understanding of land changes in an integrated framework for global environmental
change.
1.3.1 Coupling Human and Environmental Systems
Land changes are both causes and consequences of earth system changes, including
the biophysical and the socioeconomic processes. Models taking specific drivers
into considerations have tried to include factors from both subsystems. One major
challenge arises from the integration of data and processes representing biophysical
conditions and human decision making. Difficulty lies in the different levels of
aggregation and spatial unit of observation (Rindfuss et al. 2004). In socialdemographic analysis, data are usually collected at some levels of aggregation,
whereas direct measurements and remote sensing techniques have been more
commonly used in extracting biophysical variables (Jensen 1983). As a result,
research of the coupled human-environmental systems has to deal with the problem
of (i) integrating different types of data (e.g., raster and vector), (ii) integrating
spatial data at different scales, (iii) integrating spatial data from different dimensions (e.g., point, line, polygon), and (iv) integrating data acquired at different
locations (Gotway and Young 2002). These four types of spatial data integration
problems are often intertwined, which leads to even more challenges.
The issue of coupling human and environmental systems is also related to the
scale issues in that statistical modeling and machine learning are designed at the
scale of the coupled system as a whole while cell-based models can represent
multilevel dynamics in both dimensions. Moreover, it is quite challenging to fully
represent the processes in the human subsystems due to the lack of specific data on
human decision-making and a high level of uncertainty. Towards a comprehensive
understanding of the coupled system, the potential interactions and feedbacks
within the land change system need to be incorporated in the models. In this
sense, the structures of agent-based models and integrated models seem promising
for integrating human behaviors and biophysical feedbacks. Its capability in
representing temporal dynamics further facilitates the realization of simulating
system feedbacks in land change processes.
1.3.2 Scale Dependency and Multilevel Interactions
Research on the coupled human-environmental systems is further complicated by
the issue of scale dependency and the multilevel interactions within the system. One
of the early steps in spatially explicit modeling is to identify an appropriate scale
(e.g., extent and resolution) for analyzing the spatial phenomena, such as land
changes. This is known as the Modifiable Areal Unit Problem (MAUP) in
geospatial science, that is, the correlation between variables may change with scales
10
T. Liu and X. Yang
change.
1.3.1 Coupling Human and Environmental Systems
Land changes are both causes and consequences of earth system changes, including
the biophysical and the socioeconomic processes. Models taking specific drivers
into considerations have tried to include factors from both subsystems. One major
challenge arises from the integration of data and processes representing biophysical
conditions and human decision making. Difficulty lies in the different levels of
aggregation and spatial unit of observation (Rindfuss et al. 2004). In socialdemographic analysis, data are usually collected at some levels of aggregation,
whereas direct measurements and remote sensing techniques have been more
commonly used in extracting biophysical variables (Jensen 1983). As a result,
research of the coupled human-environmental systems has to deal with the problem
of (i) integrating different types of data (e.g., raster and vector), (ii) integrating
spatial data at different scales, (iii) integrating spatial data from different dimensions (e.g., point, line, polygon), and (iv) integrating data acquired at different
locations (Gotway and Young 2002). These four types of spatial data integration
problems are often intertwined, which leads to even more challenges.
The issue of coupling human and environmental systems is also related to the
scale issues in that statistical modeling and machine learning are designed at the
scale of the coupled system as a whole while cell-based models can represent
multilevel dynamics in both dimensions. Moreover, it is quite challenging to fully
represent the processes in the human subsystems due to the lack of specific data on
human decision-making and a high level of uncertainty. Towards a comprehensive
understanding of the coupled system, the potential interactions and feedbacks
within the land change system need to be incorporated in the models. In this
sense, the structures of agent-based models and integrated models seem promising
for integrating human behaviors and biophysical feedbacks. Its capability in
representing temporal dynamics further facilitates the realization of simulating
system feedbacks in land change processes.
1.3.2 Scale Dependency and Multilevel Interactions
Research on the coupled human-environmental systems is further complicated by
the issue of scale dependency and the multilevel interactions within the system. One
of the early steps in spatially explicit modeling is to identify an appropriate scale
(e.g., extent and resolution) for analyzing the spatial phenomena, such as land
changes. This is known as the Modifiable Areal Unit Problem (MAUP) in
geospatial science, that is, the correlation between variables may change with scales
10
T. Liu and X. Yang