情報地質 第27巻 第2号 108-109頁 2016年
Geoinformatics, vol.27, no.2, pp.108-109, 2016

ＧＲＡＳＳ ＧＩＳ ＰｙｔｈｏｎスクリプトとＲを用いた衛星画像推定水深のためのアルゴリズムの実装
ポリヤプラム ヴィネーラズ*・ベンカテッシュ ラガワン*
ルーカー デラッチー**・根本 達也***・升本 眞二***

Implementation of Algorithm for Satellite Derived Bathymetry using
GRASS GIS Python Scripting and R
Poliyapram VINAYARAJ*, Venkatesh RAGHAVAN*, Luca DELUCCHI**
Tatsuya NEMOTO*** and Shinji MASUMOTO****大阪市立大学大学院創造都市研究科 Graduate School for Creative Cities, Osaka City University,
3-3-138 Sugimoto, Sumiyoshi-ku, Osaka 558-8585, Japan. E-mail: vinay223333@gmail.com.
**Fondazione Edmund Mach Research and Innovation Centre, GIS and Remote Sensing Unit, Italy.
***大阪市立大学大学院理学研究科 Graduate School of Science, Osaka City University,
3-3-138 Sugimoto, Sumiyoshi-ku, Osaka 558-8585, Japan. .

キーワード：衛星画像推定水深，GRASS GIS，Python，R, i.image.bathymetry
Key words：Satellite derived bathymetry, GRASS GIS, Python, R, i.image.bathymetry

1. Introduction
Near-shore bathymetry is likely to be the coastal
variable that most limits the investigation of coastal
processes and the accuracy of numerical models incoastal areas, as acquiring medium spatial resolutiondata in the near-shore is highly demanding and costly. As
such, the ability to derive bathymetry using remotesensing techniques is a topic of increasing interest incoastal monitoring and research. Many authors (Lyzengaet al., 2006; Stumpf et al., 2003) have been proposed
different methods to estimate bathymetry. However, thiscontribution focuses on the implementation of the linear
transform algorithm to obtain satellite-derived
bathymetry as GRASS GIS module using python
scripting and R. Proposed GRASS GIS module(i.image.bathymetry)automates
thebathymetry
estimation directly from multi-spectral bands.

2. Materials and Methods
Python scripting in GRASS and PyGRASS ispowerful interface to call python functionality in toGRASS. i.image.bathymetry module is also using
existing GRASS scripts and R packages. Mainfunctionalities of the module are delineating water region,
atmospheric and water column correction, Geographical
Weighted Regression (GWR). R package called ‘GWmodel’
has been used to estimate GWR between corrected
spectral bands as independent variables and calibration
depth points as dependent variable.
2.1 Delineating water regionDelineatingwaterregion
without
visualinterpretation is potential to produce error. To prevailover this problem rule based combination of NDVI andband ratio between green band and infrared band wasused. NDVI has used to delineate water from land. Bandratio has used to separate the delineated water fromclouds, ice etc (Vinayaraj et al., 2015). GRASS module‘r.mapcalc’ has been used to delineate the water region.
2.2 Atmospheric and water column correction
The radiance observed by a satellite sensor on
shallow water basically consists of four components,
namely, atmospheric scattering component, surface
reflection component, in-water volume scattering
component, and bottom reflection component. This study
adopted more refined way of retrieving bottom
reflectance originally proposed by Lyzenga et al. (2006).
Assumes that algorithm can effectively eliminatecomponents except bottom reflectance. The following
definition of Lyzenga et al.’s algorithm is used forcorrection.

X(λ)i = log (Lλi − α0 − α1 (LλNIR/Lλi )(1)

Where, X(λ)i is the transformed radiance, Lλispectral radiance of shallow water pixel (area of interest),
α0 and α1 are coefficients estimated by least squares
using the L∞(λ) and LλNIR values of the deep-waterpixels. Using the estimated values of α 0 and α1 for theshallow-water pixels, we evaluate X(λ)i of theshallow-water pixels for each band by using the equation(1). GRASS module r.mapcalc has used to compute theequation (1).

109

Figure 1. Flowchart of workflow of i.image.bathymetry.

2.3 Geographical Weighted Regression
The transformed radiance (X(λ)i) has been generated
from all the visible bands (equation 1), and is assumed
that the transformed radiance is linearly related to thedepth and water attenuation coefficient. Thus we will
apply a linear least square regression between LiDAR
depth and transformed radiance to estimate coefficients.
Further, these coefficients have been used to estimate thedepth. R package (‘GWmodel’) has been used to compute
GWR with adaptive bandwidth functionality. Since‘GWmodel’ is memory consuming, big data cannot process
in low memory computers. Computer memory
specification also should be increased to use the adaptive
GWR. Therefore, in the computers that cannot process‘GWmodel’ due to low memory will use ‘r.gwr’ module inGRASS to compute GWR with fixed bandwidth
functionality. Workflow of the i.image.bathymetry has
shown in Figure 1.

3. Results and Discussion
The module has been tested and evaluated withmany study areas with different satellite images
irrespective spatial, radiometric resolution and size ofthe data. In this study we demonstrate bathymetry
estimation in coastal area of Iwate prefecture, Tohoku,
Japan (Figure 2).
Freely available Landsat8 data has been used
estimate bathymetry using 500 depth points surveyed byecho sounder. SWIR band (1.57-1.65µm) has been used as‘band for correction’. All the available multispectral
bands in the visible domain have been used forestimation.
Figure3isascreenshot
ofi.image.bathymetry showing details of required input
data, optional input data of i.image.bathymetry and theresulted bathymetry. The domain extent of thebathymetry estimation is set from the input calibration
points. Therefore, the limited calibration points given by
the user should cover the entire region need to beestimated. Evaluation of the result has been carried out
by comparing the estimated bathymetry and echosounder depth data which is not used for estimation.
Result illustrate that the module produces good accuracy
bathymetry in terms of correlation coefficient (R=0.96),
coefficient of determination (R2=0.92) and Root Mean

Figure 2. Study area used to test the module.

Figure 3. Screenshot of i.image.bathymetry

Square Error (RMSE =1.17m).

4. Conclusion
There are several methods to estimate near-shore
bathymetry from satellite images, but there is not muchsoftware to automate the estimation procedure. Therefor
this study implements a module in open source GIS
software (GRASS GIS) using python scripting and R.
Study demonstrates good performance of the moduleusing a case study. Results of this case study issuggesting that the developed module reliable toautomate near-shore bathymetry estimation over any
other study area, where the assumptions of bathymetry
estimation algorithm satisfies.

References
Lyzenga, D. R., Malinas, N. R. and Tanis, F. J., (2006),
Multispectral Bathymetry using a Simple Physically
Based Algorithm. IEEE Trans. Geosci. Remote Sensing,vol.44, no.11, pp.2251-2259.
Stumpf, R. P., Holderied, K. and Sinclair, M., (2003),Determination of Water Depth with High-Resolution
Satellite Imagery Over Variable Bottom Types. Limnol.
Oceanogr., vol.48, no.2, pp.547-556.
Vinayaraj, P., Raghavan, V., Masumoto, S. and Glejin, J.,(2015), Comparative Evaluation and Refinement ofAlgorithm for Water Depth Estimation using Medium
Resolution Remote Sensing Data. International
Journal of Geoimformatics, vol.11, no.3, pp.17-29.