Lawal Billa

An effective and efficient planning of an urban
growth and land use changes and its impact on the environment
requires information about growth trends and
patterns amongst other important information. Over the
years, many urban growth models have been developed and
used in the developed countries for forecasting growth
patterns. In the developing countries however, there exist a
very few studies showing the application of these models
and their performances. In this study two models such as
cellular automata (CA) and the SLEUTH models are
applied in a geographical information system (GIS) to
simulate and predict the urban growth and land use change
for the City of Sana’a (Yemen) for the period 2004–2020.
GIS based maps were generated for the urban growth
pattern of the city which was further analyzed using geostatistical
techniques. During the models calibration process,
a total of 35 years of time series dataset such as
historical topographical maps, aerial photographs and
satellite imageries was used to identify the parameters that
influenced the urban growth. The validation result showed
an overall accuracy of 99.6 %; with the producer’s accuracy
of 83.3 % and the user’s accuracy 83.6 %. The
SLEUTH model used the best fit growth rule parameters
during the calibration to forecasting future urban growth
pattern and generated various probability maps in which
the individual grid cells are urbanized assuming unique
‘‘urban growth signatures’’. The models generated future
urban growth pattern and land use changes from the period
2004–2020. Both models proved effective in forecasting
growth pattern that will be useful in planning and decision
making. In comparison, the CA model growth pattern
showed high density development, in which growth edges
were filled and clusters were merged together to form a
compact built-up area wherein less agricultural lands were
included. On the contrary, the SLEUTH model growth
pattern showed more urban sprawl and low-density
development that included substantial areas of agricultural
lands