C. Project description


ITR/AP(BCS/GRS): Computational dynamic landscape manipulation and optimization for OPEN source GIS



C.1 Introduction


New mapping and environmental monitoring technologies generate large volumes of high resolution spatio-temporal data and offer unique opportunity to dramatically improve the land management, alleviate environmental pressures and preserve biodiversity. The major challenge for the rapidly evolving geographical information science is to provide methods and tools for effective use of these data for environmentally and economically sound land use management, with focus on proactive prevention rather than just remediation of environmental problems. Despite significant uncertainty about the effectiveness and long-term impact of environmental programs, huge resources are being invested into watershed and coastal management. For example, hundreds of millions of dollars are appropriated for the Illinois River Watershed conservation program, with similarly large investments for North Carolina beach re-nourishment, and Florida's Everglades restoration, among many others. Numerous federal, state and local government regulations aimed at environmental protection pose significant challenges for land owners, developers and regulatory agencies due to lack of management tools, particularly in the case of localization and control of non-point source pollution. It is therefore crucial to speed-up the relevant landscape modeling and management research so that it can provide the tools to evaluate the impact of changes before they are implemented and to support design of new, effective conservation and pollution prevention strategies.


The landscape-scale land management strategies are extremely difficult to test experimentally because of the cost and time requirements of such experiments and because of limited possibilities to evaluate their functioning under extreme conditions. Laboratory experiments, while useful, often behave quite differently from large-scale real-world systems, thus limiting the applicability of their results. Field experiments can support the studies of certain properties of conservation measures, however, they are constrained both spatially and temporally and cannot capture the long-range interactions typical for fluxes in complex landscapes. According to the National Research Council's report on "New strategies for America's watersheds, "one area of special promise to address these issues is simulation modeling which can give decision makers interactive tools for both understanding the system and judging how management actions might affect that system" (NRC, 1999)". Computational spatial simulations are "young" methodologies developed only over the past few decades and their progress is closely tied to the advances in computer performance. As such, they have their own rules, challenges, successes and limitations. The role of model development and verification, algorithms, data structures, advanced visualization and parallel computing are crucial and require close collaboration between traditional research disciplines and computational science. In spite of significant progress in Geographical Information Systems (GIS) technology and environmental modeling (Goodchild et al. 1993, 1996, 1997, GIS/EM4 2000, Boyle et al. 1998, Kellershohn et al. 1999, Mitasova et al. 1995, Wilson and Lorang 1999, Band et al. 2000) there are persistent problems with accuracy and reliability of spatial simulations (National Research Council 1999), preparation of data is often time consuming and running the models requires substantial expertise. Finally, the modeling efforts have focused on analysis of the current state and prediction, while their use for development of new land management and conservation strategies is largely unexplored.



C.2 Objectives


We propose to develop a new methodology for landscape modeling and management support that builds on experience from other scientific disciplines such as design of new materials in physics and chemistry using virtual environments and simulations. For example, the virtual prototyping of materials employs a combination of experimental data, simulation methods and manipulation to develop new structures and predict their properties in silico, ie, using computational tools and human-computer interaction. In the case of landscape management the manipulated object is "too big" for direct experiment and therefore we propose to use its virtual representation and simulations to find favorable "landscape structures" and interactively investigate various land management alternatives to achieve a sustainable, evolving landscape. This approach to computational land use management requires new developments in several areas of geographic information science with this project focusing on (Figure 1):



The new methods and tools will be tested using two study areas: a) the 1200-acre Centennial Campus at North Carolina State University - a newly sited "technopolis" village being rapidly developed in a formerly rural farm and forest landscape and b) the actively migrating sand dunes that comprise Jockey's Ridge State Park on the Outer Banks of North Carolina. The dunes are surrounded by housing and other human activities, and strategies for management of future dune migration are of great interest to local stake holders. Development and application of the proposed methods will enable to carry out landscape simulations in 3D space over the range of seamlessly embedded scales, with modeled processes adapting to scale and real time field measurements. The project aims to reach beyond risk assessment and prediction, towards planning and landscape-scale design through data adaptive simulations and spatial optimization.



C.3 Methods: general plan and design of activities


A multidisciplinary approach involving close collaboration between Computer Science, Earth and GIScience, and Computational Physics will drive the research effort, which will build upon collective team expertise in human-computer interaction, GIS development and landscape process simulation, and process-based field studies. This new research partnership will provide the synergy to catalyze innovative advances in landscape simulations and planning. The project will be leveraged by several current, on-going studies and proposed future work which will support processing of test data sets, monitoring and mapping activities, calibration and testing of models, and data exploration and interface building tools.




Figure 1. Interactive, dynamic landscape model supported by monitoring and simulations



C.3.1 Multi-resolution, 3D dynamic landscape model linked with field sensors


GIS supported model of the studied landscape will provide the basis for landscape monitoring, simulation and manipulation. Multi-resolution landscape model will be based on the combination of regional scale model with embedded high resolution models for monitoring of "hot spots" which will be implemented through nested grids (Mitas and Mitasova 1998b, DHI 2001). The low-resolution models will be created using available USGS data at 10 to 30 m resolution while rapidly changing areas will be mapped and monitored at much higher levels of detail using such state-of-the-art mapping technologies as LIDAR and real-time-kinematic (RTK) GPS, among others.

An important component of the landscape model will be its two way link with mobile mapping and field monitoring units, with the model providing support for field observations and assimilation of field data to update the model. The concept of extending the 3D GIS from workstation to field computational unit will be tested by exploring the use of augmented reality for effective selection of monitoring and high resolution mapping sites. Using the field computer the researcher will be able to explore virtual representations of the landscape at future development stages and identify locations where monitoring would be critical during the construction and after the development. We will compare the practical aspects of using the traditional laptops, wearable computers (e.g. Xybernaut Wearable) and handheld devices (e.g., eMap GPS type).


C.3.2 Multi-scale spatial simulation of landscape processes based on duality of particles and fields


Landscape process simulations will provide insights into the current state of landscape processes, predictions for evaluation of the current and planned land use impacts and tools for intelligent landscape manipulation and optimization with a potential to develop innovative conservation strategies.


Process-based modeling of geospatial phenomena is difficult and often involves a much higher level of uncertainty than, for example, simulations of microscopic systems in physics or chemistry which are based on virtually exact theories, such as quantum mechanics. Key reasons for these modeling difficulties are the complexity of landscape phenomena, the multitude of processes acting across a range of scales, non-equilibrium phenomena and/or lack of experimental data. The practical solutions rely on the best available combination of physical models, empirical evidence, experience from previous studies and available measurements. Landscape processes are therefore often described by a combination of physically-based and empirical models, and integration with field measurements using data adaptive simulation is especially important for increasing the reliability of predictions (e.g., Cantin and Fortin 2000). Typically, the physically-based models have the following components (Mitasova and Mitas, in press):



Spatial processes described by the governing partial differential equations are usually solved by

discretization techniques such as finite difference (e.g., Doe et al. 1996) and finite element methods (e.g., Vieux et al. 1996). Besides these approaches we also plan to explore other alternatives, in particular, path sampling method. Similar methods are known as path integrals in physics (Ceperley 1995) or random walks in stochastic processes (Karlin and Taylor 1981, Gardiner 1985). The path sampling is based on the fact that essentially any field can be represented by an ensemble of sampling points. For example, a scalar field can be determined by the distribution (spatial density) of sampling points in the corresponding region of space; conversely, ensembles of particles are often described by continuous fields. The duality of field <=> particle density is routinely employed in physics and helps to reformulate and solve complicated problems involving interacting systems with many degrees of freedom. The path sampling method has been successfully used in physics (Ceperley and Mitas 1996), chemistry (Hetenyi et al. 1999, Forsythe and Makri 1998), finance (Ingber 2000), etc., for linear or weakly nonlinear transport or time propagation problems which involve processes such as diffusion, advection, rate (proliferation/decay), reactions and others. It has several important advantages when compared with more traditional approaches. It is very robust, can be easily extended into arbitrary dimension, is mesh-free and is very efficient on parallel architectures including heterogeneous clusters of PCs and workstations. Path sampling is also rather straightforward to implement in a multi-scale framework with data adaptive capabilities. The method has been successfully used for distributed modeling of overland water and sediment flow and erosion/deposition studies (Mitas and Mitasova 1998a), including multi-scale applications (Mitas and Mitasova 1998b), and modeling of dissolved and suspended substances in lakes, estuaries and coastal areas (Dimou and Adams 1993, DHI 2000).


Many natural processes involve more than a single scale and exhibit multi-scale, multi-process phenomena (Steyaert 1993, Green et al. 2000). Problem of multiple scales permeates several scientific disciplines such as materials research, physics, and biology. Many multi-scale problems can be partitioned into a hierarchy of effective models which are nested in the direction from fine to coarse scales. At a given scale the model incorporates simplified or "smoothed out" effects coming from finer, more accurate levels. In the direction from coarse to fine scales, one develops a set of effective embeddings which determine boundary and/or external conditions for the processes at finer scales. The fine scale processes are then modeled at high resolution only in "hot spots" of the studied system (Mitas and Mitasova 1998b, Mitasova and Mitas in press).


Coupling simulations with field data can greatly improve the accuracy and reliability of the models. We will investigate varying sampling locations and intervals to optimize the strategies for expensive field measurements, for example, by combining short-time-interval/high-spatial-resolution monitoring during active construction with long-time-interval/low-spatial-resolution after development is finished. The models will use the field data to adapt the simulation by changing such conditions as terrain shape and transport parameters or to calibrate the accuracy of simulated water depths to measured ones. The proposed path-sampling methods are particularly appropriate for adaptive strategies because the path-represented fields can be readily adjusted to the experimental data. Sampling points can be biased by re-weighting or by repositioning through translations, rotations or space-deforming transformations, or both. Since there is no need to rebuild any underlying finite-element mesh, such procedures are very fast and straightforward to implement.
























Figure 2. Solving water flow contibuity equation by path sampling method.

Animation is at http://www2.gis.uiuc.edu:2280/modviz/gisc00/f70psexamp.html . Related, more complex simulation of sediment flow is at http://www2.gis.uiuc.edu:2280/modviz/gisc00/f75psedim.html





Figure 3. Multiscale path sampling simulations. Animation is at http://www2.gis.uiuc.edu:2280/modviz/gisc00/f90psmultisc.html



Based on the experiences with the previous successful development and application of the path sampling method, the underlying algorithms will be further enhanced and implemented as robust simulation modules/components which will allow to build more sophisticated models of various spatial processes. The components will be tested by building 2D and 3D water, sediment and pollutant transport models.


C.3.3 Spatial optimization


An important component of the virtual landscape manipulation environment will be the optimization of complex spatial systems supporting land use design, distribution of conservation measures and similar land management tasks (e.g., Bennet and Armstrong 2000). One of the key issues will be the development of an appropriate framework for optimization of spatially variable objects. In order to formulate, quantify and solve these tasks we will use the 'cost or penalty' functional with the following two types of inputs. The first part is a set of fields {fk (r,t)}, which represent the natural processes and phenomena of interest, for example, the sediment flux resulting from a rainfall event. The second set of fields {gl(r,t)} describes the spatial distribution of quantities or measures which represent anthropogenic activities to be optimized, i.e., distribution of grass filter strips. We assume that once the fields {gl(r,t)} are specified we can find the corresponding {fk(r,t)} either by solving the appropriate model or using measured data or both. The cost functional C is then written as an integral over the space O and time T


C [{fk(r, t)}, {gl (r, t)}, {pm}]= €O, T F [{fk (r, t)},{ gl(r, t)}, { pm}] drdt           (1)


where the function F determines the local cost for a given configuration of fields {fk(r,t)}, and {gl(r,t)}, { pm} denote any other types of necessary inputs or parameters and k,l,m are enumerating indices. The optimal solution is given by a configuration of fields { glopt(r, t)} which fulfill the minimization condition


C [ {fk( r, t)}, { glopt( r, t)}, { pm}] = min             (2)


and any additional constraints such as non-negativity of a particular field gl(r, t)> 0, prescribed interval of values, continuity or compactness in spatial distribution.


In order to transform the variation of the distributed fields into a variation of usual variables we will employ expansions in several types of basis sets . The multi-variate field can be expressed as a linear combination of basis set functions and the expansion coefficients then assume the roles of variational variables. The basis sets we plan to explore include raster or block-raster basis, local continuous functions such as gaussians and regularized spline with tension; all of these can be used in arbitrary dimension. The choice will depend on the actual application and will be determined by considerations such as required resolution and accuracy, continuity or character of constraints and computational feasibility.



Figure 4. Impact of spatial distribution of protective grass cover on sediment flow a) existing pattern, b) computer aided land design based on erosion simulations (Mitas and Mitasova 1998a). Animation showing change of pattern depending on maximum acceptable erosion (Mitas et al. 1997) is at http://www2.gis.uiuc.edu:2280/modviz/lcgfin/movie/luopt.mpg


The optimization of large number of expansion coefficients can quickly become intractable and the choice of the number of basis functions, desired accuracy and choice of optimization methods must be judicious. There are essentially two limiting types of the "cost surfaces" which also determine the choice of appropriate optimization strategies. In the case of a single or a few local minima, standard methods from optimization libraries, such as efficient quasi-Newton method can be used. In the other limit the cost landscape has a large number of local extremes with almost the same cost and/or with hierarchical structures. For these cases we plan to use robust minimization techniques based on simulated annealing or genetic algorithms (Kirkpatrick 1983, Goldberg 1989). Most likely in real situations we will include a combination of both methods to achieve the desired generality and robustness.


In addition, we will explore the combination of computer optimization with human-computer interactive optimization steering. It is a common problem in large-scale optimizations that even combinations of strategies are inefficient in finding the "basin" where acceptable solutions are located. Regions containing the sought-for extrema can emerge on a qualitatively different level of detail and representation than those addressed by the optimization methods. We hypothesize that human cognitive capabilities coupled with computer optimization supported by appropriate visualization would significantly speed-up convergence towards the acceptable or nearly optimal solutions. Preliminary trials on similar problems indicate that a combination of human and computer capabilities is surprisingly robust and efficient overall, as detailed in the following section.

C.3.4 Intelligent virtual environment for interaction with evolving landscapes

A main focus will be the development of intelligent virtual environment which will allow users to visualize and interactively modify the 3D dynamic landscapes during simulations. Direct interaction with landscape processes will allow the users to "feel" the impact of their actions and explore various solutions. This is an especially important element of the decision-making process when stake holders with specific, and often differing views of the problem evaluate the impacts of various simulation scenarios (Arias et al. 2000, Johnston and Srivastava 1999). The development will involve research in the following two interrelated areas.


Techniques for interactive simulation control.

The control of complex simulations is an active area of research in Artificial Intelligence (AI), particularly in the area of mixed-initiative systems. In a mixed-initiative system, a human user and an automated system contribute to a problem solution--formulation, development, analysis, repair--without the need for constant exchange of information. Ideally, a mixed-initiative system supports the dynamic assignment of system/user responsibility for directing and analyzing a complex process, such as a simulation. In a landscape context, the system contains representations of basic processes and their parameters: e.g., water and sediment fluxes, their sources and sinks, landscape cover and soil properties. An intelligent assistant simplifies the user's control of the simulation by allowing delegation of repetitive or demanding tasks. Such an assistant contributes in a number of ways: systematic consideration of alternative analysis procedures, autonomous exploration action consequences (lookahead), and automated analysis based on general and domain-specific evaluation rules, among many other possibilities (Pegram et al. 1999, St. Amant 1997, St. Amant and Cohen 1998a, 1998b). As an example in conservation design, the exploration of landscape might involve the user's selecting an abstract object representing e.g. a stream buffer, sedimentation pond, or dry dam, and then selecting a general location. The system's automatic contribution would involve exploration of plausible alternatives for the shape, properties, and exact placement of the object, based on a topographic analysis and simulation of relevant processes. The overall goal for such interactions is to integrate human judgment into an automated problem-solving process, relying on an explicit representation of strategic knowledge of the domain. Translating the currently used mixed-initiative techniques to landscape simulation will involve some knowledge engineering, but much of the conceptual work should generalize in a straightforward way.


Techniques for flexible visualization.

A key factor in effective simulation control is an appropriate visual representation of landscape properties and behaviors (Mitas et al. 1997). We have developed a theoretical framework to explain the role of interactive software tools; the framework helps designers identify and categorize system properties that facilitate appropriate action on the part of the user (St. Amant 1999, St. Amant and Riedl, in press). To continue the conservation design example, the system might identify a dozen plausible refinements to the user's abstract action. Rather than presenting these results sequentially, or attempting to reduce them to a more concise (but usually less expressive) textual representation, the system can constrain and organize the visual environment to facilitate the user's exploration within a restricted space of "good" solutions. Just as graphical user interfaces gray out inappropriate menu selections and highlight windows and buttons needing immediate attention, a simulation system can restrict user operations appropriately and provide visual cues as to the best solutions available. In the conservation example, the system might fix the set of properties common to the best solutions and then visually highlight a decision (e.g., presenting three "ghost" objects in different locations, requiring a user selection) that most effectively reduces the remaining choices. The role of visualization in such examples is to provide an external representation that allows the user to perceive, evaluate, and potentially modify the system's actions. Appropriate techniques draw on several areas of our expertise: support for navigation in information spaces (St. Amant 1997); end-user programming through visual techniques (St. Amant et al. 2000), and visualization support for intelligent data analysis (Healey and St. Amant 1999).


C.3.5 Open source implementation.


Standard GIS and modeling tasks will be performed using a combination of commercial off-the-shelf products and open source software. The prototype virtual landscape environment for creating and manipulating 3D dynamic landscapes will be implemented in open source GIS based on the latest release of GRASS5 (GRASS5 2001). GRASS (Geographic Resources Analysis Support System) is one of the top ten open source projects (Neteler 2000) and it has played a pioneering role in integrating GIS and environmental modeling. Numerous environmental models have been linked to or integrated with this system (e.g., SWAT: Srinivasan and Arnold 1994, ANSWERS: Rewerts and Engel 1991, CASC2d: Saghafian 1996, r.water.fea: Vieux et al. 1996, TOPMODEL: GRASS5 2001). The capabilities of this GIS are substantially enhanced by links to additional open source tools, such as R statistical language (Bivand and Neteler 2000) and PostgreSQL database system (PostgreSQL 2000).


The 3D interactive landscape environment will be based on the GRASS5 multidimensional visualization tool NVIZ2.2 which provides the capabilities to display multiple surfaces, vector and site data in 3D space, supports interactive spatial query, distance measurement, and flow tracing and a wide range of additional functionality (NVIZ 2001, Brown et al. 1995). Interactive landscape manipulation will be implemented using a direct link to map algebra and other GIS functions and specially developed tools providing link to proposed simulation and optimization capabilities.


Path sampling simulation tools will be implemented in the form of specialized library functions supporting building of models for multi-scale simulation of transport and fate of various constituents in 2D and 3D space. The implementation will be coordinated with the international GRASS5 development team. It will contribute to the rapidly growing open source geospatial computing infrastructure and benefit from modifications and enhancements by international team of GIS developers. The developed and tested tools will be distributed through GRASS software web sites: 2 main sites in USA and Germany and several mirror sites worldwide, see http://www.baylor.edu/~grass/).


C.3.6 Study areas


The prototype interactive landscape environment will be developed and tested at two contrasting but scientifically complementary sites: a) North Carolina State University's (NCSU) Centennial Campus, a 1200-acre rural site undergoing substantive and rapid landscape changes that will double the land area occupied by the University (http://centennial.ncsu.edu/), and b) Jockey's Ridge State Park, a dynamic, natural portion of the North Carolina Outer Banks characterized by migrating sand dunes that threaten nearby coastal development.


Centennial Campus is NCSU's vision of the village of the future - a "technopolis" of university, corporate and government research facilities and business incubators, town center with conference center and hotel, housing, and recreational amenities, including a golf course. The site (Figure 5) is quickly emerging as the Research Triangle Area's fastest growing development. The Centennial Campus area is now partially forested and includes buildings with about 4000 employees and students; projected campus will include buildings for 25,000 employees and 7,000 housing residents. Most of the presently forested area will be transformed into a golf course developed as a demonstration of environmentally friendly and sustainable design, construction and operation (Figure 5). The field measurements and monitoring, (supported by the current and proposed leveraging projects funded by the NCSU College of Agricultural and Life Sciences, and potentially also by the Army Research Office and North Carolina Water Resources Research Institute) will address both long-term, watershed-scale impacts and short-term, sub-watershed scale impacts. Long-term impacts will be estimated using measurements of accumulated sediment in sediment retention reservoirs, and larger impoundments using high-resolution swath bathymetry equipment. Short-term impacts will be quantified using high-resolution measurements of channels, gullies and large rills using RTK GPS, laser-rangefinder technologies and geo-referenced video surveys. Video surveys will be conducted at the outset and the end of the study; and sites exhibiting extensive changes will be surveyed more often. NCSU is also monitoring storm flows and water quality at several small streams. Detailed surveys will be conducted in areas of active earth-moving, including the locations of temporary and permanent sediment control features.


The monitoring program will capture the transformation of the Campus from mostly forested, natural area to an anthropogenic landscape with urban land use, golf course and parks, and evaluate the impact of this change on the environment, including the areas adjacent to the Campus. Interactive land use design and optimization tools will be used to improve the conservation and pollution prevention measures and ensure sustainability of this new type of urban community. The Centennial Campus project supported by the State of North Carolina, NCSU and industry with overall funding of more than $500 million over the period of next ten years, offers a unique opportunity to develop, test and apply ideas which would be extremely useful and stimulating for the development of similar projects around the Nation.









Figure 5. 3D view of future Centennial Campus and its current neighborhood.



North Carolina Coast. Jockey's Ridge is the largest active dune field on the East Coast of the United States, covering about 400 acres and rising nearly 30 m above sea level (Judge et al., 2000). Topographic changes in the park, both natural and anthropogenic, are of great interest to the nearly 1 million park visitors and local hang-gliding concessions. Coastal development has altered natural transport patterns of wind-blown sand, introduced vegetation, and otherwise perturbed landscape processes. Dunes migrate across park boundaries, and a landscape-management program for the park has been actively solicited by park managers.


Jockey's Ridge is an ideal prototype location for development of interactive landscape manipulation environments:



We will use the new simulation techniques to visually and interactively illustrate several environmental effects thought to influence dune height and migration at Jockey's Ridge State Park, including: changes in sand supply, including bulldozing and diminution of natural supplies; encroachment or removal of vegetation; and installation and removal of sand fences.



C.4 Benefits, education and outreach


The proposed project extends the idea of effective design by using virtual environments to landscapes and their sustainable development. The proposed interactive dynamic landscape concept can have a profound impact on planning and decision-making process, by substantially increasing the capabilities of the stake holders to evaluate the impacts of proposed actions and explore various alternatives. By bringing geospatial information into the discussions about the planned development and its alternatives, the tools can improve the effectiveness of the decision making process and assist in finding the optimal solutions.


The developed applications will be used as a demonstration and educational tool for stake holders in watershed and coastal communities by providing easy to understand, visual representations of human impacts on landscape processes. The virtual environment will also enable training of students and professionals in applications of the advanced GIS, simulation, and visualization for land use management.


The project will contribute to the open source efforts by developing new capabilities for the open source GIS and the developed and tested tools will be distributed worldwide through GRASS software web sites. The open source philosophy will enable to harness the creativity of the international GIS community and help to distribute the basic tools to support protection of the environment and conservation of natural resources at places with limited financial resources. It will also support an infrastructure for geospatial commercial activities build upon the "add-on" capabilities, large project designs and consultation services.


C.5 Participants and international collaboration


The project will be performed as a collaborative effort among the GIScience lead by Dr. Helena Mitasova, computer science lead by Dr. Robert St. Amant, earth science lead by Dr. Thomas Drake, and computational physics lead by Dr. Lubos Mitas.


The project PI Dr. Lubos Mitas has over 10 year experience in applying mathematical, physical and computational methods to problems in geosciences, materials research and quantum chemistry. The multi-variate spatial interpolation methods which he co-developed have been implemented into large commercial and open source GIS. Recently, he has been exploring the path sampling methods to solutions of terrestrial surface transport problems. He is also one of the experts and developers of electronic structure quantum Monte Carlo methods.


Dr. Thomas Drake has extensive experience in combining field, laboratory, computational and theoretical approaches in studies of earth surface processes, including rivers, beaches, sand dunes and their effects on human activities.


Dr. Helena Mitasova has been active in open source GIS development for over 10 years and was awarded an "Excellence in development" award in 1994 from the Open GIS foundation. As a research scientists at US Army Construction Engineering Laboratories and University of Illinois' Geographic Modeling Systems Laboratory, she has served as PI for terrain and landscape process modeling projects for over 8 years. She is also a recognized expert in dynamical multi-dimensional cartography and visualization in GIS.


Dr. St. Amant research focuses on the area of intelligent user interfaces, a relatively recent synthesis of artificial intelligence and human-computer interaction research. He has initiated the first honors course in the Department of Computer Science at NCSU which fits into a larger effort in the College of Engineering to incorporate open source concepts and software into the engineering curriculum. The effort has received support and software contributions from RedHat and IBM.


NCSU has an excellent infrastructure for geospatial research by providing university licence for a major GIS, and supporting open source development based on the recently announced initiative with IBM/RedHat. The Department of Marine, Earth and Environmental Sciences is well equipped for state-of-the-art field measurements and participates in numerous monitoring programs.


The project will include an intensive international collaboration (see supplemental documentation, Section I) through coordination of the open source GIS development with the international team of developers lead by Markus Neteler from the Institute of Physical Geography and Landscape Ecology, University of Hannover, Germany. The development and testing of landscape modeling tools for environmental projects in Slovakia will be performed in collaboration with Dr. Jaroslav Hofierka, Geomodel s.k., Bratislava, Slovakia. Geomodel s.k. is a promising small software and consulting company established by the new generation of young Slovak geoscientists with the main focus on GIS applications, environmental projects, method testing and development. They have successfully completed several environmental projects funded by the Slovak government and by European Union research initiatives.


C.6 CONCLUSION


This project will contribute to the evolution of computational geography (Berry 2000) by introducing new computational methods for solving geospatial problems. The unique combination of real-time monitoring, simulations and human-computer interactions will open new perspectives both for fundamental progress in GIScience and for practical applications such as land use management and decision making. The developed methods and tools have a potential for a worldwide impact through their implementation into a well established and rapidly growing Open source GIS.





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