changes given the complexity of the coupled human-environmental systems
(Rindfuss et al. 2004). Advances in geospatial theories, technologies and data
provide great opportunities for addressing various challenges and for developing
the next generation of LCMs. In the following sections, we first discuss the
theoretical foundations and major characteristics of various modeling approaches.
We then identify some outstanding issues for the LCM communities. Finally, we
describe several examples to illustrate how land change modeling can be coupled
with other ecological modeling techniques for integrated global environmental
change research.
1.2 Land Change Modeling Approaches
This section discusses several frequently used land change modeling approaches,
including statistical regression models, artificial neural networks, Markov chain
models, cellular automata, economic models, and agent-based models. The above
modeling approaches were identified based on the authors’ knowledge and a
personal archive of relevant publications, and a search on Web of Science using
the Keywords: (Topic ¼ “land change” or “land use change” or “land cover
change” or “land use and land cover change” or “urbanization” or “urban growth”
or “urbanization” or “deforestation” or “farmland”) AND (Topic ¼ model or simulation). In the following subsections, we will briefly present the theoretical and
methodological basics and the relative strengths and weaknesses of each modeling
approach with selected examples.
1.2.1 Statistical Regression Models
The basic structure of statistical regression models is based upon empirical analyses
that link between land use and land cover changes (i.e., dependent variable) and a
set of environmental and socio-economic explanatory variables. The derived relationships are usually used to generate maps of land transitional probability to
predict potential land changes in the future. Some frequently used statistical
methods for land change modeling include logistic regression (Hu and Lo 2007),
generalized linear models (Aspinall 2004), generalized additive models (Brown
et al. 2002), and Bayesian statistics (Agarwal et al. 2005). A popular example is the
CLUE-S (Conversion of Land Use and its Effects at Small regional extent) model
developed by Verburg et al. (2002). The CLUE-S model consists of a non-spatial
demand module and a spatially explicit allocation module. The non-spatial module
estimates the aggregate demand of land changes, and the spatial module allocates
the land demands at various locations on a raster space based on stepwise logistic
regression. Logistic regression is a form of multivariate models when the dependent
variable has a categorical output, e.g., change or no-change of land use. Logistic
1 Land Change Modeling: Status and Challenges
5
(Rindfuss et al. 2004). Advances in geospatial theories, technologies and data
provide great opportunities for addressing various challenges and for developing
the next generation of LCMs. In the following sections, we first discuss the
theoretical foundations and major characteristics of various modeling approaches.
We then identify some outstanding issues for the LCM communities. Finally, we
describe several examples to illustrate how land change modeling can be coupled
with other ecological modeling techniques for integrated global environmental
change research.
1.2 Land Change Modeling Approaches
This section discusses several frequently used land change modeling approaches,
including statistical regression models, artificial neural networks, Markov chain
models, cellular automata, economic models, and agent-based models. The above
modeling approaches were identified based on the authors’ knowledge and a
personal archive of relevant publications, and a search on Web of Science using
the Keywords: (Topic ¼ “land change” or “land use change” or “land cover
change” or “land use and land cover change” or “urbanization” or “urban growth”
or “urbanization” or “deforestation” or “farmland”) AND (Topic ¼ model or simulation). In the following subsections, we will briefly present the theoretical and
methodological basics and the relative strengths and weaknesses of each modeling
approach with selected examples.
1.2.1 Statistical Regression Models
The basic structure of statistical regression models is based upon empirical analyses
that link between land use and land cover changes (i.e., dependent variable) and a
set of environmental and socio-economic explanatory variables. The derived relationships are usually used to generate maps of land transitional probability to
predict potential land changes in the future. Some frequently used statistical
methods for land change modeling include logistic regression (Hu and Lo 2007),
generalized linear models (Aspinall 2004), generalized additive models (Brown
et al. 2002), and Bayesian statistics (Agarwal et al. 2005). A popular example is the
CLUE-S (Conversion of Land Use and its Effects at Small regional extent) model
developed by Verburg et al. (2002). The CLUE-S model consists of a non-spatial
demand module and a spatially explicit allocation module. The non-spatial module
estimates the aggregate demand of land changes, and the spatial module allocates
the land demands at various locations on a raster space based on stepwise logistic
regression. Logistic regression is a form of multivariate models when the dependent
variable has a categorical output, e.g., change or no-change of land use. Logistic
1 Land Change Modeling: Status and Challenges
5