Design, development and enhancement of dynamic multidimensional tools in a fully integrated fashion with the GRASS GIS

Geographic Modeling and Systems Laboratory,
Department of Geography, 220 Davenport Hall,
University of Illinois at Urbana-Champaign, Urbana, IL 61801

National Center for Supercomputing Applications,
University of Illinois at Urbana-Champaign,
Urbana, IL 61801

1. Introduction:

Effectiveness of numerical simulations of landcsape processes is significantly influenced by the quality of supporting tools for processing, analysis and visualization of complex input and output fields (Rhyne and others, 1993). The role of visualization has increased in the past 10 years due to the dramatic improvements in hardware and software for computer graphics. Although the term was not defined until the late 1980s, Visualization in Scientific Computing (ViSC) has been used by cartographers since the early beginnings of GIS in the 1960s (Tobler, 1970). Soon, dynamic 3D surfaces appeared in cartographic visualizations (Moellering, 1978; 1980), using a joystick and dials for real time control of viewing parameters rather than today's ubiquitous mouse pointer and graphical user interfaces. While cartographers today can use a wide selection of computing platforms and software solutions for creating sophisticated dynamic 3D visual models (Kennie and McLaren, 1988; Raper, 1989; Nielson and others, 1991; Ervin, 1993; Kraak, 1993; Slocum, 1994; Stephan, 1995), there are still only a few examples of full integration of such visualization and cartographic capabilities within a single GIS providing seamless sharing of data or object types.

Implementation of dynamic landscape simulation has demanded integration of GIS and computer cartography with specially designed scientific visualization ( Brown and Gerdes, 1992: SG3d; Mitasova, Brown, and Hofierka, 1994; Brown and others, 1995; Brown and Astley, 1995: NVIZ), and this integration has proved to be very successful in supporting the development and applications of complex spatio-temporal models. This full integration provides us with methods and interactive tools for efficiently creating dynamic cartographic models representing landscape phenomena and processes.

Dynamic cartographic models are used as either a process of research and discovery (MacEachren and Ganter, 1990; Monmonier, 1990; Openshaw, Waugh, and Cross, 1994) with visualizations feeding a refinement of the model, or as a method of communicating complex measured or modeled geographic phenomena (Brown and others, 1995; Stephan, 1995; Hibbard and Santek, 1989; Hibbard and others, 1994; Fisher, Dykes, and Wood, 1993; Rhyne and others, 1993). This concept is closely related to the range of map use in geographical inquiry defined by DiBiase (1990) with the presented methods and tools supporting both visual thinking and visual communication.

To provide insight into spatial and spatio-temporal relations of studied phenomena, the cartographic models are created using multiple dynamic surfaces and isosurfaces, together with draped raster, vector and point data in an appropriate projection of 3D space. Visual exploration and analysis of data is facilitated by interactive manipulations of visualization environment parameters such as viewing position, z-scale, cutting planes for fence diagrams, light position and brightness etc., and by animating the sequences of images created by changing the viewing parameters or by displaying evolving series of data (Mitasova, Brown and Hofierka, 1994; Brown and others, 1995). Interactive query capabilities, retrieving original attributes directly from the GIS data base, facilitate the modeling process. Integration within the GIS also encourages greater use of all available data due to the ease of data access and manipulation and stimulates interdisciplinary research involving specialists from various disciplines who use GIS to perform different tasks on the same data sets.

In this report we provide an overview of visualization tools developed for GRASS GIS together with examples of their practical applications for visual analysis and communication of complex landscape characterization data and landscape process simulation results.

2. Methods

For the development of visualization tools fully integrated within GIS, we required the use of a few external libraries and developed foundation libraries which may be reused with little or no modification for a number of tools. External libraries are widely available on a variety of UNIX platforms, and only one of them (OpenGL) has a licensing fee associated with it. All libraries we developed are implemented in K&R C programming language.

Overview of visualization tools and library dependencies.

3. Applications

The visualization tools are illustrated by numerous examples on the World Wide Web at Here we present new 3D applications to spatial modeling of soil properties from 3D point data from the Scheyern experimental farm. All volume models have vertical exageration 100.

Soil horizons

Model of soil horizons was created by interpolating horizons from 3D point data. We present also a long term land use map to allow visual comparison of distribution of chemicals and land use.

sites (Fig. 1) movie land use

The comparison of the following 3D models of soil properties demonstrate the impact of land use on spatial distribution of chemicals in soil.

Soil chemistry analysis

a) soil reaction: ph (Fig. 3)

Volume model incorporates the vertical relationship into interpolation and allows more efficient visual analysis

The highest acidity is on terrain surface in grass area and it extends over most of the area in deeper horizons

b) organic carbon

The highest concentration of organic matter is in the long term grass area. The amount of organic matter rapidly decreases with depth.

c) plant available K

The study area has surplus of Potassium with maximum concentrations in permanent grass locations which indicates its function as a filter or accumulation area. Concentrations decrease with depth, except for a hop field in valley where the higher concentrations extend well bellow the A horizon.

d) plant available P

Area has surplus of Phosphates, with maximum concentrations in valleys (depressions). Concentrations decrease with depth. (Fig. 2)

Location of optimal and surplus K, P presented as intersection of complex solid bodies

Note that the optimal concentrations od K,P cover only a small surface area and, especially for K, they are located mostly bellow surface in lower horizons.

e) total nitrogen

Again, the highest concentrations of nitrogen are located in the area with permanent grass and the concentrations rapidly decrease with depth.

f) bulk density

Size fraction analysis

Derived soil parameter - hydraulic conductivity

The values of hydraulic conductivity were derived for each 3D point based on the particle size distribution using equations from the WEPP manual. The values were then interpolated to a 3D raster which can be used as an input for 3D infiltration model or visualized using isosurfaces or crossections:

4. Conclusion

These examples demonstrate the extension of modern computer cartography from a tool for automatization of paper map production towards providing methods and techniques aimed at exploration and communication of complex georeferenced data and their spatial and spatio-temporal relationships.

Further development and wider applications of the presented cartographic visualization techniques will be driven by full integration of multi-dimensional data structures and their support within GIS, by larger volumes of spatio-temporal data available and by improvements in speed and quality of graphics on personal computers (at least to the level now available only on graphical workstations). While visualization cannot and does not solve the landscape simulation problems which require improvement of algorithms and extensive field experiments for calibration, visualization methods enable researchers to better understand the processes and identify potential errors and solutions.

5. References

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