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MAPPING SHALLOW WATER SEAGRASS WITH

LANDSAT TM SATELLITE DATA IN TORRES STRAIT

Mervyn Thomas

Brian Long

Thomas Taranto

June 1997

REPORTMR-GIS 97/6


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T O R R E S S T R A I T S E A G R A S S M A P P I N G 2

Executive Summary

The objective of this study was to map the shallow water seagrass beds of northwesternTorres Strait, using Landsat TM satellite data. The study area was 4,545 km2. It was

sampled by divers in November / December 1993 at 251 sites. Percentage cover ofseagrass, water depth and substrate type were recorded at each site. A spatial statisticalmodel was developed, relating seagrass cover with the pixel values of blue, green and redlight recorded by the satellite. The usefulness of this model for prediction of seagrassdensity from satellite imagery was assessed. Landsat TM satellite data did not provide anacceptable basis for spatial prediction of seagrass density outside the area sampled.Nevertheless, Landsat TM data may be useful in improving the interpolative mapping ofseagrass density withinthe sampled area. In this study it improved the predicted residualsums of squares statistic by 2.3%.

IntroductionSeagrass is critical habitat for dugongs, turtles, and some commercially important prawnsand fish. Torres Strait has one of the largest areas of seagrass in Australia; seagrass bedsin Torres Strait are larger than the Gulf of Carpentaria by a factor of 10 and areequivalent to the total estimated area of seagrass along the Queensland coast (Long andPoiner, 1997). Torres Strait supports one of the largest populations of dugongs in the

world which is a reflection of the importance of seagrass there.

The attenuation of light through the water column is a major limiting factor for the useof Landsat TM technology to map subtidal habitats. Red light penetrates to 5 m, green

to 15 m and blue to 30 m in waters with moderate suspended sediments (typical ofcoastal waters in the Great Barrier Reef lagoon. Much of central Torres Strait, however,is shallow - with large tracts of seabed < 15 m deep. Thus Landsat satellite data mayprove useful for mapping the shallow water sub tidal habitats in much of the TorresStrait region. The purpose of this study was to test the utility of Landsat TM satellite datato map seagrass beds in northwestern Torres Strait where water depths are < 15 m formost of the study area.

Materials and Methods

Description of the study area

Torres Strait lies between the NW coast of Cape York Peninsula and the S coast ofPapua New Guinea, and connects the Coral and Arafura Sea (Fig. 1). Wolanski et al.(1988), Harris (1988) and Bode and Mason (1994) have described the physicaloceanography and sedimentary geology of the Torres Strait. The Straits are shallow (< 15m) with strong tidal currents due to large pressure gradients between the Arafura andCoral Sea (Bode and Mason, 1994). Water speeds exceeding 2.5 m.s-1occur in the narrowchannels between some islands and reefs (Admiralty, 1973).


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100 km500

Cape York

Moa Is.Badu Is.

Orman Reefs

Dauan Is.

Boigu Is.

Buru Is.

Aldai Reef

Mabuiag Is.

AUSTRALIA

Torres Strait

PNGMai River

142 143 E

10 S

N

Figure 1. Map of Torres Strait, showing the boundaries of the study area sampled for seagrass inNovember 1993.

The strong tidal currents have created sand waves in many areas of Torres Strait (Harris,1988) including north western Torres Strait. There are two distinct seasons in TorresStrait: a dry season and a wet season. The dry season runs for seven months from May toNovember with an average rainfall of 21.4 mm month-1. The wet monsoon season lastsfor five months from December to April with an average monthly rainfall of 311 mm at

Thursday Island (Admiralty, 1973). The prevailing winds for the two seasons are alsodistinct. During the dry season, south-east trade winds blow from E and SE 90% of thetime. Wet monsoon winds are more variable; blowing from the NE, N and NW for 30%of the time. The average wind speed is lower in the wet monsoon, 5 knots.h -1, than dryseason, 7.9 knots.h-1, and the number of calm days is also lower in the dry season, < 1

day.month-1 than wet monsoon, 2.1 days.month-1. There are more gales during themonsoon than dry season (6 and < 1 days.month-1respectively). There is little net flowof water through Torres Strait although there are seasonal differences in the direction ofnet flow. The dry season has a net westerly flow with the south-east trade winds There isa net eastwardly flow over the wet monsoon season, when westerlies and north westerliesprevail (Wolanski et al., 1988). The winds and currents stir up the bottom sediments inshallow water areas of central Torres Strait which results in a turbidity maximum zone incentral Torres Strait (Harris, 1988).

The southern limits of the study area were Badu and Moa Island, situated mid-way acrossthe Straits. Boigu and Dauan Island near the S coast of Papua New Guinea formed thenorthern boundary. The eastern limit of the study area was formed by a line NE from


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Moa Island through the Orman reefs and N to Dauan Island The 142 nd meridian oflongitude formed the western boundary (Fig. 1).

Field sampling: Inter-reefal areas

The seagrass at 251 subtidal sites in the study area were sampled in November /December 1993 (Fig. 2). The study area was first divided into primary sampling units

which were each 4.5 km east-west and 4.2 km north-south. The primary sampling unitarea, 18.9 km2, was chosen on the basis of three factors:

estimated time taken to sample a site (15 min),

time to travel between sites,

total time (three weeks) available for field sampling.

All primary sampling units were sampled, giving complete sampling coverage of thestudy area. It was impractical to sample the whole primary sampling unit (18.9 km2), and100 m2 sites were sampled in each unit. The position of each site within each primarysampling unit was chosen randomly. Global Positioning System (GPS) satellite navigation

was used to locate the sites in the field. At all sites divers searched an area ofapproximately 100 m2and estimated the percentage cover of seagrass and algae as well asrecording descriptions of the substratum. Seagrass samples were also collected, sortedand enumerated to species level, but for this study we only used the presence/absencedata. Water visibility (m) and water depth (m) were also recorded at each site and asediment sample was taken for grain-size analysis.

Figure 2. Map of Torres Strait study area showing sites sampled for seagrass.

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0 25 50 75 Kilometers

N

# Recentsamplesites

Torres100k Basemapforeshoreislandmainlandreef


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Data analysis

The data analysis occurred in two stages. In the first stage, and exploratory analysis wasused, to identify the appropriate functional form of predictive relationships, and tocharacterise large scale spatial trends and small scale spatial dependencies. The second

stage involved estimating the parameters of a predictive relationship, using generalisedleast squares, based on a spatially structured error covariance matrix. The predictiverelationship was then assessed using cross validation.

The exploratory analysis was based on a generalised additive model (GAM) (Hastie andTibshirani, 1990). Square root of percentage cover of seagrass was analysed using anormal error structure with a constant variance. Smoothing spline models were fitted toeach wavelength, to and to sample depth. Large-scale spatial trends were modelled usinga loess smoother in two dimensions (Easting and Northing). Partial residual plots wereused to display the fitted relationships for each predictor.

Small-scale spatial dependence was investigated using the residuals of the GAM model. Arobust semi-variogram (Cressie and Hawkins, 1980) was calculated and plotted. Thesemi-variogram displays half the variance of the difference between two residuals, as afunction of the respective inter-site distances. There is evidence of small-scale spatialdependence if the semi-variogram increases as distance between the sites increases.

An exponential theoretical semi-variogram model was fitted by eye. The model was ofthe form:

ij = e-dij

where

ij is the correlation between sites iandj, and

dijis the distance between the sites.

Informal fitting was appropriate, since the successive distance variance pairs of thesemi-variogram are not statistically independent. In this circumstance estimation by leastsquares has little advantage over a less formal approach.

In the second stage of the analysis, parameters were estimated using generalised least

squares, and a spatially structured covariance matrix incorporating the fitted semi-variogram. F tests were used to test the importance of large-scale spatial trends, depthinformation and image variables.

Model assumptions were investigated using graphical displays of residuals.

The importance of the Landsat TM image variables was investigated using crossvalidation. Cross-validated predicted residual sums of squares (PRESS statistics) wereobtained for models with and without the image variables. That is, each observation wasdropped in turn, and generalised least squares estimates of the parameters were obtained.

These parameters, together with the covariance of the errors for the dropped point and

all other points were used to predict the sea grass cover for the dropped observation. Theprediction may be regarded as a form of universal Kriging (Ripley, 1981). After


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predictions were produced, the dropped observation was re-instated and the nextobservation dropped. The process was repeated until all observations had been droppedin turn, and predicted residual sums of squares (PRESS) were accumulated.

All calculations were performed with Splus

Results

Seagrass

Seagrass was found throughout the study area but highest covers were generally found inthe southern half of the study area (Fig. 3).

Figure 3. Bubble plot of percentage seagrass cover in the Torres Strait study area.

Water depth

The depth of water in the study area was shallow (< 17 m) and deepest areas were in thenorth east (Fig. 4). Only 17.5 km2 was deeper than 15 m, the penetration limit of greenlight in this water type. More than half the area (54%) was shallower than 7 m (Table 1).

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Torres 100k Basemapforeshoreislandmainlandreef

%Seagrass cover# < 1%# 1-15# 15- 35# 35-60

# 60- 95

0 25 50 75 Kilometers

N


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Table 1. Breakdown of areas by 1 m depth intervals for the Torres Strait study area.

Depth interval Area (km2) Depth interval Area (km2)0-1 25.061-2 160.27 9-10 375.28

2-3 233.57 10-11 257.893-4 258.06 11-12 156.314-5 406.42 12-13 60.615-6 747.72 13-14 27.926-7 662.97 14-15 21.267-8 651.03 15-16 16.828-9 482.49 16-17 0.78

0 10 20 30 40 50 Kilometers

Depth

1-56-78-9

10-1112-17

N

Figure 4. Depth contours (m) for the Torres Strait study area.


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Exploratory analysis of Seagrass Cover

The functional form of the relationship fitted for each predictor was displayed usingpartial residual plots. Figure 5 shows the smoothing splines and partial residuals forintensity in the Red, Green and infrared bands and for depth. The solid lines represent

the fitted smoothing spline, and the broken lines represent the standard error. Given thesize of the standard error, the Red band intensities seem to be adequately modelled bylinear trends, but there does seem to be some evidence of curvature for the Green andinfrared bands, and for depth. Curvature in the Green band relationship seems to berestricted to the highest few values, suggesting that percentage coverage of Seagrassdrops off more rapidly with intensity of the green band at high vales of the green bandthan at low values. All of the plots show a high degree of variation in seagrass coverage.

Red Band

SmoothTransforma

tion

20 25 30 35

-0.4

0.0

0.4

0.8

Green Band

SmoothTransforma

tion

25 30 35 40 45

-1.5

-0.5

0.5

Infra Red Band

SmoothTransformation

5 10 15

-0.4

0.0

0.4

Depth

SmoothTransformation

6 7 8 9 10

-0.4

0.0

0.4

0.8

Figure 5. Smoothing Splines Fitted to Predictor Variables

Figure 6 shows the spatial trend fitted by loess estimation. There is a high density regioncentred on Longitude 142.3, Latitude 9.8, and a low density region in the north eastcorner of the study area.


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Longitude

Latitude

142.0 142.1 142.2 142.3 142.4 142.5

-10.0

-9.8

-9.6

-9.4

-0.2 0.2

Figure 6:The Spatial Trend Surface fitted to partial residuals by Loess estimation

No formal tests of linear or non linear dependence of seagrass cover on predictorvariables were performed in this analysis. The purpose of the exploratory investigation ismerely to identify appropriate models and error structures.

A histogram of residuals was plotted, and residuals were plotted against fitted values(Fig. 7). Several problems were evident. First, a number of linear bands of residuals canbe seen in the plot of residuals versus fitted values. These bands were caused by themany observations with zero cover. Secondly, there seemed to be some suggestion thatthe variance of the residuals increased with the predicted value - despite the square roottransformation used. This phenomenon was not, however, improved by using astronger transformation (such as the logarithm), and was also a result of the presenceof many observations with no seagrass. There was also some suggestion that the residualdistribution was skewed with a longer tail to the right.


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Predicted

Residuals

0.0 0.2 0.4 0.6 0.8

-0.4

0.0

0.4

Predicted

AbsoluteR

esiduals

0.0 0.2 0.4 0.6 0.8

0.0

0.2

0.4

0.6

-0.4 -0.2 0.0 0.2 0.4 0.6

0.0

1.0

2.0

3.0

Residuals

Quantiles of Standard Normal

res

-3 -2 -1 0 1 2 3

-0.4

0.0

0.4

Figure 7. Distribution of Residuals from Exploratory Analysis

The estimated semi-variogram for the residuals (showing half the variance of thedifference between two residuals plotted against their distance in metres) is shown in Fig.8. The solid line was a fitted exponential semi-variogram with a range of 2000 m and asill of 0.028. There seemed to be a clear spatial dependency, with the variance increasingrapidly to a plateau, as observations move apart up to a distance of 10 km. There is clearevidence of spatial dependence between observations up to 10 km apart. This spatialdependence invalidated any F statistics that might have been calculated in the exploratoryanalysis, and was a more serious problem for analysis than the many observations withzero coverage. The final analysis must make explicit provision for this dependence.


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distance

gamm

0 10000 20000 30000 40000

0.

0.01

0.02

0.03

Figure 8. Robust Semi-variogram for Residuals from Exploratory Analysis

Spatial dependency analysis

F tests to estimate the effect of image variables, depth and polynomial spatial trend were

obtained using generalised least squares, and are based on the fitted semi-variogram(Table 2). All F statistics are significant, with the largest effect being caused by theLandsat TM image variables.

Table 2. F-statistics to estimate the effect of image variables, depth and polynomial spatial trend. All F-

statistics are significant.

Effect MeanSquare

df FRatio PValue

Image 0.151 3 6.02 0.001

Depth 0.088 2 3.54 0.031Spatial

Trend0.072 5 2.87 0.015

0.025 225 - -

The PRESS statistic for the model with all variables was 5.49 and the PRESS statistic forthe model excluding the image variables was 5.63 indicating that there is indeed someimprovement in prediction from including the image variables. Nevertheless, thisimprovement is very slight. The correlation between predicted values and observed sea

grass covers in cross validation was only 0.5 indicating that the densities obtained in asingle site are very variable.


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Discussion

The analysis reveals clear evidence of a relationship between the Landsat TM imagevariables and the seagrass coverage. This is the largest single effect in the generalised leastsquares analysis of variance.

The presence of large-scale spatial trends, and of small scale local spatial dependence,implies that it is not possible to map seagrass reliably, using satellite imagery andbathymetry alone. Even taking account of relationships between seagrass cover and bothdepth and imagery, seagrass varies smoothly from region to region. This implies thatextrapolation outside the spatial envelope of the samples will not produce reliable mapsof seagrass distribution.

The large Fratio associated with the image Variables (6.02 on 3 and 225 df) indicates thatsatellite imagery may, however, be used to improve interpolative maps of sea grassdensity. That is the map of seagrass density obtained for the study area may be

improved by using information from the Landsat TM image.

This study provides a useful caveat against the uncritical acceptance of correlationsbetween seagrass coverage and Landsat TM images. Despite clear evidence for arelationship between seagrass density and image variables, the use of Landsat TMimagery has provided only a marginal improvement in the interpolative mapping withinthe study area. The presence of large-scale spatial trends and local dependencies impliesthat the analyses conducted here are of no value in extrapolative mapping outside thestudy area, on the basis of Landsat imagery. These caveats are even more pressing in thecontext of this study - which provides an almost optimal arena for the use of Landsat

TM satellite data to map seagrass (large shallow areas, with little variation in depth).

Acknowledgements

Many thanks to AFMA for providing funds.

References

Admiralty (1973). Australia Pilot. Vol. III. 6thed. Oxford University Press, 320 pp.

Bode, L. and Mason, L.B. (1994). Numerical modelling of tidal currents in Torres Strait

and the Gulf of Papua. Report to Victorian Institute of Marine Science, 65 pp.

Cressie, N. and Hawkins, D.M. (1980) Robust estimation of the variogram, I. Journal ofthe international Association for Mathematical Geology, 12, 115-125.

Harris, PT (1988). Sediments, bedforms and bedload transport pathways on thecontinental shelf adjacent to Torres Strait, AustraliaPapua New Guinea. ContinentalShelf Research, 8:9791003

Hastie, T. and Tibshirani, R. (1990). Generalised Additive Models. Chapman and Hall,London.


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Long, B.G. and Poiner, I.R. (1997). Seagrass communities of Torres Strait. Final report toTorres Strait Fisheries Scientific Committee, March 1997.

Ripley, B. (1981) Spatial Statistics. Wiley, New York.

Wolanski, E., Ridd, P. and Inoue, M. (1988). Currents through Torres Strait. Journal ofPhysical Oceanography, 18: 15351545