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user's accuracy and producer's accuracy represented the accuracy for each class and
the overall accuracy and Kappa represented the accuracy for the entire landscape. In
both models, the best predicted class was the residential area (with 66.97 %/59.03 %
user's accuracy and 77.32 %/53.91 % producer's accuracy) and the worst prediction
class was barren/soil (with 40.78 %/20.48 % and 1.43 %/11.74 %). The barren/soil
class, although had the least area in the study area, were easy to be confused with
other classes such as industrial/commercial or residential area. The incorporation
socioeconomic data into the model improved the simulation on the residential or
industrial/commercial classes which made the barren/soil having the least accuracy.
One disadvantage of incorporating socioeconomic data into the model was the
overestimation of residential area in which led to a relative underestimate in the
industrial landscape as well as other natural landscapes such as woodland and grass-
land. This might be improved as more and more socioeconomic data were incorpo-
rated as driving forces in the model. The analysis of the model validation showed
that the appropriate ancillary parameters were necessary for the CA model to derive
a solid result. In fact, the value of the simulation approach lied in its exploratory
nature which enabled the improvement of models with additional variables later.
Meanwhile, the CA model had an “aggregate” function to smooth the heterogeneous
pattern within the urban and suburban area. One solution to solve this problem was
to incorporate better data source into the model, such as higher spatial resolution
images or sub-pixel classifications, to improve the accuracy of CA models.
12.6
Conclusion
The spatiotemporal CA model of urban landscape patterns using multi-temporal TM
and MSS imagery enabled us to characterize the internal structure of landscapes
and monitor the landscape dynamics for Houston. Moreover, we also explored the
potential of socioeconomic variables to detect how human forces affect the urban
spatial pattern.
The CA model, coupled with the Markov transition probability, has indicated
the capability of trend projection for the landscape change. This spatiotemporal
model provided not only the quantitative description of change in the past but
also the direction and magnitude of change in the future. However, based on the
experimental results and exploratory analysis, several limitations still exist within
the current study:
Since the modeling process involves the usages of data from multiple sources, the
accuracy of prediction result will be closely related to the individual accuracy
with each type of data, especially different remote sensing data sources. The
development of a robust method to incorporate data in different spatial resolution
was still an interesting issue.
Although the Markov transition probability was calculated on the census block
level, it was stationary and unable to accommodate the unpredictable influence
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