water quality monitoring systems that are both repetitive ... chlorophyll concentrations and Secchi disk depth (SDD) ...... European International Space.
REMOTE SENS. ENVIRON. 46:268-280 (1993)
Water Quality Monitoring in Estuarine Waters Using the Landsat Thematic Mapper Paul Lavery, *'t Charitha Pattiaratchi,* Alex Wyllie, and Peter Hick § D u e to the impacts of rural and urban development on southwestern Australian estuaries, and the general isolation of these water bodies, there is a need to develop water quality monitoring systems that are both repetitive and cost-effective. The literature suggests that Landsat Thematic Mapper (TM) has spectral and spatial characteristics that are suited to monitoring small coastal water bodies. This study examined the potential for the satellitebased TM sensor to serve as a regular monitoring tool. Atmospherically corrected TM digital data acquired on four dates over summer 1990/91 and concurrent field measurements collected at the time of the satellite overpass over the Peel-Harvey Estuarine System (32°30~S lat. 115°40PE long.) were used to obtain multitemporal, empirical algorithms for predicting pigment concentration, Secchi disk depth (SDD), and salinity. Highly significant, predictive algorithms were developed for these parameters. It is concluded that Landsat TM has the resolution and accuracy to be a potentially very useful monitoring tool. However, cloud cover and delays in data acquisition seriously diminish its usefulness for monitoring on anything less than a seasonal basis. Laboratory-based radiometric studies also indicated that Landsat TM was unlikely to be useful in determining the taxonomic composition of phytoplankton blooms in coastal waters.
INTRODUCTION The southwest region of Western Australia has over 40 estuarine systems ranging from small systems perma-
~Centre for Water Research, University of Western Australia, Nedlands, Australia. Present address: Botany Department, University of Western Australia, Nedlands, WA, Australia. Remote Sensing Applications Centre, Department of Land Administration, Western Australia, Australia. CSIRO Division of Exploration Geoscience, Western Australia, Australia. Address correspondence to Charitha Pattiaratchi, Centre for Water Research, Univ. of Western Australia, Nedlands, WA 6009, Australia. Received 14 July 1992; revised 6 March 1993.
268
nently closed to the ocean to large and permanently open systems. Population growth and intensive agriculture are having significant impacts on these estuaries and near coastal environments in the region. Among the more noticeable effects of discharging increasing loads of nitrogen and phosphorus into these systems have been increased frequencies of phytoplankton blooms in several estuaries and embayments and an associated decline in seagrass meadows (Hillman, 1986). Several systems, such as the Peel-Harvey Estuarine System (Lukatelich and McComb, 1986), have been monitored, and management strategies put into place; but a large number of estuaries are relatively inaccessible, and the cost of establishing regular monitoring programmes using conventional techniques are prohibitive. There is a need for cheaper and repetitive quantitative techniques for measuring water quality that will allow adequate management. Previous success in using remote sensing to assess water quality (Carpenter and Carpenter, 1983; Lillesand et al., 1983; Verdin, 1985; Rimmer et al., 1987; Lathrop and Lillesand, 1986; Dwivedi and Narain, 1987; L6pez-Garcfa and Caselles, 1990) and the relative cost effectiveness of satellite remote sensing (Haddad and Harris, 1985) indicate that satellite remote sensing is potentially an ideal survey system. Airborne multispectral scanner data have been used to assess water quality in Bristol Channel, U.K., with strong correlations between suspended sediment concentration (SSC) and visible TM-equivalent bands (Collins and Pattiaratchi, 1984; Rimmer et al., 1987). High correlations were also obtained with salinity but no correlation was found with chlorophyll concentrations, due mainly to spectral interference from the high SSC values. In contrast, Tassan (1987) found that TM data were at least comparable to CZCS data for predicting chlorophyll concentrations and concluded that TM Channels 1-4 should yield suitable data, if appropriate correction procedures are applied. This conclusion is supported by other studies that have found significant correlations between TM data in Channels 1-3 and chlorophyll concentrations and Secchi disk depth (SDD) 0034-4257 / 93 / $6. O0 ©Elsevier Science Publishing Co. Inc., 1993 655 Avenue of the Americas, New York, NY 10010
Monitoring Estuarine Water Quality
for coastal and inland water bodies (Lathrop and Lillesand, 1986; L6pez-Garcia and Caselles, 1987; 1990; Dwivedi and Narain, 1987; Bagheri and Dios, 1990). While the above suggests that Landsat TM data have the potential for estimating water quality in small coastal bodies, the practicality of developing algorithms and assessing their value to managers has been addressed in only a few cases. This study undertook an assessment of TM data as a tool for monitoring southwestern Australian estuaries using the Peel-Harvey Estuary as the study site. The Peel-Harvey Estuarine System (Fig. 1), located 70 km to the south of Perth along the western Australian coast, comprises two interconnected lagoons, with a maximum depth of 2.5 m, and a total area coverage of 130 km 2. Three major rivers discharge into the system: Murray and Serpentine into the Peel Inlet and the Harvey into the Harvey Estuary (Fig. 1) with 90% of the annual discharge occurring during the winter months. The catchment of the Harvey River consists of pasture land, which has been fertilized with superphosphate over the last 3 decades. The excess fertilizer runoff is the main source of nutrients into the system (Lukatelich and McComb, 1986). There have been in-
Figure 1. Location of study area showing the sea-truth sampling positions.
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creasing symptoms of eutrophication since the 1960s. Large quantities of rotting algae are blown onto what were once clean, sandy beaches in Peel Inlet in late spring and early summer, and there are massive blooms of the blue-green alga Nodularia in the Harvey Estuary. The chlorophyll concentrations within the blooms exceed 600 mg L-l and the Secchi disc depth is 0.20 m (Lukatelich and McComb, 1986). The seasonal cycle of the chlorophyll concentrations is related to the river (nutrient) discharge and the increase in salinity through evaporation. In general, the chlorophyll concentrations are low (4), and the ratio of the sum of the squared random errors (C,) and the number of coefficients in the regression analysis (p) should be < 1 (i.e., Cp/p< 1). Regression equations (algorithms) were then applied to the corrected data image to produce thematic maps of the various water quality parameters.
Radiometric Measurements Radiometric characteristics of different algae at the Landsat TM visible wavelengths were determined under laboratory conditions. This eliminated atmospheric interference in the radiance data, thereby optimizing conditions for determining differences in the algae with different pigment arrays. An Exotech Model 100A radiometer, with four wavebands approximating those of TM Channels 1-4, was used to measure reflectance from algal cultures in
the laboratory. Samples were illuminated by a Quartzhalogen lamp (UNIMAT, 1000 W) with spectral qualities close to those of sunlight (white light). Two species of phytoplankton were used: Nodularia (Cyanophyta), an algae which forms dense blooms in Harvey Estuary, and Chaetoceros gracilis (Bacillariophyta), a component of post-Nodularia and winter diatom blooms in the estuary. A concentrated sample of Nodularia spumigena was obtained from Peel Inlet while a culture of Chaetoceros gracilis (CS 176) was provided by the CSIRO Marine Laboratories. Phytoplankton samples were placed in a plastic container on a white (BaSO4) background with previously determined reflectance characteristics. The reflectance values of the phytoplankton solution for the four wavelengths were recorded for a 10 cm depth of the solution. Samples were successively diluted by 50% eight times, using filtered seawater. At each dilution the reflectance values were recorded and chlorophyll a, c and phaeophytin concentrations determined. The data were analysed using the regression techniques described earlier (the previous subsection).
RESULTS AND DISCUSSION
Field Data Between 1 December 1989 and 24 February 1990 there were 10 satellite overpasses of Peel Inlet. Of these, only four provided suitable conditions for remote sensing of the estuary (i.e., no cloud cover, high visibility, and relatively uniform atmospheric conditions over the region). Field reference data for these dates are presented in Table 1. Total pigment concentrations displayed a wide range on each sampling occasion. The maximum range of 2-280 gg L -1 was observed in December and was associated with the spring Nodularia bloom and consisted predominantly of chlorophyll a. The January pigment concentrations were associated with the postNodularia diatom bloom. On 14 January, chlorophyll c dominated. The minimum range of 2-31 gg L -1 was observed on 25 February and followed the decline of the diatom bloom (Table 1). Harvey Estuary had generally higher pigment concentrations than Peel Inlet. Salinity and Secchi depths also showed considerable variation on the sampling dates. The data set for 24 January 1990 coincided with the onset of the post-Nodularia diatom bloom in Peel Inlet and the occurrence of a bright green, and as yet unidentified, bloom. This resulted in considerable spatial heterogeneity in water color on that date.
Simple Regression Analyses The regression analyses and associated significance levels for each water quality variable, in relation to individ-
Monitoring Estuarine Water Quality
271
Table 1. Range of Environmental Variables Measured in the Peel-Harvey Estuary
Date 14 24 31 25
Dec Jan Jan Feb
Pigment Concentration (llg L - z)
Salinity (°/0o)
Temperature (o C)
2-280 1-208 1-210 2-31
22.6-34.7 31.0-37.5 32.7-37.5 34.1-36.2
22.3-24.4 20.4-23.3 19.2-22.7 24.7-27.5
Secchi Disk Depth (m)
Comments
0.25-3.0 0.5-4.5 0.6-6.5 0.5-4.5
Nodularia bloom, low water at 11:45 a.m. Diatom + "green" bloom, low water at 12:46 p.m. Diatom + "green" bloom, low water at 12:15 p.m. "Green" bloom, low water at 8:30 a.m.
Table 2. Results of Multidate Linear Regression Analysis (Expressed as re) Showing Only Significant Channels a Channel Variable
1
2
3
4
log~0 C SDD Salinity
0.045* 0.173t 0.324t
0.087* 0.204? 0.383t
0.194' 0.147t 0.446t
5
0.642t
0.364t
6
1/2
0.386t
0.122t 0.082t
1/3
2/3
0.130# 0.426t
0.505* 0.300? 0.139t
" Bands imagine outside visible wavelengths were not included in log C and SDD regressions. * denotes significance at 0.05; + denotes significance at 0.001.
ual remotely sensed channel data and ratios, are presented in Table 2. All visible and ratios of visible bands (Bands 1-3) were significantly predictive of log C with the exception of the Band 1/2 ratio. Variations in log C were most adequately explained by the ratio of Bands 2 and 3, which explained 50% of the variation in a highly significant regression (p= 0.0001). These same bands were also significantly predictive of variations in SDD (p = 0.0001 in all cases), although no single variable explained more than 42% of the variation. Variation in Band 4 (near infrared) explained 64% of the variation in salinity, although seven other single bands or ratios of bands were also significantly correlated to variations in salinity.
Pigment Concentration
Pigment concentration, measured as the total chlorophyll concentration (log C), in the combined PeelHarvey Estuaries, was significantly correlated with the ratio of Bands 2 and 3, and accounted for 71% of the variation with a standard error (s.c.) of 0.27 (Table 3). Treated in isolation the Harvey Estuary pigment data showed greater correlation with TM data (r2=0.76, s.e. = 0.21), than when treated in conjunction with Peel Inlet data. This is evident in the reduced scatter of the observed versus predicted plot for log C (Fig. 2) and is probably due to the almost one-dimensional variation in surface water quality of Harvey Estuary, along the north-south axis. The band selection for the algorithms in this study contrast those of other studies, such as Tassan's (1987) TM algorithm for log C, which used the ratio of visible Bands 1 and 2. Tassan's algorithm was derived from the CZCS algorithm for Case 1-oceanic waters-whereas the Peel-Harvey Estuary is closer to a Case 2 water body with materials other than chlorophyll contributing to water color. This probably accounts for the inclusion
Multiple Regression Analyses The multiple regression algorithms for all water quality parameters showed differences in either variable selection or statistical quality between the two estuarine systems in three ways (i.e., Peel and Harvey Estuaries together, Peel Inlet and Harvey Estuary each in isolation).
Table 3. Results of the Multidate Stepwise Multiple Regression Analyses for Log Chlorophylla System Harvey(H) Peel (P) P+H
Bands Ratios Intercept 2/3 2/3 2/3
3.65 3.09 3.28
A2J3
re
s.e.
F / F~r
Cp
- 1 . 9 1 0.758 0.211 16.402 0.045 - 1.61 0.719 0.267 33.061 0.071 1.73 0.712 0.273 45.454 0.074
C~ / p p-Value 0.022 0.036 0.037
0.0001 0.0001 0.0001
° FIF~, Cp/p, and Cp refer to the selection criteria of Whitlock et al. (1982): see text. Equations are of the form Y= intercept +AiR~, where R~ is the radiance in the ith band and A~ is the coefficient for that ratio, p is the significance level.
272 Lavery et al.
3
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(b)
(b)
Figure 2. Comparison between pigment concentra-
tions (log C) predicted from the algorithms developed in this study and the measured values for a) Harvey Estuary and b) Peel Inlet.
Figure 3. Comparison between pigment concentrations (log C) predicted from the Tassan (1987) algorithm and the measured values for a) Harvey Estuary and b) Peel Inlet.
of Bands 2 and 3 in the algorithms. Variation in water constituents may account for the differences in variable selection for this study and those in other studies of coastal waters (L6pez-Garcia and Caselles, 1990; Bagheri and Dios, 1990), although these other studies did not include ratios of channels in their analyses. In a similar study to that reported here, undertaken in a coastal embayment in which only chlorophyll was contributing to the upwelling radiance, that is, Case 1 waters, Pattiaratchi et al. (1992) found that the ratio of
Bands 1 and 2 produced the best model for the prediction of the chlorophyll concentration. This demonstrates that, as expected, band selection in the models are dependent on the constituents in the water column responsible for the upwelling radiance. The empirical algorithms developed in this study were compared with the log C algorithm developed for TM data by Tassan (1987) by applying both algorithms to the TM data collected for this study. In all cases of Peel and Harvey together or Peel and Harvey Estuaries
Monitoring Estuarine Water Quality
273
Table 4. Results of the Multidate Stepwise Multiple Regression Analyses for Secchi Disc Depth ° System
Bands
Int.
A2
All3
H P+H P
2, 1/3 3, 1/3 1/3, 1/2, L2/3
1.06 0.745 0.491
- 0.047
0.540 1.80 9.37
A3
A213
A112
re
s.e.
F / F,,,
0.749 0.107 0.808 0.402 0 . 4 9 0.753 0.362
- 0.05 -6.30
Cp
C~,/ p
p
7.50 0.012 0.004 0.0001 39.91 0.161 0.053 0.0001 18.20 0.131 0.033 0.0001
F/F 1 5 / , t g L -l (Robinson, 1985). In this case, no statistically significant regression was found when data points over 15 /2g L -x, 50 /2g L ~, and 100/2g L -~ were analyzed in isolation. The preferred regressions for prediction of log C then are (Table 3) Peel inlet: TM2 log C = 3.09 - 1.61 -TM3' -
r2 = 0.72, s.c. = 0.27;
Harvey Estuary: TM2 log C = 3.65 - 1.91 - TM3'
r2 = 0.76, s.c. = 0.22.
The band selections for these algorithms are satisfactory from a physical basis, being visible wavelength bands, and from a statistical basis with high r ~ values, and the selection criteria of Whitlock et al. (1982) being met in all cases (Table 3). The regression standard errors vary from 0.21 to 0.27 log units, corresponding to + 1.6-1.9 /~g L - ~. These standard error values are not much more than a realistic estimate of the errors associated with the spectrophotometric determination of chlorophyll concentration in the range found in Peel Inlet over spring and summer. As such, the algorithms could be confidently expected to predict upward of 70% of the true quantitative chlorophyll variation in the system and will be excellent predictors of the qualitative variation. Secchi Disk Depth Secchi disk depth (SDD) was modeled with different variables for the two estuaries treated together and in isolation (Table 4). In Peel and Harvey Estuaries combined, SDD was predicted from Band 3 and the ratio of visible wavelength Bands 1 and 3. A plot of predicted versus observed SDD shows little bias over the range of observed values (Fig. 4). The regression
explained 81% of the variation in SDD with a standard error of 0.40. Peel Inlet SDD data in isolation yielded a correlation with three variables and a decreased r2 of 0.75. Harvey Estuary data also yielded a similar model (re = 0.75). Therefore, the combined Peel and Harvey regression equation, having the highest re and satisfying the selection criteria of Whitlock et al. (1982), was considered the most satisfactory (Table 4): TM1 SDD = 0.74 - 0.05 TM3 + 1.80 TM3'
r e = 0.81, s.c. =0.40.
Again, factors other than chlorophyll may contribute to the use of different variables to predict SDD and the increased r ~ compared with the pigment algorithms. As well as providing a useful prediction of SDD, this algorithm could be used to determine attenuation coefficients in the system using relationships between Secchi disk depth and attenuation coefficients (Lavery et al., 1989). Salinity The salinity algorithms varied between estuaries, but in both cases explained at least 74% of the variation in Figure 4. Comparison between predicted Secchi disk depths (SDDs) and the observed values. 6 R2 = .80 i= v t'~
4 ¢-
0
I•
E
I-
0
I
I
2
4
Observed S D D (rn)
6
274
Lavery et al.
Table 5. Results of the Multidate Stepwise Multiple Regression Analyses for Salinity~ System Bands P+H P H
4, 5 4 7
Int. 36.27 36.72 35.55
A4
A5
- 0 . 5 4 0.295 -0.27
A~
r~
s.e.
F/F~.
Ct,
Cp/p
p
0.741 1.477 38.198 2.181 0.727 0.0001 0.749 1.140 44.761 1.299 1.650 0.0001 - 0 . 9 2 0.779 1.748 17.284 3.055 1.502 0.0001
° Only the final regression is presented in each case. F / E . , Cp/p, and Cp refer to the selection criteria of Whitlock et al. (1982). Equations are of the form Y= intercept + A~R,, where Ri is the corrected radiance in the ith band and A, is the coefficient for band. p is the significance level.
salinity (Table 5). For the combined Peel and Harvey Estuaries, salinity was initially correlated with Bands 3, 4, and 5 (r2 = 0.74). Omission of the visible wavelength Band 3 only slightly affected the regression (Table 5), which showed no bias in predicting salinity over the entire range of observed values. For the Harvey Estuary data, infrared Band 7 was selected in the regression analysis (r 2 = 0.78), but was a poorer predictor of low salinities (Fig. 5). The low number of data points at the lower end of the salinity range may have exaggerated this effect. Strong intercorrelation between water color and salinity was evident in Peel Inlet where initially two visible wavelength bands (2 and 3), as well as the infrared channel (4), were selected in the regression analysis. However, omission of the visible bands still yielded a significant regression (Table 5). Since the Harvey Estuary regression had a slightly higher r 2 value than the combined Peel and Harvey regression, it seemed appropriate to use separate salinity algorithms for Peel Inlet and Harvey Estuary. Variations in salinity for the two systems are described by (Table 5)
40
'i
R2 = .779
30
b3
20 20
a
I
t
30
40
Observed Salinity (ppt)
4O
r 2 = 0.75, s.e. = 1.14;
Harvey Estuary: salinity = 35.55 - 0.92 TM7,
o /
•
(a)
Peel Inlet: salinity = 36.72 - 0.27 TM4,
Figure 5. Comparison between salinity predicted from the algorithms developed in this study and the measured values for a) Harvey Estuary and b) Peel Inlet.
R2 = .749
J
r 2 = 0.78, s.e. = 1.75.
The reason for the inclusion of the SWIR band data, which has also been reported in other studies, may be based on the inverse relationship between salinity and land-derived dissolved organics (gelbstoffe) that have an absorption peak in the infrared region (Khorram, 1982; Rimmer et al., 1987). The inclusion of visible data should have increased the r2 of the regressions, through the effect of salinity on the phytoplankton composition and the indirect relationship between water clarity and salinity, that is, the clearer ocean waters have a different salinity when compared with the pigment laden waters in the estuary. There may also have been an indirect relationship related to the surface conditions. All infrared energy should have been absorbed at the surface of the water. However, omission of these data was justified
r
/
I
35 00
00
•
o•
3O
25 25
I
I
30
35
Observed Salinity (ppt) (b)
40
Monitoring Estuarine Water Quality 275
since they represent correlations prone to seasonal variation, rather than truly predictive models.
Spectral Measurements of Phytoplankton Chaetoceros pigment concentrations ranged from 17 /~g L -1 t o 3900 /~g L-1 and comprised chlorophyll a, phaeophytin a, and chlorophyll c, the last accounting for the majority. Nodularia pigment concentrations, measured as chlorophyll a + phaeophytin a, were in the range 0.5-10,000 Hg L-l; however, the phycocyanin pigment contribution was not measured. Simple Regression Analyses In both algal types, there were significant correlations between the pigment concentration (as log C) and Bands 1, 2 and 3, which were also highly intercorrelated (Table 6). Chaetoceros pigments were most strongly correlated with Band 1, while Nodularia pigments were most significantly correlated with Band 3. For both algae, ratios of bands provided the highest correlations against log C. It was found that the ratio consisting of the lowest correlated band with algal types (Band 4) and the highest correlated band with algal types (Bands 1 or 3) provided the most significant relationships. Multiple Regression Analyses Log C of the diatom samples was significantly correlated to ratios of Bands 1 and 2, and Bands 1 and 4 (rz = 0.71 and 0.98). The final multiple regression equation, which also included Band 1 data, accounted for 99% of the variation in pigment concentration (Table 6).
For Nodularia, Band 3 and ratios of Bands 1 and 4 and of Bands 3 and 4 share the maximum simple regression (r 2 = 0.91) but the multiple regression analysis used only the ratio of Bands 3 and 4 (Table 6). Intercorrelation may prevent the addition of Bands 1 / 4 or 3 contributing any significance to the regression. As with the simple regression, the multiple regression accounted for 91% of the variation in log C. Despite the selection of different bands in the regression models for diatom and blue-green algae, the high intercorrelation of all visible bands reduces the validity of these regressions, and therefore the usefulness of those bands in determining the taxonomic composition of phytoplankton responsible for zones of chlorophyll concentration. Laboratory spectral measurements were free from atmospheric interference and should represent the optimum conditions for detecting variations in the predictive bands for different algal groups. Since these results were at best inconclusive, it is unlikely that satellite TM data will provide any useful information on taxonomic characteristics.
Phytoplankton and Circulation Dynamics In addition to providing quantitative correlations with log C, SDD, and salinity, the data also allowed monitoring of other processes within the estuarine system. The development of the spring phytoplankton bloom in Harvey estuary and its subsequent dispersal into Peel Inlet are displayed in the time-series images of log C in Figures 6 and 7. Initially, there are high concentrations
Table 6. Cross Correlation Matrices for Pigment vs. Radiometric Data of Chaetoceros (Top) and Nodularia (Bottom)a Chaetoceros gracilis Pigment Conch Band1 Band2 Band3 Band4 Band1~2 Band1/4 log C Total Band Band Band Band Band Band
pig. 1 2 3 4 1/2 1 /4
-1.000
0.981 0.837 1.000
0.977 0.731 0.981 1.000
0.977 0.865 0.998 0.967 1.000
0.681" 0.970 0.717 0.580 0,754 1.000
0.707 0.471" 0.789 0.791 0.776 0.383* 1.000
0.984 0.834 1.000 0.982 0.998 0.710 0.787 1.000
Nodularia spumigens Pigment Conch log C Total pig. Band 1 Band 2 Band 3 Band 4 Band 3 / 4
1.000
Band 1
Band2
Band3
Band4
0.951 0.595* 1.000
0.947 0.549* 0.995 1.000
0.954 0.614" 0.999 0.990 1.000
0.155" 0.781 0.035* 0.084* 0.013" 1.000
One asterisk means not significant at a = 0.05.
Band3/4 0.954 0.648* 0,995 0.981 0.998 0.030* 1.000
276 Lavery et al.
(a)
(b)
(c)
(d)
Figure 6. Maps of pigment concentration in Peel Inlet predicted from the multitemporal algorithms: a) 14 De-
cember 1989; b) 24 January 1990; c) 31 January 1990; d) 25 February 1990.
of pigments (log C) throughout Harvey Estuary in December and January (Figs. 7a,b,c) that decline by midFebruary (Fig. 7d), with the higher concentrations restricted to the poorly flushed, southern extremity of the Estuary. The log C images of Peel Inlet show late summer decline in biomass even more dramatically (Figs. 6a-d). The Peel Inlet images also reveal a consistently higher concentration of pigments in the southeast of the Inlet (Fig. 6). The elevated concentrations in this area are due largely to the banks of unattached benthic macroalgae, which accumulate in the shallows of this embayment.
The images depicting Secchi disk depth reveal areas of relatively high light penetration on all occasions (Fig. 8). These areas are associated with incoming clearer oceanic water. This indicates both the extent of incoming tidal exchange, and therefore oceanic flushing, as well as allowing an interpretation of the predominant circulation patterns in the system. On most occasions, the incoming tidal waters moved into the deep central basin of Peel Inlet, where relatively high SDDs were observed. Presumably the tidal inflow does not penetrate as effectively into the shallow peripheral platforms of the system. The image for 25 February 1990 (Fig.
Monitoring Estuarine Water Quality 277
(a)
(b)
(c)
(d)
Figure 7, Maps of pigment concentration in Harvey Estuary predicted from the multitemporal 'algorithms: a) 14 December 1989; b) 24 January 1990; c) 31 January 1990; d) February 1990.
8d), obtained in the early part of the incoming flood tide (low water at 0830, Table 1), clearly shows the oceanic waters (higher SDD) moving from the entrance channel at the north and spreading south, east, and west in the deep basin with a distinct difference in the clarity of the incoming waters and those in the estuary. In particular, the tidal waters moved east towards the Murray and Serpentine River mouths, along the edge of a peripheral platform where the water depth was < 1 m but not onto the platform itself (Fig. 8d). An image taken approximately 3 h before low water (on 24 January 1990, Fig. 8b) shows the existence of an anticlockwise eddy located in Peel Inlet.
Hence, the thematic maps of pigment concentration and Secchi disk depth not only show the temporal variability of the water properties within the estuarine system, but also indicate some fundamental mixing and transport patterns within the system. CONCLUSIONS The results confirm that satellite remote sensing, and in particular Landsat TM data, can be used effectively to determine surface water characteristics of coastal water bodies. Similarly, remote sensing can also provide a synoptic view of processes occurring in an estuary
278 Lavery et al.
i .j
t~
(a)
(b)
!,
L
(e)
(d)
Figure 8. Maps of Secchi disk depth predicted from the multitemporal algorithms: a) 14 December 1989; b) 24 January 1990; c) 31 January 1990; d) 25 February 1990.
and time-series images can allow at least a qualitative assessment of processes, such as algal bloom development and tidal intrusion. In contrast, the study revealed that radiance measurements, especially from satellites, are unlikely to be useful in determining the taxonomic nature of algal blooms. Algorithms were successfully developed for pigment concentration (log C), SDD and salinity. Log C algorithms accounted for upwards of 72% of the variation over the entire concentration range likely to be encountered. The algorithm for Secchi disk depth was applicable to the entire system and could also be used to estimate attenuation coefficients. Separate salinity algo-
rithms for Peel Inlet and Harvey Estuary explained up to 78% of the variation in salinity. Both the SDD and salinity algorithms determine areas where clearer, oceanic water dominate and, therefore, in addition to their primary functions, they provide a tool for qualitative estimation of circulation patterns. All of the algorithms were statistically significant and satisfied the criteria established for selecting water quality algorithms from remotely sensed data, as suggested by Whitloek et al. (1982). Each algorithm had a sound physical basis and fulfilled the expectations based on the reflectance parameters. This strongly supports the validity of TM data as a tool for estimating water
Monitoring Estuarine Water Quality
quality in small coastal water bodies and confirms, to a degree, its value as a monitoring tool. While the results of this study indicate the potential for TM data in estimating water quality parameters, it is acknowledged that this study was empirically based and, therefore, offered the best probability of developing successful water quality algorithms. The physical principles determining the band selection for the various algorithms in this study will be applicable to other systems. However, since the algorithms are empirical, the parameters are likely to be different in other systems. This highlights the importance of reducing the reliance on empirical data, if the full management potential of satellite imagery is to be realized. In the course of this study, it became apparent that the incorporation of remotely sensed data into a monitoring program would involve overcoming a number of logistical problems. The principal problems are: cloud cover, time delays in data acquisition, and data processing and availability of suitable computing facilities. There was a low success rate (4 out of 10 days) in gaining suitable atmospheric conditions coinciding with satellite overpasses. The 40% success rate was lower than the percentage of cloud free days observed over this period (range of 47-57% for December 1989 to February 1990) and was considerably lower than the long-term average of numbers of cloud-free days for this period. This indicates that despite an excellent predictive capacity, there is a low potential for satellite remote sensing as an independent monitoring tool in areas with a moderate probability of cloud cover. In these areas, however, satellite remote sensing may provide a supplementary tool to reduce the cost of overall monitoring programmes. Given that in many cases the managers may only require irregular, qualitative information on a particular system, satellite data may still be an attractive alternative to the current paucity of monitoring. The authors gratefully acknowledge the assistance of Mr. G. Davies and Mr. V. Hosja (Waterways Commission), Mr. W. Russell (CSIRO), and Mr. R. Stovold (Department of Land Administration), in collection of ground-truth data; the Department of Land Administration and the CSIRO kindly provided image processing facilities. This research project was funded by the Waterways Commission of Western Australia and the Environmental Protection Authority of Western Australia.
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