i.image.bathymetry - GRASS GIS manual

Note: This document is for an older version of GRASS GIS that has been discontinued. You should upgrade, and read the current manual page.

NAME

i.image.bathymetry - Estimates Satellite Derived Bathymetry (SDB) from multispectral images.

KEYWORDS

imagery, bathymetry, satellite

SYNOPSIS

i.image.bathymetry

i.image.bathymetry --help

i.image.bathymetry [-fb] [blue_band=name] green_band=name red_band=name nir_band=name band_for_correction=name calibration_points=name [area_of_interest=name] [additional_band1=name] [additional_band2=name] [additional_band3=name] [additional_band4=name] depth_estimate=name [tide_height=float] calibration_column=name [--overwrite] [--help] [--verbose] [--quiet] [--ui]

Flags:

-f: select if only want to run Fixed-GWR model
-b: select kernel function as bi-square
--overwrite: Allow output files to overwrite existing files
--help: Print usage summary
--verbose: Verbose module output
--quiet: Quiet module output
--ui: Force launching GUI dialog

Parameters:

blue_band=name: Name of input raster map
green_band=name [required]: Name of input raster map
red_band=name [required]: Name of input raster map
nir_band=name [required]: Name of input raster map
band_for_correction=name [required]: Name of input raster map
calibration_points=name [required]: Name of input vector map; Or data source for direct OGR access
area_of_interest=name: Name of input vector map; Or data source for direct OGR access
additional_band1=name: Name of input raster map
additional_band2=name: Name of input raster map
additional_band3=name: Name of input raster map
additional_band4=name: Name of input raster map
depth_estimate=name [required]: Name for output raster map
tide_height=float: Tide correction to the time of satellite image capture
calibration_column=name [required]: Name of the column which stores depth values

DESCRIPTION
NOTES
EXAMPLES
AUTHORS
SEE ALSO
REFERENCES

DESCRIPTION

i.image.bathymetry is used to estimate Satellite-Derived Bathymetry (SDB). Module estimates bathymetry over near-shore region using limited reference depth points. The maximum depth can be estimated by the module is depending up on many factors such as quality of the water, suspended materials etc.,(Lyzenga et al., 2006, Kanno and Tanaka, 2012). Our experiments with several multispectral optical images indicate that the depth estimates are reliable for when water column is below 20 meter.

Delineation of land and water area are based on combining the result of NDVI and band ratio. NDVI has used to delineate water from land, band ratio between green band and infrared band used to separate the delineated water from clouds, ice, etc. Atmospheric and water corrections applied according to the Lyzenga et al., 2006. Corrected spectral bands will be used for weighted multiple regression to estimate depth. R library GWmodel has been used to compute the Geographically Weighted Regression used for depth estimation.

NOTES

The input image must include deep water pixels (far away from the coast) which are used to assist water surface and water column correction.if there is no deep water pixels included in the satellite imagery, atmospheric and water corrections are carried without using deep water pixels. Sparse depth points extracted from hydrographic charts or depth pints derived from LiDAR survey or derived from Sonar survey can be used as reference depth for calibration. The calibration depth points provided by the user are used to ditermine the Area of Interest, therefore it is suggested to provide calibration depth points in order to cover user's estimation region boundary. In addition, an optional parameter is also available to provide a polygon vector file for user's to ditermine the area to be estimated (see first example).

The tide height at the time of reference depth collection and satellite imagery capture should be normalized if it is not. An option is available in the module to provide tide hieght at the tide of image captured and the module will correct the reference depth accordingly. This option asuumes that the reference depth given is corrected zero tide height. The tide lower than zero can be added as negative value.

The GWmodel adaptive GWR model is memory intensive and may not be used to process large images. For large images, the estimation is carried out by using non-adaptive GWR implemented in r.gwr module in GRASS GIS. R > 3.1 should be installed to run GWmodel in order to proccess adaptive GWR model for better depth estimation. Default gaussian kernel will be used to estimate geographically weighted regression coefficients.The flag 'b' can be used to change the kernel function gaussian to bi-square.

EXAMPLES

In i.image.bathymetry green band, red band, near-infrared band, band for correction and calibration depth points are mandatory input. Additional bands available in the visible wavelength can be used for better depth estimation as optional input. Short Wave Infrared (SWIR) band is suggested to use as "band_for_correction" if it is available (for e.g. satellite images like Landsat-7, Landsat-8 and Sentinel-2).An example of depth estimation using Sentinel-2 (MSI) image is shown below, where depth value is stored in column named 'Z'

i.image.bathymetry blue_band='B2' green_band='B3' red_band='B4' 
nir_band='B8' band_for_correction='B11' 
calibration_points='Calibration_points' calibration_column='Z' 
depth_estimate='output' area_of_interest='AOI'

If SWIR band is not available near-infrared band can be used as "band_for_correction" (for e.g. satellite images like RapidEye and ALOS AVINIR-2). An example of depth estimation using RapidEye image is shown below image is shown below, where depth value is stored in column named 'value'.

i.image.bathymetry blue_band='B1' green_band='B2' red_band='B3' 
Additional_band1='B4' nir_band='B5' band_for_correction='B5' 
calibration_points='Calibration_points'  calibration_column='value' 
depth_estimate='output'

AUTHORS

Vinayaraj Poliyapram (email:vinay223333@gmail.com), Luca Delucchi and Venkatesh Raghavan

REFERENCES

Vinayaraj, P., Raghavan, V. and Masumoto, S. (2016) Satellite derived bathymetry using adaptive-geographically weighted regression model, Marine Geodesy, 39(6), pp.458-478
Su, H., Liu, H., Lei, W., Philipi, M., Heyman, W., and Beck, A., 2013, Geographically Adaptive Inversion Model for Improving Bathymetric Retrieval from Multispectral satellite Imagery. IEEE Transaction on Geosciences and Remote Sensing, 52(1) : 465-476, Accessed January 2013, doi:10.1109/TGRS.2013.2241772.

SOURCE CODE

Available at: i.image.bathymetry source code (history)

Latest change: Monday Jun 28 07:54:09 2021 in commit: 1cfc0af029a35a5d6c7dae5ca7204d0eb85dbc55