Geography Reference
In-Depth Information
the socioeconomic development, which were not taken into account in the scenario
design. Therefore, it is necessary to implement more detailed analysis of socio-
economic drivers of land use change in the future study. In addition, the impacts of
socioeconomic factors on the land use change are represented in approach
parameterization, which have some limitations since they failed to thoroughly
reveal how socioeconomic development influences land use change. Meanwhile,
only three scenarios, which can set general direction of socioeconomic develop-
ment, were designed in this study, but they cannot provide more specific details. In
this aspect, more scenarios should be designed in the future research so as to fully
reveal the best land use change to adapt to the climate change, e.g., more specific
scenarios of the same type can be designed under the CES scenarios.
3.2 Scenarios of LUCC in China
The core part of researches on LUCC includes driving force, driving mechanism,
their effects and model simulation of LUCC. In the past decades, scholars of
different fields have paid great attention to LUCC, mainly focusing on the
spatiotemporal change, driving mechanism, eco-environmental impacts and sim-
ulation of LUCC (Hasselmann et al.
2010
). The research on the spatiotemporal
analysis of LUCC mainly focuses on the change in quantity and spatial pattern
(Patarasuk and Binford
2012
). While the research on driving mechanism of LUCC
makes great contribution to revealing basic processes of LUCC and its driving
factors, further predicting future changes and formulating their corresponding
policies. Currently, there have been various models to reveal the mechanism,
explore their driving factors and simulate dynamic process of LUCC (Liu et al.
2008
; Munroe and Müller
2007
).
Previous models for forecasting LUCC in the future mainly covered empirical
statistical models, agent-based models, methods based on relationships of adjacent
grids in a dynamic simulation of land system (Zhao et al.
2011
). The empirical
statistical models can extract those major driving factors of LUCC and explore the
reasons through its spatiotemporal processes. The Conversion of Land Use and
Effects (CLUE) model and Conversion of Land Use and Effects at Small Region
Extent (CLUE-S) model are two representative empirical statistical models
(Veldkamp and Fresco
1996
). However, there is generally a very large spatial scale
and low resolution used in the simulation with the CLUE model, while the CLUE-S
is mainly applied in dynamic simulation of regional land use at small scales
(Verburg et al.
1999
). The simulation of the structural change of land use with the
Agent-based Model (ABM) has many advantages, but it generally concentrates on
small study area. The Cellular Automaton (CA) simulates the processes of cellular
evolution rules, but it requires a variety of spatial statistical methods to assist in this
detection (White and Engelen
2000
). Many scholars have tried to explore land use
change through other methods and models, such as land-use dynamic degree model
(Liu et al.
2003
), identification model of driving forces (Geist and Lambin
2002
),
