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Detection of shallow water depth using remotely sensed data
I. Z. Poonawala , S. D. Ranade, S. Selvan, C. Gnanaseelan , Anuja Rajagopalan Rajagopalan Central Water and Power Research Station Pune - 411 024
Water depth is an important parameter in solving various
coastal engineering problems such as erosion, accretion, shoreline stability,
port and harbor construction, evaluation of tidal storage, dredging, maintenance
of navigation routes etc.. The field data collection at a site is an expensive
and time consuming and sometimes extremely difficult in shallow water regions.
For various reasons like wide area coverage, data dependency on depth and
repetivity, etc. satellite data can be used to determine shallow water depths.
For calibration/validation of the bathymetric model, ground truth data at a few
selected locations is essential.
The digital image data acquired by the remote sensing satellites consist of the reflectance of the surface of the earth and the atmospheric constituents. Considering the case of water bodies, there will be significant change in the reflectance due to various parameters including water depth, turbidity and bed characteristics. Assuming the other two parameters uniform, it is obvious that the intensity of reflected electromagnetic energy will vary inversely with the depth of the water column. Preliminary analysis of digital image data of IRS-1A and IRS-1B show that the band 4 data in the spectral range of 0.77 to 0.86 microns contain significant variation in intensity level for the shallow water region. Algorithms for water depth mapping of coastal areas, using satellite data were developed by Lyzenga (1978) and Paredes and Spero (1983). This paper describes a new bathymetric model based on statistical approach, which transforms the intensity levels of the satellite data to depth values. Bathymetry of a small region is correlated with intensity levels to achieve the transformation and the transformed data contains depth information of shallow water region. Area of study The Jamnagar and Sikka region of Gujarat, in the Gulf of Kuchchh was selected as the study area. The area is about 25 sq. km. between the latitudes 22o 20’ 48" N and 22o 28’ 48" N and longitudes 69 o 48’ 00" E and 69 o 56’ 24" E. Satellite Imagery Digital satellite imagery data of 3rd Oct., 1994, covering an area of 25 sq. Km. between the latitudes 22o 20’ 48" N and 22o 28’ 48" N and longitudes 69 o 48’ 00" E and 69 o 56’ 24" E was used for the study and shown in Plate 1. The above imagery is geocoded and contains 1024 x 1024 pixels with 25 m ground resolution. The time of satellite pass is around 1100 hours. The tidal levels before and after the time of satellite pass were determined from India Tide Tables 1994. The tidal level at the time of satellite pass was computed using sinusoidal interpolation method and was +5.12 m with respect to Chart Datum. Bathymetric data Bathymetry of the region was available covering area between latitudes 22 o 25’ 50" N and 22 o 28’ 30" N and longitudes 69 o 51’ 45" N and 69 o 53’ 00" N. The data was digitized over a grid of 100 m x 100 m. It was then resampled using two dimensional interpolation method to obtain bathymetry at a grid interval of 25 m x 25 m, which is compatible to the pixel size of satellite imagery. Then it is transformed to depth values using the transformation equation, dij = TL - bij where dij is depth at location ij, TL is the tidal level at the time of satellite pass and bij is bathymetry at location ij. Methodology A Domain Rippling Scheme (DRS) has been developed to estimate the depths of the shallow water region. This scheme starts with a rectangular domain D which specifies a sub area in the region of interest, where both reflectance and bathymetry are known. It estimates the depth values of the immediate neighbouring pixels of D using a transformation equation in terms of its statistical parameters like covariance, variance etc., and updates the domain by including the newly estimated pixels. The next layer is also calculated using the same principle and proceeds towards the boundary like a ripple as shown in Fig. 1. Necessary care should be taken when the domain touches the boundary of the region of interest. 
Fig. 1. Domain Rippling
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