Content uploaded by Nurfatin Izzati Ahmad Kamal
Author content
All content in this area was uploaded by Nurfatin Izzati Ahmad Kamal on Nov 28, 2019
Content may be subject to copyright.
Disaster Advances Vol. 12 (12) December (2019)
1
Spatio-temporal variability of heat exposure in
Peninsular Malaysia using land surface temperature
Nurfatin Izzati Ahmad Kamal, Zulfa Hanan Ash’aari* and Ahmad Makmom Abdullah
Department of Environmental Science, Faculty of Environmental Studies, Universiti Putra Malaysia, MALAYSIA
*zulfa@upm.edu.my
Abstract
The increasing of extreme heat event across the world
has become a new threat that was caused by the
changing climate. It is important to understand the
spatiotemporal dynamics of extreme heat and suggest
feasible adaptation strategies to reduce the heat
exposure. In this study, daytime land surface
temperature (LST) has been retrieved from MODIS
Aqua Earth observation satellite from NASA to
characterize the latest spatio-temporal variability of
heat exposure in Peninsular Malaysia with the
reference of short-term mean calculated from year
2003 until 2012. It was found that the LST is increasing
by 0.0477°C per year during the period. Trend analysis
using Mann-Kendall test shows that the LST increases
significantly during annually especially during
southwest monsoon.
Based on the z-score of mean LST for each district from
year 2003 until 2018, heat exposure index (HEI) was
obtained and exhibited the high HEI in mostly northern
and urban areas. The HEI value will be one of the
inputs for heat vulnerability assessment in the future
research. Through cluster analysis, it was found that
the northern part of Peninsular Malaysia is considered
as the hot spot of extreme heat while cold spot is
located in centre part of the region.
Keywords: Land surface temperature, MODIS, Peninsular
Malaysia, extreme heat, exposure.
Introduction
Over the past century, the concentration of CO2 in the
atmosphere has increased significantly which induced the
average global temperature to increase by 1.0 °C as
compared to pre-industrial era25. This can possibly cause
notable changes in the climate system such as the increase of
extreme heat event. Recent studies show that there is
increases in the frequency and magnitude of extreme heat.
According to Russo et al43, the probability of heat waves
occurrence with magnitude greater than heat wave event in
Russia in 2010 will increase globally. Projection based on
climate models shows that there will be increasing of
extreme heat occurrence in some of tropical, Mediterranean
and central Asia region if global temperature is expected to
rise between 1.5°C to 5.0°C42.
* Author for Correspondence
Year 2016 is considered as the hottest year ever recorded
with frequent occurrence of extreme heat events particularly
in Northern Eurasia, Southeast Asia and Southern India24.
Malaysia also experienced the impacts of extreme heat in
recent decades. During the recent heat wave event in year
2016, about 200 cases of heat-related illness were recorded
with one death caused by heat stroke in Johor. It also
severely affects the food production by reducing 13% of rice
yields31, caused haze and drought that lead to water rationing
in Kuala Lumpur and Selangor state27 and increase the usage
of electricity in Peninsular Malaysia by 37.8% compared to
month of January 20161. Even though Malaysia is not one of
the top Asian countries that experienced high exposure of
extreme temperature, the risk is still there as Malaysia
experiences increase in average temperature too58.
Therefore, increased attention has been paid to understand
the spatial and temporal patterns of temperature change in
Malaysia. Recorded temperature data are very useful to
observe these changes. For example, based on
meteorological measurements from 11 stations, Tangang et
al52 revealed that Malaysia showed significant warming
trends between year 1961 until 2002 especially in region of
Peninsular Malaysia and northern Borneo. This is supported
by study done by Suhaila and Yusop49 where there is
increase of temperature range between 2°C and 5°C/100
years based on observations from 10 meteorological stations
from year 1980 until 2011. These findings are consistent
with projections from climate models29. Nevertheless,
research studies specifically discussed on exposure towards
extreme heat in Malaysia were found to be less extensive.
Therefore, it is important to understand the spatial and
temporal distribution of heat exposure in order to assist
decision makers in preparing adaptation and mitigation
strategies to reduce the impacts caused by the significant
changes. Exposure can be generally defined as the measure
of direct hazard that was caused by significant climate
variation25. Temperature is one of the meteorological
variables that has been used to trace the extent of heat
exposure. Unfortunately, the assessment made based on in
situ meteorological data mostly covered urban areas and
does not cover certain areas such as mountainous areas and
rural areas. In order to overcome such limitations, satellite
remote sensing provides a feasible way to improve the
spatio-temporal accuracy39.
Land surface temperature (LST) is considered as skin
temperature of land that is derived from solar radiation16,19
and a key variable for land-surface processing studies at both
Disaster Advances Vol. 12 (12) December (2019)
2
regional and global scale. LST measured by satellite remote
sensing provides spatial-temporal data at high resolution and
is available for daily, monthly and yearly observation. This
can help regions with sparse distribution of meteorological
station. It plays an important role in climatology and
meteorological and helps in wide range of other fields such
as hydrology, environmental monitoring and agriculture.
The application of satellite-derived LST data was
increasingly used to measure heat exposure as it offers
spatially detailed heat-related information.60
Hence, the application of satellite-derived LST data can be
employed to analyse the spatio-temporal variability of heat
exposure in Peninsular Malaysia region. This research
should be helpful to understand the mechanism and impacts
whilst highlighting the areas prone to heat exposure. The
outcome of this study will also be a crucial component of in
heat hazard risk management, preparedness and mitigation.
Methods
Study Area: Malaysia is located in Southeast Asia and
famous for its multicultural and multiethnicity. It consists of
13 States and three Federal Territories. 11 of the States and
two of the Federal Territories located in Peninsular
Malaysia. Many important sectors such as agricultural,
industrial and mining industries are situated in this region.
Since it is located nearby the equator, the climate system is
mainly influenced by the tropical climate. Natural climate
phenomena such as El-Nino Southern Oscillation (ENSO),
Indian Ocean Dipole (IOD) and Madden-Julien Oscillation
(MJO) modulate the climate variability in Malaysia53.
The temperature is relatively uniform throughout the year
with the mean temperature ranging between 26°C to 28°C.
The rainfall patterns depend on the seasonal variations and
that more than 350mm of rainfall is recorded in a year. Over
the past 43 years, positive trends in temperature increase
have been observed32. In response to that, Malaysia
government has come up with National Policy on Climate
Change in 2009 to address the challenges of climate change
and provide a framework to guide government agencies33.
Data Collection and Pre-processing: In this study, heat
exposure is represented as LST which is obtained from the
thermal emissivity of land surfaces stored in remote sensing
images. LST data from remote sensing does not directly
represent air temperature but studies have shown that there
is strong correlation between those two data48,57. There are
several remote sensing satellites that can derive potential
datasets such as Advanced Very High-Resolution
Radiometer (AVHRR), Landsat, Advanced Spaceborne
Thermal Emission and Reflection Radiometer (ASTER) and
Moderate Resolution Imaging Spectroradiometer (MODIS).
Each of the datasets area is available at different resolution,
accuracy and time periods.
MODIS is the most widely used source for air temperature
estimation compared to other remote sensing satellite
because of its availability and resolution41. MODIS was
launched in 1999 and consists of two onboard satellites
which are Terra and Aqua. The instruments capture image in
36 spectral bands ranging from 0.405 µm until 14.385 µm at
three spatial resolutions which are 250m, 500m and 1000m.
There are nine products of LST data products with three
different spatial resolution (1km, 6km and 0.05° by 0.05°)
and four different temporal resolution (swath, daily, eight
days and monthly)59. The products were taken in both
daytime (Terra: 10.30am and Aqua: 1.30pm) and nighttime
(Terra: 10.30pm and 1.30am). Various temporal and spatial
resolutions of LST help in wide range of field such as air
temperature estimation, urban heat island and drought
vulnerability assessment41.
Due to data availability and time taken, MODIS Aqua Land
Surface Temperature/Emissivity 8-day at 1-km2
(MYD11A2) image was acquired for daytime from 2003
until 2018 to assess the temporal and spatial pattern. Aqua
daytime image was chosen because the image was taken at
the period where most of the maximum temperature
occurred12. In order to ensure the quality of the data, pixels
affected by atmospheric disturbance and clouds were
removed by using quality assurance file. Then, the images
underwent mosaicking, sub setting and the projection was
converted to WGS 1984 to match the Peninsular
administrative boundaries dataset.
Calculation of LST value: The LST value was extracted
through pixel-based calculation. The value of each pixel was
calculated by using equation 1:
𝐿𝑎𝑛𝑑 𝑠𝑢𝑟𝑓𝑎𝑐𝑒 𝑡𝑒𝑚𝑝𝑒𝑟𝑎𝑡𝑢𝑟𝑒 (𝑖𝑛 𝐶𝑒𝑙𝑐𝑖𝑢𝑠) =
(𝑝𝑖𝑥𝑒𝑙 𝑣𝑎𝑙𝑢𝑒 × 0.02)− 273.15 (1)
In this study, temperature anomalies were obtained based on
the comparison of monthly mean LST with the reference of
calculated short-term mean. Short-term mean was used as
reference period to compare with the recent years. Recent
and shorter averaging periods provide adequate information
as 30-years averaging periods62. The mean was calculated by
averaging 10 years monthly image from year 2003 until
2012. Monthly mean LST was then calculated using formula
proposed by Patel et al40 where the summation of 8-days
image in particular month was divided by all non-zeros
occurrence of the month.
Zonal statistics for each district was calculated using
ArcGIS10. The range of LST data for each district was
standardized using z-score methodology. Z-score is the
number of standard deviations away from the mean. Z-
scores were calculated so that increasing values correspond
to increasing vulnerability; values for all variables were
summarized to create the index score under the assumption
that each variable has an equal impact on the overall
exposure. Therefore, index variables were equally weighed.
Based on previous research, Z-scores were categorized into
5 quantiles. The top 20% quantile represent districts with
Disaster Advances Vol. 12 (12) December (2019)
3
“very high” exposure while the bottom 20% quantile
represent districts with “very low exposure”.
In between those two quantiles are described as “high”,
“moderate” and “low”12,36,63. This method was chosen as it
can be easily understood by general audience, clean data
breaks and can assist in decision making10. This index will
help in calculation of heat vulnerability index in the future.
Statistical Analysis: Trend detection analysis was done by
using Mann-Kendall test. It is widely used to detect trends
of meteorological variables. Mann-Kendall test is a non-
parametric test that has been used to detect the presence of
monotonic trends in series of climate data. Trend analysis
has been carried out on monthly, annual and seasonal basis.
The purpose is to detect any significant trends between
period of 2003 until 2018. Mann-Kendall trend analysis
produce three outputs that will determine whether the trends
exists or not. Positive z-statistic value shows an increasing
trend while negative z-statistic value shows a decreasing
trend. The trend is considered as significant if the p-value is
less than 0.05 (95% confidence level). Sen’s slope is
interpreted as the magnitude of the trend per year.
In order to identify group of districts that were significantly
prone to heat exposure, spatial cluster and outlier analysis,
Anselin Moran I was conducted. This analysis helps to
identify hot spots, cold spots and spatial outliers by
statistically assessing whether there is similarity in
magnitude of attribute values5. The output of the analysis
will be based on four categories which are: high-high cluster
(areas considered as hot spot), low-low cluster (areas
considered as cold spot), high-low outliers (areas with high
heat exposure but surrounded with areas with low heat
exposure) and low-high outliers (areas with low heat
exposure but surrounded with areas with high heat
exposure).
Results and Discussion
Data validation analysis was conducted to confirm the
correlation between LST obtained from remote sensing
image and air temperature data obtained from weather
monitoring station. The LST was found to significantly
correlate with air temperature obtained from Department of
Environment (r2 = 0.6018, p = <0.001). The annual and
seasonal mean of LST was analysed using Mann-Kendall
test for the whole Peninsular Malaysia. Parameters in Mann-
Kendall test such as z-scores and Sen’s slope were
considered to identify the significant trend in time series of
LST.
Temporal patterns of heat exposure in Peninsular
Malaysia: Analysis of annual and monthly LST data was
undertaken to detect the variability and trend of temperature
change in Peninsular Malaysia for periods of 2003-2018.
Figure 1 shows the temporal distribution of annual LST of
the period. Statistical analysis to determine the annual rate
of change was carried out to provide a primary indication on
the trend existence in the study period using ordinary least
squares (OLS) regression method3,21,64. The rate of change is
defined by the slope of the regression line which is 0.0477°C
per year. Regardless, the low value of coefficients of
determination (R2) indicated that the LST value at regional
level exhibits less significant trend for past 16 years.
Annual and seasonal LST in Peninsular Malaysia: Figure
2 illustrates the temperature anomalies of Peninsular
Malaysia from 2003 until 2018. The anomalies of mean
annual LST showed inter-annual variability with several
peaks that indicate large temperature anomalies. Oceanic
Nino Index (ONI) (obtained from Climate Prediction Center,
National Oceanic and Atmospheric Administration
(NOAA)) was compared with the temperature anomalies to
assess the reason behind the large temperature anomalies.
Malaysian Meteorological Department reported in year 2005
and 2010, weak and moderate El-Nino47 which explain the
high temperature anomalies in both years (2.1 °C and 1.4°C
respectively).
El-Nino event also occurred in year 2015. The El-Nino index
for March 2015 is +0.6°C and it is considered as moderate
El-Nino event8. The highest anomaly recorded was 3.2°C on
March 2016. Based on the temporal patterns derived, it is
confirmed that the climate of Malaysia is influenced by lot
of natural climate variability as it is located in Maritime
Continent. Hence, the temperature variability is due to the
influence of natural climate event such as Southeast
Maritime Continent Monsoon and El-Nino Southern
Oscillation53.
According to Thirumalai et al55, the study estimated that El-
Nino event accounts 49% of the 2016 Southeast Asia
temperature anomaly while 29% is caused by global
warming. This situation not only occurred in Southeast Asia
but also occurred globally7. In India, most of the heat waves
intensified during El-Nino years35 while El Nino influenced
the rise of extreme summer temperature in China18.
However, not all extreme heat events are caused by El-Nino.
Figure 3 shows the box and whisker plot of monthly LST
(2003-2018) in Peninsular Malaysia. The point marker
denotes the mean value while the solid line in the middle is
the median. The height of the boxplot is the difference
between the first quartile and third quartile. The mean LST
in the study area ranges from 27.89°C (December) to
31.89°C (March) with average annual LST of 29.82°C. The
highest monthly LST was recorded in March (34.5°C) and
the lowest monthly LST was recorded in Nov (26.5°C).
Other climate variability such as monsoons can lead to
extreme heat.
Malaysia’s climate is regulated by two types of monsoon
northeast monsoon and southeast monsoon. Northeast
Monsoon occurred when north-easterly winds prevail during
the boreal winter monsoon which is from November until
February and brings the dry season at the western part of
Disaster Advances Vol. 12 (12) December (2019)
4
Peninsular Malaysia while eastern part experiences wet
season.
During Southeast Monsoon, south-westerly winds prevail
during the boreal summer monsoon from May to August.
Western region will experience wet season while eastern part
experiences dry season. The phase that occurs in between
monsoon is called inter-monsoon. Inter-monsoon occurs
during month of March-April and September-October53.
Besides than monsoons, atmospheric blocking and air
circulation influence the occurrence of extreme heat
event15,56. For example, in year 2014, no El-Nino occurred,
but the high temperature was caused by hot and dry spell that
has affected the whole Peninsular Malaysia. According to
National Weather Forecast Centre Director, this is due to the
inactive monsoon trough that was caused by the lack of
monsoon surge from China and non-influence of easterly
winds from Western Pacific Ocean54.
Trend analysis: In table 2, the analysis of Mann-Kendall
trend test revealed the trend of LST for annual and season in
Peninsular Malaysia. Only annual LST and LST during
southwest monsoon exhibit significant increasing trends
based on the positive value of z-statistic and p-value that
computed are lower than α = 0.05 level of significance.
Based on the Sen’s slope value, the annual temperature
increased by 0.0438°C/year, lower compared to the
temperature during southwest monsoon which increases by
0.05°C/year.
Northeast monsoon and inter monsoon do not show any
significant increasing trend for the past 16 years. The trend
analysis conducted found that the annual LST and LST
during southwest monsoon are significantly increasing from
2003 until 2018.
This finding is supported by studies conducted by Suhaila
and Yusop49 and Amirabadizadeh et al4 where the significant
increasing trends were found to be higher annually and
during southeast monsoon compared to other seasons such
as northern monsoon and inter monsoon. Apart from that, in
NOAA monthly global climate report, ever since 2005, most
of the hottest month of the year globally is the month of July
which explains the reason why LST during southwest
monsoon significantly increased38.
Spatial patterns of heat exposure in Peninsular
Malaysia: Heat exposure index was derived from the z-
score of the average LST of the past 16 years from 2003 until
2018. Based on figure 4, Cameron Highland district have the
lowest exposure index compared to other districts. About 8
districts located in Perak (Kuala Kangsar, Batang Padang
and Ulu Perak), Kelantan (Jeli and Gua Musang) and Pahang
(Lipis, Raub and Jerantut) are categorized as low heat
exposure. These districts are located nearby the Main Range
or Banjaran Titiwangsa where mostly are covered with
forest areas. 33 districts in Perlis, Kedah, Selangor, Negeri
Sembilan, Melaka, Johor Bharu, Terengganu and Kelantan
are categorized as high heat exposure. Among the 87
districts, 6 districts have the highest exposure index and
mostly located in urban areas. The rest of the districts are in
moderate category.
Based on the spatial pattern of the heat exposure, LST
variation is influenced by the land use and land cover in the
areas11,23. Urban area tends to have higher LST compared to
fores area. This is because the ability of vegetation to
conduct evapotranspiration can reduce and adjust the
temperature of land surface and greenhouse gases emission.
Therefore, area with high vegetation cover such as forest
area will experience low LST. Hence, low heat exposure
index occurs. Meanwhile, high heat exposure is present
mostly in urban areas, bare land and industrial land cover
where there is low vegetation cover20,28. Urbanization also
contributes to the high LST. This is caused by urban heat
island. Urban heat island occurs when the temperature of
urban areas is higher than the rural areas6,51. It is known as
one of the effects of urbanization. The occurrence of urban
heat island in Malaysia was confirmed by multiple studies in
several location such as Kuala Lumpur, Penang, Putrajaya
and Selangor2,17,34
Cluster analysis of heat exposure: Figure 5 reveals the
result of cluster analysis. Among 87 districts in Peninsular
Malaysia, 10 districts were identified as hot spots of high
heat exposure based on the cluster analysis.
Fig. 1: Temporal distribution of annual LST from 2003 until 2018 in Peninsular Malaysia
y = 0.0477x - 66.042
R² = 0.2638
29
29.5
30
30.5
31
2003 2005 2007 2009 2011 2013 2015 2017
Temperature (°C)
Year
Temperature Linear (Temperature)
Disaster Advances Vol. 12 (12) December (2019)
5
Fig. 2: Temperature Anomalies and Oceanic Nino Index of period 2003-2018 in Peninsular Malaysia
Fig. 3: Box and whiskers plot of LST in Peninsular Malaysia
Fig. 4: Heat Exposure Index for Peninsular Malaysia
-2
-1
0
1
2
3
-3.0
-2.0
-1.0
0.0
1.0
2.0
3.0
4.0
Jan-03
Jul-03
Jan-04
Jul-04
Jan-05
Jul-05
Jan-06
Jul-06
Jan-07
Jul-07
Jan-08
Jul-08
Jan-09
Jul-09
Jan-10
Jul-10
Jan-11
Jul-11
Jan-12
Jul-12
Jan-13
Jul-13
Jan-14
Jul-14
Jan-15
Jul-15
Jan-16
Jul-16
Jan-17
Jul-17
Jan-18
Jul-18
Oceanic Nino Index (°C)
Temperature Anomaly (°C)
Year
Temperature El-Nino
26.0
27.0
28.0
29.0
30.0
31.0
32.0
33.0
34.0
35.0
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Temperature (°C)
Month
Disaster Advances Vol. 12 (12) December (2019)
6
Fig. 5: Cluster analysis of heat exposure in Peninsular Malaysia using Anselin Moran I
Table 1
Data used in this study
Index
Variables
Data description
Year
Data source
Exposure
Land surface
temperature
LST daytime at 1km2 resolution
2003-
2018
Level-3 MODIS Land Surface
Temperature and Emissivity –
8 days composite (MYD11A2)
Table 2
Annual and seasonal Mann-Kendall trend test result of LST (2003-2018)
z-statistic
p-value
Sen’s slope
Annual
2.3862
0.0170*
0.0438
SWM
2.2061
0.0274*
0.0500
NEM
0.8554
0.3923
0.0278
Inter-monsoon 1 (March-April)
1.3957
0.1628
0.0746
Inter-monsoon 2 (September-October)
1.3057
0.1916
0.0367
* indicate statistically significant results
These districts are located in two states in Kedah (Kubang
Pasu, Kota Setar, Pokok Sena, Yan, Pendang and Kuala
Muda), Pulau Pinang (Seberang Perai Utara, Seberang Perai
Tengah and Seberang Perai Selatan) and Perak (Kerian).
Meanwhile, the cold spot consists of 9 districts located in
three states which are in Perak (Kuala Kangsar, Hulu Perak
and Batang Padang), Pahang (Lipis, Jerantut, Raub and
Cameron Highland) and Kelantan (Jeli and Gua Musang).
The northern part of the Peninsular Malaysia usually
recorded the highest temperature during the extreme heat
event. According to Deni et al14, the northwestern parts of
Peninsular Malaysia are classified as the driest area during
northeast monsoon based on the dry spell indices. The
position of Banjaran Titiwangsa (Main Range) at the centre
of Peninsular Malaysia blocks the heavy precipitation winds
that was caused by northeast monsoon in eastern region. This
caused the western region to become drier during northeast
monsoon49. This condition worsens during heat wave event
in 2016 due to the present of Super El-Nino32.
The index and cluster analysis help in assessing heat
vulnerability by providing likelihood of heat exposure in
Peninsular Malaysia. It is particularly valuable in guiding
local planners and disaster agency to develop more efficient
mitigation and adaptation planning in developing countries
with limited cost, time and labour.
Conclusion
The derivation of heat exposure from LST provided by
remote sensing has been analysed using GIS technologies for
Peninsular Malaysia at district level. Temperature anomalies
were assessed based on short-term mean that was calculated
from 10-years data (2003 until 2012). Mann-Kendall trend
Disaster Advances Vol. 12 (12) December (2019)
7
analysis was applied and it was proved that there is increase
in trends between the 16 years of study period. Heat
exposure index obtained from the average LST from 2003
until 2018 revealed districts with high heat exposure is
mostly at urban areas. Urban areas experienced higher LST
compared to rural areas. The cluster analysis highlights the
northern region as the hot spot area of heat exposure with
center of the region as cold spot area. This study gives some
insights and understanding of relationship between spatio-
temporal variability of heat exposure and point out the
changes and extreme heat that occurs over the period of
study. The application of LST generated from remote
sensing and geographic information system gives the
opportunity to the decision makers to assess the spatial and
temporal variation of heat exposure at no-cost and less
complicated computation work.
Furthermore, the value of heat exposure index can be used
to assess heat vulnerability assessment. This can help to
come up with adaptation and mitigation strategies to combat
heat exposure. As a suggestion for future work, more
parameters such as wind speed and humidity can be
considered for a more holistic approach in determining the
heat exposure.
Acknowledgement
The author would like to acknowledge the Department of
Environmental Malaysia for providing the daily maximum
temperature data and to the Ministry of Education Malaysia
for the funding of this research under Fundamental Research
Grant Scheme (FRGS) [FRGS/2019/5540229].
References
1. Adnan A.S., Dunia makin panas, cetus fenomena cuaca ekstrem,
Berita Harian, Retrieved on 24 October 2018 from
https://www.bharian.com.my/node/215442 (2016)
2. Ahmad S. and Hashim N.M., Effects of soil moisture on urban
heat island occurrences: case of Selangor, Malaysia, Humanity &
Social Sciences Journal, 2(2), 132-138 (2007)
3. Al-Timimi Y.K. and Al-Khudhairy A.A., Spatial and Temporal
Analysis of Maximum Temperature over Iraq, Al-Mustansiriyah
Journal of Science, 29(1), 1-8 (2018)
4. Amirabadizadeh M., Huang Y.F. and Lee T.S., Recent trends in
temperature and precipitation in the Langat River Basin, Malaysia,
Advances in Meteorology, 2015, Article ID 579437 (2015)
5. Anselin L., Local indicators of spatial association—LISA,
Geographical Analysis, 27(2), 93-115 (1995)
6. Antics A., Pascal M., Laaidi K., Wagner V., Corso M., Declercq
C. and Beaudeau P., A simple indicator to rapidly assess the short-
term impact of heat waves on mortality within the French heat
warning system, International Journal of Biometeorology, 57(1),
75-81 (2013)
7. Arblaster J.M. and Alexander L.V., The impact of the El Niño‐
Southern Oscillation on maximum temperature extremes,
Geophysical Research Letters, 39(20), L20702 (2012)
8. Becker E., March 2015 ENSO discussion: El Niño is here,
NOAA, Retrieved on 30 April 2019 from
https://www.climate.gov/news-features/blogs/enso/march-2015-
enso-discussion-el-ni%C3%B1o-here (2015)
9. Bisquert M., Sánchez J.M. and Caselles V., Evaluation of
disaggregation methods for downscaling MODIS land surface
temperature to Landsat spatial resolution in Barrax test site, IEEE
Journal of Selected Topics in Applied Earth Observations and
Remote Sensing, 9(4), 1430-1438 (2016)
10. Buscail C., Upegui E. and Viel J.F., Mapping heatwave health
risk at the community level for public health action, International
Journal of Health Geographics, 11(1), 38 (2012)
11. Chaudhuri G. and Mishra N.B., Spatio-temporal dynamics of
land cover and land surface temperature in Ganges-Brahmaputra
delta: A comparative analysis between India and Bangladesh,
Applied Geography, 68, 68-83 (2016)
12. Chen Q., Ding M., Yang X., Hu K. and Qi J., Spatially explicit
assessment of heat health risk by using multi-sensor remote sensing
images and socioeconomic data in Yangtze River Delta, China,
International Journal of Health Geographics, 17(1), 15 (2018)
13. Christenson M., Geiger S.D., Phillips J., Anderson B., Losurdo
G. and Anderson H.A., Heat vulnerability index mapping for
Milwaukee and Wisconsin, Journal of Public Health Management
and Practice, 23(4), 396-403 (2017)
14. Deni S.M., Suhaila J., Zin W.Z.W. and Jemain A.A., Spatial
trends of dry spells over Peninsular Malaysia during monsoon
seasons, Theoretical and Applied Climatology, 99(3-4), 357 (2010)
15. Dong L., Mitra C., Greer S. and Burt E., The Dynamical
Linkage of Atmospheric Blocking to Drought, Heatwave and
Urban Heat Island in South Eastern US, A Multi-Scale Case Study,
Atmosphere, 9(1), 33 (2018)
16. Duan S.B., Li Z.L. and Leng P., A framework for the retrieval
of all-weather land surface temperature at a high spatial resolution
from polar-orbiting thermal infrared and passive microwave data,
Remote Sensing of Environment, 195, 107-117 (2017)
17. Elsayed I.S., A study on the urban heat island of the city of
Kuala Lumpur, Malaysia, Journal of King Abdulaziz University:
Metrology, Environment and Arid Land Agricultural Sciences,
142(588), 1-27 (2012)
18. Freychet N., Sparrow S., Tett S.F.B., Mineter M.J., Hegerl G.C.
and Wallom D.C.H., Impacts of anthropogenic forcings and El
Niño on Chinese extreme temperatures, Advances in Atmospheric
Sciences, 35(8), 994-1002 (2018)
19. Good E.J., An in situ‐based analysis of the relationship between
land surface “skin” and screen‐level air temperatures, Journal of
Geophysical Research: Atmospheres, 121(15), 8801-8819 (2016)
20. Grover A. and Singh R., Analysis of urban heat island (UHI) in
relation to normalized difference vegetation index (NDVI): A
comparative study of Delhi and Mumbai, Environments, 2(2), 125-
138 (2015)
Disaster Advances Vol. 12 (12) December (2019)
8
21. Hereher M.E., Time series trends of land surface temperatures
in Egypt: a signal for global warming, Environmental Earth
Sciences, 75(17), 1218 (2016)
22. Holmes T., Hain C., Anderson M., Kustas B. and Crow W.,
Microwave based implementation of a two-source energy balance
model to estimate evapotranspiration, EGU General Assembly
Conference Abstracts, 19, 17624 (2017)
23. Ibitoye M.O., Aderibigbe O.G., Adegboyega S.A. and Adebola
A.O., Spatio-temporal analysis of land surface temperature
variations in the rapidly developing Akure and its environs,
southwestern Nigeria using Landsat data, Ethiopian Journal of
Environmental Studies and Management, 10(3), 389-403 (2017)
24. Imada Y., Shiogama H., Takahashi C., Watanabe M., Mori M.,
Kamae Y. and Maeda S., Climate change increased the likelihood
of the 2016 heat extremes in Asia, Bulletin of the American
Meteorological Society, 99(1), S97-S101 (2018)
25. IPCC, Climate Change 2014, Impacts, Adaptation and
Vulnerability, Part A: Global and Sectoral Aspects, Contribution
of Working Group II to the Fifth Assessment Report of the
Intergovernmental Panel on Climate Change, eds., Field C.B. et al
(2014)
26. Intergovernmental Panel on Climate Change, Global Warming
of 1.5°C, An IPCC Special Report on the Impacts of Global
Warming of 1.5°C Above Pre-Industrial Levels and Related Global
Greenhouse Gas Emission Pathways, in the Context of
Strengthening the Global Response to the Threat of Climate
Change, Sustainable Development and Efforts to Eradicate
Poverty, Intergovernmental Panel on Climate Change (2018)
27. Ithnin H.B., Influence of Climate Change on Water Resources:
A Study of 2014 Prolonged Dry Spell in the state of Selangor, The
2014 West East Institute International Academic Conference
Proceedings (2014)
28. Kayet N., Pathak K., Chakrabarty A. and Sahoo S., Spatial
impact of land use/land cover change on surface temperature
distribution in Saranda Forest, Jharkhand, Modeling Earth Systems
and Environment, 2(3), 127 (2016)
29. Kwan M.S., Tangang F.T. and Juneng L., Present-day regional
climate simulation over Malaysia and western Maritime Continent
region using PRECIS forced with ERA40 reanalysis, Theoretical
and Applied Climatology, 115(1-2), 1-14 (2014)
30. Liu S., Su H., Zhang R., Tian J. and Wang W., Estimating the
surface air temperature by remote sensing in Northwest China
using an improved advection-energy balance for air temperature
model, Advances in Meteorology, 2016, Article ID 4294219 (2016)
31. Malay Mail, Heatwave may see drop in domesic rice output,
saya Mardi senior official. Retrieved on 24 October 2018 from
https://www.malaymail.com/s/1084469/heatwave-may-see-drop-
in-domestic-rice-output-says-mardi-senior-official (2016)
32. Met Malaysia, Laporan Tahunan, Jabatan Meteorologi
Malaysia, Retrieved on 13 April 2018 from
http://www.met.gov.my/content/pdf/penerbitan/laporantahunan/la
porantahunan2016.pdf (2016)
33. Ministry of Natural Resources and Environment Malaysia
(NRE), National Policy on Climate Change, Retrieved on 13 April
2018 from https://www.pmo.gov.my/2019/07/national-policy-on-
climate-change (2009)
34. Morris K.I., Salleh S.A., Chan A., Ooi M.C.G., Abakr Y.A.,
Oozeer M.Y. and Duda M., Computational study of urban heat
island of Putrajaya, Malaysia, Sustainable Cities and Society, 19,
359-372 (2015)
35. Murari K.K., Sahana A.S., Daly E. and Ghosh S., The influence
of the El N iño Southern Oscillation on heat waves in India,
Meteorological Applications, 23(4), 705-713 (2016)
36. Mushore T.D., Mutanga O., Odindi J. and Dube T.,
Determining extreme heat vulnerability of Harare Metropolitan
City using multispectral remote sensing and socio-economic data,
Journal of Spatial Science, 63(1), 173-191 (2018)
37. National Aeronautics and Space Administration (NASA),
Global Temperature, Retrieve on 18 June 2019 from
https://climate.nasa.gov/vital-signs/global-temperature (2019)
38. NOAA, National Centers for Environmental Information, State
of the Climate: Global Climate Report for July 2019, published
online August 2019, retrieved on September 25, 2019 from
https://www.ncdc.noaa.gov/sotc/global/201907 (2019)
39. Oyler J.W., Dobrowski S.Z., Holden Z.A. and Running S.W.,
Remotely sensed land skin temperature as a spatial predictor of air
temperature across the conterminous United States, Journal of
Applied Meteorology and Climatology, 55(7), 1441-1457 (2016)
40. Patel S., Joshi J.P. and Bhatt B., An assessment of spatio‐
temporal variability of land surface temperature using MODIS
data: A study of Gujarat state, India, Geography Compass, 11(4),
e12312 (2017)
41. Phan T.N. and Kappas M., Application of MODIS land surface
temperature data: a systematic literature review and analysis,
Journal of Applied Remote Sensing, 12(4), 041501 (2018)
42. Perkins-Kirkpatrick S.E. and Gibson P.B., Changes in regional
heatwave characteristics as a function of increasing global
temperature, Scientific Reports, 7(1), 12256 (2017)
43. Russo S., Sillmann J. and Sterl A., Humid heat waves at
different warming levels, Scientific Reports, 7(1), 7477 (2017)
44. San Francisco Department of Public Health, Understanding the
risk: an assessment of San Francisco’s vulnerability to extreme
heat events, Retrieved on 28 July 2018 from
http://www.sfphes.org/component/jdownloads/finish/42 (2013)
45. Safieddine S., Clarisse L., Whitburn S., George M. and
Clerbaux C., Earth’s Land Surface Temperature as a Tracer for
Climate Change, 14th iCACGP Quadrennial Symposium and 15th
IGAC Science Conference (2018)
46. Shi L., Liu P., Kloog I., Lee M., Kosheleva A. and Schwartz J.,
Estimating daily air temperature across the South Eastern United
States using high-resolution satellite data: A statistical modeling
study, Environmental Research, 146, 51-58 (2016)
Disaster Advances Vol. 12 (12) December (2019)
9
47. Simon J., El-Nino: Current Progress, Possibility and Severity
of Occurrence in 2015, El Nino Seminar 2015 by Malaysian Palm
Oil Council (MPOC), Retrieved on 28 November 2018 from
http://www.mpoc.org.my/upload/Paper-1-Jailan-Simon.pdf
(2015)
48. Sohrabinia M., Zawar-Reza P. and Rack W., Spatio-temporal
analysis of the relationship between LST from MODIS and air
temperature in New Zealand, Theoretical and Applied
Climatology, 119(3-4), 567-583 (2015)
49. Suhaila J. and Yusop Z., Trend analysis and change point
detection of annual and seasonal temperature series in Peninsular
Malaysia, Meteorology and Atmospheric Physics, 130(5), 565-581
(2018)
50. Tan J., Zheng Y., Tang X., Guo C., Li L., Song G. and Chen
H., The urban heat island and its impact on heat waves and human
health in Shanghai, International Journal of Biometeorology,
54(1), 75-84 (2010)
51. Tan K.C., San Lim H., Mat Jafri M.Z. and Abdullah K., Landsat
data to evaluate urban expansion and determine land use/land cover
changes in Penang Island, Malaysia, Environmental Earth
Sciences, 60(7), 1509-1521 (2010)
52. Tangang F.T., Juneng L. and Ahmad S., Trend and interannual
variability of temperature in Malaysia: 1961–2002, Theoretical
and Applied Climatology, 89(3-4), 127-141 (2007)
53. Tangang F.T., Juneng L., Salimun E., Sei K.M., Le L.J. and
Muhamad H., Climate Change and Variability over Malaysia:
Gaps in Science and Research Information, Sains Malaysiana,
41(11), 1355–66 (2012)
54. The Star Online, Hot and dry weather expected throughout
M'sia until Monday, Retrieved on 18 April 2018 from
https://www.thestar.com.my/news/nation/2014/02/06/scorching-
days-cool-nights-hot-and-dry-weather-expected-throughout-msia-
until-monday (2014)
55. Thirumalai K., Di Nezio P.N., Okumura Y. and Deser C.,
Extreme temperatures in Southeast Asia caused by El Niño and
worsened by global warming, Nature Communications, 8, 15531
(2017)
56. Tomczyk A.M. and Bednorz E., Heat waves in Central Europe
and their circulation conditions, International Journal of
Climatology, 36(2), 770–782 (2015)
57. Vancutsem C., Ceccato P., Dinku T. and Connor S.J.,
Evaluation of MODIS land surface temperature data to estimate air
temperature in different ecosystems over Africa, Remote Sensing
of Environment, 114(2), 449-465 (2010)
58. Vinke K. et al, A Region at Risk, The Human Dimensions of
Climate Change in Asia and the Pacific (2017)
59. Wan Z., Collection-5 MODIS land surface temperature
products users’ guide, ICESS, University of California, Santa
Barbara (2007)
60. Weber S., Sadoff N., Zell E. and de Sherbinin A., Policy-
relevant indicators for mapping the vulnerability of urban
populations to extreme heat events: A case study of Philadelphia,
Applied Geography, 63, 231-243 (2015)
61. Wong C.L., Yusop Z. and Ismail T., Trend of Daily Rainfall
and Temperature in Peninsular Malaysia Based on Gridded Data
Set, International Journal of GEOMATE, 14(44), 65–72 (2018)
62. World Meteorological Organization, The Role of
Climatological Normals in a Changing Climate, WCDMP-No. 61,
WMO-TD/No. 1377 (2007)
63. Zhang W., McManus P. and Duncan E., A Raster-Based
Subdividing Indicator to Map Urban Heat Vulnerability: A Case
Study in Sydney, Australia, International Journal of
Environmental Research and Public Health, 15(11), 2516 (2018)
64. Zhao W., He J., Wu Y., Xiong D., Wen F. and Li A., An
Analysis of Land Surface Temperature Trends in the Central
Himalayan Region Based on MODIS Products, Remote Sensing,
11(8), 900 (2019).
(Received 01st October 2019, accepted 10th November 2019)
