of Ecosystem Service
Project Framework /
Objectives / Approach
advancement of GIS technology, land use models having the abilities of
simulating spatial and temporal land use change were emerged. Various
land use models from different realms such as equation-based models,
econometric models, empirical statistical models, and spatial system
models, have been widely applied to simulate land use change, but they
were often focused on growth of cities and regions. Agent-based models
(ABM) and cellular automata (CA) have recently become the most widely
used models to simulate land use change based on the state of a pixel,
the conditions in the surrounding pixels, and a set of transition rules
from a bottom-up perspective. However, the evolution of urban system
depends not only on its pervious and surrounding states but also on
driving forces such as energy inflows and imported goods and services
which affect the change of land use. Ecological models based on general
system theory have also been developed to take into account energy flows
as driving force of the system (Huang et al., 2007; Lee et al., 2008).
This project intends to integrate ABM and ecological modelling approach
for the purpose of simulating the interaction among system components
within the urban ecosystem and assess the resilience of urban system due
to global environmental change and land use change in peri-urban area.
ideal of agent-based models can be traced back to the theoretical
machine capable of reproduction addressed by John von Neumann. After the
improvement by Stanislaw Ulam and John Conway, CA and Game of Life, the
predecessor of ABM, were developed (Sun and Cheng, 2000). Craig Reynold
was the first person who applied ABM to model the reality of lively
biological agents. Swarm, the first general purpose ABM systems, was
developed by the Santa Fe Institute in Santa Fe, New Mexico, USA during
late 1980s and early 1990s. Because of the serious impacts from human
decision making on land use and land cover change, agent-based models
were started to receive attention for simulating the actions and
interactions of autonomous individuals in modelling land use change.
Agent-based models consist of dynamically interacting rules based agents
and simulate their interactions through the bottom-up approach (Castel
and Crooks, 2006). The models can analyze effects of human decision on
land use and interactions between them which were often ignored by other
models (Ojima et al., 2005). The widely applied ABM software includes
RePast, Ascape, StarLogo, MASON, FEARLUS and NetLogo. On the other hand,
in order to model the complex dynamic interactions of water,
environmental inputs and habitants, General Ecosystem Models (GEM) was
developed by Costanza et al. (1995) with algorithms of ecosystem
processes. GEM is a grid-based spatial dynamic model and can integrate
human and environmental systems for simulating the effects of land use
scenarios on ecosystem processes. GEM has been widely used in different
fields such as habitat change, watershed management, species migration,
etc. (Costanza and Voinov, 2004).
1. Castle, C. J. E. and Crooks, A.T.
(2006). UCL Working Papers Series Paper 110: Principles and Concepts
of Agent-Based Modelling for Developing Geospatial Simulations,
London: Centre for Advanced Spatial Analysis, University College London.
2. Costanza, R., Wainger, L., Bockstael,
N. (1995). Integrated ecological economic systems modeling: theoretical
issues and practical applications, In Integrating Economic and
Ecological Indicators, edited by J. W. Milton and J. F. Shogren, pp.
45-66, London: Praeger.
3. Costanza, R. and Voinov, A. (2004).
Landscape Simulation Modeling- A Spatially Explicit, Dynamic
Approach, New York: Springer.
4. Huang, S. ˇVL., Kao, W. ˇVC., and Lee, C.
ˇVL. (2007). Energetic mechanisms and development of an urban landscape
system, Ecological Modelling, 201 (3-4): 495-506.
5. Lee, C. ˇVL., Huang, S. ˇVL., and Chan,
S. ˇVL. (2008). Biophysical and system approaches for simulating land-use
change, Landscape and Urban Planning, 86: 187-203.
6. Ojima, D., Moran, E., McConnell, W.,
Smith, M. S., Laumann, G., Morais, J., and Young, B. (2005). Global
Land Project: Science Plan and Implementation Strategy, IGBP Report
No.53/ IHDP Report No. 19, Stockholm: IGBP.
7. Sun, Y. and Cheng, L. (2000). A survey
on agent-based modelling and equation-Based modeling, Proceedings of
Department of Computer Science (http://www.cs.gsu.edu/~csclicx/Csc8350/model.pdf),
Georgia: Georgia State University.