Urban green space spatio-temporal change influences on land surface temperature in Kuala Lumpur, Malaysia
Urban green space (UGS) is a nature-like environment established in the urban structure of a city. It plays a vital role in providing vegetation cover to provide shade and act as a natural cooling eco-system to reduce the city’s heat by releasing oxygen for sustaining a healthy ecological environ...
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| Main Author: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
2020
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| Subjects: | |
| Online Access: | http://psasir.upm.edu.my/id/eprint/99210/1/FRSB%202021%2011%20IR.pdf |
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| Summary: | Urban green space (UGS) is a nature-like environment established in the urban
structure of a city. It plays a vital role in providing vegetation cover to provide shade
and act as a natural cooling eco-system to reduce the city’s heat by releasing oxygen
for sustaining a healthy ecological environment. However, given the developments
brought about by urbanisation, UGS has been sacrificed to allow for the urban
growth activity. The continual development of new construction, road networks and
buildings has eradicated UGS areas thus contributing to the rising of land surface
temperature (LST). Accordingly, this study aims to monitor the UGS changes and
LST pattern in Kuala Lumpur (KL) for the past six years and to develop an automated
prediction model of these scenario for the year 2025 via temporal and spatial
variation, using high-resolution aerial imagery data supported by the use of advanced
technology mapping. The research utilised high-resolution aerial imagery for 2014,
2016, and 2019 that firstly used to map the spatial-temporal evolution of UGS over
the past six years and to examine the UGS loss within the boundary of KL city.
Secondly, to assess the pattern of LST change for the past six years and investigating
the correlation between UGS changes and the effect on the LST. Thirdly, to develop
an automated spatial prediction model that could potentially predict the UGS
changes and their effect on the LST pattern. This research also tested the suitability
of object-based classification methods of high-resolution aerial imagery using the
support vector machine (SVM) classifier regarding its capability to correctly classify
and recognise UGS patterns. The study also applied land surface emissivity (LSE)
algorithm to determine the LST value extracted from the Band 10 parameter of
Landsat 8 OLI/TIRS. A linear regression technique was employed to investigate the
correlation between both scenarios using spatial statistical analysis and further
predicting the UGS pattern and LST gradient for 2025 using the Artificial Neural
Network - Cellular Automaton (ANN-CA) model. This model confidently predicted
these scenarios logically, in which the expansion of built-up areas (BUA) in KL for
following six years increased by body areas (WBA) slightly decreased by 4.57%. This led to an increase in the mean
LST gradient for 2025 (32.15°C, which was about 3.22°C higher than the value
recorded in 2019 (28.93°C). The prediction model employed in this study provides
a significant benefit in monitoring the UGS changes and impact on the LST pattern
for the past, present and future scenarios. The new automated model utilising highresolution
aerial imagery has great potential to assist city planners and professionals
in extracting, updating and detecting land use changes, particularly for UGS by
applying a comprehensive procedure through a geographical information system
(GIS) platform. The broad range of output generated from the multiple temporal of
high-resolution aerial imagery could henceforth improve the reliability of collected
data and develop a high-performance outcome in interpreting real visualised
scenarios.11.62%, the UGS decreased by 28.88%, and water body areas (WBA) slightly decreased by 4.57%. This led to an increase in the mean
LST gradient for 2025 (32.15°C, which was about 3.22°C higher than the value
recorded in 2019 (28.93°C). The prediction model employed in this study provides
a significant benefit in monitoring the UGS changes and impact on the LST pattern
for the past, present and future scenarios. The new automated model utilising highresolution
aerial imagery has great potential to assist city planners and professionals
in extracting, updating and detecting land use changes, particularly for UGS by
applying a comprehensive procedure through a geographical information system
(GIS) platform. The broad range of output generated from the multiple temporal of
high-resolution aerial imagery could henceforth improve the reliability of collected
data and develop a high-performance outcome in interpreting real visualised
scenarios. |
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