International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B8, 2012
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia
SHALLOW-WATER BATHYMETRY OVER VARIABLE BOTTOM TYPES USING
MULTISPECTRAL WORLDVIEW-2 IMAGE
G. Doxani®, M. Papadopoulou®, P. Lafazani’, C. Pikridas®, M. Tsakiri-Strati*
* Dept. Cadastre, Photogrammetry and Cartography, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
Dept. Geodesy and Topography, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece -
gdoxani, papmar, lafazani, cpik, martsaki@topo.auth.gr
Technical Commission VIII/4
KEY WORDS: Multispectral Bathymetry, Linear Regression, Radiometric Correction, GPS, Echo Sounder
ABSTRACT:
Image processing techniques that involve multispectral remotely sensed data are considered attractive for bathymetry applications as
they provide a time- and cost-effective solution to water depths estimation. In this paper the potential of 8-bands image acquired by
Worldview-2 satellite in providing precise depth measurements was investigated. Multispectral image information was integrated
with available echo sounding and GPS data for the determination of the depth in the area of interest. In particular the main objective
of this research was to evaluate the effectiveness of high spatial and spectral resolution of the new imagery data on water depth
measurements using the Lyzenga linear bathymetry model. The existence of sea grass in a part of the study area influenced the linear
relationship between water reflectance and depth. Therefore the bathymetric model was applied in three image parts: an area with sea
grass, a mixed area and a sea grass-free area. In the last two areas the model worked successfully supported by the multiplicity of the
imagery bands.
1. INTRODUCTION
Accurate bathymetric measurements are considered of
fundamental importance towards monitoring sea bottom and
producing nautical charts in support of marine navigation. Until
recently, bathymetric surveying of shallow sea water has been
mainly based on conventional ship-borne echo sounding
operations. However, this technique demands cost and time,
particularly in shallow waters, where a dense network of
measured points is required. Taking all these under
consideration, during the last decades remotely sensed data have
provided a cost- and time-effective solution to accurate depth
estimation (Lyzenga, 1985; Stumpf et al., 2003; Su et al., 2008).
The initial attempts for automatic estimation of water depth
were based on the combination of aerial multispectral data and
radiometric techniques (Lyzenga, 1978). With the advent of
Landsat images, the methods of monitoring sea floor were
increased and ameliorated, so as to be efficiently applied on
optical satellite images (Lyzenga, 1981; Spitzer and Dirks,
1987; Philpot, 1989; Van Hengel and Spitzer, 1991). In the
following years, the advance of remote sensing technology
expanded the use of these methodologies to data with improved
spatial and spectral resolution, i.e. Ikonos (Stumpf et al., 2003;
Mishra et al. 2006; Su et al., 2008), Quickbird (Conger et al.,
2006; Lyons et al, 2011) and Worldview-2 data (Kerr, 2010,
Bramante et al. 2010). The main hindrances while applying
these processes were reflectance penetration and water turbidity
(Conger et al., 2006; Su et al., 2008). However, the bathymetric
approaches involving satellite imagery data are regarded as a
fast and economically advantageous solution to automatic water
depth calculation in shallow water (Stumpf et al., 2003; Su et
al., 2008).
A wide variety of empirical models has been proposed and
evaluated for bathymetric estimations by establishing the
statistical relationship between image pixel values and field-
measured water depth values. The most popular approach was
proposed by Lyzenga (1978, 1981, 1985) and was based on the
fact that the bottom-reflected reflectance is approximately a
linear function of the bottom reflectance and an exponential
function of the water depth. Jupp (1989) introduced an
algorithm for determining firstly the depth of penetration (DOP)
zones for every band and then for calibrating depths within
DOP zones. Stumpf et al. (2003) presented an algorithm using a
ratio of reflectance and demonstrated its benefits to retrieve
depths even in deep water (725m) contrary to standard linear
transform algorithm. Moreover a modified version of Lyzenga's
model has been proposed by Conger et al. (2006) employing a
single colour band and LIDAR bathymetry data rather than two
colour bands in rotating process.
The aim of this paper was to evaluate the contribution of the
eight bands of Worldview-2 imagery in the estimation of sea
depths. High spectral and spatial image resolution was tested in
shallow waters by using the well known and time-tested
Lyzenga's linear model. Particularly, three critical issues in
shallow water bathymetry were investigated concerning a) the
removal of the sun glint that exists on imagery data of very high
resolution and depends mainly on the solar and image
acquisition angles, b) the atmospheric correction over the sea
surface and c) the confrontation of the bottom reflectance
variations effects on the bathymetry model, taking advantage of
the multiplicity of the imagery bands.
2. SUN GLINT REMOVAL AND ATMOSPHERIC
CORRECTION
Sun glint removal and atmospheric correction of remotely
sensed data are essential processes prior to the application of a
bathymetry model. There are not rules about the sequence of
these two procedures. Many researchers begin with the sun glint
