1 REMOTE SENSING BASED BATHYMETRY ON THE HIGHLY DYNAMIC AMAZONIAN COAST ...

13 Pages·2005·1.89 MB·English

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REMOTE SENSING BASED BATHYMETRY
ON THE HIGHLY DYNAMIC AMAZONIAN COAST
Nicolas Gratiot1, Antoine Gardel2 and Laurent Polidori3
1 UR ELISA, IRD, BP 165, 97323 Cayenne, French Guiana. [email protected]
2 Laboratoire GéoDal, ULCO, Dunkerque, France. [email protected]
3 US ESPACE, IRD, BP 165, 97323 Cayenne, French Guiana. [email protected].

Abstract
The coast of French Guiana is one of the world’s muddiest. Usual techniques of bathymetric
measurement are not appropriate in such a specific environment, so that there is a need for
alternative techniques. A remote sensing-based method using optical SPOT images is
presented. Impact of environmental constraints on the bathymetric output accuracy is
discussed, as well as other limitations related to image quality and mud/water image contrast.
The results do not provide a very accurate bathymetric map, but they can be merge with a
priori geomorphic knowledge in order to gain a better understanding of the evolution of the
muddy sea bottom. This study also highlights the potential of high temporal resolution remote
sensing data in providing bathymetric measurements on the very specific Amazonian
mudbank system.

Keywords : Amazonian mudbank system, bathymetry, remote sensing, SPOT.

1.Introduction
Bathymetric data are of major importance for coastal and estuarine environment monitoring
and for the understanding of coastal sedimentary processes. Such data have become a
standard information, available on nautical maps for most coastal zones. Although
conventional surveying and echo sounding remain the most commonly used techniques, they
cannot be applied easily to shallow waters and intertidal areas, nor to muddy, highly dynamic
sea bottoms where these methods turn out to be tedious and unsuitable for frequently repeated
implementation.
This is the case of the coast of French Guiana, part of the muddiest coastal system of the
world, 1600km long between the Amazon river mouth (Brazil) and the Orinoco river mouth
(Venezuela). This system is principally governed by the huge sediment discharge of the
Amazon, estimated at 1.2 109 T/y. Part of the sediment supply (15-20%) is liquefied and
transported as a near shore fluid mud layer by waves and currents (Meade, 1985.; Froidefond
et al., 1988). This involves the formation of mud banks with intertidal mudflat areas that
typically extend over 2-4 km off shore. In such a specific environment, the main economical
impacts are related to dredging and shore defence. This last point is of the utmost importance
in the extensively cultivated coastal area of the north-west part of French Guiana. From an
ecological point of view, the understanding of the dynamics of mudflats ecosystems and their
relation to biodiversity remains a major scientific theme.
The present paper aims at investigating the possibility of deriving bathymetry from a high
rate of remote sensing images acquisitions. Several approaches based on remotely sensed data
have been developed in the past, and are briefly presented in section 2, with the objective of
examining their potential adaptation to the highly turbid and dynamic Amazonian coastal
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system. The shoreline detection method (noted SDM hereafter) seems to be appropriate to the
context of French Guiana and is investigated further. The method is tested from a set of six
images acquired during the summer 2003. The images initially covered 60km x 60km. A 30
km length coastline between the cities of Macouria and Kourou was extracted from the initial
data. This part of French Guiana coast is characterized by the presence of a huge mud bank
that has progressively migrated northwestward since 1991 and that now obstructs access to
the industrial port of Kourou which is permanently dredged since 2000. The difficulties in
establishing a Digital Elevation Model (DEM) for this specific coastal system from SDM are
successively pointed out in section 3.
As will be seen in the conclusion, this work does not provide a very accurate bathymetric
map, but the results could be merged with a priori geomorphic knowledge in order to gain a
better understanding of the evolution of the muddy sea bottom.

2. Bathymetric measurements from remote sensing data – presentation of various methods
Three remote sensing-based techniques have been recently investigated for bathymetric
measurements :
a) Water surface radiometry can be related to depth, allowing derivation of bathymetry
from multispectral imagery.
b) Depth variations produce an alteration of surface roughness, to which radar echoes are
sensitive.
c) Water-line delineations from remote sensing images at different tide levels provide a
set of contour lines that can be interpolated to generate a bathymetric map of the
intertidal area.

2.a) Variation of the water color with depth
To examine the suitability of this technique in the context of French Guiana, we use results of
a bathymetric survey realized during the summer 2002 along the 30km stretch of coast line
extending eastward of the Cayenne peninsula. The GPS horizontal positioning accuracy is
about –10 m and the water depth measurement precision is about –0.5 m. Measurement
techniques coupled echo sounding and conventional surveying to limit misinterpretation of
acoustic echoes which are strongly damped by the soft muddy bottom. Some bed sediment
samples were also collected in order to examine the sediment density distribution. Their
resolution is about –5%.
Results of this bathymetric survey are reported in figure 1a, superimposed on a SPOT scene
acquired during the measurement campaign. The inter-bank area shows an almost 1/1000
linear slope (or slightly convex shape) from -15 m to 0 m and a pronounced concave profile
above. The corresponding muddy bed sediment has a density of about 1450 kg.m-3. The mud
bank areas are rather different. They show a convex profile and are characterized by a mean
bottom layer density of about 1350 kg.m-3. The intertidal and adjacent subtidal areas of
mudbanks are characterized by very low slope, of the order of 1/2000. Beyond the 1m
isobath, the slope increases to about 1/1000 m. The under-consolidated bottom layer of the
mud bank extends 13-15 km offshore.
Under quiet hydrodynamic forcing by waves and currents, optical images can be
significantly correlated with water depth (Lafon et al., 2002 among others). This is the case of
the image acquired on 08/05/2002, as illustrated in figure 1a. In such a situation, a significant
correlation can be obtained between the contour of the most turbid coastal waters and isobaths
0-1m. This situation is characteristic of summer conditions, but is not usual. Most of the time,
the coastal fringe is characterized by highly turbid water as shown in figure 1b. In such a
situation, the turbid coastal waters are transported far from their source zones by turbid
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plumes easily detected on satellite images. The optical signature is no longer correlated with
water depth, and so this technique does not seem very appropriate for the Amazonian coastal
system.

SPOT@CNES August 5, 2002 SPOT@CNES July 2, 2001

Atlantic ocean
Turbid plumes

Mudflats
CAYENNE

Mahury river

Kaw river

Figure 1: a : Bathymetry and optical image (after Gratiot et al., 2003) ; b : (after Froidefond et al., 2004).

2.b) Variation of the sea surface roughness with depth
A recent study was carried out by Baghdadi et al. (2004) to examine the utility of RADAR
and optic images to studies of the dynamic coast of French Guiana.

a b

Mud lake

0 1 2 km

Smooth surface over a RADARSAT image
mudlake 2002/10/14

-5

Back scattering

coefficient
(dB) -10

c
-15
-0.1 0.3 0.7 1.1 1.5 1.9 2.3
dPerpothf o(mn) deur (m)
Figure 2 : Bathymetry from RADAR images (after Baghdadi et al, 2004).

The mudbanks delineations are more easily detected on low-incidence radar images than on
high –incidence ones. RADAR images turned out to be particularly suitable for the detection
of particular geomorphic features such as ‘mudlakes’ (see figure 2a and 2b) because of the
specular reflection. On this surface the fluid mud layer becomes so viscous that capillary
waves are utterly damped. For areas over mud bank, a slight dependence is noted between the
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RADAR signal and water depth where the latter is less than some 30cm (see figure 2c). This
dependence only occurs for very shallow water and disappears where the depth exceeds half a
meter. This is clearly not sufficient to realize a complete bathymetric survey of the intertidal
area.

2.c) Variation of the waterline delineation with depth
The last remote sensing method considered is the shoreline detection method. This method
has received increasing attention over the last ten years but to our knowledge, SDM has never
been applied to the Amazonian coastal system.
Among the previous studies, we can cite the work of Mason and collaborators, from ERS-
based images (Mason et al., 1995, 1997, 2001) and the work of Aarninkhof et al., (2003) from
video-based images. This latter investigation provides rapid and low-cost bathymetry
measurements over a single tidal cycle. It is particularly attractive in urban sandy beach
seafronts as it can be easily mounted on buildings or other structures. Several research
projects using this technique (ARGUS) were presented during ICS 2004 (JCR SI n°39, in
press). Theoretically, DEM can be obtained daily so that the even very rapid geomorphic
changes related to storm events could be routinely surveyed. The application to large
inhabited areas would require the existence of promontories to fix the cameras at elevations
that are sufficiently high as to cover portion of intertidal areas.
Along the coast plain of French Guiana, the extent of the intertidal area (~10km2) and its
instability necessitated a rethinking of this approach. Aerial video overcomes these
limitations, but the field of view is two small and the lack of permanent details on the ground
limits image-to image-registration. High resolution satellite remote sensing data proved to be
much more suitable to implement this method. The approach of Mason et al. (1995, 1997,
2001) in the study of large estuarine areas from ERS based-images fits much more with the
objectives and constraints met herein. It is considered as a guide for the present investigation.

3. Application of the shoreline detection method (SDM)
The SDM implies both instrumental and environmental constraints that are shown on the
flowchart in figure 3. The generation of a digital elevation model (DEM), from the set of
images available can be conceptually divided into three consecutive operations (blue cells on
fig 3). The first is the classification ; it provides isolines from the set of images at our
disposal; then, the shoreline elevation model uses the time dependent water elevation to
calculate isobaths from the isolines, and finally, the non-uniformly distributed numerical
information into a matrix through interpolation.
While Mason et al. (1995, 1997, 2001), based their work on ERS images, we chose optical
images. This choice was motivated by the investigation of Baghdadi et al. (2004) which
highlighted certain difficulties in discriminating the shoreline from RADAR images (band C)
along the coast of French Guiana, while MIR (Mid Infrared) and NIR (Near Infrared) spectral
bands of optical images revealed much more accurate results. The application of the SDM
was considered from a set of six SPOT satellite images, acquired over a month (08/13/03 to
09/19/03). Their characteristics are summarized in table 1 while images are presented in
figure 4.
A statistical analysis of the climate was performed to define the period over the year offering
the optimal conditions to test the SDM. Meteorological conditions (wind, rain, sunshine) are
of importance for the detection of the shoreline on each images, while the hydrodynamic
forcing (by waves and currents) limits the temporal window during which mud banks do not
present major geomorphic changes.
The equatorial climate of French Guiana is characterized by an important cloud cover which
often limits the use of optical sensors. Fortunately, wind conditions occurring in the coastal
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fringe involve frequent cloudless conditions at the hour of satellite acquisition (11-12 AM,
local time). This behavior is observed daily, all over the year, and is well illustrated by figure
4. Conditions of sunlight are optimal during the so called ‘dry season’, from August to
November with a mean monthly insulation of about 240 hours. Coastal winds blows gently
from June to August with a mean ten metre altitude value of about 3m/s. The hydrodynamic
forcing by waves and current is also moderate, with a mean statistical wave height of 1-1.2m
from June to September (over the period 1960-2003). Finally, the July-December period
corresponds to the retroflexion of the north-brazil current so that coastal waters are
significantly less turbid. The combination of all of these environmental constraints makes the
July- September period particularly attractive and justifies the temporal window considered
herein (Table 1).

Instrumental conditions Environmental conditions
satellite revisit period georeferencing
image resolution meteorological forcings hydrodynamic forcings
sun , clouds, rain tide, waves, turbidity, currents
image cost
classification shoreline elevation
model
set of
images yes isobathes
isolines
no
interpolation
DEM
geomorphic
evolutions
(x,y,z,t)

Figure 3 : Flowchart of the shoreline detection method.

Dated yaetea r 03 SSaatt.. RReessooll.. SBpaencdteras lS bpaencdtrs OOrriiggiinne
13A auogûuts t2 1030 3 SSPPOOTT 55 1100mm MMIIRR--PNIIRR--RR--VG IISSIISS

29A auogûuts t2 2090 3 SSPPOOTT 55 1100mm MMIIRR--PNIIRR--RR--VG IISSIISS

30A auogûuts t2 3000 3 SSPPOOTT 44 2200mm MMIIRR--PNIIRR--RR--VG PPNNEECC

8 sSepetp t2 080 3 SSPPOOTT 55 1100mm MMIIRR--PNIIRR--RR--VG IISSIISS

15 Sseepptt 21050 3 SSPPOOTT 44 2200mm MMIIRR--PNIIRR--RR--VG IISSIISS
19 Sseepptt 21090 3 SSPPOOTT 55 1100mm MMIIRR--PNIIRR--RR--VG IISSIISS

Table 1: set of images and main characteristics.

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13/08 0,87 29/08 0,91

30/08 1,12 08/09 1,3

15/09 1,68 19/09 2,21

Figure 4 : set of remote sensing images considered to test the application of the Shoreline Detection method.

3.a) Classification
With six images acquired over a month, the dataset has the highest repetition frequency never
reached along the coast of French Guiana. SPOT is an heliosynchronous satellite so that it
orbital cycle differs from the tidal cycle. Consequently, images can be acquired over the entire
tidal range. The images have a coverage of 60 x 60 km and a spatial resolution of a few tens
of meters. Thus, they are well adapted to the monitoring of the entire intertidal mud bank area
with an appropriate resolution. The optical sensors, which provide three or four spectral bands
(band xs1: 500-590 nm; band xs2: 610-680 nm; band xs3: 780-890nm; band xs4: 1580-
1750nm) in the visible and infrared domains offer a good tool for the identification of muddy
shoreline features. All images were geometrically processed in order to be compatible with a
cartographic referential. The datum used is WGS84 and the projection North UTM zone 22.
This processing was realized using a digitized topographic base map edited by the Institut
Géographique National (IGN n° 4711, 1:25000, 1989). Because of the instability of the
coastline, this part of the analysis is rather difficult and GPS (global positioning system)
points are necessary to geo-reference recent images. These initial treatments were realised
using ER Mapper (©) software version 6.21. Once geo-referenced, the spatial error is
estimated to be a half pixel, i.e. 5-10 meters. As the typical intertidal mudflats extension in the
cross shore direction is of 2-3 km, the relative error induced by the georeferencing is about
one per cent.
Because of the substantial effort and subjectivity involved in manual delineation, we opted
for an unsupervised classification method to identify the mud/water limit. Masks were applied
on both supra-tidal (ocean) and hyper-tidal (earth) areas on each image to restrict the
classification to the area of interest. Six isolines are determined, one for each image. They are
presented on figure 5.
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08/13/03
08/29/03
08/30/03

08/09/03
09/15/03
09/19/03 2500m

Figure 5 : Delineation of the shoreline from the unsupervised classification method.

Some segments considered as water-mudflat delineations by the unsupervised classification
method were manually rejected because they actually correspond to water-mangrove
delineations from which no bathymetric information can be provided. The application of the
SDM is restricted to bare mud areas so that particular attention has to be paid when applying
the classification to the upper mudflat area. This zone, subjected to very rapid plan
colonization processes, may be misinterpreted over a short time delay (about a month)
because of plant growth. However, zones of pioneer colonization usually have a specific
signature in optical images (PIR wavelengths) that differs clearly from the bare mud areas.
The spatial resolution attained at the end of the classification is not easily quantified while it
is almost impossible to make a field validation covering the whole intertidal area.
Nevertheless, field trips realized with a small boat at the vicinity of river mouths indicate that
the classification error can reasonably be estimated at about 30-50 meters. Furthermore, the
error varies with the local slope, which is itself a result of the DEM.
By examining the isolines presented in Figure 4, it appears that the blue one (acquired at
data 09/08/2003) crosses the isolines acquired at previous dates several times. These cross-
overs largely exceeds the 50m spatial error. Because mudflats do not exhibit cliffs or even
important slopes, a single X,Y position should necessarily be associated with a single altitude.
This postulate being rejected, it is almost sure that a major geomorphic event occurs during
the period of images acquisition. In the upper left corner, the blue line is very smooth when
compared to the previous dates. It is likely that the area has been covered by a massive supply
of fluid mud that smoothed out pre-existing geomorphic features. Such extreme events can
lead to the formation of a mudlake of several square kilometers in a few days (Lefebvre et al.,
2004).
To go further with the analysis, we examined wave activity during that period. As no buoy
data are available along the coast of French Guiana, we used predictions provided by Météo
France. The model simulates the generation of swells from wind stress measurements over
the Atlantic Ocean and calculates their propagation along the coast of French Guiana. The
data are reported in figure 6 with ticks labels at the date of images acquisition. Even if the
overall period is of moderate wave activity (H(cid:1)1.15m), a noticeable peak occurred on
s
September 5 (H(cid:1)1.35m), followed by a another on September 9 and 10 (H(cid:1)1.35m). This was
s s
the maximum wave height for about one and a half months so that it is reasonable to assume
that it could be at the origin of the modifications observed in the early September.

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Figure 6 : Variation of the significant wave height with time (time is associated to a number coded by
month*1e4+day*1e2+hour).

In figure 5, we may wonder why red and purple isolines, acquired after the blue isoline, do
not cross the black, green and yellow isolines. As will be seen hereafter, they were deduced
from images acquired at tide levels significantly higher so that only an extreme storm event
could have led to cross-over.
At the end of the classification, we can ever conclude that even during a statistically
appropriate season, the acquisition of a complete set of images over a month may be too long
to satisfy the condition of geomorphic stability of mud banks. This because some isolines
cross over many times. Within the scope of the objective of understanding the coastal
dynamics of this region it seems reasonable to accord more importance to the frequency of
DEM repetition rather than to the precision of individual DEMs.

3.b) Shoreline elevation model
This consists in the second operation of the SDM method. As illustrated on figure 3, the
hydrodynamic forcing by tide, wind and waves is of a greater importance here.
Conceptually, an isoline can be interpreted as an iso-bathymetric line, provided that the
mudflat remains stable during the one month observation, and that tidal modeling over
mudflat is satisfying, and that waves and wind-generated storm surges are adequately taken
into account. In most of coastal environments, these criteria can be validated or rejected
through objective information such as in situ measurements and numerical modeling, but this
approach is not fully appropriate along the coast of French Guiana. The first limitation comes
from the very poor permanently available field instrumentation. Along the entire coast of
French Guiana, they are very few tidal gauges and only two meteorological stations in
operation. There have been occasional measurements of coastal currents, temperatures and
salinites as part of scientific programmes and and sparse wave measurements. The second
limitation is due to the rapid geomorphic changes occurring in shallow water. As a
consequence of this, there are only few up-to-date bathymetric maps so that the application
and validation of shallow water hydrodynamic model is a challenging task for the future. For
the moment, it can not be implemented to the shoreline elevation model operation.
The tidal signal in the specific coastal area considered herein is estimated from online
prediction proposed by the French National Hydrographic and Oceanographic Service
(SHOM) at Devil’s Islands. The measurement point is located at about 20 km offshore of the
Kourou river mouth. A tidal gauge was moored on the islands during 892 consecutive days by
SHOM starting in 1992. 94 frequency components were extracted and are currently used to
predict the tidal signal. Tides are semi-diurnal and the tidal range is mesotidal. The time of
image acquisition allows estimation of the isobaths from isolines as reported in table 2
(www.shom.fr). The tidal range is well covered by the set of images at our disposal. Two tidal
gauges are presently running at the Kourou river mouth to ensure the navigational
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management activity. However they are strongly influenced by the river flow so that they are
not of significant interest in the context of this study.

Date Hs (in m) Hs (in m)
Year 2003 (SHOM) including T
s
August 13 0.87 0.92
August 29 0.91 1.00
August 30 1.12 1.20

September 8 1.3 1.22

September 15 1.68 1.77

September 19 2.21 2.21

Table 2: Prediction of the water level from SHOM at the Devil’s islands ; estimation at the Macouria river
mouths, taking into account the time delay of tide signal propagation.

The tidal signal observed on the mudflat can differ from the predicted one because of the
precision of the tidal estimate, but also for other reasons. While propagating along the coast,
the tidal signal can be significantly modified by the very shallow water of the mudflat areas. It
can also exhibit a time shift due to the distance between the mudflat and the Devil’s Islands.
Furthermore, local effects such as river flow can be additional sources of divergence in the
estimation of the tidal signal.
Tidal prediction, devil’s island

Tidal prediction Cayenne
Water depth measurement (wdm)
T(t)=z(t-13’)+155
wdm fitted on tidal
T(t)=z(t+19’)+169
predictions
wdm fitted on tidal
predictions
0 4 8 12 16 20
hours

Figure 7: Comparison of the tidal prediction and in situ water depth fluctuations.

To examine the suitability of the tidal prediction, we realized water pressure measurements
on five major intertidal mudflats along the coast of French Guiana (Fiot et al., 2004). The
Cayenne-Kourou mud bank, studied herein, was one of the study sites. The instrument was
moored over two tidal cycles in front of the Macouria river mouth (June; 1, 2004). Its
approximate location is reported in figure 4 as white dots. The time dependent water elevation
is presented in figure 7.
Once adjusted together, tidal signal T(t), and measured water depth z(t), show a good
correlation. There is a time shift of about -13’ when considering the eastward Devil’s Island
station and +19’ with the eastward Cayenne station, but overall the tidal signal is not too
much distorted when propagating over the mudflat. Small fluctuations superimposed on the
field data are due to the sea surface agitation, principally by waves, currents and river flood.
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(cid:1)(cid:2)(cid:3)(cid:4)(cid:5)(cid:6)(cid:2)(cid:7)(cid:8)(cid:6)(cid:9)(cid:2)(cid:10)(cid:11)(cid:6)(cid:9)(cid:12)(cid:4)(cid:13)(cid:11)(cid:9)(cid:14)(cid:2)(cid:9)(cid:12)(cid:4)(cid:15)(cid:16)(cid:17)(cid:18)(cid:11)(cid:14)(cid:10)(cid:19)(cid:17)(cid:20)(cid:4)(cid:5)(cid:21)(cid:7)(cid:12)(cid:9)(cid:6)(cid:22)(cid:4)
These fluctuations exhibit a mean standard deviation of about 5-7 cm that occurs at a high
frequency. When applied to other mudflats, the inter comparison of adjusted field data and
tidal prediction shows a maximal standard deviation of 7cm. This value is relatively low and
may be only indicative of error during moderate hydrodynamic forcing conditions.
This result is promising and highlights the suitability of the tidal prediction (using SHOM
data) to predict sea surface fluctuations. Providing that the time shift can be estimated
properly, the expected accuracy is even better than that reported by Mason et al. (1995), when
ran a hydrodynamic model (std=17.5cm).

3.c) Error and Interpolation
As a result of the first two operations, the Shoreline Detection Method provides non-
uniformly distributed X,Y,Z,T data points with a corresponding error. The time of image
acquisition can be considered as exact in the context of our application. The horizontal
precision was discussed in 3a while the vertical error was briefly introduced in 3b. This latter
is mainly due to the error in the estimation of the time shift of the tidal signal and of a poor
estimate of the wind and wave effects. As illustrated on figure 8, semi diurnal and monthly
tidal fluctuations imply a non linear response in the estimation of (cid:2)z. Overall, the error
increases with the time delay, if uncorrected. It is more important for spring tides than neap
tide because the water fluctuations are more rapid. In any case, the maximum error occurs at
mid tide level, because the first order derivate is maximal and the minimum error occurs at
high and low tide levels. Wind- and waves-generated surge are not dependent on the time
shift. From the previous section, they are estimated to be about 7cm during the moderate
forcing season. They could be much more important during the high-energy season (January-
May).

Figure 8: Variation of the vertical error with the tidal cycle.

The set of contour lines obtained from image segmentation at different dates has to be
converted into a surface model through a resampling operation. Since most output grid points
do not belong to any input contour line, the depth values need to be interpolated. A large
variety of interpolation algorithms are generally proposed by geoprocessing toolkits, and the
algorithm must be chosen carefully since it may have a major impact on the morphology of
the surface model.
In our situation, it was not possible to use an exact interpolator, i.e. an algorithm that would
preserve the values of input points, such as those based on a triangulation. Indeed, some
contour lines crossed each other due to local depth estimation errors, and this would have
produced a topological nonsense. To overcome this difficulty it is necessary to use an
averaging algorithm. Following the work of Mason et al. (1997), we opted for a smoothed
krigging method (Surfer© software). Averaging interpolators tend to minimize signal energy,
so that the output surface model is generally smoother than reality. According to our field
experience, the mud bank was naturally very smooth. Therefore, we consider that the
interpolation step has a limited impact on its geomorphic depiction
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