erosion and sediment delivery. Land use classes are not directly involved in
calculating the soil erosion component of SEDEM. But the probability of land
conversion and soil erosion rate are both affected by the same factor of slope
gradient. The simulated future land use patterns were used as input for the sediment
transport component in SEDEM, with a transporting capacity coefficient estimated
for each land use class.
Reidsma et al. (2006) assessed the relationship between land use intensity and
related biodiversity in agricultural landscapes. For land use simulation, an integrated model was applied to quantify the area changes in agricultural land use and
the CLUE model was used for land use allocation. Biodiversity in this study was
measured using the ecosystem quality, which is expressed as the mean abundance
of species originally present in the natural ecosystems relative to their abundance in
undisturbed situations. Following the land use scenarios, the ecosystem quality of
agricultural landscapes can be calculated as conditioned by land use. Then the
impact of agricultural land use changes on overall biodiversity was assessed by
comparing the relative size of nature area and the average ecosystem quality of
natural ecosystems.
1.5 Conclusions
Land changes are processes in which human and natural systems interact over space
and time to reshape the earth’s surface. They are both causes and consequences of
global change that interacts with other components of the earth system. Land
change science has recently emerged as a fundamental component of global
environmental change and sustainability science. However, the complexity of
land systems leads to many challenges for the research communities. Among the
research components in land change science, land change modeling appears to be
promising in improving our understanding of land use and land cover change as a
coupled human-environmental system.
A wide variety of modeling approaches has been developed to simulate the
processes of land changes. This chapter has reviewed some commonly used
approaches, including statistical regression models, artificial neural networks
(ANN), Markov chain modeling, cellular automata, economic models, and agentbased models (ABM). These different approaches are built upon various theoretical
and methodological foundations. The order of these approaches generally represents the theoretical transition of land change modeling from aggregate to individual modeling frameworks. The best model to use depends on specific applications
given their unique strengths and weaknesses.
The complexity for land change modeling is owing to their need to represent the
spatiotemporal dynamics of the coupled human-environment systems. For coupling
the factors from human and environmental systems, development of data integration techniques can help address the differences in spatial data. However, more
comprehensive understanding and representation of the integrated processes within
1 Land Change Modeling: Status and Challenges
13
calculating the soil erosion component of SEDEM. But the probability of land
conversion and soil erosion rate are both affected by the same factor of slope
gradient. The simulated future land use patterns were used as input for the sediment
transport component in SEDEM, with a transporting capacity coefficient estimated
for each land use class.
Reidsma et al. (2006) assessed the relationship between land use intensity and
related biodiversity in agricultural landscapes. For land use simulation, an integrated model was applied to quantify the area changes in agricultural land use and
the CLUE model was used for land use allocation. Biodiversity in this study was
measured using the ecosystem quality, which is expressed as the mean abundance
of species originally present in the natural ecosystems relative to their abundance in
undisturbed situations. Following the land use scenarios, the ecosystem quality of
agricultural landscapes can be calculated as conditioned by land use. Then the
impact of agricultural land use changes on overall biodiversity was assessed by
comparing the relative size of nature area and the average ecosystem quality of
natural ecosystems.
1.5 Conclusions
Land changes are processes in which human and natural systems interact over space
and time to reshape the earth’s surface. They are both causes and consequences of
global change that interacts with other components of the earth system. Land
change science has recently emerged as a fundamental component of global
environmental change and sustainability science. However, the complexity of
land systems leads to many challenges for the research communities. Among the
research components in land change science, land change modeling appears to be
promising in improving our understanding of land use and land cover change as a
coupled human-environmental system.
A wide variety of modeling approaches has been developed to simulate the
processes of land changes. This chapter has reviewed some commonly used
approaches, including statistical regression models, artificial neural networks
(ANN), Markov chain modeling, cellular automata, economic models, and agentbased models (ABM). These different approaches are built upon various theoretical
and methodological foundations. The order of these approaches generally represents the theoretical transition of land change modeling from aggregate to individual modeling frameworks. The best model to use depends on specific applications
given their unique strengths and weaknesses.
The complexity for land change modeling is owing to their need to represent the
spatiotemporal dynamics of the coupled human-environment systems. For coupling
the factors from human and environmental systems, development of data integration techniques can help address the differences in spatial data. However, more
comprehensive understanding and representation of the integrated processes within
1 Land Change Modeling: Status and Challenges
13