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

DOI: https://doi.org/10.14710/geoplanning.3.2.117-126
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Article Info
Submitted: 18-09-2016
Published: 25-10-2016
Section: Articles

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.

 

Keywords

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

  1. Masita Dwi Mandini Manessa 
    Graduate School of Science and Engineering, Yamaguchi University, Ube, Japan, and Center for Remote Sensing and Ocean Science (CReSOS), Udayana University
  2. Ariyo Kanno 
    Graduate School of Science and Technology for Innovation, Yamaguchi University, Ube , Japan
  3. Masahiko Sekine 
    Graduate School of Science and Technology for Innovation, Yamaguchi University, Ube , Japan
  4. Muhammad Haidar 
    Center for Thematic Mapping and Integration, Geospatial Information Agency , Indonesia
  5. Koichi Yamamoto 
    Graduate School of Science and Technology for Innovation, Yamaguchi University, Ube , Japan
  6. Tsuyoshi Imai 
    Graduate School of Science and Technology for Innovation, Yamaguchi University, Ube , Japan
  7. Takaya Higuchi 
    Graduate School of Science and Technology for Innovation, Yamaguchi University, Ube , Japan

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