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sparsely vegetated land will mainly gather in the arid desert areas centering on
Taklimakan Desert in Tarim Basin, Qaidam Basin, etc. In the northwest part of
China, including the Alpine arid regions in Qinghai-Tibet Plateau, middle part of
Inner Mongolia, northwest part of Gansu Province, etc.
3.2.3 Concluding Remarks on Scenarios of the Future
LUCC
In this study, three scenarios of the future LUCC in China are designed on the
basis of the trends of future socioeconomic development and national policies
(e.g., Grain for Green). The simulation results showed that spatial pattern of land
use/cover in China under the three scenarios is consistent on the whole, but with
some regional difference. The simulation results based on different scenarios
reflect spatial pattern of land use/cover of China in the future to some extent,
which have important policy implications and scientific supporting on land use
planning and sustainable development of the society and can provide the input
underlying surface data for the climate models.
There are still some uncertainties in the results of scenario simulation of future
land use/cover change due to those uncertain driving factors since the land system
is a complex system that is closely associated with human activities and natural
conditions. Moreover, this study uses the land use/cover classification system
correspondence from the GCAM model with five categories of classification into
USGS with 24 categories, which also lead to the risk of uncertainties. Therefore,
the simulation results cannot represent actual change of area of different land use/
cover types and their spatial pattern, but they can still make good sense in rea-
sonable confidence interval to a certain extent due to the robustness of the model.
3.3 Reclassified LUCC for the Simulation of Regionalized
Impacts
The temporal land cover datasets have been widely used in numerous climate
simulation projects. Most attention has been paid to effects of the accuracy of the
land cover data on the climate simulation. The accuracy of temporal land use data
from CAS is higher than 90 %, but the high-precision land cover data is absent.
We overlaid the land cover maps of the IGBP, Global Land Cover2000 (GLC),
University of Maryland Data (UMD) and Data Center for West China (WESTDC),
and the grids with agreement of classification were selected as the sample grids.
We can combine the land cover data with the land use data to generate land cover
data of high accuracy for the climate simulation. By comparing results obtained
with different decision tree classifiers with the WEKA toolkit for data mining, this
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