Geography Reference
In-Depth Information
Analysis on driving mechanism of land use change
Scenarios analysis of land use change
Transform
probability
Environmental
condition
Climate
Population
Economic
Management
Policy
Characteristics
of historic
land use
change
Transform rules
Driving
mechanism
for patterns
and process
of land use
change
Driving
forces of
land use
change
Prediction tendency
of land use change
Change of regional
land use structure
Scenarios analysis of
regional land use change
Patterns of land use change
Balance of land use demand between
various industrials
Balance of land use demand between
various regions
Patterns of land use change
Spatial distribution of land use change
Fig. 6.6
Framework of DLS model
spatial analysis module, the conversion rules module, and spatial analysis modules
(Fig.
6.6
). Scenario analysis module is used to express the changed needs of a
variety of land use types under different scenarios. Spatial analysis module is used
to calculate the probability values of various land use types in each grid unit
through spatial regression analysis for driving factors. Transfer rules module is
used to express possibility and ease of a certain type of land transfer to another
type of land on each grid cell. Space allocation module implements spatial dis-
tribution pattern of various land use types under different scenarios on the grid.
There are mainly four steps to carry out dynamic simulation of land use based
on DLS. First, conversion rules module analyzes statistical relationship between
land use types distribution and driving factors from the two scales of region and
grid, measures effects of the natural environment and socioeconomic factors on
temporal patterns of regional land use, and extracts the key factors which affect
land use types distribution. Second, based on the history of land use characteristics
and the status of regional land use changes, spatial analysis module predicts the
trends that key factors influence land use patterns, and then select a reasonable
scenario. Third, according to supply-demand situation of different industries on
land under this scenario during the time cross-section of forecast period, scenario
analysis module allocates area demand of different land types to various industries.
Finally, by balance analysis of grid-scale land type area's demand and supply,
spatial allocation module achieves spatial distribution of different kinds of land use
types on the grid scale and generate spatial pattern of land use.
According to the estimated result of experiential model, the contribution on
land use change of various independent variables can be calculated. Based on this,
prediction of land use in 2010 and 2050 in Southern Jiangsu can be worked out.
Under the linear hypothesis, land use change process can be presented as the
following formula.
