C.3.2 Multiscale spatial simulation of landscape processes based on duality of particles and fields.

This is mostly cut-and-paste text, Lubos and I will clean it up and modify to fit the project

Landscape process simulations will provide the necessary diagnostic tools for assessment of the current state of the landscape, prediction tools for evaluation of the current and planned land use impacts and tools supporting intelligent landscape manipulation, optimization with a potential to develop innovative conservation strategies.

Process-based modeling of geospatial phenomena involves substantially more uncertainty than modeling in physics or chemistry. One of the key reasons is the complexity of landscape phenomena. The practical solutions then have to rely on the best possible combination of physical models, empirical evidence, intuition and available measured data. Landscape processes are therefore often described by a combination of deterministic (physically based) and empirical models and integration with field measurements (data adaptive simulation) is crucial for obtaining reliable predictions. Physically based models of spatial processes are based on:

To emphasize the distributed and dynamic character, the modeled quantities can be characterized as continuous fields (scalar, vector, tensor). For numerical modeling, discrete representation of the fields based on particle(s) can be used with transformation between particles and fields performed in the sense that density of particles in space defines a field and vice versa, i.e, field can be represented by particles with corresponding spatial distribution of their densities. Using this duality between particles and fields, processes can be modeled as evolution of fields or evolution of spatially distributed particles.

Spatial processes described by the governing differential equations are usually solved in a discretized form. Typical approaches include finite difference (REF, Julien and Saghafian, ...) and finite element methods (REF: Burnett, 1987,  SMS, GMS, WMS). We propose to use an alternative, newer method, which has a substantial potential for many geoscientific applications. The approach is called path sampling (other names such as random walks are also common, Gardiner (1985) and relies on the duality: field and particle density. The path sampling method has been successfully used for solving linear partial differential equations in physics, chemistry, finance and other disciplines (REF LUBOS add). It has several important advantages when compared with more traditional approaches. The method is very robust, can be easily extended into arbitrary dimension, is mesh-free and is very efficient on parallel architectures (embarrassingly parallel, making it suitable for clusters, including internet based clusters etc.) The method is also rather straightforward to implement in a multi-scale framework with data adaptive capabilities.  For landscape applications, the method has been used for modeling of overland water and sediment flow  and erosion/deposition (Mitas and Mitasova 1998).

Many of the natural processes involve more than a single scale and exhibit multiscale, multiprocess type of phenomena (Steyaert1993, Green et al. 2000). Problem of multiple scales now permeates a number of scientific disciplines such as materials research,geosciences, and biology. Some multiscale problems can be partitioned into a system of nested models in the direction from fine tocoarse scales. This basically requires to develop effective model on each scale level which incorporates simplified or "smoothed out" effects coming from finer, more accurate levels. On the other hand, 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 on finer scales. The high accuracy, resolution and processes of fine scales are then used only in "hot spots" of the studied system which require such a treatment.Implementation of multiscale get from Vienna paper EQUATIONS?

To improve the accuracy and reliability of the models the simulations will be coupled with field data. We will investigate the impact of location and temporal interval of sampling and possibilities to find the optimal/cost effective relation between the models and measurements (at which point additional measurements are not necessary, adapting ttime interval of sampling to the monitored conditions - e.g. short interval during storms, long interval during stable dry weather, short interval/higher resolution during construction, long interval/lower resolution after development is finished). The models will use the field data to adapt the simulation by  changing the conditions of simulation (terrain shape, flow velocity, etc) and improving the accuracy by adapting the simulated water depth to the measured one.

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 general simulation modules / components which will allow to build more sophisticated models of various spatial processes.

Figure 3 Multiscale path sampling simulations fig - different effects at dif. scales?
 

C.3.3 Spatial optimization

To support the development of intelligent tools for manipulation of landscape,  optimization of complex spatial systems will be explored. The methods will be aimed at finding optimal compromises between conflicting demands or finding  landscape pattern solutions within prescribed constraints.

One of the important developments which we plan to undertake is to find a sufficiently general framework for distributed optimization tasks with optimization of multi-variate objects (e.g, spatial distribution of conservation measures). In order to formulate, quantify and solve these tasks we will use a general approach based on the 'cost or penalty' functional, with two types of key inputs. The first input, denoted as fk (r,t), represents a set of fields which describe the natural processes and phenomena of interest (e.g., sediment flux). The second one, gl(r,t), is a set of fields which describe the spatial distribution of quantities or measures related to anthropogenic factors or influences (e.g., distribution of grass strips). In addition, we suppose that as soon as we specify gl(r,t) and provide the rest of inputs from the GIS database, we can find the corresponding fk(r,t) by solving the appropriate set of equations describing the relevant landscape process (in our case sediment and contaminant transport). The possible ranges of the fields then specifies the configuration space, in which we will search for a solution. Available information and models such as initial field values, are provided by a GIS and are used as inputs into the master equations and their solvers.

In general, the cost functional can be written as an integral over the space region O and time interval T

C[fk(r, t), gl (r, t), pm]= \int_{O, T} F [fk (r, t), gl(r, t), pm] d(r)dt           (1)

where fields fk(r,t ) and gl(r,t) describe natural processes and anthropogenic factors, respectively, while pm denote non-distributed variables, e.g., related to actual cost expressions or other relations and k,l,m are enumerating indices. The functional can depend also on other fields and parameters which do not change during the optimization and therefore are not explicitly mentioned. In addition, the dependence of C on gm(r, t), pm can be both direct and indirect (by modifying the solution of the equations for gl(r, t), pm). The optimal solution is achieved by finding sets of complete multi-variate fields gl^{opt}(r, t) and parameters pm^{opt} which minimize the cost functional

B C[ {fk( r, t), gl^{opt}( r, t), pm^{opt}] = min            (2)

and at the same time fulfill the prescribed constraints. The constraints can have various character such as non-negativity of a particular field gl(r, t)> 0, prescribed interval of values, continuity or compactness in spatial distribution. For the actual optimization of the functional (5) we need to specify how to vary efficiently the fields gl(r, t) and the corresponding choice of the optimization techniques.

In order to transform the variation of the multi-variate fields into a variation of simple variables we will employ the basis set concept. The multi-variate fields will be expanded in appropriate basis sets and the corresponding expansion coefficients will play a role of variational variables. The variational variables can then be optimized by approaches described below. Regarding the basis sets, we expect to use mostly the following possibilities: raster or block-raster basis, local continuous function basis such as gaussians and regularized spline with tension basis. The choice will depend on the actual application and will be determined by various considerations such as required resolution, continuity or character of constraints.

Next, we need to specify efficient methods for minimization of the cost functional which will be sufficiently robust to accommodate the mentioned types of basis sets. There are essentially two extremes for how the "cost landscape" can behave and this characterization determines the choice of appropriate methods. In the case of a single or a few local minimas, standard methods from optimization libraries, most likely an efficient quasi-Newton method can be used. In the other extreme, the cost landscape can be very complicated with a large number of local minima with almost the same cost or with hierarchical cost structures. For these cases we plan to use a robust minimization techniques based on simulated annealing or genetic algorithms (Kirkpatrick 1983, Goldberg 1989). Most likely in real situations one would need to include a combination of both methods to achieve the desired generality and robustness.


C.3.4 Intelligent virtual environment for interaction with evolving landscapes

The main focus of the proposed project will be the development of intelligent virtual environment which will allow users to visualize and interact with 3D dynamic landscapes by introducing modifications of landscape during an ongoing simulation. The interaction with landscape processes will allow the users to "feel" the impact of their actions and explore various solutions. This would be especially important for the decision making process when the stake holders with very specific, and often differing views of the problem (e.g. developers, local government, landowners, environmental groups) will be able to present their proposals and concerns and the entire group would be able to view and evaluate the impacts.


The development will involve research in three related areas.


a) Data management tools.

The foundation of any intelligent environment lies in the power and flexibility of the tools it provides the user. In a landscape simulation and visualization environment, users will have access to data management tools that can handle large, spatio-temporal multiresolution datasets. Support will include the ability to carry out interactive analyses of data in and across different time scales and spatial scales, to focus attention on localized regions, to filter and combine data and results, and comparable activities. For example, the SE Triangle watershed will be modeled at 10-30m resolution to provide an overall framework for exploration and analysis. Centennial Campus will be modeled within the framework of to capture flows into and out of the study area. The embedded Centennial Campus area will be modeled at 2m resolution, with the stream areas embedded at 10-30cm. For an example of the kinds of analysis possible in this framework, consider a simulation that combines general changes at a construction site, modeled at a daily interval, with water and sediment flow during a storm on the construction site modeled a minute

interval. The tools to construct and manage such a simulation facilitate user action as well as intelligent assistance in the process.


b) Techniques for interactive simulation control.

The control of complex simulations is an active area of research in 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, this begins with an environment that contains representations of basic processes and their parameters: fluxes (e.g. water and sediment flow rates), source and sinks (e.g. rainfall intensity), and so forth. In addition, 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). An an example in conservation design, the exploration of landscape might involve the user's selecting an abstract object such as a 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

mixed-initiative techniques to landscape simulation will involve some knowledge engineering, but much of the conceptual work should generalize in a straightforward way.


c) Techniques for flexible visualization.

A key factor in effective simulation control is an appropriate visual representation of landscape properties and behaviors. The PIs 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 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).


additional references

see alsoVR CAVE interactive environment which allowed to explore the dynamic landscape, start/stop rainfall, watch the overland flow evolve, etc. (no landscape manipulation though and flow model was very simple) http://www.gis.uiuc.edu/research/hpgis/cave.htm
Visualization of relatively complex simulations of water and sediment flow for spatially variable land use conditions - this is the model that we plan to use, again, no landscape manipulation or interactive change of simulation conditions (see slides PSwater, PSsedim, PSmultiscale) http://www2.gis.uiuc.edu:2280/modviz/gisc00/duality.html

(examples from physics: VR manipulation of molecules developed by Prof. Schulten's group at UofI , UNC is adding simulations to their nanoMano environment to support "what if" scenarios in exploration of nanomaterials - this is at a completely different scale, but they work with topography too.see: http://www.cs.unc.edu/Research/nano/doc/biovisit.html)
our first steps towards this goals can be illustrated by figures and movie4 in
http://www2.gis.uiuc.edu:2280/modviz/lcgfin/cg-mitas.html
some "primitive" experiments with "building" a pond and a terrace with standard GIS tools are in Figure 13c at:
http://www2.gis.uiuc.edu:2280/modviz/reports/cerl97/rep97.html


C.3.5 OPEN source implementation.


In the fall of 2000 Dr. St. Amant initiated the first honors course in the Department of Computer Science at NCSU. Student projects during

the course supported a long-term goal of generating an open source environment tailored to the needs and interests of CSC students. This

course fits into a larger effort in the College of Engineering to incorporate Open Source concepts and software into the engineering

curriculum. This effort has received support and software contributions from RedHat and IBM.


GRASS5 visualization tool will be used as a basis ... NVIZ2.2 open gl based 3D visualization tool with tcltk - multiple surfaces, vector and site data, a multiscale-spatially variable resolution - improve efficiency, interactive manipulation, query, distance measurement, flow tracing. Interactive manipulation can be implemented through call map algebra and other GIS functions(Buffer, ...), site symbols. Improve effectiveness, add manipulation modules and interface

The prototype virtual landscape environment for creating and manipulation 3D dynamic landscapes and spatial simulation modules will be implemented in OPEN source environment supported by Open source GIS.

I will write more about OPEN source GIS and we need to discuss some technical details of the implementation
Open source GIS used and enhanced by this project will be  based on the latest release of GRASS5. GRASS (Geographic resources analysis support system) is one of the top ten open source projects (link).....it has played pioneering role in integrating GIS and environmental modeling, with numerous environmental models linked to it (ANSWERS, AGNPS, SWAT, CASC2d, TOPMODEL, .....). Link to R stats package and OSSIM...

This type of development and implementation will contribute to the rapidly growing open source geospatial computing infrastructure and benefit from modifications and enhancements by international team of GIS developers.


C.3.6 Study areas


The interactive computational landscape manipulation environment will be developed/tested using 2  different landscapes: the rapidly developing  Centennial Campus (http://centennial.ncsu.edu/) and monitored dynamic section of North Carolina coast.

Centennial Campus is North Carolina State University's vision of the village of the future-a "technopolis" of university, corporate and government R&D facilities and business incubators, with an exciting town center, executive conference center and hotel,  housing, and recreational amenities. This 1,192-acre site, adjacent to NC State's main campus, is quickly emerging as the Research Triangle Area's fastest growing development. A futuristic fixed guideway transportation system will link NC State's  main campus.  with this environmentally sensitive, mixed-use, academic village.

The Centennial Campus area is now partially forested (add % LU composition) and includes XX buildings with about 4000 employees and students. Projected at build-out will include XX buildings with planned 25,000 employees, 7,000 housing residents and most of the forested area will be transformed into a championship golf course developed as a demonstration of environmentally sustainable design, construction and operation (to be opened in 2002) (Figure 2 here comparing current and future land use ).

The field measurements (supported by ARO, NCSU CALS and WRRI) 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 ponds, sediment retention reservoirs, and larger impoundments using high-resolution swath bathymetry equipment. Short-term impacts will be quantified using high-spatial-resolution measurements of channels, gullies and large rills using RTK GPS, ancillary 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 earthmoving, 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 anthropogenic landscape with substantial urban type land use, golf course and parks and evaluate the impact of this change on the environment (including the areas beyond the Campus). The changing impact of ongoing development on landscape processes will be simulated and if unexpected or potentially damaging impacts are predicted the feedback will be provided to the stakeholders (developers, contractors). Interactive land use design and optimization tools will be used to improve the conservation and pollution prevention measures and ensure sustainability of the new, futuristic type of village being built in the Triangle.

The development of this community provides a unique opportunity for: add here something nice.....to contribute to what could  become a model for other technopolis developments over the country....

North Carolina Coast - Tom can you please advice on this (which area and what problems/features we should study)- this project should leverage the NRC effort and I will be working on it if I get the NRC fellowship.


C.4 Impact/benefits data sharing, education and outreach

The proposed project will bring ideas of using simulations and use of virtual object representation for design and manipulation of objects which are difficult to experiment with due to the scale and temporal constraints - an approach used in physics, material science (nanotubes) will be extended  to landscapes, land use management, design of conservation and environmental protection.

The proposed virtual landscape environment will be also used as a demonstration and educational tool for stakeholders in watershed and coastal communities, local government,  NC museums(?), providing visual representations of human impacts on landscape processes. The  environment will be also used to train students and professionals to use the advanced GIS, simulation, visualization and mobile measurements to support land use management.

Rob should we include here your open source class?

The project will also contribute to the Open source infrastructure by developing new capabilities for the  OPEN source GIS.

In long term future it is possible to envision a full analogy with nanoMano when the lanscape will be modified using robotic/automated machinery... already experiments in precision agriculture (REF) and construction (REF ?). It may also have a impact on real time response to natural disasters, etc


C.5 Participants and international collaboration, qualifications of the investigator and the grantee organization

Rob, Tom please add here whatever is appropriate

The project will be performed as a collaborative effort among the GIScience lead by Dr. Helena Mitasova, computer science lead by Dr. Robert StAmant, earth science lead by Dr. Tom Drake, and Physics lead by Dr. Lubos Mitas. We will also collaborate with Soils science department team lead by Dr. Rich McLaughlin.

Co-PI 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 OPEN GIS foundation. She has served as PI on terrain and landscape process modeling projects for DoD and Illinois over the past 8 years.

NCSU is building open source , sponsored by IBM and Red Hat, already LINUX based environment is used by students and faculty

International collaboration: We will coordinate the OPEN source GIS development with the international team of developers lead by Markus Neteler from University of Hannover, Germany and collaborate on the development of landscape modeling tools with Geomodel s.k. in Slovakia (Jaroslav Hofierka).


D. References

see separate document

E. Biographical sketches

Helena
Lubos
Rob
Tom

F. Supporting letters

DoD, IBM?, RedHat?,
 


Notes

I will add some info about WESNIS (wireless environmental sensor network and info system here - you can check it at  http://www.calit2.net/environment_civil.html (note that cal-it2 is a 10year over 100million initiative)
measurements from WRRI proposal should be added