of Lyzenga’s method for improving accuracy. Coastal Engineering Journal, 53(4), 431–450.

Kanno, A., Tanaka, Y., Kurosawa, A., & Sekine, M. (2013). Generalized Lyzenga’s predictor of shallow water depth for multispectral satellite imagery. Mar Geod, 36, 365–376. doi: 10.1080/01490419.2013.83997

Lillesand, T. M. & Kiefer, R. W. (1987). Remote sensing and image interpretation (2nd ed.). Canada.

Manessa, M. D. M., Kanno, A., Sekine, M., Ampou, E. E., Widagti, N., & As-syakur, A. R. (2016). Lyzenga multispectral bathymetry formula for Indonesian

shallow coral reef: Evaluation and proposed generalized coefficient. In: C. H. Bostater, X. Neyt, C. Nichol, & O. Aldred, (Eds). Proc. Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions. Edinburgh, UK: SPIE.

Manessa M. D. M., Haidar, M., Hartuti, M., & Kresnawati, D. K. (2017). Determination of the best methodology for bathymetry mapping using SPOT 6 imagery: A study of 12 empirical algorithms. International Journal of Remote Sensing and Earth Sciences, 14(2) 127–136.

Pacheco, A., Horta,, J., Loureiro C., Ferreira, O. (2015). Retrieval of nearshore bathymetry from Landsat 8 images: A tool for coastal monitoring in shallow waters. Remote Sens Environ. 159, 102–116. doi: 10.1016/j.rse. 2014.12.004

Pushparaj, J., & Hegde, A. V, (2017), Estimation of bathymetry along the coast of Mangaluru using Landsat-8 imagery. Int J Ocean Clim Syst 8, 71–83. doi: 10.1177/17593 131166

Setiawan, K. T., Manessa, M.D.M., Winarso, G., Anggraini, N., Giarrastowo, G., Asriningrum, W., … Supardjo, A. H. (2018). Bathymetry estimation Of SPOT 7: Case study of Gili Matra West Nusa Tenggara waters. Journal of Remote Sensing and Digital Image Data Processing, 15(2). doi: 10.30536/j.pjpdcd.2018.v15.a3008

Vinayaraj, P., Raghavan, V., & Masumoto, S. (2016). Satellite-derived bathymetry using adaptive geographically weighted regression model. Mar Geod 39, 458–478. doi: 10.1080/01490419.2016.12452

Yuzugullu, O. & Aksoy, A. (2014). Generation of the bathymetry of a eutrophic shallow lake using WorldView-2 imagery. J. Hydroinformatics 16(50). doi: 10.2166/ hydro.2013.133


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