5 th AGILE Conference on Geographic Information Science, Palma (Balearic Islands, Spain) April 25th-27 th 2002
Optimal spatial sampling design for mapping estuarine sediment management areas Sandra Caeiro1 , Pierre Goovaerts 2 , Marco Painho3 , Helena Costa4 and Sandra Sousa3 1. IMAR, Department of Exact Sciences and Technologies of the Portuguese Open University, Lisbon, Portugal. e-mail:
[email protected] 2. Environmental and Water Resources Engineering, Department of Civil and Environmental Engineering, The University of Michigan, Ann Arbor, USA. e-mail:
[email protected] 3. ISEGI/CEGI, Institute for Statistics and Information Management of the New University of Lisbon, Portugal. e-mail:
[email protected];
[email protected] 4. IMAR, Faculty of Science and Technology of the New University of Lisbon, Portugal. e-mail:
[email protected] Abstract. Estuaries have unusual difficulties in characterizing the spatial distribution of the properties that collectively define their quality. In this study an estuary optimal spatial sampling design is presented, using prior information on the estuarine sediments spatial variation. This research represents an initial phase towards the development of sediment environmental management areas for a Portuguese Estuary, integrated in a GIS. The sampling strategy was designed within the digitized boundaries of the estuary. Preliminary analysis of sampled data collected is showing robust and precise interpolation results.
Introduction In estuaries large scale patterns of spatial variability include the longitudinal salinity gradient along the continuum between the estuarine drainage basin and the coastal ocean. Superimposed onto this trend are sources of small-scale spatial variability, unique to or amplified for estuaries. These sources include distributed point sources, features of water circulation such as fronts, edits or convergences that create localized turbidity maxima, patchiness resulting from irregularities in bottom topography, and biological-mediated spatial differences in processes such as primary production and biogeochemical transformation of reactive constituents (Jassby et al., 1997). Due to the variability of these estuarine conditions, greater sampling efforts are often necessary to describe estuarine environments, compared to other aquatic systems. That is why in coastal management studies where, the collection of data is sometimes very difficult and time consuming, an optimal sampling survey with minimal number of sampling locations and with sufficient precision, is a prerequisite for the detection of existing spatial heterogeneities (Kitsiou et al., 2001). Sampling design is also very important when the objective is to interpolate in an optimal fashion and to compute contour maps for a variable within a region (Haining, 1990). Many authors have been using sediment to monitor aquatic contamination, showing great advantages when compared to traditional water sampling (e.g. Elliot and Mcmanus 1989, Ramos, 1996). Sediment is a compartment where contaminants such as heavy metals or organic compounds tend to accumulate first. In most cases the sediment contaminant levels exhibit small variations over short time periods reflecting the average conditions of month periods (Luoma, 1990). The aim of this study is to design an optimal spatial sampling strategy, using prior information on the spatial variation in the estuarine sediments. This survey is to define boundaries of environmental management areas for the Portuguese Sado Estuary. These areas are defined through sediment characterization indicators. Few studies such as Kitsiou et al. (2000) have been conducted in estuarine environments using this kind of approach. This research represents an initial phase towards the development of an environmental management system for Sado Estuary integrated in a GIS. The sampling strategy was designed within the digitised boundaries of the estuary. Sampling designs There are three major basic forms of point sampling in a geographic region (Clark & Hosking, 1986; Jassby et al, 1997, Haining, 1990; Webster, 1999): Simple random sampling: This is the simplest case. Each sampling point is chosen uniformly and independently of all other to give a set of points. Spatial variables are almost always auto correlated at some scale, and in these circumstances simple random sampling is inefficient in the sense that it requires more effort to achieve a given precision than any other schemes. Stratified sampling: The points are chosen uniformly and independently in each strata. Strata are tessellating cells of equal area that may be squares, rectangles, triangles or hexagons. Stratified sampling is more precise
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5 th AGILE Conference on Geographic Information Science, Palma (Balearic Islands, Spain) April 25th-27 th 2002
than simple random sampling. In general the smaller the cells the smaller is the within-stratum variance. The strata need not be sampled equally. Systematic sampling: One point is sampled at the nodes of a regular grid. This method provides the most precise estimates for a given sampling effort. Unfortunately systematic sampling provides no entirely satisfactory assessment of the estimation variance because the sampling points are not randomized once the grid had been placed on the land. A potential hazard of systematic sampling is bias arising if a sampling grid is offset to one side or another of a region in which there is a trend in the variable of interest (Webster, 1999). One solution is to modify the grid by deliberately shifting the sampling points from the grid nodes in a random and controlled way. The results is a systematic unaligned sampling suggested by Berry and Baker (1968). The bias is reduced and the resulting design has greater precision than any of the other methods mentioned. This approach avoids the periodicities of the systematic approach, it gives good coverage over an area, it is efficient, and it deals with most distributions (Clark & Hosking, 1986). Geostatistical analysis An optimal sampling strategy for estuarine environments survey requires prior information on the spatial variation in the estuary, which can be quantified using the semi-variogram (Burgess & Webster, 1984, Jassby et al., 1997, Van Groenigen, et al., 1999, Van Groenigen, et al., 2000, Kitsiou et al., 2001). The use of previous samples to direct additional sampling is important for minimum kriging variance of regional variables (Van Groenigen, et al, 1999). ^
The semi-variogram
γ( h) measures the dissimilarity between a pair of regionalized variables z ( u α ) , α =
1,……., n. with respect to the spatial separation, h (Goovaerts, 1997): ^
1 γ ( h) = 2N( h)
N (h )
∑ [z(u
α ) − z( u α
+ h )]
2
eq. 1
α =1
Where N(h) is the number of pairs of data locations a vector h apart. To the experimental semi-semivariogram is fitted a model of spatial variability assumed to be characteristic of the sampled data (fig. 1). Then a generalized linear regression is carried out in accordance with the covariance function model to establish the weights with which data from other locations u α , α = 1,……., n, contributes to the value at the point x0. Such spatial regression operation is known as kriging. The semivariogram reaches a plateau, called sill, at a distance referred to the range of correlation (a) since data separated by a larger distance are considered spatially independent. This distance is important for the sampling plan in that to collect non-redundant observations they must be at least the range of correlation apart. In fig. 1, on the vertical axis of the fitted model, the variance has two components, C0 and C1. The C1 component of the variance represents the structural variation, while C0 combines random variance factors, such as sampling and analytical error, along with any spatial variability that may exist at distance smaller than the sampling interval.
Fig. 1 – A typical semi-variogram and fitted model ( Flatman et al, 1987).
Methods Study area
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5 th AGILE Conference on Geographic Information Science, Palma (Balearic Islands, Spain) April 25th-27 th 2002
The Sado Estuary is the second largest in Portugal with an area of approximately 24,000 ha. It is located in the West Coast of Portugal, 45 Km south of Lisbon. Most of the estuary, except for the city of Setubal, its port and a considerable part of its surrounding area, is classified as a Nature Reserve. The Sado estuary is subject to intensive land use practices and plays an important role in the local and national economy. There are many industries mainly on the northern margin of the estuary. Furthermore the harbour-associated activities and the city of Setúbal along with the mines on the Sado watershed use the estuary for waste disposal purpose without suitable treatment. In other areas around the estuary intensive farming, mostly rice fields, is the main landuse together with traditional salt pans and increasingly intensive fish farms (Painho et al, 1996). Some of these activities have negative effects on water, sediment and biotic communities namely because they discharge to the estuary contaminants like heavy metals, hydrocarbons, pesticides and fertilisers (Costa et al, 1998, Cerejeira et al, 1999, Caeiro et al, 2002). At the present moment there are expansions of tourism complexes in the south edge of the Estuary resulting in several pressures on the estuary. Sampling design To define the boundaries of environmental management areas of the Estuary, a systematic unaligned sampling design proved to be the most adequate strategy. Grid unit length was assessed through analysis of experimental semivariograms estimated using observations of a previous study (Rodrigues and Quintino, 1993). This survey analysed sediment granulometry, a parameter strongly correlated with sedimentary environment, at 133 sampling sites distributed along the estuary bay. According to Flatman et al. (1987), the distance between sample locations should be half the range of correlation of experimental variograms of previous data, in case of small nugget effect. In case of large nugget effect, sample distance should be less than two-thirds of the range of correlation. The grid should be laid out with no vertices unsampled (Flatman et al., 1988). Semivariograms were computed and modeled using Variowin 2.2 software. Coastal boundary digitalisation The aerial ortophotos of 1995 (1:40.000, 1 meter of resolution) of Portuguese Center of Geographic Information were used in this study for Sado estuary coastal boundaries delimitation. The flight plan contain information about date/time of each photo, that were used to determine the tide height based on tidal tables of local harbours of Sesimbra and Setúbal. We experimented two different approaches to compile thematic maps through image processing: manual image classification and digital image classification (Robinson et al., 1995): i) Manual Image Classification - This feature, extraction approach, is a combination of manual interpretation and digital image display. Using the mouse the polygon of the interpreted features was traced from the image displayed on the colour monitor. Polygons appear to be drawn on the image as they are digitised, and are also stored as a shapefile and included in a GIS database. ii) Digital Image Classification - This second uses an image processing software to classify each pixel, based on the reflectance value in each spectral band. An ArcView 3.2 ® and Image Analysis® extension was used. Experimentation of both methods led to the choice of manual digitising. All aerial ortophotos were taken in low tide, allowing better digitalisation of coastal boundary.
Results and discussion The surface semi-variogram (fig. 3) of fine fraction particles shows a clear anisotropy, with the maximum continuity observed in the direction of azimuth120º. This is due to the fact that the variability in the estuary bay is greatest in the direction perpendicular to the water flow, which agrees with other studies (Martins et al, 2001).
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5 th AGILE Conference on Geographic Information Science, Palma (Balearic Islands, Spain) April 25th-27 th 2002
Fig. 3 – Surface variogram of fine fraction percentage. In the case of anisotropic surfaces the optimal strategy is to elongate the grid in the direction of the strongest correlation (maximum continuity) (Haining, 1990). From the 133 sampling points, only 80 with small distance between them were used for semi-variograms computation. The semi-variograms computed with all the sampling points have shown big variations between lags due to small areas of different granulometry. These areas exist in the North Channel (Rodrigues and Quintino, 1993). Fine fraction semi-variograms were computed with lag distance equal to 0,25 km along the direction of azimuth 120º and 0,23 km for perpendicular direction 30º, both with angular tolerance equal to 30º. These parameters were the ones producing semi-variograms that were the most easily interpreted. Figure 4 depicts the final grid definition with 750 m in the direction of maximum continuity and 500 m in the perpendicular direction.
a = 1000 m a = 1500 m Fig. 4 – Semi-variograms of fine fraction percentage in the direction of maximum continuity 120º and in the perpendicular direction, with the model fitted. This sampling design was already successfully used for mapping estuarine sediment management areas of Sado Estuary (fig. 5), to sample sediment characterization indicators. The final grid includes 153 sites covering the estuary bay until the entrance of Aguas de Moura and Alcacer Channels. The random sampling point in each grid was sampled every time the boat moved and reached a grid rectangle. Preliminary analysis of sampled data collected is showing robust and precise interpolation results.
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5 th AGILE Conference on Geographic Information Science, Palma (Balearic Islands, Spain) April 25th-27 th 2002
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Fig. 5 – Sado Estuary sediment sampling design.
References Berry, B. J. L. and Baker, A. M., 1968. Geographic sampling. In, ed. B. J. L. Berry and D. F. Marble. Spatial Analysis. Prentice Hall. 91 – 100. Burgess, T. M., Webster, R., 1984. Optimal sampling startegies for mapping soil types. I. Distribution of boundary spacing. Journal of Soil Science. 35. 641 - 654 Caeiro, S., Painho M., Costa, M. H. and Ramos, T.B., 2002. Sado Estuary Ecosystem: a management methodology. Procedings of International Conference on Sustainable Management of Coastal Ecosystems. (in press). University of Fernando Pessoa. 3 - 5 November 1999. Porto. Portugal. Cerejeira, M. J., Pereira, T, Espirito-Santo, J. Viana, P., Brito, F and Morbey, M., 1999. Influência da utilização de pesticidas em arrozais para o meio aquático. Estudos de campo e de laboratório. In: ed. Santana, F.; Vasconcelos, L.; Partidáro, M. R.; Seixas, M. J. and Sobral, M. P. Proceedings 6º Conferência Nacional sobre a Qualidade do Ambiente. Vol 2. 20 – 22 Outubro, Lisboa. 133 – 142. Clark, W. A. V. Hosking, P. L., 1986. Statistical Methods for Geographers. John Wiley & Sons, Inc. 513 pp. Costa, M. H., Brito, F., Correia, A. D. and Costa, F. O., 1998. Toxicity assessment of coastal dredge materials in Portugal: current status, regulation and research requirements. Presentation of a toxicity test using a local species. In: Seminário sobre dragagens, dragados e ambientes costeiros. Associação Eurocoast-Portugal. Porto. (1998): 91-104. Eliot, M. and McManus, J., 1989. Advances and Future Direction in Marine and Estuarine Studies. In: ed. MacManus, J. and Elliot, M. Proceeding of EBSA 17 Symposium. Denmark. 1 – 6. Flatman, G.T. Englund, E. J. Yfantis, A. A., 1987. Geostatistical Approaches to the Design of Sampling Regimes. ed. Keith, L. H.. Principles of Environmental Sampling. ACS Professional Reference Book. Americam Chemical Society. 73 – 92. Goovaerts, P., 1997. Geostatistics for natural resources evaluation. Oxford University Press. 483 pp. Haining, R., 1990. Spatial data analysis in the social and environmental sciences. Cambridge University Press. 409 pp. Jassby, A. D., Cole, B. E. ,Cloern, J. E., 1997. The Design of Sampling Transects for characterizing Water Quality in Estuaries. Estuarine, Coastal and Shelf Science. 45. 285 - 302 Kitsiou, D., Tsirtsis, G., Karydis, M., 2001. Developing an Optimal Sampling Design. A case Study in a Coastal Marine Ecosystem. Environmental Monitoring and Assessment. 71. 1 – 12. Luoma, S. N., 1999. Processes affecting metal concentrations in estuarine and coastal marine sediments. In: ed. Furness, R. W.; Rainbow, P. S. Heavy Metals in the Marine Environment. CRC Press. 51 – 66. Martins, F. , Leitão, P. , Silva, A. , Neves, R., 2001. 3D modelling in the Sado estuary using a new generic vertical discretization approach. Oceanologica Acta. 24. S51 - S62. Painho, M., Vasconcelos, L. T., Farral, H., 1996. Tendências evolutivas territoriais em gestão ambiental: o caso do Estuário do Sado In: ed. Santana, F.; Vasconcelos, L.; Partidáro, M. R.; Seixas, M. J. and Sobral, M. P. Proceedings 6º Conferência Nacional sobre a Qualidade do Ambiente. Vol 2. 20 – 22 Outubro, Lisboa. 637 - 646.
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5 th AGILE Conference on Geographic Information Science, Palma (Balearic Islands, Spain) April 25th-27 th 2002
Ramos, T. B., 1996. Sistemas de Indicadores e Índices de Qualidade da Água e Sedimento em Zonas Costeiras. Master Thesis. Universidade de Aveiro. Robinson, Arthur H.; Morrison, Joel L.; Mulhrcke, Phillip C.; Kimerling, A. Jon; Guptill, Stephen C., 1995. Elements of Cartography. 6th ed. Jonh Wiley & Sons. Rodrigues, A. M. and Quintino, V. 1993. Horizontal Biosedimentary Gradients Across the Sado Estuary, W. Portugal. Netherlands Journal of Aquatic Ecology. 27: 449-464. Van Groenigen, J. W., Pieters, G., Stein, A., 2000. Optimizing spatial sampling for multivariate contamination in urban areas. Environmetrics. 11. 227 – 244. Van Groeningen , J. W., Siderius, W., Stein, A., 1999. Constrained optimisation of soil sampling for minimisation of the kriging variance. Geoderma. 87. 239 - 259 Webster, R., 1999. Sampling, Estimating and Understanding Soil Pollution. Ed. Gómez-Hernández, J. ,Soares, A., Froidevaux, R. GeoEnvII 98 - Geostatistics for Environmental Aplications. Quantitative Geology and Geostatistics. Kluwer Academic Publishers. 25 – 37.
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