the coupled system is one of the major challenges for land change modeling. To
deal with the influences of spatial dependency, multi-scale analysis is necessary to
address the Modifiable Areal Unit Problem (MAUP). Another important issue is
to model the interactions and feedbacks among multiple scales in the land change
processes. New models need to take into consideration of the multilevel processes
and to integrate alternative perspectives into the existing modeling framework. In
modeling land change processes, a temporally dynamic modeling framework is
critical to capture the necessary behavior changes during the modeling time
periods. Moreover, the factor of time lags needs to be considered to avoid biased
simulation.
The advances in land change modeling offer great opportunities to study global
environmental change in an integrated framework. The examples reviewed in this
chapter should shed light on the progress of coupling land change modeling with
other ecological modeling and analysis techniques for analyzing the interactions
between land changes and other components of global environmental change. Many
of the integrated frameworks are based on the use of simulated land use patterns or
other land use/cover derived variables as input to the ecological models. More
complex examples make use of the process-based models that integrate land change
models and ecological models through individual decision-making using outputs
from each model.
Acknowledgments The authors like to thank the Florida State University for the time release in
conducting this work. The research was partially supported by the Florida State University Council
on Research and Creativity, CAS/SAFEA International Partnership Program for Creative
Research Teams of “Ecosystem Processes and Services”, the Natural Science Foundation of
China through the grant “A Study on Environmental Impacts of Urban Landscape Changes and
Optimized Ecological Modeling” (ID 41230633).
References
Agarwal C, Green GM, Grove JM, Evans TP, Schweik CM (2002) A review and assessment of
land-use change models: dynamics of space, time, and human choice. GTR NE-297.
U.S.D.A. Forest Service, Northeastern Research Station, Newton Square
Agarwal DK, Silander JA, Gelfand AE, Dewar RE, Mickelson JG (2005) Tropical deforestation in
Madagascar: analysis using hierarchical spatially explicit, Bayesian regression models. Ecol
Model 185(1):105–131
Akin A, Clarke KC, Berberoglu S (2014) The impact of historical exclusion on the calibration of
the SLEUTH urban growth model. Int J Appl Earth Obs Geoinf 27:156–168
Aspinall R (2004) Modelling land use change with generalized linear models - a multi-model
analysis of change between 1860 and 2000 in Gallatin Valley, Montana. J Environ Manag
72(1–2):91–103
Batty M (2005) Agents, cells, and cities: new representational models for simulating multiscale
urban dynamics. Environ Plan A 37(8):1373–1394
Batty M, Xie Y (1994) From cells to cities. Environ Plan B Plan Des 21:31–38
Bockstael NE (1996) Modeling economics and ecology: the importance of a spatial perspective.
Am J Agric Econ 78(5):1168–1180
14
T. Liu and X. Yang
deal with the influences of spatial dependency, multi-scale analysis is necessary to
address the Modifiable Areal Unit Problem (MAUP). Another important issue is
to model the interactions and feedbacks among multiple scales in the land change
processes. New models need to take into consideration of the multilevel processes
and to integrate alternative perspectives into the existing modeling framework. In
modeling land change processes, a temporally dynamic modeling framework is
critical to capture the necessary behavior changes during the modeling time
periods. Moreover, the factor of time lags needs to be considered to avoid biased
simulation.
The advances in land change modeling offer great opportunities to study global
environmental change in an integrated framework. The examples reviewed in this
chapter should shed light on the progress of coupling land change modeling with
other ecological modeling and analysis techniques for analyzing the interactions
between land changes and other components of global environmental change. Many
of the integrated frameworks are based on the use of simulated land use patterns or
other land use/cover derived variables as input to the ecological models. More
complex examples make use of the process-based models that integrate land change
models and ecological models through individual decision-making using outputs
from each model.
Acknowledgments The authors like to thank the Florida State University for the time release in
conducting this work. The research was partially supported by the Florida State University Council
on Research and Creativity, CAS/SAFEA International Partnership Program for Creative
Research Teams of “Ecosystem Processes and Services”, the Natural Science Foundation of
China through the grant “A Study on Environmental Impacts of Urban Landscape Changes and
Optimized Ecological Modeling” (ID 41230633).
References
Agarwal C, Green GM, Grove JM, Evans TP, Schweik CM (2002) A review and assessment of
land-use change models: dynamics of space, time, and human choice. GTR NE-297.
U.S.D.A. Forest Service, Northeastern Research Station, Newton Square
Agarwal DK, Silander JA, Gelfand AE, Dewar RE, Mickelson JG (2005) Tropical deforestation in
Madagascar: analysis using hierarchical spatially explicit, Bayesian regression models. Ecol
Model 185(1):105–131
Akin A, Clarke KC, Berberoglu S (2014) The impact of historical exclusion on the calibration of
the SLEUTH urban growth model. Int J Appl Earth Obs Geoinf 27:156–168
Aspinall R (2004) Modelling land use change with generalized linear models - a multi-model
analysis of change between 1860 and 2000 in Gallatin Valley, Montana. J Environ Manag
72(1–2):91–103
Batty M (2005) Agents, cells, and cities: new representational models for simulating multiscale
urban dynamics. Environ Plan A 37(8):1373–1394
Batty M, Xie Y (1994) From cells to cities. Environ Plan B Plan Des 21:31–38
Bockstael NE (1996) Modeling economics and ecology: the importance of a spatial perspective.
Am J Agric Econ 78(5):1168–1180
14
T. Liu and X. Yang