Land Cover Types and Their Effect on the Urban Heat Signature of University Campuses using Remote Sensing
Published at : 29 Apr 2018
Volume : IJtech
Vol 9, No 3 (2018)
DOI : https://dx.doi.org/10.14716/ijtech.v9i3.1932
Wibowo, A., Salleh, K.O., 2018. Land Cover Types and Their Effect on the Urban Heat Signature of University Campuses using Remote Sensing. International Journal of Technology. Volume 9(3), pp. 479-490
| Adi Wibowo | 1. Department of Geography, Faculty of Arts and Social Sciences, University of Malaya, 2. Department of Geography, Faculty of Mathematics and Natural Sciences, Universitas Indonesia |
| Khairulmaini Osman Salleh | Department of Geography, Faculty of Arts and Social Sciences, University of Malaya, Kuala Lumpur 50603, Malaysia |
The campus, as an educational area, has a variety of land cover with varying surface temperatures. Knowledge of land use in a localized urban environment and its effect on the Urban Heat Signature (UHS) of a university campus is insufficient, so it is essential to assess UHS-related localized urban environments. The objective of this study is to assess land cover and its effect on the UHS of two university campuses. The research used spatial-temporal analysis employing satellite images during the period 2013-2014. The areas studied were the University Malaya (UM) and University Indonesia (UI) campuses. The results show that the land cover of both university campuses has the same localized urban environment pattern. Based on Landsat 8 TIRS (100 m ground resolution) resolution, we estimated that both university campuses had UHS profiles related to vegetation cover of 25-33oC, with a mean of 28oC as the lowest temperature, and building cover with a profile of 33-39oC, with a mean of 35oC as the highest temperature caused effect from Land cover types. Google Earth visual interpretation and digitalization provided the land cover based on 10m×10m vector square grids with their attributes validated by field survey. The research shows a trend of UHS change between 2013 and 2014, with the maximum temperature of >30oC on the UM and UI Campuses with increased of temperature 1oC. The study concludes that the UHS behavior is an effect of its temporal relation with land cover, which is new knowledge on university campuses about localized urban environments.
Land cover; Spatial and temporal analysis; Urban heat signature profiles
In Southeast Asia, for example, Malaysia, observations
in the past 20 years have noted a temperature increase of 0.5oC to
1.5oC, in line with the intensity of land use change (Malaysian
Meteorological Department, 2009). Urbanization has initiated alterations in the
natural ecosystem become an urban landscape (Tran et al., 2006). Even though
the increased temperature is a natural phenomenon in urban areas, but it could
be a threat in tropical cities when the temperature is higher than 30oC
(Tursilowati, 2008; Ichinose et al., 2008; Tursilowati et al., 2012). Such
increased temperatures will impact on human life (Salleh & Ghaffar, 2009;
Johnson et al., 2012).
Land cover type as a representation of land use (Ren et al., 2012) can absorb and reradiate sun Radiation and generate urban heat (Wong & Yu, 2005; Srivanit & Hokao, 2013). In general, the urban land cover associated with buildings, roads and pavements, highways, green parks and also bare soil (Ahmad & Hashim, 2007). Land cover type as climatic (temperature) characteristics of local climates. Temperature and land use/cover information, which allows monitoring of the urban environment and human activities, enhances our understanding of the urban environment (Asmat et al., 2003).
Urbanized areas have
significantly higher daylight surface temperatures compared to those of
surrounding rural areas with relatively more vegetation (Tran et al., 2006).
Urbanized areas are associated with high temperatures, while green spaces are
associated with low temperatures (de la Barrera et al., 2015) caused by urban
surface temperature reflect from the solar radiation (Li et al., 2010). Urban
areas, were the temperature higher in densely built cities, have different heat
intensity (Su et al., 2012), the other hand the urban heat phenomenon is a
natural consequence related to the land cover type and solar radiation. (Kim
& Baik, 2005; Ichinose et al., 2008; Memon et al., 2009; Mirzaei &
Haghighat, 2010; Stewart & Oke, 2012). Built-up surfaces in urban areas are
related to high temperatures (Taha, 1997; Tran
et al., 2006), while green surfaces with moist soil might reach a temperature
of only 18°C (Gartland, 2008). According to Chen and Ng
(2012), urban heat has become critical in zones
where inadequate shading and green spaces are unable to intercept and balance
the heat from direct solar gains (Srivanit & Hokao, 2013). Urban air
temperatures can be on average 2°C higher than in rural areas (Taha,
1997) and the maximum surface temperatures are also associated with high-rise
city core areas (Tran et al., 2006). According to Shahidan et al. (2012) and
Tran et al. (2006), the highest outdoor thermal stress observed during bright
sunny days with a calm wind in the summer season. However, in tropical climate
zones, the most considerable thermal anxiety may occur during the yearly hot,
dry periods at noon due to higher solar radiation exposure (Srivanit &
Hokao, 2013). Land cover in localized urban environments has natural
consequences related to sun radiation which high-low temperature urban land
cover is Urban Heat Signature.
Remote
sensing techniques are beneficial and efficient in analyzing the relationship
between land surface temperature and land cover in tropical regions (Asmat et
al., 2003; Ahmad & Hashim, 2007; Mirzaei & Haghighat, 2010; Ku?cu &
Sengezer, 2011; Mallick et al., 2012; Tursilowati et al., 2012; Senayake et
al., 2013; Rozentein et al., 2014; Roy et al., 2014).
Thermal bands with spatial resolutions are retrieved from land satellites (Wong
& Yu, 2005; Wong et al., 2007; Tursilowati, 2008; Mirzaei & Haghighat,
2010; Tursilowati et al., 2012, Wibowo, et al., 2013; Weng et al., 2014).
Furthermore, LST retrieved from the thermal images can show that areas covered
by vegetation and farmland have lower LST, while urban areas have much higher
LST (Srivastava et al., 2009). Wong et al. (2007) concluded from thermal images
that there was a reddish color of thermal distribution on and around buildings,
that a greenish color appeared in high-density plantation areas, with a
yellowish color between these areas. The colors in this research are also
related to Mallick et al. (2012), who argue that the spatial distribution of
land surface temperature is related to land cover distribution. LST is an
essential factor in global change studies, heat balance, and as a control for
climate change (Srivastava et al., 2009). This
temperature information provides a powerful way to monitor the urban
environment (Asmat et al., 2003).
According to the introduction to the paper, campuses in a tropical area were chosen as the study region to answer the objectives of the research, these areas located at the University of Malaya (Malaysia) and University of Indonesia (Indonesia). The study uses LST from Landsat 8 TIRS (satellite image) data (Mirzaei & Haghighat, 2010; Ku?cu & ?engezer, 2011; Tursilowati et al., 2012; Senayake et al., 2013; Rozenstein et al., 2014; Roy et al., 2014). Landsat 8 had two thermal bands; this research used Band 10 to process becoming LST data, as according to Wang et al. (2015), and thermal band 11 has considerable data uncertainty (Wang et al., 2016). It is the latest series of the Landsat system, carrying two sensors: an Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS). This study mostly employed the TIRS sensor, which has two bands (TIR-1 and TIR-2), with 100-meter spatial resolution and 16-day temporal resolution. The limitation of using remote satellite sensing is cloud cover.
2.1. Datasets
The research started from the acquisition of data on the land cover using Google Earth data and validated survey ground truth, and the land surface temperature data obtained from (employed) a thermal band of Landsat 8 TIRS. The Landsat 8 data for the UM campus with Path 127 Row 058 in the year 2013 used data from 22/4/13, 27/7/13, 12/8/13, 31/10/13 and 16/11/13 and for the year 2014 used data from 4/2/14, 8/3/14, and 12/6/14. The Landsat 8 data for the UI campus with Path 122 Row 064 in the year 2013 used data from 22/6/13, 25/8/13, 10/9/13 and 26/9/13 and for the year 2014 used data from 9/6/14, 28/8/14, 13/9/14 and 29/9/14. The remainder of the source data obtained from http://glovis.usgs.gov/.
2.2. Data Processes
The generated data of land cover of both campuses obtained from Google Earth. Data collection and extraction started from downloaded satellite imagery from Google Earth (Park, 2009, Wibowo et al., 2016). The satellite image data from Google Earth was mosaicked using ArcGIS software. The result of the mosaicked images does not have geographic references. A georeferenced tool was used for the mosaicked images to obtain a georeferenced image based on Universal Transform Mercator (UTM) projection (Wibowo et al., 2016). Land cover types were obtained from Google Image Data by visual interpretation and digitalization was based on 10m×10m vector square grids with their attributes (Shim, 2014; Wibowo et al., 2015; Wibowo et al., 2016). To validate the land use cover data from Google Earth (survey). The data was saved in geodatabase storage and processed to produce a summary of the total area of each land cover type, set within standard symbols, colors and layout using ArcGIS with a cartographic standard as a land cover map (Wibowo et al., 2016).
The data processing is generated Land
Surface Temperature using Equation 1 (L? = spectral radiance (wm-2sr-1?m-1); M
= Multiplicative digital number value at band 10; DN of Band 10 = Digital
Number of Band 10 Landsat 8 OLI; A = Additive value of spectral radiance at
band 10 according to http://landsat.usgs.gov/Landsat8_Using_Product.php).
3.1. Land cover at UM and UI campuses.
Land cover, as the localized urban environment on UM and UI campuses, can be seen in Table 1. About the comparative land cover of UM and UI, it found that the two urban campuses had a similar cover, but different total percentage areas. Land cover on the UM campus about building cover was 20.9% greater than on the UI campus, which had building cover of only 10.9%. The UM campus was covered by 59.1% dense vegetation, which was lower than the UI campus cover of 65.3%. The spatial distribution of land cover on the UM and UI campuses could see in Figure 1.
3.2. Spatial-Temporal UHS on UM and UI campuses
The spatial UHS pattern in 2013 and 2014 at UM could see in Figure 2, with color gradation from yellow to red (low to high temperature). Based on the spatial distribution of UHS on the UM campus, distribution of low temperatures was clustered in the middle and to the north of the campus, and high temperatures clustered to the south of the campus.
Table 1 Land cover distribution on UM and UI campuses 2013-2014
|
No |
Land cover |
UM campus |
UI campus |
||
|
Area (ha) |
Percentage (%) |
Area (ha) |
Percentage (%) |
||
|
1 |
Building cover (faculties, colleges, administrative buildings) |
67.3 |
20.9 |
33.0 |
10.9 |
|
2 |
Paved open spaces |
52.9 |
16.4 |
35.8 |
12.6 |
|
3 |
Bodies of water |
3.0 |
0.9 |
22.9 |
7.5 |
|
4 |
Open green surfaces (grassland, shrubs, isolated tree strains) |
23.3 |
7.2 |
19.3 |
6.4 |
|
5 |
Dense vegetated surfaces |
189.0 |
59.1 |
197.4 |
65.3 |
|
|
|
|
100.0 |
|
100.0 |
Figure 1 Spatial pattern land cover on UM and UI campuses
UHS in 2013 with low temperatures was very limited, while on the other hand red and yellow-red colors were more dominant and distributed around the UM campus. Spatial distribution of UHS in 2014 was similar to that of 2013, but with very clear yellow in June 2014, with an area distribution covering more than 40% of the UM campus. The spatial density of UHS at UM is explained by the percentage of covered areas with high, moderate and low temperatures, as seen in Table 2. The spatial densities varied every month, depending on cloud cover. In general, the UM campus had moderate UHS cover most months.
Figure 2 UHS spatial pattern 2013-2014 on the UM campus
Table 2 Spatial density with percentage cover of UHS on the UM Campus
|
No |
Urban Heat Signature |
Apr 2013 |
Jul 2013 |
Aug 2013 |
Oct 2013 |
Nov 2013 |
Mar 2014 |
Apr 2014 |
Jun 2014 |
Oct 2014 |
|
1 |
Low |
23.5 |
32.6 |
44.4 |
20.5 |
5.5 |
37.5 |
23.6 |
53.6 |
43.0 |
|
2 |
Moderate |
49.9 |
60.0 |
48.0 |
38.7 |
47.7 |
56.1 |
35.6 |
43.1 |
32.5 |
|
3 |
High |
26.6 |
7.3 |
7.7 |
40.8 |
46.8 |
6.4 |
40.7 |
3.3 |
24.5 |
|
Total |
100% |
100% |
100% |
100% |
100% |
100% |
100% |
100% |
100% |
|
To understand the monthly land cover effect on the temporal UHS behavior on the UM campus could see in Figure 3. The behavior on the UM Campus in 2013 started on 22/4/2013 at 25-31oC; followed by 27/7/2013 at 20-24oC; 12/08/2013 at 17-23oC, 31/10/2013 at 20-31oC; and 16/11/2013 at 17-24oC. The corresponding behavior on the UM campus in 2014 started on 4/2/14 at 28-38oC, 8/3/14 at 30-39oC, 12/6/14 at 10-17oC; and 15/10/14 at 25-33oC. These results show that UHS between 2013 and 2014 on the UM campus had a positive trend, with an average trend temperature 1oC. The temporal trend for temperature maximum had a positive trend line (y = 4.2745x).
Figure 3 UHS behavior 2013-2014 on UM campus
The spatial UHS patterns in 2013 and 2014 on the UI campus could see in Figure 4. That figure shows a distribution of low temperatures clustered within the center of the north part of campus, and high temperatures clustered in the center and to the south. The spatial pattern of UHS in June 2013-2014 shows a small area with red and yellow color distribution around the campus; in August 2013-2014 there is a spatial distribution of red color clustered to the south of the campus in 2013, but in 2014 there is a distribution of yellow color around the campus. UHS with low temperatures dominated the spatial distribution in Sept/Oct 2013 and Sept 2014, with moderate temperature cover in June, August and Sept 2013, and Sept/Oct 2014. Furthermore, the red color had a dominating spatial distribution pattern in June 2014. The spatial density of UHS on the UI Campus could see in Table 3. In general, the UI campus had moderate UHS cover most months.
Figure 4 UHS spatial pattern 2013 and 2014 on UI campus
The temporal temperature of UHS on the UI campus in 2013 can be seen in Figure 5, which shows UHS behavior on 22/6/2013 of 22-28oC; on 25/8/2013 of 26-32oC; 10/9/13 of 27-34oC; and on 26/9/13 of 27-34oC. The corresponding behavior in 2014 was a UHS on 9/06/14 of 20-28oC; on 28/08/14 of 25-32oC; on 13/09/14 of 25-33oC, and on 29/9/14 of 21-33oC. These results show that the UHS between 2013 and 2014 on the UM campus had a negative trend, with an average trend temperature of 1oC. However, the maximum temporal trend was positive (y = 4.973x).
Based
on Landsat 8 TIRS (100 m ground resolution) the result on both university
campuses had UHS related to vegetation cover of 25-33oC with a mean of 28oC as the lowest
temperature. The building covered 33-39oC with a mean of 35oC as the highest temperature,
its effect on land cover types likes build-up area. Google Earth provided
the land cover for visual interpretation, and digitalization based on a 10m×10m vector square grid with
their attribute and land cover attribute validated with field survey.
Our research has shown the critical trend of UHS
change in the period 2013-2014, with a maximum temperature of >30oC on
the UM and UI campuses increased in
temperature with 1oC. Finally, this study has concluded that UHS
behavior is an effect of the temporal relation between land cover and solar
radiation. The new knowledge for
university campuses UHS is relation land cover
with solar energy in the localized urban environment.
This research support by Department of Geography,
Faculty of Mathematics and Natural Sciences, Universitas Indonesia
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