第29回日本情報地質学会講演会 講演要旨集 011-012頁 2018年

Investigation of Land Cover Change for Simulating Future Urbanization in
Colombo City, Sri Lanka
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Pavithra JAYASINGHE* and Venkatesh RAGHAVAN**

空白* 大阪市立大学大学院創造都市研究科, Graduate School for Creative Cities, Osaka City University,
3-3-138 Sugimoto, Sumiyoshi-ku, Osaka 558-5858, Japan. E-mail: niroshansnj@gmail.com** Graduate School of Engineering, Osaka City University, 3-3-138 Sugimoto, Sumiyoshi-ku,
Osaka 558-5858, Japan.

Key words: Urban Simulation, FUTURES, LULC, LST

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１．Introduction
Colombo is the commercial capital of Sri Lanka whichis experiencing significant spatial changes over past few
decades. As Colombo is in a stage of rapid development, abroader understanding and consideration on urbanization
process and spatial changes has become a timely need.
Many Land use (LU) Land Cover (LC) change detection
studies have carried out over decades concerning urban
expansion, conversion of LU/LCs, impacts on different
environmental aspects etc. On the other hand, urban
simulation studies are focusing on identify future LULC
changes. There is a lack of studies that focus both theaspects to understand the existing or current urbanizing
process as well as predicting the future growth and its
associated consequences that can occur if the current
urbanizing trend is continued. Accordingly, the attempt of
this study was to first examine the LC change of Colombo
District (CD) and study the spatial changes andexpansion of built-up areas. Secondly, to simulate future
urban growth using FUTURES model (Meentemeyer etal.,2013) and predict the expected growth in 2030. The
third step was to investigate the relationship between
Land Surface Temperature (LST) increase in relation
with identified spatial changes in terms of vegetation
cover and built up areas. Further, study tried to evaluate
corresponding impacts for the predicted change.

２．Methodology
In the first step Supervised Classification wasperformed for LC classification to identify three major LC
classes namely, built-up, non-built and water using
Maximum Likelihood Classification (MLC) algorithm for
four Landsat images in 1997, 2005, 2009 and 2016.
Twenty ground truth reference polygons were created foreach class based on Google Earth images. In order toimprove the classification, results were visually comparedwith Google images and misclassified reference polygons
were discarded and new ones were created; the process
was repeated until the results become observably
accurate (Fonji and Taff, 2014). The accuracy for four LCmaps was assessed using 125 points for each map throughcareful visual inspection using random sampling method.
Using classification results, built-up areas were extracted
to compare the pattern of urban expansion. Extracted
urban patches were used as input data for FUTURES
simulation model which is the second step.

FUTURES model is a multilevel modelling framework
consists of 3 sub models namely, POTENTIAL, DEMAND
and PGA. POTENTIAL sub-model quantifies thedevelopment potential of a cell based on multilevel
relationships between land change and hypothesizedenvironmental, infrastructural, and socioeconomic factors.
DEMAND sub-model quantifies differences in per capita
land demand among sub-regions based on increases in
population concurrent with the rate of development
specific to each sub-region or level. PGA is a stochastic
patch-growing algorithm that bridges field-based andobject-based representations of change by constructing
discrete land conversion events prescribed by DEMAND
from cell-level state transitions on the POTENTIAL
surface. Application of each sub-model is performed using
r.futures extension in GRASS GIS. FUTURES model wasapplied in two cases, first is to predict for 2016 using
1997,2005 and 2009 development trend (developmentstarting point=2009, end point=2016) and second ispredict for 2030 assuming the same development trend
will continue further considering development starting
point at 2016 and end at 2030.
Finally, spatial change (Vegetation cover -NDVI, builtup areas - NDBI) detection is the third step to analyse its
impact on LST. It is assessed under this step and 1997,2005 and 2016 years with approximate time gaps. As
previous two phases of the methodology tried to identify
the rapid expansion of built-up areas, final stage of thisstudy tried to further analyse its impact on LST.

３．Results and Discussion
LC change detection in first phase distinguishd thespread of built-up areas from coastal side towards theinland. From 1997 to 2005, built-up areas have increased
by 36.11km2 and it is 58.10km2 between 2005 to 2009.
After 2009 it showed a rapid increase up to 380km2 of
total built up areas in 2016 which was in total 135.25km2
during 1997. It has more than doubled (64.46% totalincrease) within 19-year period compared to the start ofanalysis time.
According to urban simulation in second phase, model
resulted an extensive amount of urban patches converting
all existing sites for the generated map of 2030
development considering current trend. It emphasizes
that if the current trend will continue, the built areas will
be more than available sites that can influence on12conversion of conservation areas which were excluded
from the model (protected areas).
So, two other scenarios infill growth and sprawl wastested using the same model by changing modelparameters. The results are shown in Figure 1 comparing
with Business as Usual scenario for 2030 prediction. The
development map resulted which encourages infill growth
clearly showed the built-up areas are emerging along road
network as a development corridor. Sprawl growth
resulted emerging built areas with dispersed patternwhich convert more lands in discrete locations fromundeveloped status to developed.

Figure 2. LST classes-(a). 1997 (b). 2005, (c). 2016

Moreover, relationship with NDVI vs LST and NDBI vsLST analysis using bivariate scatter plot and regression
analysis for each year. Relation between built-up areas
and LST showed a high positive correlation with R2 =0.714 in 2016 and relation between NDVI showed asignificant negative correlation with R2 = 0.468.

5. Conclusion

Figure 1. 2030 Prediction-(a). Business as usual scenario, (b).
Infill Growth, (c). Sprawl

NDVI analysis and comparison of three maps in 1997,2005 and 2016 clearly illustrate the rapid declining rateof area covered by vegetation over 19 years period.
Simultaneously, rapid expansion of built-up areas wasobserved through NDBI analysis. LST estimation wasperformed to understand the impact of spatial change.
LST distribution for this period shows a significant
increase of areas with raised temperature. Urban Heat
Islands (UHI) where areas with accumulated heat could
observe along the coast with highest intensity. Compared
to 1997, number of UHI distribution (Figure 2) and spreadof areas with raised temperature have increased. LST
values were categorised in to 5 classes; (1). 21-24 0C, (2).
24-27 0C, (3). 27-30 0C, (4). 30-33 0C, (5). 33-38 0C. Areas
under 1st, 2nd and 3rd categories showing a decreasing
trend while 4th and 5th categories showed a total increaseof 72.73% and 97.81% respectively. The result
demonstrates the increase of high temperature anddecrease of moderate and lower temperature areas.

Based on findings of the study, it is clear that Colombo
is experiencing a rapid expansion of built-up areas andresulting growth of UHIs within the time span considered.
Rapid decline of vegetated areas in CD should be
minimized by promoting more green spaces even insideurban areas to discourage UHI effect. Understanding
natural urban expansion pattern will help to introduce
practical urban development scenarios. According topredicted growth, using FUTURES model, promoting
compact vertical development along road network byencouraging infill growth scenario can be proposed as abetter solution to preserve sensitive environments bypolicy initiatives.

ReferenceFonji, S. and Taff, G. (2014). Using satellite data tomonitor land-use land-cover change in North-eastern
Latvia. SpringerPlus, 3(1), p.61.
Meentemeyer, R., Tang, W., Dorning, M., Vogler, J.,
Cunniffe, N. and Shoemaker, D. (2013). FUTURES:
Multilevel Simulations of Emerging Urban–Rural
Landscape Structure Using a Stochastic Patch-Growing
Algorithm. Annals of the Association of American
Geographers, 103(4), pp.785-807.