SMART AND SUSTAINABLE PLANNING FOR CITIES AND REGIONS 2015
SSPCR 2015, 19-20 NOV.
MODELING FUTURE LAND USE AND LAND
COVER CHANGE
IN ASYUT REGION USING MARKOV CHAIN AND
CELLULAR AUTOMATA
Hatem Mahmoud
Assistant professor, Dept. of Architecture, Aswan
University. Egypt.
Prasanna Divigalpitiya
Associate professor, Dept. of Architecture and Urban
Design, Faculty of Human Environment Studies,
Kyushu University
Background
•
•
The Urban sprawl in Egypt is one of the main problems that reduce the limited
highly fertile lands in the Nile valley of Egypt
The rapid urban sprawl has caused agricultural lands to be decreased by 36% or
about 1.5 million acres (6300 million m2) in Egypt
Agricultural area
lost by Housing in
the region of El
Fath between
2004-2008
Source: ETH Studio
Basel, 2009 (Edited
by the author)
Since 1970’s, the Egyptian governments formulate plans and policies to save the
agricultural lands and redistribute the population horizontally through establishing
new urban settlements across the desert areas outside the Nile valley, as a way to
reduce the urban pressure on the old agricultural land. However, these policies
failed in conducting a real success especially in Upper Egypt region. So, decision
makers in Egypt face unprecedented challenges with regard governing, urban
planning, and land use management.
Therefore, knowledge concerns spatial temporal LULC change and predicting
changes that might be take place have play an important role in the decision
making process. Monitoring growth helps to develop an understanding of past
trends and growth patterns, while the urban prediction models provide insights into
possible future development. Both approaches are necessary for implementing
appropriate strategies regarding the urban planning decision making process
The study aims to
characterize the past urban growth process and investigate the future scenario that
help the decision makers in redrawing their policies to realize a sustainable
development in absorbing the urban sprawl outside the Nile Valley towards the new
cities to save the agriculture areas.
Method
In this study, the status of LULC of
Asyut region was mapped using
multi temporal data of satellite
images and then the future of
LULC change was predicted using
Markov Cellular Automata
“Markov-CA.
Cellular Automata CA may be defined as discrete models, useful in complexity science to
understand spatial dynamics of change over time. CA, applied to urban growth, rely on the
iteration of a given dimensional cell based on supporting socio-economic and geographical
data, to change into urban or nonurban form within a given time-frame.
Markov-CA was used. It is a combined of CA, Markov Chain, Multi-Criteria, and MultiObjective Land Allocation (MOLA) land cover prediction procedure that adds an element of
spatial contiguity as well as knowledge of the likely spatial distribution of transitions to
Markov chain analysis.
Driving forces
Land Use and Land Cover (1990-2003, 2003-2015)
Distance to major roads
Unsupervised classification
Distance to minor roads
Distance to Asyut city
Distance to three cities
Classified images
Calibration with
the base map
Distance to New Asyut
Distance to built-up areas
Built Up area change
Markov model
Distance to Nile River
Slope - Heights
Logistic Regression
Conditional Probability
images
Transition Probability
Matrix
Nile River
Calibration with
ROC map
Ordered Factors
AHP Weights
Calibration with ROC
Markoc-CA
Calibration
Simulated Future
LULC
MCE
Transition suitability
images
Study area:
The case study is considered a typical case in Upper Egypt region, so the results can be
applied for the whole Upper Egypt region. The study introduces an approach for Egypt
case to help choosing the best strategic development that help in the decision making
process within a quantitative analytical approach.
• Despite the fact that most of the urban sprawl occurred on agricultural land, thus
decreasing the latter by 9.46%, there was an increase in new agricultural land,
especially in the eastern desert area, next to the NAc.
• The increase in agricultural areas more than compensated for the loss due to the
urban sprawl, even resulting in an increase in the total agricultural areas in this
period by encroachment onto agricultural lands; however the new extensions
towards the new city succeeded in creating a good environment for agriculture
sprawl.
Predict
2015
Prediction
Driving
Forces
Compare
86%
2015 Map
2003 map
A Markov chain “MC” model is utilized to quantify transition probabilities of
several land cover categories from discrete time steps. It focuses on quantity in
predictions for land use changes and the spatial parameters are weak in model
Conditional probability images based on projection date 2030
Independent Variable
intercept
Heights constraint Che
Water body constraint Cw
Existing built up areas
constraints Cbu
Distance to major roads Fmr
Distance to minor roads Fmir
Distance to built up area Fdbu
Distance to the three cities Fdci
Distance to Asyut city Fdac
Distance to New Asyut city Fdna
Distance to water Fdw
Distance to border Fdbo
East West direction Fx
North south direction Fy
Direction along with Nile Fni
Direction perpendicular on Nile
Fpni
Vramer’s V
90-03
03-15
Coefficient
90-03
03-15
Odds ratio
90-03
03-15
0.183
0.059
0.604
0.183
0.059
0.782
23.187
4.566
20.118
-34.428
-26.845
3.718
21.090
14.289
-
0.250
0.316
0.603
0.356
0.327
0.215
0.292
0.216
0.150
0.169
0.089
0.249
0.250
0.316
0.566
0.353
0.328
0.250
0.292
o.200
0.150
0.169
0.089
0.249
-0.021
-0.097
-0.513
-0.094
-0.377
-1.013
-0.086
-0.468
0.232
-0.122
0.481
0.047
-0.059
-0.187
-0.617
-0.088
-0.080
-0.676
-0.180
-0.216
0.132
-0.198
0.231
-0.017
90-03
Rank
03-15
-
a2
a3
a1
a1
a3
a2
0.979
0.907
0.598
0.910
0.685
0.363
0.917
0.626
1.261
0.885
1.617
0.943
0.829
0.540
0.916
0.923
0.509
0.835
0.806
1.141
0.820
1.260
9
6
2
7
4
1
8
3
11
5
12
10
9
5
2
7
8
1
6
3
11
4
12
10
1.048
0.983
Extracted weights based on AHP
Predicting future urban expansion
The Multi Criteria Evaluation MCE (Eastman J. E., 1995) and
extracted weights of urban driving forces, which cover the natural
and socioeconomic variables, were used to generate the group of
suitability images of 2015 and 2030
The suitability images 1990-2003 to predict 2015
The suitability image 2003-2015 to predict 2030
The suitability image for urban change for 1990-2003 were validated using ROC test
(Relative Operating Characteristic). ROC value ranges from 0 to 1, where 1 indicates a
perfect fit and 0.5 indicates a random fit. In our study, the initial result with 10%
percentage sampling test. It was performed by comparing the suitability image of
1990-2003 (to predict 2015) with the image derived from the actual 2015. The value
of ROC was 0.9168.
The predicted image for 2015 was compared with the classified satellite-derived image
on the Kappa statistic. The Markov-CA’s overall simulation success is 77.235% which
means a “substantial” degree in the strength of agreement
Gains(red) and losses(blue) in land use classes 2015-2030
(Percentages %)
Contributions of each land use class in urban growth 2015-2030
(% change)
The Results
The simulated future LULC changes indicate increasing pressure on agricultural lands (one of the most important resources in
Egypt).
Socioeconomic conditions and the roads network have played important roles to produce these spatial patterns. Spatial diffusion
of built areas spread outward from the core of existing built-up areas along with the roadways. It is mainly due to road expansion
and increasing the population with weak regulation, citizens haven’t strong motivations to move outside the valley towards the
new city.
Results
1
Most land conversion will be attributed to the replacement of agricultural
lands with urban areas. So the net growth rate of built up area is
expected to be about 42.16%.
2
Despite the fact that the main aim for building New Asyut city is
absorbing the urban sprawl out of the agricultural lands, most of the
urban sprawl will occur on agricultural land, thus agricultural lands may
loss 18% of present lands.
3
Looking at the spatial patterns of land change in the future, the evidences
show that the rate of conversions from non-built to built-up areas is a
quite rapid, with scattered patches of urban development in agricultural
areas characterizing the urban sprawl in the Nile valley.
4
Socioeconomic conditions and the roads network have played important roles to
produce these spatial patterns. Spatial diffusion of built areas spread outward from
the core of existing built-up areas along with the roadways. It is mainly due to road
expansion and increasing the population with weak regulation, citizens haven’t strong
motivations to move outside the valley towards the new city.
Conclusion
Strong evidence suggests that urban expansion will continue to occur in Asyut
region throughout the next fifteen years. The temporal mapping of built up areas
and simulation models for the next fifteen years indicate that the projected urban
expansion will be directed mainly near from New Asyut city, existing built up area,
and the agricultural lands border.
The main swap in land use will occur between urban and agricultural lands, however
it should be happened between urban and desert lands. This problem mainly
because of
a) the relative location of these agricultural lands nearby existing built-up boundaries
b) the lack of regulations that ensure protecting the agricultural lands
c) New Asyut city isn’t able to absorb all the urban expansion.
It is clear that the decision makers should act strong on other urban development
driving forces that used in this study to control the future urban growth, whether
natural or socioeconomic driving forces to control the future urban growth.
QUESTIONS?