Water has an outstanding importance for the life on earth. From this results the ..... Figure 6 shows a stick-plot describing the changes of a fluid-flow along time.
Evaluation of Marine Data by Visual Means Heidrun Schumann1
Bodo Urban2
1 Universität Rostock, Fachbereich Informatik, Institut für Computergarphik Albert-Einstein-Str. 21 D-18059 Rostock Germany 2 Fraunhofer-Institut für Graphische Datenverarbeitung, Außenstelle Rostock Joachim-Jungius-Str. 9 D-18059 Rostock Germany
Abstract. Water has an outstanding importance for the life on earth. From this results the necessity for the monitoring and interpretation of marine data. For that, the visual analysis is a suitable and effective tool, whereby special demands arise from the heterogeneity of data (different data types, different data sources), the quality of data (missing values, incorrect values), and the large quantity of data. The visualization of marine data is particularly important within both their geographic context and their temporal course. First, this paper introduces a classification for the visualization of spatial and time related data, which is not only appropriate for marine data. Following special visualization and interaction techniques for marine data are discussed. Thereby we do not raise the claim, to create new visualization paradigms. Rather we want to show solution concepts and special methods using well known paradigms for a special and complex application area, but also to address limits of the visualization paradigms in these applications.
Introduction Water is essential for life on Earth and ties together the Earth’s land, ocean, and atmosphere into an integrated physical system. The water quantity on our planet has been estimated to be about 1,400 billions km3, where the major part - 97,3% - is the salty water of the oceans. The movement of water in its various phases (gaseous, liquid, solid) across the Earth’s surface constitutes the global hydrological cycle, and the exchange of energy associated with these phase changes are a fundamental driving force for our weather and climate systems. Despite its importance, some aspects of the global hydrological cycle and its underlying mechanics are still poorly understood. This lack of knowledge prevents accurate understanding of hydrological processes on a global basis and limits our ability to understand and predict the response of global hydrology and anthropogenic and/or natural climate change. Moreover the aquatic living space is the source for drinking water, food, and protein but it is also the basis for transport, production of energy, and recreation. By this it is an important factor for both the economy and the ecology. Therefore monitoring, understanding and protection of the aquatic living space as an essential part of the ecosystem earth is very important for the future existence of mankind. In this context, national and international research and monitoring
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programs have been set up. For the area of the North Sea and the Baltic Sea, the research initiatives of the European program MAST and the German ‘Bund-Länder-Meßprogramm’ belong to this. Visual analysis is a suitable and efficient aid for monitoring and interpretation of marine data. In this paper we discuss several methods for the visualization of such data. Although we consider particularly marine data, the discussed techniques are applicable also for other heterogeneous multiparameter data sets in space and time, especially for the visualization of environmental data. 1. Specifics of marine data and its exploration Marine data are characterized by a strong heterogeneity. That affects both the data type and the distribution in space and time, as well as the quality of the data, because these can be generated by completely different methods. Marine data can be divided into the following classes: • Biological and hydrological measured data This class contains data like fish stocks and species data, temperature, salinity, and oxygen, but also fluid-flow data. They are measured in different spatial and temporal frames of reference and are scattered. Moreover, they represent different data types (scalars, vectors). Often the quality of the measured data does not correspond to the requirements of a detailed analysis (missing values, statistical characteristics, undesirable differences in measuring time resulting from data acquisition at monitoring cruises). • Remote sensing data These are defined for a fixed time and are uniformly distributed over a specific geographic area (raster data). To extract marine data, the raster data have first to be divided into ashore and offshore data. The offshore data represent properties of the surface of the marine world. Only a few parameters can be extracted from them directly, e.g., the temperature of the water surface. • Simulation data These data originate from model calculations and contain in general uniformly distributed values for hydrological and biological data in a fixed spatial and temporal frame of reference. • Context data Topographical data (like two-dimensional geographical data or three-dimensional terrain data) belong to this class as well as position and time of the measurements and information about the measuring processes themselves. During monitoring and exploration of these data classes, a lot of interpretation goals have to be supported. For example, one could want to recognize the following aspects in the data: • Alteration of measured values in the spatial distribution (e.g. Identification of places with high concentration of lead) • Alteration of measured values along the depth (e.g. Recognition of jumping levels) • Alteration of measured values over time (e.g. Recognition of break-ins of salt water) • Recognition of coherence by correlation of measured values (e.g. Recognition of ecological coherence between salinity and distribution of fish) • Verification of model calculations through measurements The challenge for the visualization is to condense these heterogeneous data and present them in a way suitable for comparison under consideration of the context necessary for interpretation. By this, a comprehensive evaluation of the marine ecosystem can be supported.
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2. Requirements on the visualization There are specific requirements on the visualization of marine data in order to meet the special demands of monitoring marine ecosystems. The following data characteristics have to be considered: • Heterogeneity of data It must be possible to visualize completely different data types simultaneously. • Quantity of data Visualization of high-dimensional data is necessary. In order to support efficient data condensation, powerful mechanisms for both data extraction and extraction of essential properties about the data are required. • Quality of data It must be possible to point out missing values, incorrect values, and limits of exactitude as well as to get information about sources of data. Moreover, effective visualization of marine data within an ecological monitoring system requires the design and use of many diverse visual representations: • Conventional representations They are used for the interpretation of marine data for years and have proven to be useful. These representations (e.g., depth-plots, diagrams, box and whisker) are intuitive and well interpretable. However, they present only few of the many aspects of the data to be analyzed. • Specific representations Particular aspects of the data can be shown by specific representations which support specific aims of the visual analysis. The presentation of values within the geographical context belongs to this class as well as the presentation of changes along time. Simultaneous display of different data items allows visual correlation analysis. • General representations They give an overview over the stock of data. Because of the large quantity of data, they present no individual values, but instead general properties of the data set. Moreover, the visualization of marine data requires special interaction techniques for both reducing the complexity of data and manipulating data values. This includes special mechanisms for data enquiry as well as methods for navigation in space and time. Access to original data must be possible anytime to, e.g., correct errors or simulation parameters. There will be no complete solution, which considers all the named requirements simultaneously. Up to now, different and problem-specific solutions exist. Keim suggests in [9], [10] the visualization of large data sets with pixel based methods, which are able to illustrate a time frame using special arrangements. However, a presentation of geographical relations is not possible. Moreover, most methods for the presentation of multiple parameters do not consider the geographical context (see [1], [8], [11] and others). On the other hand, icon based techniques are well suited for presenting the distribution of values within a spatial (mostly 2-dimensional) reference system (see [3]). Problems arise from the overlapping of icons when data values are positioned too close to each other. Schumann et.al. [15] separate direct and indirect visualization of spatial relations and give examples for this. The specification of a quantitative limit is still an open question. By most visualization techniques the heterogeneity of data types is also not considered. Instead they assume a special data class and provide a solution adapted to this. There exist only a few exceptions, especially in the area of flow visualization where scalar and vector values are visualized simultaneously (see [7] and [12]). However, the number of scalar values is always limited and a detailed spatial geometry like a 3-dimensional depth profile is not supported. In the same way, most representations convey no information about the quality of the data. Examples are given in [5] and [2]. [7] and [16] include examples for a visual data fusion. [13] discusses an
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interesting attempt for data condensation. In [6] and [14], special problems arising from the visualization of oceanographic data are investigated. We too do not claim to solve all named problems completely. Instead we will discuss fundamental strategies and back them up by examples. 3. Visualization strategies for spatial related and time related data For monitoring, control and interpretation of marine data special visualization strategies are necessary, which are able to present large heterogeneous data sets in space and time as well as integrate corresponding control and interaction mechanisms. Hereby the presentation in space and time plays a decisive role. For this reason, we first introduce a classification for spatially related and time related representations. 3.1. Classification of spatial related visual representations For the creation of spatially related visual representations, several factors have to be considered. The most important ones are summarized in figure 1. They include: • Scope of the parameters The values refer to individual measure points, located on lines (e.g., measure points on a river), on planes (e.g., measure points on the surface of a lake or ocean), or within volumes (e.g., measure points within the water body). In the visual representation, these associations must be clearly recognizable. • Abstraction level of the representation of the spatial reference system The more realistic the representation of the spatial reference system, the more intuitive spatial dependencies can be interpreted. However, the number of values being presentable within the spatial reference system is limited. This requires a higher level of abstraction for large data sets. Therefore, we will distinguish between direct and indirect presentation of the spatial relation, depending on whether the visualization of the data values is realized directly within the spatial reference system or outside the spatial reference system but with additional references. Moreover, figure 1 shows the distinction between point oriented, line oriented, surface oriented, and volume oriented presentation of parameters according to the usual classification (see, e.g., [4]). 3.2. Classification of time related visual representations The parameter time may not be treated in the visualization like an arbitrary (or independent) parameter, since in that case the meaning of this parameter can not be converted adequately into the visual representation for the analysis of the underlying process. Through a classification of time related data, a better assignment to visual representations can be realized. We will therefore distinguish between • dynamic data changing continuously with time and • static data referring to a fixed point in time or being quasi-static, e.g., only two measurements per year. For visual representations we distinguish between • dynamic representations changing with time and • static representations not changing with time.
8th EG Workshop on ViSC, Boulogne sur Mer, 28-30 April, 1997 Scope of Parameters
Point related
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Figure 1: Spatially related visual representations
Time dependence
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Time relation by annotation
Navigation within observation and data space at a fixed time
Time Diagrams Multiple Windows Special Icons
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Figure 2: Examples of time-related visual representations
Volume based
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Figure 2 summarizes the possible combinations at the representation of the time relation. For each possible combination examples are given. Static representations for dynamic data are most suitable, if quantitative statements are in the foreground. Contrarily, temporal associations can be perceived most intuitively by dynamic representations. 4. Visualization of marine data 4.1. Evaluation of marine data Within monitoring processes large amounts of data are acquired by measurements. The measurement processes differ from each other, and obtained data contain erroneous values and inaccuracies. Especially for correlated analysis of different data originating from different measurements, these errors have to be removed in a pre-process. Therefore, the validation of measured data before analysis is a basic condition. The validation process must support the visualization of both original and validated data values, the presentation of numerical values and assigned meta information (e.g., about the measurement process, or position and time of measurement), as well as interactive modification of incorrect values. The interactive tool VALIDATOR supports the simultaneous visualization of different data sets. Single parameters and sections of the data set as well as different scales can be selected. Besides the visual presentation, also the numerical values are presented. Assigned meta information will be displayed on demand. Incorrect values can be modified by automatic filters, by interactively changing the visual presentation or simply by modifying the numerical values. Figure 4 shows measured values (from a measuring probe) and corrected values for oxygen, salinity, and temperature in a depth-plot. The third window shows the alphanumeric interface for the correction filter. 4.2. Conventional visualization methods for marine data Here we present intuitive visualization methods, which are broadly accepted and used by the marine community. In contrast to the validation, the spatial relation is very important here. Most conventional visualization methods simultaneously present only a few parameters. Thus, the direct spatial relation as introduced in section 4 can be used. Usually marine data are visualized within a 2-dimensional geographic map. For this, a visualization system has to support the handling of geographical maps, e.g., selection of map data, definition of geographical section, and the selection of geographical projection. Most systems for the visualization of marine data do not support this functionality. The system VISMAR has been designed for the analysis of marine data within their geographical context by using different conventional visualization methods. Figure 5 shows the interface for the selection of map data, geographical section and projection as well as the control of visual parameters and geographical amendments (e.g. trails of ships, positions of buoys). The system supports the interactive visualization of marine environmental data in a geographical context using different visualization methods. Because of the heterogeneity of marine data, the dynamic interface adapts to the data types and suggests appropriate visualization methods. These are mostly point and vector based (see figure1). VISMAR supports graphs, bar charts, pie charts, box and whisker, arrows, icons, isolines, cross sections, 3D wire frames, and 3D surfaces. The figures 6 - 9 give examples. Figure 6 shows a stick-plot describing the changes of a fluid-flow along time. The measurement position is marked by a small triangle in the lower left corner of the plot. Figure 7 shows a depth-plot for various parameters. Again, the measurement position is marked by a triangle in the lower left corner of the chart. Figure 8 represents the visualization of lead concentration and temperature in a combined simultaneous 2-dimensional and 3-dimensional presentation. The lead concentration is coded as isolines while the temperature is coded by color or as a 3D-parameter field respectively. Finally, figure 9 shows both the temperature coded by color and the fluid-flow coded by arrows within a geographical map.
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If the number of parameters per point to be visualized increases, icon based methods can provide a good overview over a large amount of data. Figure 10 shows a star-like icon for the presentation of multiple hydrological parameters of a lake, extracted from the database of lakes and rivers in the country Mecklenburg-Vorpommern. A small icon indicates average values while a big icon attracts attention and indicates, e.g., abnormal values. Figure 11 also presents an icon based visualization of sediment data (calcium, iron, nitrogen, and phosphate per gram of dried mass)and hydrological data (oxygen, variosence, and depth of measurement) measured at the surface and in a certain depth. If the number of parameters to be visualized increases further, the icon based visualization is no longer sufficient. In this case an indirect spatial relation would be more intuitive, and we could use, e.g., the parallel coordinates presentation. 4.3. Specific visualization methods for marine data Based on an example, the system VINEU, we want to discuss now special methods for the visualization of marine data. The system VINEU provides a large set of special visualization methods. It supports navigation both in the geographical context as well as in time. Moreover, it is connected to a simulation system to calculate the distribution of fish within the aquatic world. Figure 3 shows the basic structure of the system VINEU. VINEU processes heterogeneous data from five different sources. These include relief data, which describe the depth profile of the Baltic Sea, as well as geographical coordinates of measurement positions, where hydrological and biological quantities have been measured, and data about the distribution of fish originating from a simulation process. The quantity of these data is so large, that before the visualization a data reduction step is absolutely necessary. This is done by means of area sections, which are defined on a boundary map and restrict the area of investigation. These geographical sections can be defined interactively on a contour map of the area or through an explicit declaration of geographical coordinates. The investigation area can be visualized by a 3dimensional terrain presentation that allows an intuitive recognition of the depth profile of the Baltic Sea. Points of measurement are marked and can be selected for the display of measured values. The hydrological data of the selected measurement points can be visualized by depth-plots with direct relation to the measurement point within the 3-dimensional terrain. Depth-plots show measured values (temperature, salinity, absolute and relative oxygen concentration) for different depths. It is important here also to provide statements about the quality of the data. For this reason, actually measured values are marked with a flag. If a flag is missing, the according value has been interpolated because a measured value was not available. Beside each depth-plot, arrows indicate that measurements exist for different times and allow navigation through time. Moreover, different points in times can be selected explicitly by a time editor. Figure 12 shows the visualization of marine data by a depth-plot and illustrates the functionality to select geographical sections and different times. For the presentation of values for multiple points in times, a visualization with indirect spatial relation is necessary because of the large amount of data. Selected measurement points are marked within the terrain representation, and the assigned values are visualized, e.g., by a separate parallel coordinates representation (see figure 13). A static representation of the dynamic data was chosen on purpose in order to support quantitative analysis. The navigation in space has been realized in a way similar to the navigation in time. The system supports two operation modes: • Automatic navigation The user selects points of measurement. From this list an automatic animation is created, which visits all selected points of measurement one after the other and visualizes the assigned data values.
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•
Manual navigation The user navigates freely in the 3-dimensional presentation of the terrain by using mouse input. An important goal supported by VINEU is the analysis of the distribution of fish in dependence of hydrological parameters. Figure 14 shows the simultaneous visualization of measured hydrological and biological data. The biological data reflect the distribution of cod in different groups of age, visualized by bar charts. After a well-directed manipulation of hydrological parameters, a simulator connected to the visualization system forecasts the distribution of fish depending on a certain hydrological situation. Neuronal Networks form the basis for the simulation system, and the visualization functionality of the system VINEU is based on IRISEXPLORER. VINEU supports several special presentation techniques for marine data. In section 3, we stated the necessity of general representations. Therefore, the connection of VINEU to a tool for processing satellite images is planned. Figure 15 shows the distribution of temperature, derived from a satellite image, as an offset surface connected to the according depth profile. Figure 16 goes one step ahead. It shows next to the satellite image and the depth profile an additional surface, which depicts the distribution of phosphate values in this area. 5. Conclusion The visualization of marine data requires the representation of heterogeneous multiple parameter data sets in space and time with corresponding interaction mechanisms for navigation and correctness evaluation. In this paper we presented special methods for this application area. Last but not least we want to address two further topics: 1. Data management: Connection to data bases Typically, measured hydrological and biological data are stored in databases (e.g., insituDB, monitoring-DB). Therefore, a database connection with corresponding retrieval mechanisms has to be realized for supporting a visual analysis of these marine data. Most current visualization systems do not include this functionality. The system VISMAR realizes a connection to the MUDAB database, a monitoring database maintained by the ‘Bundesamt für Seeschiffahrt und Hydrographie Hamburg’ and the ‘Institut für Ostseeforschung Warnemünde’. VISMAR delivers a graphical user interface to the MUDAB database, not only supporting the visualization of marine data in spatial and time context with various presentation methods, but also enabling data condensation by implementing special retrieval methods. The system VINEU on the other side uses the neutral file format NetCDF as input format. In a preprocessing step, the data to be analyzed are converted from the database into the NetCDF-format. Here, integrated solutions would be desirable, as they are presented nowadays, e.g. by AVS/EXPRESS. Data preprocessing Because the data to be visualized originate from different sources and are related to different reference systems, they must first be mapped through a suitable projection process into an uniform reference system. The presented systems VISMAR and VINEU provide several methods for this.
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Reliefdaten
sample points
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contour map
area selection
selected relief data
measured data hydrological data
VISUALIZATION 3D-terrain display with sample points data visualization
INTERACTION N navigation in space navigation in time manipulation of hydrological data
fish data
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Figure 3: Principal structure of the system VINEU
2. Levels of detail In section 3 we already stated the necessity of both supporting general presentations for overviews and special presentations for details. Further work focus on visualizations, which will show not only the measured values itself but also the changes in a general presentation. Though the third surface in figure 16 could present the change of oxygen within the water instead of the distribution of phosphate. When identifying extreme data values by clicking on their presentation, special visualization techniques could be initiated to represent the concrete data values exactly for these areas in detail. In the same way, the simulation process for calculating the distribution of fish and the following visualization could also be initiated. Furthermore, levels of detail could be used effectively for the navigation in space.
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If measured data do not differ very much within a certain area, we do not have to present all the values for every point of measurement within this area. To realize the sketched ideas, we will need a further intensive cooperation with the users, biologists, and hydrologists, not only to find the right thresholds but also to understand the interrelations within the complex marine ecosystem in order to create and evaluate appropriate visualization methods. Acknowledgment: At this point we would like to thank our students who have developed and implemented different visualization techniques for marine data with engagement and inventiveness. We thank especially D. Schultz (Figure 10), M. Hagemann (Figure 11), M.Kreuseler (Figures 12, 13, 14) and S. Friedl (Figures 15, 16). We also thank H.-R. Vatterrott for providing the pictures of VALIDATOR and VISMAR. Moreover we thank K.-U. Graw and U. Rauschenbach for proofreading our English. 6. References 1. Beddow,J.: Shape Coding for Multidimensional Data on a Microcomputer display, Proceedings Visualization’90, San Francisco, IEEE Computer Society Press, 1990 2. Buttenfield,B.,Kate Beard,M.: Graphical and geographical components of data quality, in Hearnshaw,H.;Unwin,D.J.: Visualization in Geographical Information Systems, John Wiley & Sons, Chichester, 1994 3. Dorling,D.: Cartograms for Visualizing Human Geography, in Hearnshaw,H.;Unwin,D.J.: Visualization in Geographical Information Systems, John Wiley & Sons, Chichester, 1994 4. Earnshaw,R.A.; Wiseman,N.: An introductory guide to scientific visualization, SpringerVerlag, Berlin, 1992 5. Goodchild,M.; Buttenfield,B.; Wood,J.: Introduction to visualizing data validity, in Hearnshaw,H.;Unwin,D.J.: Visualization in Geographical Information Systems, John Wiley & Sons, Chichester, 1994 6. Haus,J.: Visualization of real and simulation data in physical oceanography, in Earnshaw,R.A.; Watson,D.: Animation and Scientific Visualization, Academic Press, London, 1993 7. Hong,L; Mao,X.; Kaufman,A.: Interactive Visualization of Mixed Scalar and Vector fields, Proceedings Visualization’95, Atlanta, IEEE Computer Society Press, 1995 8. Inselberg,A.; Dimsdale, B.: A Tool for Visualizing Multidimensional Geometry, Proceedings Visualization’90, San Francisco, IEEE Computer Society Press, 1990 9. Keim,D.; Kriegel,H.-P.; Ankerst,M.: Recursive Pattern: A technique for visualizing very large amounts of data, Proceedings Visualization’95, Atlanta, IEEE Computer Society Press, 1995 10. Keim,D.: Pixel oriented Database Visualizations, SIGMOD Record, Vol.25, No.4, Dez.1996 11. LeBlanc,J.; Ward,M.O.; Wittels,N.: Exploring N-dimensional Databases, Proceedings Visualization’90, San Francisco, IEEE Computer Society Press, 1990 12. Leeuw,W.; van Wijk,J.: A Probe for Local Flow Field Visualization, Proceedings Visualization’93, San Jose, IEEE Computer, Society Press, 1993 13. Post,F.J.; Walsum,T.; Post,F.H.: Iconic Techniques for Feature Visualization, Proceedings Visualization’95, Atlanta, IEEE Computer, Society Press, 1995 14. Rosenblum,L.;Kamgar-Parsi,B.: Progress and problems in ocean visualization, in Rosenblum u.a. (ed.) Scientific Visualization, Aademic Press, London, 1994
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15. Schumann,H.; Lopez,N.; Graw,K.-U.: Visual representations of multiparameter data with spatial dependence, Proceedings of the 7th Eurographics Workshop on Visualization in Scientific Computing, Prag, 1996 16. Friedl,S.: Graphisch unterstütze Fusion mariner Daten, Diploma theses, University of Rostock, 1996
Color Plates
Figure 4: Simultaneous visualization of measured and validated values (System VALIDATOR)
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Figure 5: Interface for the visualization of 2-dimensional geographical maps (System VISMAR)
Figure 6: Stick-plot for fluid-flow presentation in time (System VISMAR)
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Figure 7: Depth plot for oxygen, salinity, temperature, variosence, and sound (System VISMAR)
Figure 8: Combined visualization of lead concentration and temperature (System VISMAR)
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Figure 9: Combined visualization of temperature and fluid-flow data (System VISMAR)
Figure 10: Visualization of hydrological data from the database of lakes and rivers in the country Mecklenburg-Vorpommern
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Figure 11: Use of multiple windows for the presentation of different aspects of a lake upper left: Visualization of the calcium carbonate concentration by Shephard procedure; upper right: Correlated visualization of calcium carbonate and calcium by a color coded Voroni diagram lower left: Visualization of sediment data (calcium, iron, nitrogen, and phosphate per gram of dried mass) by star icons lower right: Visualization of hydrological data (oxygen, variosence, and depth of measurement) measured at the surface and in a certain depth
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Figure 12: Visualization of hydrological data of the Baltic Sea top: Visualization of temperature (white), salinity (red), oxygen (absolute - green, relative blue) by a depth plot bottom: Editors for the selection of geographical sections and points of time
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Figure 13: Visualization of hydrological data for a selected measurement point (marked by a white spot) over time (The years of measurement can be selected by sliders, and the season of measurement (spring or/and autumn) by buttons)
Figure 14: Simultaneous visualization of hydrological and biological data left: Hydrological data visualized by a depth plot right: Spread of cod visualized by bar charts (the single bars represent different groups of age)
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Figure 15:
Combined visualization of temperature (calculated from a satellite image) and the according depth profile for a geographical section of the Baltic Sea
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Figure 16: Combined visualization of temperature (calculated from a satellite image), phosphate (interpolated by Shephard procedure), and the according depth profile by three offset planes
