DOI: https://doi.org/10.14710/geoplanning.3.2.117-126

SATELLITE-DERIVED BATHYMETRY USING RANDOM FOREST ALGORITHM AND WORLDVIEW-2 IMAGERY

*Masita Dwi Mandini Manessa  -  Graduate School of Science and Engineering, Yamaguchi University, Ube, Japan, and, Japan
Ariyo Kanno  -  Graduate School of Science and Technology for Innovation, Yamaguchi University, Ube, Japan
Masahiko Sekine  -  Graduate School of Science and Technology for Innovation, Yamaguchi University, Ube, Japan
Muhammad Haidar  -  Center for Thematic Mapping and Integration, Geospatial Information Agency, Indonesia
Koichi Yamamoto  -  Graduate School of Science and Technology for Innovation, Yamaguchi University, Ube, Japan
Tsuyoshi Imai  -  Graduate School of Science and Technology for Innovation, Yamaguchi University, Ube, Japan
Takaya Higuchi  -  Graduate School of Science and Technology for Innovation, Yamaguchi University, Ube, Japan

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Abstract

In empirical approach, the satellite-derived bathymetry (SDB) is usually derived from a linear regression. However, the depth variable in surface reflectance has a more complex relation. In this paper, a methodology was introduced using a nonlinear regression of Random Forest (RF) algorithm for SDB in shallow coral reef water. Worldview-2 satellite images and water depth measurement samples using single beam echo sounder were utilized. Furthermore, the surface reflectance of six visible bands and their logarithms were used as an input in RF and then compared with conventional methods of Multiple Linear Regression (MLR) at ten times cross validation. Moreover, the performance of each possible pair from six visible bands was also tested. Then, the estimated depth from two methods and each possible pairs were evaluated in two sites in Indonesia: Gili Mantra Island and Panggang Island, using the measured bathymetry data. As a result, for the case of all bands used the RF in compared with MLR showed better fitting ensemble, -0.14 and -1.27m of RMSE and 0.16 and 0.47 of R2 improvement for Gili Mantra Islands and Panggang Island, respectively. Therefore, the RF algorithm demonstrated better performance and accuracy compared with the conventional method. While for best pair identification, all bands pair wound did not give the best result. Surprisingly, the usage of green, yellow, and red bands showed good water depth estimation accuracy.

 

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Keywords: Satellite-derived bathymetry; Worldview-2; Random Forest; Multiple Linear Regression

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Section: Original Research
Language : EN
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  1. Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32.

  2. Diesing, M., et al. (2014). Mapping seabed sediments: Comparison of manual, geostatistical, object-based image analysis and machine learning approaches. Continental Shelf Research, 84, 107–119.

  3. Digital Globe. (2012). Radiometric Use of WorldView-2 Imagery. Available online: http://www.digitalglobe.com/downloads/Radiometric_Use_of_WorldView-2_Imagery.pdf">    

  4. Doxani, G., et al. (2012). Shallow-water bathymetry over variable bottom types using multispectral Worldview-2 image. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 39(8), 159–164. [https://doi.org/10.5194/isprsarchives-xxxix-b8-159-2012">CrossRef]

  5. 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 Transactions on Geoscience and Remote Sensing, 53(7), 3539–3549. [https://doi.org/10.1109/tgrs.2014.2377300">CrossRef]

  6. Flener, C., et al. (2012). Comparison of empirical and theoretical remote sensing based bathymetry models in river environments. River Research and Applications, 28(1), 118–133. [https://doi.org/10.1002/rra.1441">CrossRef]

  7. Jupp, D. L. B. (1988). Background and extensions to depth of penetration (DOP) mapping in shallow coastal waters. In Proceedings of the Symposium on Remote Sensing of the Coastal Zone (p. IV--2).

  8. Kanno, A., & Tanaka, Y. (2012). Modified Lyzenga’s method for estimating generalized coefficients of satellite-based predictor of shallow water depth. IEEE Geoscience and Remote Sensing Letters, 9(4), 715–719. [https://doi.org/10.1109/lgrs.2011.2179517">CrossRef]

  9. Kerr, J. M. (2011). Worldview-02 offers new capabilities for the monitoring of threatened coral reefs. In Proceedings of the Geospatial World Forum.

  10. Knudby, A., et al. (2013). Mapping Coral Reef Resilience Indicators Using Field and Remotely Sensed Data. Remote Sensing, 5(3), 1311–1334. [http://doi.org/10.3390/rs5031311">CrossRef]

  11. Lee, K. R., et al. (2011). Determination of bottom-type and bathymetry using WorldView-2. Proc. SPIE Ocean Sens. Monitoring III, 80300D--1.

  12. Liceaga-Correa, M. A., & Euan-Avila, J. I. (2002). Assessment of coral reef bathymetric mapping using visible Landsat Thematic Mapper data. International Journal of Remote Sensing, 23(1), 3–14. [https://doi.org/10.1080/01431160010008573">CrossRef]

  13. Lyzenga, D. R. (1978). Passive remote sensing techniques for mapping water depth and bottom features. Applied Optics, 17(3), 379. [http://doi.org/10.1364/AO.17.000379">CrossRef]

  14. Lyzenga, D. R., Malinas, N. P., & Tanis, F. J. (2006). Multispectral bathymetry using a simple physically based algorithm. IEEE Transactions on Geoscience and Remote Sensing, 44(8), 2251–2259. [http://doi.org/10.1109/TGRS.2006.872909">CrossRef]

  15. Paredes, J. M., & Spero, R. E. (1983). Water depth mapping from passive remote sensing data under a generalized ratio assumption. Applied Optics, 22(8), 1134. [http://doi.org/10.1364/AO.22.001134">CrossRef]

  16. Stumpf, R. P., Holderied, K., & Sinclair, M. (2003). Determination of water depth with high-resolution satellite imagery over variable bottom types. Limnology and Oceanography, 48(1part2), 547–556. [http://doi.org/10.4319/lo.2003.48.1_part_2.0547">CrossRef]

  17. Yuzugullu, O., & Aksoy, A. (2014). Generation of the bathymetry of a eutrophic shallow lake using WorldView-2 imagery. Journal of Hydroinformatics, 16(1), 50. [http://doi.org/10.2166/hydro.2013.133">CrossRef]

  18.  


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