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
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.
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.
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'
- 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.
r.gwr,
r.regression.multi
Vinayaraj Poliyapram (email:
vinay223333@gmail.com),
Luca Delucchi and Venkatesh Raghavan
SOURCE CODE
Available at:
i.image.bathymetry source code
(history)
Latest change: Monday Jan 30 19:52:26 2023 in commit: cac8d9d848299297977d1315b7e90cc3f7698730
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GRASS GIS 8.4.1dev Reference Manual