SEGi Review ISSN: Vol.9, December 2015

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1 SEGi Review ISSN: Vol.9, December 2015 Preliminary study of the impacts of land use and land cover change on land surface temperature with remote sensing technique A case study of the Klang Valley and Penang Island, Malaysia Yan Ping Leong, Loi Kok Chng, Jenny Ong, Chee Ming Choo, and Nordin Laili Faculty of Engineering and the Built Environment SEGi University Corresponding johnchng@segi.edu.my Abstract Urban heat island (UHI) phenomenon has been observed in many high density cities due to regional climate change while urbanization has been recognized as a major contributor to this regional climate change. The aim of this study is to evaluate the impact of land use/land cover (LULC) change on land surface temperature (LST) in the Klang Valley and Penang Island using the remote sensing technique. LULC change, Normalized Difference Vegetation Index (NDVI) and LST values were derived from the Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) images by using ERDAS Imagine software. Correlation graph between LST and NDVI was generated to evaluate the relationship between LULC, NDVI and LST. The results showed negative relationship between NDVI and LST. The higher the NDVI, the lower is the temperature, and vice versa. The overall results showed that LULC change affects the Leaf Area Index (LAI) of an area which is indicated by NDVI, which in turn affects the land surface temperature of that particular area. Keywords LULC Change; NDVI; LST; Remote Sensing; LAI I. INTRODUCTION Urbanization has been a major cause of land use/land cover change in human history (Weng, 2001). It has transformed the land covers into built environment due to population and economic growth. The uncontrolled urban expansion as a result of improper planning and management would eventually affect the local environment. Replacement of vegetation covers by impervious surfaces may alter the surface temperature, solar radiation and heat storage in urban areas (Tan et al., 2009). Eventually, this causes variation in temperature between the urban and nearby rural areas which is known as the UHI effect. UHI has always been associated with the increase in energy consumption for cooling purposes such as airconditioning. One research showed that 5 to 10% of community-wide electricity demand is used to compensate for the UHI effect (Akkbari, 2005). Air quality deterioration occurs due to increased electricity demand whereby most of the power utility companies actually generate electricity by burning fossil fuel. As a result, this process increases the emissions of air pollutants such as sulfur dioxide, nitrogen oxide, particulate matter as well as GHGs like carbon dioxide and methane. These pollutants may cause the formation of ground level ozone and acid rain. Furthermore, the release of GHGs may contribute to climate change. Besides that, UHI effects may result in health problems such as respiratory difficulties, heat 5

2 cramps, exhaustion and heat stroke. The UHI effect may also worsen the impact of heat waves, which is the period of abnormally hot and humid weather. Some sensitive groups of people such as children and the elderly are at high risks during these events (Heat island impacts, 2013). Application of remote sensing technique in the study relating to LULC and LST has been widely recognized and adopted by many researchers. Many similar studies have been done worldwide and in Malaysia. The areas which have been studied include Chiang Mai Lamphun Basin in Thailand (Dontree, 2010), Beijing in China (Jiang & Tian, 2010), Lokoja town in Nigeria (Oluseyi et al., 2009), Penang Island (Tan et al., 2009), and the Klang Valley in Malaysia (Tehrany et al., 2013). Despite the difference in geographical characteristics and nature of land covers between various study areas, several common findings have been observed from the various researches done. Studies have shown a strong negative relationship between LST and vegetation. Areas with dense vegetation will experience lower surface temperature than the areas with sparse or no vegetation at all. For instance, the lowest LST had been observed in evergreen forests whereas the highest LST had been observed in urban settlements and bare lands. The density of vegetation is usually expressed as NDVI which is an indicator of the greenness, relative density and health of vegetation as depicted through satellite images that provide information on the vegetation distribution. NDVI values range from -1 to 1 where the negative values (values approaching -1) correspond to water, while values close to zero (-1 to 0.1) usually correspond to barren areas of rocks, sand or snow. Sparse vegetation such as shrubs and grasslands have low, positive NDVI values (approximately 0.2 to 0.5) while dense vegetation such as temperate and tropical forests have high NDVI values (values approaching 1) ( NDVI, the foundation, 2011). Studies have also found a strong positive relationship between land use change and LST which is correlated with impervious surface. Based on the aforementioned relationship between LST and NDVI, LULC change will indirectly affect the LST due to the variation in NDVI among different LULC types. For example, forested areas have lower LST than urban areas due to higher NDVI value. It has been noticed that urbanization is the major cause of land transformation. This LULC conversion is believed to be driven by both economic and population growth as well as expansion for settlements to cater for the increasing population. The aim of this study is to identify the relationship between LULC and LST in the Klang Valley and Penang Island based on the Landsat satellite images. The specific objectives are: (1) To identify the LULC types and LST changes in the Klang Valley and Penang Island and (2) To identify the correlation between NDVI and LST in the Klang Valley and Penang Island. II. METHODOLOGY Study Site The Klang Valley is a form of conurbation in Malaysia. It is comprised of Kuala Lumpur and its surrounding suburbs as well as the adjoining cities and towns in Selangor. The state of Selangor is located on the west coast of Peninsular Malaysia, within longitudes 101 o E to 101 o E and latitudes 02 o N to 03 o N (Google Earth, 2014). The Klang Valley covers an area of approximately 1,750 km 2. It shares a border to the north with the state of Perak, to the east with the state of Pahang, to the south with the state of Negeri Sembilan and to the west faces the Straits of Malacca (Figure 1). 6

3 The Klang Valley is made up of 10 municipalities which are governed by their respective local authorities. These include Dewan Bandaraya Kuala Lumpur (DBKL), Perbadanan Putrajaya, Majlis Bandaraya Shah Alam (MBSA), Majlis Bandaraya Petaling Jaya (MBPJ), Majlis Perbandaran Klang (MPK), Majlis Perbandaran Kajang, Majlis Perbandaran Subang Jaya (MPSJ), Majlis Perbandaran Ampang Jaya (MPAJ) and Majlis Perbandaran Sepang (MPS) (Figure 2). Fig. 1.. Location map of Klang Valley (Google Earth, 2014) Penang Island is part of the Penang state and is located on the northwest coast of Peninsular Malaysia, between latitudes 5 o 15 N to 5o30 N and longitudes 100 o 10 E to 100o20 E (Google Earth, 2014). Penang shares a border with the state of Kedah to the north and east, and with the state of Perak to the south, while facing the Straits of Malacca to the west. The state of Penang consists of two parts - Penang Island and a coastal strip on the mainland, called Seberang Perai (formerly known as Province Wellesley). These two regions are connected by the two Penang Bridges. Penang Island covers an area of approximately 295km 2 with an estimated population of 720,000. The east side of the island is highly urbanized with industrial, commercial or residential areas. Meanwhile, the west side of the island is mainly composed of hilly terrain and thus it is less developed compared with other areas in the island (Figure 3). 7

4 Fig. 2.. Municipalities of Klang Valley (KWPKB, 2014) 8

5 Fig. 3.. Location map of Penang Island (Google Earth, 2014) Data used Landsat 5 TM and Landsat 7 ETM+ images were utilized in this project. Imagery from these satellites can be obtained for free from the following USGS website: Landsat 5 TM images consist of six spectral bands (Bands 1-5 and 7) and one thermal infrared band (Band 6). Landsat 7 ETM+ images consist of six spectral (Bands 1-5 and 7), one thermal band (Band 6), and one panchromatic (Band 8). In this study, Bands 1-5 and 7 were used for LULC classification while Band 6 was used for LST extraction for both Landsat 5 and 7 images (USGS, 2013). Remote Sensing The procedures employed in this study are illustrated in Figure 4. The acquired images were pre-processed before proceeding to the subsequent stages. Next, LULC map, LULC change map, LST map and NDVI map were generated. Lastly, correlation graphs between NDVI and LST were created to evaluate the relationship between LULC change, NDVI and LST. 9

6 Fig. 4.. Flow chart of methodology Data acquisition Two Landsat images, acquired on the 27 th of December 1999 (Landsat 7 ETM+) and the 16 th of February 2010 (Landsat 5 TM) were used for the study of the area in Penang Island. As for the study of the areas in the Klang Valley, two Landsat 5 TM images, dated the 15th of June, 1989 and the 2nd of March, 2006 were utilised. Selection of the images was based on the following criteria: (1) the satellite images must have <10% cloud coverage over the total study area or be cloud-free if possible; (2) the satellite images should be available for a long time series, to maximize the separability and differentiate the classes of land use, (3) the images must have been taken during the day time. Image pre-processing Data pre-processing is required for the restoration of appropriate image data from distorted raw data prior to any analysis of the satellite image. The landsat imagery came in GeoTIFF image format with each spectral band in a separate file. These spectral bands were stacked in one image file by excluding unnecessary bands for further action. Bands 1 to 5 and 7 were used for LULC classification while Band 6 was used for LST extraction for both Landsat 5 and 7 images. After that, the original scenes were subset by regions covering the study area in order to minimize file size and any possible errors in classification. 10

7 Image classification and LULC map The purpose of image classification is to categorize all pixels in the digital satellite images into different land cover classes based on their spectral characteristics. Iterative Self- Organizing Data Analysis (ISODATA) algorithm which is an unsupervised classification method was chosen. LULC in the Klang Valley was classified into six types, which were urban, water, forest, grassland, soil, and cloud. Meanwhile, the LULC in the Penang Island was classified into four types, including urban, water, forest, and grassland. However, the accuracy assessment for the LULC classification was not done due to the lack of reference data. LULC change detection map LULC change detection maps for the Klang Valley and Penang Island were generated through the Image Difference function in ERDAS Imagine by using the original classified images as input. Older images were used as a base map in the comparison of images with different dates. LST map LST was extracted from the satellite images by using ERDAS Imagine Spatial Modeler. Band 6 of both Landsat 5 TM and Landsat 7 ETM+ images were used. Temperature data was recorded by TIR sensors and stored as digital numbers (DNs). Several steps were performed to convert the DNs into actual temperature values. Firstly, the DN was converted into spectral radiance and this was followed by the conversion of spectral radiance to temperature in Kelvin. Lastly, the temperature map was generated after converting Kelvin to Celsius. NDVI map NDVI values were extracted from the satellite images through the unsupervised classification function for NDVI in ERDAS Imagine. The NDVI value of each pixel of the images was calculated based on the following formula: NDVI = (NIR-RED)/(NIR + RED) (1) where NIR is near-infrared band and RED is the red band. The values were then classified into several classes. Lastly, NDVI map was generated. LST-NDVI correlation graph LST and NDVI values at three random points of each LULC types were used to generate the correlation graph to identify the relationship between LST and NDVI. III. RESULTS & DISCUSSION LULC map, LULC change detection map, NDVI map, LST map, LST-NDVI correlation graph, and the relationship between LULC, NDVI and LST will be discussed in this section. LULC types of the Klang Valley in 1989 and 2006 were classified and illustrated in Figure 5. Figure 5(a)(i) and 5(b)(i) reveal that the urban areas were the most dynamic LULC class that have been expanded significantly by substituting nearby vegetated areas. This LULC change occurred in the middle part of the Klang Valley, which mainly covers the 11

8 territories of Shah Alam, Klang, Petaling Jaya, Kuala Lumpur, Subang Jaya and Putrajaya. The urban expansion is believed to be driven by economic and population growth. In the 19th century, economic boom began in the Klang Valley due to the active tin exploitation activities as well as the growing demand for rubber. As a consequence, a large influx of migrant workers into the city area continued due to the availability of job opportunities in various sectors. Besides that, the Klang Valley is situated in a strategic geographical location which is in the center of Peninsular Malaysia. This had contributed to the vigorous urbanization of the Klang Valley due to its role as Malaysia s transportation and industrial hub. In a nutshell, urbanization occurred due to economic development as well as the need for more settlement as a result of the increased urban population. Table 1 shows the statistical analysis of LULC changes in the Klang Valley from 1989 to Obviously, the most drastic change occurred in the urban areas which had increased by % over the study period. On the other hand, the areas covered by water had slightly decreased by 5.66% and the forest area had decreased moderately by 15.94%. However, grassland area has increased by nearly 28.38%. Lastly, bare soil has been decreased moderately by 24.83%. The results suggested that the increment of urban areas was primarily made out from the conversion of the water body, forest and bare soil. This also proves that deforestation had been carried out. 12

9 Fig. 5.. LULC map of Klang Valley in (a)1989 and (b)2006 TABLE I. LULC CLASSES IN KLANG VALLEY Land use classes Area (ha) LULC change Area (ha) Percentage (%) Urban 436,308 1,028, , Water 1,760,527 1,660,894-99, Forest 4,751,528 3,993, , Grassland 1,794,287 2,303, , Soil 157, ,221-39, Cloud 417, , , LULC types of Penang Island in 1999 and 2010 were classified and shown in Figure 6. According to Figure 6(a)(i) and 6(b)(i), the most noteworthy LULC change occurred in the southeast region of the island which is Bayan Lepas. It has been noticed that a significant amount of vegetated area was converted into urban dwelling. Industrial activity was the main 13

10 driver of the LULC change as Bayan Lepas is the core industrial zone of Penang which primarily comprises of the factories of many multinational companies. This also owes to the plan that had been proposed and promoted by the Penang state government in the late 1990s that aimed to make Penang a popular state as a Tax-Free World Trade Centre. Consequently, the plan had attracted many entrepreneurs from around the world to invest in Penang and hence initiated the conversion of Penang Island from a residential island to an industrial island. In addition, an international airport is also located in Penang island. The airport connects the island to many other countries and therefore provides efficient transportation for importation and exportation which is suitable for international trade. Besides, the increased demand for new settlements due to increased urban population had also contributed to the urban expansion. Table 2 demonstrates the statistical analysis of LULC changes in Penang Island between 1999 and Similar to the results of the Klang Valley, the most drastic change occurred in the urban areas, which had increased by 52.80%. In contrast, the other LULC classes had decreased. Water area, forest area and grassland had all decreased by about 1.22%, 10.11% and 15.41%, respectively. These results indicate that the urbanization phenomenon observed in the Penang Island involved the conversion of water, forest and grassland areas into urban areas. TABLE II. LULC CLASSES IN KLANG VALLEY Land use classes Area (ha) LULC change Area (ha) Percentage (%) Urban 6, , , Water 28, , Forest 15, ,231-1, Grassland 7, , , Figure 7 illustrates different NDVI classes of Klang Valley in 1989 (a) and 2006 (b). A darker color indicates a higher NDVI value and vice versa. Table 3 shows the change of NDVI value in the Klang Valley between 1989 and 2006, NDVI class 1 and 2 indicate that the water body or urban area have been increased, NDVI class 3 corresponds to barren areas of rocks or sand which have been reduced, NDVI class 4 and 5 that correspond to sparse vegetation have been greatly increased, and NDVI class 6 and 7 that correspond to dense vegetation have been decreased. The trend of change of NDVI suggests the conversion of bare soil and dense vegetation areas such as forests into grassland, agricultural and urban areas. Besides that, NDVI class 4 and 5 had increased significantly and this suggests the increased agricultural activities in the Klang Valley during the study period. 14

11 Figure 8 illustrates the different NDVI classes of the Penang Island in 1999 (a) and 2010 (b). A darker color indicates a higher NDVI value and vice versa. Fig. 6.. LULC map of Penang Island in (a) 1999 and (b)

12 Fig. 7.. NDVI map of Klang Valley in (a) 1989 and (b) TABLE III. NDVI CHANGE IN KLANG VALLEY NDVI classes Area (ha) NDVI change Class NDVI 1999 Class NDVI

13 Fig. 8.. NDVI map of Penang Island in (a) 1999 and (b) 2010 Table 4 shows the change of NDVI values in the Penang Island between 1999 and 2010, NDVI class 1 and 2 have been reduced, suggesting that there might have been a reduction of water body. NDVI class 3 that has been greatly increased suggests an increment of the urban area. NDVI class 4 that has been reduced suggests the reduction of bare lands. Both NDVI class 5 and 6 that correspond to sparse vegetation show different trends of change, which may be due to the change of vegetation cover of different plants that give different NDVI values. NDVI class 7 and 8 has been greatly increased, indicating that the dense vegetation cover has increased. TABLE IV. NDVI CHANGE IN PENANG ISLAND NDVI classes NDVI classes Area (ha) NDVI Area (ha) Percentage Class (%)

14 NDVI classes NDVI classes Area (ha) NDVI Area (ha) Percentage Class (%) Figure 9 illustrates different LST classes of Klang Valley in 1989 (a) and 2006 (b). Darker color indicates higher LST and vice versa. 18

15 Fig. 9.. NDVI map of Penang Island in (a) 1999 and (b) 2010 Table 5 shows that the LST experienced in most of the areas in 1989 range from 16 o C 24 o C. Meanwhile, the LST experienced in most of the areas in 2006 range from 20 o C 32 o C. This shows the trend of increasing average temperature experienced in most of the regions in the Klang Valley. Besides that, some abnormal LST values such as 0 o C 12 o C and 36 o C 67 o C have been observed. The exceptionally low LST is due to the presence of clouds within the study area when the satellite image was taken (Figure 12), whereas the exceptionally high LST is due to agricultural burning activities (Figure 13). TABLE V. LST CHANGE IN KLANG VALLEY LST ( o C) Area (ha) LST change Area (ha) Percentage (%)

16 LST ( o C) Area (ha) LST change Area (ha) Percentage (%) Figure 10 shows that the exceptionally low LST observed is due to the presence of clouds. The clouds reflect the solar radiation and cool the Earth s surface below it. Fig Areas in Klang Valley with exceptionally low LST Figure 11 shows that an exceptionally high LST being observed due to the open burning for agricultural activities, which are known as the traditional slash-and-burn agricultural technique. This technique involves the slashing and burning of trees and plants in forests or woodlands to create fertile rich soil suitable for agricultural activities. The diagram indicated that the smoke spread from the north towards the south. 20

17 Fig Areas in Klang Valley with exceptionally high LST Figure 12 illustrates different LST classes of the Penang Island in 1999 (a) and 2010 (b). A darker color indicates a higher LST and vice versa. 21

18 Fig LST map of Penang Island in 1999 (a) and 2010 (b) Table 6 shows that the LST experienced in most of the areas in 1999 and 2010 range from 20 o C 29 o C. From the result, two noticeable LST changes can be observed. Areas with LST ranges of 20 o C 23 o C had reduced by about 40.98% while areas with LST ranges from 23 o C 26 o C had increased by about 82.24%. Besides that, areas with higher LST ranges (26 o C 29 o C and 29 o C 32 o C) had also increased. The trend of the LST change suggested that vegetation cover in Penang Island has been reduced gradually. This may be due to the conversion of vegetation areas into urban areas as a result of urbanization. Other than that, some exceptionally low LST values range from 0 o C 14 o C have also been observed. This is due to the presence of clouds within the study area when the satellite image was taken which has been explained in Figure 12. TABLE VI. LST CHANGE IN PENANG ISLAND LST ( o Area (ha) LST change C) LST ( o C)

19 LST ( o Area (ha) LST change C) LST ( o C) Three random points in the Klang Valley region were selected from the LULC classes of urban and forest. The LST and NDVI values from each point were used to generate LST- NDVI correlation graph in order to identify the relationship between LST and NDVI. TABLE VII. LST AND NDVI VALUES FROM LST AND NDVI MAP OF KLANG VALLEY LULC classes LST ( o C) NDVI LST ( o C) NDVI Forest Urban Figure 13 and 14 demonstrate a strong linear regression between NDVI and LST with a good coefficient of determination, R 2, which is for 1989 and for The negative values of the slopes of both graphs indicate a strong negative relationship between NDVI and LST. In other words, the higher the NDVI, the lower is the LST, and vice versa. For instance, dense vegetation such as tropical forests has a high Leaf Area Index (LAI) and thus will give a high NDVI value. NDVI is an indicator of greenness, and the density of vegetation. Thus, areas with high NDVI values indicate that there are large amounts of green vegetation that can help to lower the surface temperature by providing shades or through the process of evapotranspiration. Shading is useful in lowering the surface temperature because the leaves and branches can reduce the amount of solar radiation that reaches the area below the canopy. On the other hand, evapotranspiration helps to lower surface temperature by utilizing the heat from the air to evaporate water. 23

20 Fig LST-NDVI correlation graph of Klang Valley, 1989 Fig LST-NDVI correlation graph of Klang Valley, 2006 A random point in the Klang Valley domain was chosen to evaluate the relationship between LULC, NDVI and LST. Fig. 15 (A) demonstrates the land use change detection map and the green color areas within the box represent an area that had changed during the study period. Fig. 15 (B) shows that the area was initially a forest in 1989 whereas Fig. 15 (C) shows that the area had become part of the urban area in In 1989, the NDVI of the area was while the temperature was 24 o C. However, in 2006, the NDVI of that area had decreased to while the temperature had increased to 29 o C. The trend of change once again proved the strong linear regression between NDVI and LST. It also reveals that LULC will indirectly affect the LST due to the different NDVI values provided by different LULC types. For instance, areas with dense vegetation cover experience lower surface temperature as compared to areas with sparse or no vegetation at all. In this case, conversion of forest into urban area decreased the vegetation cover and thus lowered the NDVI value. As a consequence of lower vegetation cover, less heat will be absorbed and hence it results in higher surface temperature. 24

21 Fig. 15. Illustration of the relationship between LULC, NDVI and LST in Klang Valley. (A) LULC change detection map, (B) LULC map of 1989, and (C) LULC map of 2006 Three random points in Penang Island were selected from the LULC classes of urban and forest. The LST and NDVI values from each point were used to generate LST-NDVI correlation graph in order to identify the relationship between LST and NDVI. TABLE VIII. LST AND NDVI VALUES FROM LST AND NDVI MAP OF PENANG ISLAND LULC classes LST ( o C) NDVI LST ( o C) NDVI Forest Urban Figure 16 and 17 demonstrate a strong linear regression between NDVI and LST with a good coefficient of determination, R 2, which is for 1999 and for The strong negative relationship between NDVI and LST is indicated by the negative values of the slopes of both graphs. In short, the higher the NDVI value, the lower is the LST, and vice versa. 25

22 Fig LST-NDVI correlation graph of Penang Island, 1999 Fig LST-NDVI correlation graph of Penang Island, 2010 A random point in the Penang Island domain was chosen to evaluate the relationship between LULC, NDVI and LST. Figure 18 (A) is the land use change detection map and the green color area within the box represents an area that had changed during the study period. Figure 18 (B) shows that the area was initially a forest in 1999 whereas Figure 18 (C) shows that the area had changed into an urban area in In 1999, the NDVI of the area was while the temperature was 22 o C. However, in 2010, the NDVI of that area had decreased to while the temperature had increased to 28 o C. This result is another proof that the area with lower vegetation cover will have lower NDVI value and experience higher temperatures. 26

23 Fig Illustration of the relationship between LULC, NDVI and LST in Penang Island. (A) LULC change detection map, (B) LULC map of 1999, and (C) LULC map of 2010 IV. CONCLUSION AND RECOMMENDATIONS The aim of this study was to evaluate the impact of LULC change on land surface temperature in the Klang Valley and Penang Island by using the remote sensing technique. The results showed that the major LULC change in both areas of study is the conversion of forest land into urban areas. The conversion occurred in the core regions of the Klang Valley and Penang Island which mainly comprised of residential, commercial and industrial areas. This indicates that population growth, industrial development and economic growth are the predominant drivers of urban expansion. The results also indicate a strong negative relationship between NDVI values and temperature readings. In other words, the higher the NDVI values, the lower was the temperature and vice versa. Areas with high NDVI value indicate that there are large amounts of green vegetation that help to lower the surface temperature though evapotranspiration process as well as by providing shade. Besides that, the results of this study also reveal that LULC will indirectly affect the LST due to the different NDVI value provided by different LULC types. For instance, forested area with dense vegetation cover will have high NDVI values and experience lower surface temperature whereas urban areas with low or no vegetation cover will have low NDVI values and experience higher surface temperatures. However, this paper suggests several limitations that need to be addressed in further studies Firstly, LST retrieved from the satellite images may be different from the actual values. This is because there are other factors that may affect the LST values but are not taken into account when the LST is retrieved, for instance, surface roughness, water content, and chemical composition. In order to validate the reliability of the LST retrieved from 27

24 satellite images, ground measurement for LST should be carried out to identify the accuracy of the LST retrieved. The second limitation of this study is that the result is highly dependent on the quality of the classification results, which may be a major error due to misinterpretation. In order to increase the accuracy of the result, satellite images with higher resolution can be used as they can provide more details for the classification process. Lastly, the amount of LULC types classified from Landsat images is limited, which are only 4 to 6 classes. More LULC types should be classified for further study in order to improve the accuracy of the result. REFERENCES Akkbari, H. (2005). Energy saving potentials and air quality benefits of urban heat island mitigation. Lawrence Barkeley National Laboratory. Dontree, S. (2010). Relation of Land Surface Temperature (LST) and Land Use/Land Cover (LULC) from Remotely Sensed data in Chiang Mai-Lamphun Basin. Google Earth Klang Valley, Malaysia N, E, elevation 7m. < [Viewed 28 May 2014]. Google Earth Penang Island, Malaysia N, E, elevation 246m. < [Viewed 28 May 2014]. Heat Island Impacts. (2013). Retrieved November 27, 2013, from Jiang, J., & Tian, G. (2010). Analysis of the impact of Land use/land cover change on Land Surface Temperature with Remote Sensing. Procedia Environmental Sciences. KWPKB Greater KL/KV comprises 10 local authorities. (2011). Retrieved May 28, 2014, from NDVI, the Foundation for Remote Sensing Phenology Retrieved May 28, 2014, from Oluseyi, I.O., Fanan, U., & Magaji, J.Y. (2009). An evaluation of the effect of land use/cover change on the surface temperature of Lokoja town, Nigeria. African Journal of Environmental science and Technology Vol.3 (3), pp Tan, K.C., Lim, H.S., Matjafri, M.Z., & Abdullah, K. (2009). Landsat data to evaluate urban expansion and determine land use/land cover changes in Penang Island, Malaysia Tehrany, M.S., Pradhan, B., & Jebur, M.N. (2013). Remote sensing data reveals ecoenvironmental changes in urban areas of Klang Valley, Malaysia: contribution from object based analysis USGS Frequently asked questions about the Landsat missions. (2012). Retrieved November 29, 2013, from /band_designations_landsat_ satellites.php 28

25 Weng, Q. (2001). A remote sensing-gis evaluation of urban expansion and its impact on surface temperature in the Zhujiang Delta, China. Int J Remote Sens 22(22):