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microclimate but also affects mesoscale atmospheric circulation (Hartmann 1994 ;
Weaver and Avissar 2001 ; Yang 2004 ). The accurate land cover maps are the
foundation for land surface, ecological and hydrological modeling, carbon and
water cycle studies, and research on global climate change (Sellers et al. 1997 ).
With many land cover products from different sources becoming available for a
given region of the Earth, a challenge arises as to which product is optimal for a
land-climate modeling study. Traditional classification accuracy assessment is
primarily dependent on ground-based surveys or interpretation of high-spatial
resolution aerial photos and satellite images. By comparing the classified land
cover dataset with the ground-truth data, error metrics can be developed to report
the commission and omission errors. Measures of accuracy, such as the Kappa
coefficient of agreement, are frequently calculated to express classification accu-
racy (Congalton 1991 ; Foody 2002 ). Therefore, the researcher should take full
advantage of geographic knowledge in GIS database to support classification to
improve the accuracy of land cover classification.
Accurate representation of land surfaces is an important factor for climate
modeling. However, little attention has been paid to the effect of land cover
classification accuracy on climate simulations. In reality, land cover accuracy
rarely reaches the commonly recommended 85 % target (Ge et al. 2007 ). The
accuracy of land use classification is approximate 73-77 % using decision-tree
classification methods and thereby increasing mapping efficiency by 50 % (Homer
et al. 2004 ). In addition, most assessments of classification accuracy were con-
ducted using the same dataset as was used to train the classifier. Therefore, the
classified accuracy was overstated. Spatial data mining techniques for land cover
classification is also applied. The accuracy of land cover data that is accomplished
by different methods could reach 88.62 % (Wu et al. 2013 ). Inaccurate repre-
sentation of land cover will lead to differences in simulating sensible heat flux,
latent heat flux, and many other variables depending on vegetation and land use
parameters. Remote sensing provides accurate representation of Earth's surface at
different spatial and temporal scales and is an attractive source for creating high
accuracy land cover data. Therefore, it is feasible to take advantage of the existing
land cover data from different sources to make a high accuracy land cover data
using spatial data mining method. The information fusion strategy is proposed to
produce a higher accuracy land cover map of China (Ran et al. 2010 ), whose
classification system should be compatible with the widely accepted classification
system used for surface climate simulation.
8.2 Upgraded Models
Studies on LUCC processes are often challenged by the complex nature and
unexpected behavior of both human drivers and natural constraints. Therefore, we
need a land use change dynamic model to simulate the interdependencies and
feedback mechanisms between social, economic, and ecosystem environments.
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