Spatial structure of surface urban heat island and its relationship with vegetation and built-up areas in Melbourne, Australia

Science of the Total Environment 659 (2019) 1335–1351

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Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv

Spatial structure of surface urban heat island and its relationship with vegetation and built-up areas in Melbourne, Australia Yashar Jamei a,⁎, Priyadarsini Rajagopalan b, Qian (Chayn) Sun c a b c

School of Property, Construction and Project Management, RMIT University, Melbourne, Australia School of Property, Construction and Project Management, RMIT University, Melbourne, Australia School of Science Cluster, Department of Geospatial Science, RMIT University, Melbourne, Australia

H I G H L I G H T S

G R A P H I C A L

A B S T R A C T

• Time-series satellite imagery and spatial statistical used to determine the LST spatial structure at the local government level. • LST calculated by the modified mapping method and NDVI calculated for each acquisition dates from Landsat satellite images. • Overall, clustering patterns of LST expanded toward the north-west(NW) and south-east(SE) in Melbourne • NW part has the highest positive correlation between NDBI and LST; SE part has lowest negative correlation for NDVI and LST

a r t i c l e

i n f o

Article history: Received 21 October 2018 Received in revised form 20 December 2018 Accepted 20 December 2018 Available online 29 December 2018 Editor: Frederic Coulon Keywords: Surface urban heat island (SUHI) Land surface temperature (LST) Spatial distribution Melbourne metropolitan area

⁎ Corresponding author. E-mail address: [email protected] (Y. Jamei).

https://doi.org/10.1016/j.scitotenv.2018.12.308 0048-9697/© 2019 Elsevier B.V. All rights reserved.

a b s t r a c t Due to the intensity of urban development around the world, there is an increasing body of studies attempting to investigate urban heat island (UHI) in various spatial and temporal scales. In surface heat urban island (SUHI) studies, extended periods of time, broader regions and local government area (LGA) level have become more crucial and will shed light on causes of UHI. Moreover, the spatial pattern and structure of SUHI will be useful for policy-makers to develop mitigation strategies. This study focused on three objectives. Firstly, analyzing land surface temperature (LST), normalized difference built-up (NDBI) and vegetation (NDVI) indices. Secondly, investigating interrelationships among LST, NDVI, and NDBI. Thirdly, identifying LST patterns in the Melbourne metropolitan area. These objectives were achieved through three different methods. The modified automatic mapping method for the first objective, the correlation analysis for the second, and spatial statistical methods for the third. The methodological innovations of this study were considering LGA in interrelationship analysis among LST, NDBI and NDVI, and calculation of NDVI for each acquisition date. The results indicated that the clustering pattern of LST expanded toward the north-west and south-east during the period of the study. Furthermore, the north-west part of the city has the highest positive (0.6) correlation between NDBI and LST, and the south-east part of the city has the lowest negative (−0.8) correlation between NDVI and LST. The most significant increase and decrease in mean LST happened respectively from January 6th to 22nd 2017, and January 14th to 30th January 2014. The temperature degree altered from 19.61 °C to 27.86 °C in inner western suburbs, and from 35.49 °C to 26.88 °C in most LGA's. These findings are critical for planners to localize UHI mitigation action plans, target hot spots in LGA's and allocate resources to respond to the adverse effect of UHI. © 2019 Elsevier B.V. All rights reserved.

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1. Introduction The transformation of natural landscapes into human settlements has increased (Grimm et al., 2000), which has a considerable effect on built environments and global and local climates. Cities, which are the primary areas for human activities and interactions, have faced extensive alterations in their land use and land cover (Li et al., 2011) due to population growth and economic development. The increase in the proportion of impervious surfaces has resulted in a warm urban environment that causes the UHI effect. The replacement of natural surfaces with pavement and other urban construction types has resulted in the reduction of the cooling effects of vegetative surfaces (Santamouris, 2013; Jamei et al., 2016). UHI can be investigated at various scales. Micro-scale methods consider localized urban spaces and other meteorological factors. Meanwhile, mesoscale methods are generally applied on large geographic extents to evaluate the spatial distribution of UHI in terms of land surface temperature (LST). LST maps are created from radiation data obtained by applying remote sensing (RS) techniques on spaceborne satellite imagery. Most of the studies have applied RS techniques and have used various satellite images, such as MODIS, Landsat ETM+, and OLS, to provide LST and compare them with other factors, such as impervious surfaces (Li et al., 2011), vegetation coverage (Weng et al., 2004; Yuan and Bauer, 2007; Duncan et al., 2019), and composition and configuration of various land uses and land covers (Chen et al., 2006; Li et al., 2011). Furthermore, several other studies, such as the study by Sheng et al. (2017), have indicated that the correlation of LST with a vegetated area is higher than that of water and impervious surfaces (nearly N0.8 in the daytime spring season). In Australia, studies conducted by Sidiqui et al. (2016), Sharifi et al. (2017), and Deilami et al. (2016) have applied RS techniques in analyzing UHI. Sidiqui et al. (2016) found that the intensity of UHI in Sydney is considerable at night-time during all seasons. Sharifi et al. (2017) investigated the effect of paved areas and tree canopy covers on the temperature in Adelaide. Deilami et al. (2016) indicated that the UHI effect could be remarkably minimized through proper land use planning by creating a balance between urban and non-urban uses. Previous studies used indicators, such as normalized differencebuilt-up index (NDBI) and normalized difference vegetation index

(NDVI), to investigate the relationship between LST and various types of land cover. However, the results for an entire year are aggregated without distinguishing between various times of the year. In addition, mesoscale studies on LST in Australia are limited and the spatial variation of the relationship between NDVI, NDBI, and LST is not considered in most of the studies. Limited attention has been paid to the spatial pattern of LST, which is in fact essential for strategic planning in heat mitigation and adaptation. To fill the gaps in the existing research, this study aims to investigate the relationship between LST, NDVI, and NDBI by analyzing their spatial characteristics, especially the spatial structure of LST in Melbourne over the last five years. The objectives of this study are expressed as follows: • To analyze the LST, NDVI, and NDBI from 2014 to 2018 during the summer season. • To analyze the spatial variation of relationships between LST, NDBI, and NDVI in Melbourne. • To analyze the spatial distribution of surface urban heat island (SUHI) in Melbourne. 2. Methods 2.1. Study area The Melbourne metropolitan area (Melbourne from now on) is located in the southern coastal part of the state of Victoria in Australia, with an area of 9991.5 km2 ranging approximately 148.5 km from north to south and 137.4 km east to west (Fig. 1). With a population of 4.9 million, Melbourne is considered the second most populous city in Australia. With a current growth rate of 2.7%, the population is expected to reach more than eight million people in 2051 (ABS, 2018; DEWLP, 2016). Adversely, the increase in urban population aggravates the number of individuals who experience the disadvantageous impacts of the UHI effects, such as the intensification of heat waves (Zhou and Shepherd, 2009; Stone, 2012; Li and Bou-Zeid, 2013). The heat waves that occurred in Australia during the summer of 2012 and 2013 are examples of how fatal the UHI effect can be when it synergistically acts with an extreme heat event (Lewis and Karoly, 2013). In Melbourne, heat waves can lead to excessive mortalities (Nicholls et al., 2008;

Fig. 1. Spatial location of Melbourne and its local government (DHHS, 2016).

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Loughnan et al., 2008). In Victoria, the heat waves have no minimum duration, and can be as short as a single day. For instance, the January 2009 heatwave in Victoria had unprecedented intensity and duration, in which Melbourne encountered three continuous days of temperatures above 43 °C and little overnight relief. Another heat wave was recorded in Melbourne in 2014 with temperatures above 41 °C between January 14 and 17 (DHS, 2009; Longden, 2018). A report has indicated that Melbourne will swelter through an average of 24 days above 35 °C by 2090, which is 11 more than in 1995 (Milman, 2015). Melbourne has different types of land cover, such as rivers and associated creeks, farmlands, conservation, natural environments, and residential and industrial areas. The climate of Melbourne can be described as moderate oceanic with occasional incursions of intense heat from Central Australia, and the city is famous for its highly changeable weather conditions (BoM, 2009). The city experiences a temperate climate with mild winters (average July maximum temperature is 13.4 °C) and moderately hot summers (average January maximum is 25.8 °C) (Keay and Simmonds, 2005). 2.2. Methods The method adopted in this research has two main steps. The first is the calculation and analysis of LST, NDVI, and NDBI, and the second is the analysis of the spatial distribution of LST and its relationship with LST, NDVI, and NDBI. Considerable studies have been published on LST detection methods (Li et al., 2013; Waters et al., 2002; Zhengming and Dozier, 1996; Qin et al., 2001; Avdan and Jovanovska, 2016; Isaya Ndossi and Avdan, 2016). The second phase concentrates on the spatial distribution of LST and its relationship to NDVI and NDBI. Deng et al. (2018), Sun and Kafatos (2007), Zhang et al. (2009), Yue et al. (2007), and Yuan and Bauer (2007) provided several insights related to LST, NDVI, NDBI, and other types of land cover based on linear regression and correlation analysis. Several models on the spatial distribution of LST, such as the Kriging technique for spatial interpolation (Yang et al., 2015), geographically weighted regression for regression analysis (Li et al., 2010), and hotspot analysis (Tran et al., 2017), have been mentioned in previous studies. The research framework of this study is presented in Fig. 2. 2.2.1. Calculating LST, NDVI, and NDBI RS techniques are generally used to analyze LSTs, vegetation cover, and several other surface characteristics. The measured radiance in the thermal infrared spectrum at each cell of the captured image can be converted to a surface temperature by using Planck's Law (Njoku, 2014). High-resolution images can provide continuous data over a surface. However, the images can only be captured at discrete times because an RS device does not typically remain stationary over the Earth's surface. In addition, only upward radiance from visible surfaces can be detected by sensors, and only 2D surfaces can be rendered.

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Moreover, objects such as clouds can block radiation from the surface, causing inaccurate data recordings. These recent challenges have led to the development of various methods for accurate estimation of LST by using ENVI, ArcGIS, and Erdas software. However, the most effective method is difficult to determine. Thus, the best method to retrieve the LST from space can be determined by considering the sensor characteristics and the availability of emissivity data and atmospheric information (Li et al., 2013). In this study, apart from the application of R programming language and raster library package for the statistical characteristics of LSTs, several modifications are implemented on the automated mapping method, which is developed by Avdan and Jovanovska (2016). The first modification is related to NDVI. Previous studies mostly applied general logic in assigning the emissivity value to each class of NDVI, regardless of the alteration of vegetation, soil, and water with time. The NDVI histogram method considering different thresholds for NDVI classes for each day was applied. Thus, each selected day has different NDVI values. Furthermore, the problems associated with cloud coverage are addressed with the aid of ArcGIS and ENVI software. Also, NDBI is used as an indicator to represent the built-up areas (Chen et al., 2006) which is calculated by ENVI and ERDAS software. 2.2.2. Analyzing the spatial distribution of LST, NDVI, and NDBI The spatial distribution analysis conducted by using spatial autocorrelation methods based on feature locations and values represented by Global Moran's I Index. Moran's index values are bounded by −1.0 and 1.0. The value “1” indicates perfect positive spatial autocorrelation (high values or low values clustered together), “−1” indicates perfect negative spatial autocorrelation (a checkerboard pattern), and “0” implies perfect spatial randomness (Tu and Xia, 2008). The z-score or p-value indicates statistical significance, which indicates whether or not to reject the null hypothesis. The values associated with the randomly distributed features are considered the null hypothesis states (Griffith, 1987; Goodchild, 1986). The precise definition of Moran's I is given with a spatialized variable zi at the location I, where σ2 is the sample variance:
 ∑i; j Wij Zi−Z n : I¼ σ2

ð1Þ

The strength of Moran's I depends on its simplicity, while its significant limitation is neglecting the local variations. Therefore, a new category of tools proposed to evaluate the local level of spatial autocorrelation and to identify areas where variable values are either extreme and geographically homogeneous. This approach is valuable when several local level variables exhibit homogeneous values that do not follow the global trend. Therefore, this process leads to the identification of so-called hot spot regions where the considered phenomenon

Fig. 2. Research framework.

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is extremely pronounced across localities and spatial outliers (Anselin, 1995; Bailey and Gatrell, 1995; Cliff, 1981; Fotheringham et al., 2000). In parallel with Global Moran's index, the hot spot analysis calculates the high or low clustering that represents the concentration of the primary feature values in the study area. Local statistic methods, such as Getis-Ord Gi (Gi*), assess the value of the feature within its spatial context in terms of its neighboring features (Getis et al., 2004). Gi* is an effective and practical spatial statistic tool used to identify the spatial clusters of similar high (hot spots) or low (cold spots) values. Gi* index is suitable because it can locate unsafe regions on a global scale and can discern cluster structures of high- or low-value concentration among local observations. The estimation of Gi* is performed by using Eq. (2), where Xj is the pixel value of feature j, Wij is the spatial weight between features i and j, and n is the total number of features (Sismanidis et al., 2017). n

n

n


 ∑ j¼1 WijXj−X∑ j¼1 Wij2 ∑ j¼1 X i 2 ffi Z Gi ¼ vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ;S  2 X ¼ u n un∑n Wij2 − ∑n Wij t j¼1 j¼1 s n−1 n ∑ j¼1 X j 2 2 −X : ¼ n

ð2Þ

The standardized Gi* is essentially a Z score and can be attached to the statistical significance. A close-to-zero Gis value implies a random distribution of the observed spatial events. Conversely, positive and negative Gi* statistics with high absolute values correspond to the clusters of high- and low-valued events, respectively. However, the negative Gi* indicates a tendency of clusters of events with short incident durations. In summary, the location of a cluster is identified as a hot spot when the calculated index values are greater than that of a threshold associated with statistical significance (Songchitruksa and Zeng, 2010). 3. Results

The two main spatial forms of SUHI identified in Melbourne are focal and linear. Focal UHI includes non-residential parts of the city with a dominant compact form and residential areas that are blended in the old texture of the city. Linear UHI mainly reflects the asphalt-coated surfaces and transport lanes. 3.1.2. Descriptive statistics of LST The statistical indicators of LST in Melbourne are presented in Figs. 9 and 10, and the mean daytime LST for each local government areas during the acquisition dates is presented in Table 1 all in appendices. As shown in Fig. 9, the highest LST observed from 10 to 10:30 am on 14th January 2014, which is approximately 45 °C. Table 1 shows that the mean LST is higher compared with other dates on 14th January 2014. By contrast, the lowest LST is detected on 7th February 2017, which is approximately 33 °C. Additionally, the mean LST for most of the local government areas reaches its minimum on 24th January 2015. A great extent of variability of LST data to the mean is detected on 17th February 2018 and 24th January 2015 by comparing the coefficient of variation (CV) index on acquisition dates. Therefore, the LST values are dispersed on the above-mentioned days. The most significant variation in LST values among acquisition days occurs between 17th February 2018 and 24th January 2015. On this basis, the LST values are spread from their mean and from one another. Another remarkable indicator is the range of LST values, which is also related to the spatial order of LST. For instance, the spatial order of LST decreases, and the formation of SUHI intensifies in several parts of Melbourne with the increase in the range of LST values on 24th January 2015. Regarding the LST fluctuation, the oceanic climate in Australia based on the Köppen climate classification (Kottek et al., 2006) plays a pivotal role. Fig. 10 in appendices shows the kurtosis and skewness of LST values. The negative kurtosis indicates that the frequency of the above-mean LST is higher than the frequency of below-mean LST. Also, the distribution of LST is negatively skewed, light tail, and platykurtic. In fact, the distribution of LST data is not based on the discrepancy of the median, mode, and standard deviation from the mean in a fair and sensible manner.

3.1. LST analysis 3.2. NDVI and NDBI analysis 3.1.1. LST maps The LST maps in Melbourne at each acquisition dates presented in Fig. 3. Apart from that, the study area based on the spatial and temporal resolutions of Landsat 8 satellite sensors for each snapshot from 2014 to 2018 are reported in Fig. 8 in the appendices. The general trends of LST maps show a remarkable variation in the minimum LST from 2014 to 2018. However, the maximum temperatures remain unchanged during those years. The minimum temperature mostly occurs in the eastern and northeastern countryside, such as Yarra Ranges. The LSTs in January 2015, January 2017, and February 2016 are low in other places, such as Bayside, Stonington West, Nillumbik, and Manningham East. Apart from the LST, the sea surface temperature is found to be low in most of the acquisition dates. The distribution of diverse types of green spaces, such as parks and gardens, results in the creation of cold areas in specific parts of the cities (Norton et al., 2015). For Melbourne, the places located near the coastal areas with large amount of greeneries, such as Mornington Peninsula, are the example of the areas with lower temperature. By contrast, built-up areas compose the hottest part of the city that are mainly located at the north-west of Melbourne with dominant gridiron street patterns (Arnfield, 1990) in general sprawl urban zone (Lemonsu et al., 2015), which is positively related to UHI intensity. Several examples of these areas are Keilor, Melton, and Essendon. Large industrial districts located in the north part of the city, such as Whittlesea–Wallan, are another factor that could potentially lead to high LST. Most parts of the city that could contribute to high LST are frequently detected as built-up surfaces, transportation networks, industrial areas, brownfields, airports, and petrol stations.

NDVI and NDBI are vegetation and built area indicators that are generally used for analyzing the relationship between LST, vegetation, and built-up areas (Yue et al., 2007; Guha et al., 2018; Ma et al., 2016; Zhang et al., 2009). NDVI refers to the Normalized Difference Vegetation Index, and is broadly used due to its advantages, such as high sensitivity to chlorophyll, and the reduction of noise by normalization between −1 and 1. However, this index has limitations, such as high sensitivity to variations of the canopy. Apart from this, NDBI was developed by Zha et al. (2003) to analyze the increments of reflectance on TM 5 bands 4 and 5 for the images of urbanized and barren land areas. NDBI is considered an essential factor and indicator of LST. NDBI values range from −1 to 1. Positive values represent highly built-up land, and negative values indicate other land cover types. Fig. 4 shows the NDVI maps from 2014 to 2018 during summer seasons. The comparison between LST and NDVI maps shows that the NDVI value increases with the dense green areas and vegetations, and with the reduction of built-up areas. The LST and NDVI relationship is based on a normal distribution with a high kurtosis. On the basis of this distribution and kurtosis, a nonlinear relationship is observed between LST and NDVI in Melbourne. Moreover, the comparison between Figs. 3 and 4 indicate that high temperature is observed in parts of the city where vegetation is less dense and more sporadic. Thus, the NDVI values tend to be close to −0.50. The NDVI values could be categorized into three groups based on the LST temperature range: −0.5–0, 0–0.5, and 0.5–1. Certainly, each NDVI class could be placed in a specific LST range. The LST in the first class (−0.5–0) is approximately 22–45 °C, which contains roads, residential areas, and roofs of industrial sheds.

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Fig. 3. Snapshots of LST in Melbourne on acquisition dates.

For the second class (0–0.5), the LST ranges from 2 °C to 23 °C. This class covers places, such as newly built residential areas with a suitable amount of green areas, green open spaces, river edges, and other types of greeneries. Several surrounding areas of agricultural lands are

also considered in this class. The NDVI value becomes close to 1 in areas that are near to the mountain and coastal areas. Local Government areas of Yarra Ranges Cardinia, Macedon Ranges, and Mornington Peninsula breeze are some of the examples.

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Fig. 4. Snapshots of NDVI in Melbourne on acquisition dates.

The fluctuation of NDVI classes during the acquisition dates are presented in Fig. 11 in appendices. The lowest fluctuation between NDVI classes corresponds to water, and soil; vegetation classes, have nearly the same fluctuation. The maximum and minimum percentages of vegetation and soil are observed on 12th and 21th February 2016. The effect of soil and vegetation on LST should be appropriately understood for the spatial distribution of NDVI classes in Melbourne. Hence, a comparison

of LST with soil and vegetation percentages on different acquisition days is required. The comparison between the percentage of vegetation and soil of NDVI classes and the range of LST on acquisition days is provided in Table 2 in appendices. Table 2 shows the irregular alteration in the temperature range and percentage of vegetation and soil classes, which requires reviewing the BoM data for interpretations because precipitation and wind and cloud condensation could lead to a low

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temperature in some parts of cities (He, 2018; Williams et al., 2015; Jr and Cerveny, 1987; Grimmond, 2007). For instance, the effect of vegetation on temperature reduction is ineffective without rainfall. In fact, the amount of rainfall during the acquisitions dates is negligible based on the BoM online data (BoM, 2018). In terms of the effect of wind breeze on temperature, the online climatic wind speed data of BOM are considered. The historical data of average wind speeds at 9 am on Melbourne regional office for January and February from 1981 to 2010 are recorded as 9 km/h and 8 km/h, respectively. However, the wind speed recorded at Melbourne Olympic Park weather station on 14th January 2014 is 24 kph and is more than that of the historical average wind speed in January. The effect of wind speed is ineffective in reducing the temperature due to the high amount of temperature on 14th January 2014. The spatial pattern of NDBI in Melbourne presented in Fig. 5. NDBI is a commonly applied indicator to extract the built-up land from urban areas. The west and north-west parts of Melbourne are considered as areas with high NDBI values. Examples of that are Wyndham,

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Tullamarine, Melton, Whittlesea-Wallan. High NDBI values are also recorded in several other councils, such as Dandenong, BrunswickCoburg, Casey and Darebin-south, Maribyrnong, especially on 21th February 2016 and 22th January 2017. In fact, the NDBI value in newly developed areas is generally higher compared with the inner parts of Melbourne, such as Hobson Bay, Melbourne City, Stonington, and Glen Ira. Councils, such as Mornington Peninsula, Frankston, Knox, and Nillumbik, which are mostly covered with lakes, creeks, and vegetation, have low NDBI value. Moreover, the NDBI in some non-urbanized parts of the northern suburbs, such as the Macadeon range, is relatively low. The spatial patterns of built-up areas illustrate a downward trend of NDBI values on the east part and coastal areas of Melbourne. In fact, these areas are mostly covered with vegetation, which leads to low values of NDBI. Fig. 12 in the appendices shows the NDBI histograms during the acquisition dates. The highest frequency of NDBI values is mostly between −0.5 and 0. Moreover, the frequency of maximum value of NDBI during the acquisition days has a considerable fluctuation.

Fig. 5. Snapshots of NDBI in Melbourne on acquisition dates.

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In terms of spatial distribution, it is important to note that the high frequency of green areas did not always lead to reduction of LST in the entire city. Therefore, the spatial structure of built-up and green areas should be considered regardless of their high or low values.

3.3. The relationship between NDVI, NDBI, and LST The relationship between LST, NDVI, and NDBI is affected by many factors, and their relationship should be investigated. Many studies have discussed the inverse correlation between LST and NDVI (Deng et al., 2018; Faqe Ibrahim, 2017; Lo et al., 2010; Raynolds et al., 2008) and NDBI and NDVI (Ogashawara and Bastos, 2012; Grover and Singh, 2016; Chen et al., 2013). Meanwhile, several scholars have disclosed that LST and NDVI values have a positive correlation related to water bodies, such as rivers and beach. Therefore, their values appear to be low (Weng et al., 2004; Wilson et al., 2003). To understand the correlation and covariance between LST, NDBI, and NDVI, this study removes all the “Null” or “No-Value” data from all LST, NDBI, and NDVI raster layers. Subsequently, the covariance and correlation matrices are calculated based on the band collection statistics on ArcGIS spatial analyst tool. Figs. 13 and 14 in appendices represent the correlation and covariance values between LST, NDVI, and NDBI and density slice scatter plot. The correlation values between the mentioned variables during the acquisition dates demonstrate that NDBI generally has a stronger relationship with LST compared with NDVI. Each NDVI class and their correlation with LST in Fig. 14 are considered. Those values of NDVI, which are within the vegetation and water classes, have a negative correlation with LST compared with the soil class. The corresponding maximum LST values in the summer seasons from 2014 to 2018 alter at approximately 7 °C with the increase in the value of NDVI, which lead to an unstable relationship. The significant correlation between NDVI and LST occurs on 6th February 2014 and 17th February 2018 when the maximum temperature is 38 °C and the percentage of the vegetation class is higher than other classes of NDVI and close to 41%. Thus, the scatterplots of those days demonstrate that vegetation is more effective in reducing the temperature compared with the soil and water classes. Instead, the relationship between NDBI and LST is more stable. The stability of the NDBI and LST relationship may lead to the assumption that their relationship is not subject to seasonal effects. Certainly, the significant correlation (p b 0.05) between

NDBI and LST occurs on 21th February 2016, 30th January 2014, and 9th February 2015 when the mean LST is approximately 22 °C. The scatter plot in Fig. 14 shows overall positive correlation between NDBI and LST, indicating that increases in built areas leads to higher LST. Anomalies in the scatter plot between NDBI-LST are mostly related to the surface type of the buildings. For NDVI-LST scatter plots, most of the anomalies are related to consideration of water as one specific class in NDVI which makes the NDVI-LST relationship non-linear. In most of the acquisition dates, the NDBI values that are less than the maximum and higher than the minimum have a strong correlation with LST range between 20 °C to 30 °C. An example of this relationship from the spatial point of view mostly occurs in the porous texture of Melbourne. 3.4. Spatial distribution analysis 3.4.1. General trends of LST spatial distribution To analyze the general trends of LST spatial distribution, the spatial autocorrelation of LST values conducted based on Global Moran Index is applied in the spatial scale of 30 m. The outcome of the analysis is shown in Fig. 6. The analysis determines whether the LST distributions are dispersed or clustered and is based on the contiguity edges corners as a conceptualization of the spatial relationship of the LST values. In this concept, LST polygon features, which share a boundary, node or overlap, influence the computations for the target polygon feature. For the Global Moran's I statistic, the null hypothesis states that the analyzed LST is randomly distributed in Melbourne. However, the null hypothesis is rejected when the p-value returned by this index is low, and the absolute value of z-score is high and situated beyond the confidence interval. As shown in Fig. 6, Global Moran indexes for all acquisition dates are N0.9, which indicate that they are close to a cluster pattern, and b1% likelihood that the clustered pattern of LST could be the result of random chance based on the z-scores. This finding is another confirmation for rejecting the null hypothesis. The highest Moran Index is calculated on 24th January 2015, and the second highest Global Moran index is calculated on 6th February 2014. The two days are the hottest days among the acquisition days with a high value of LST range and high z-scores. Moreover, the Global Moran Index value should be between −0.000007 to −0.000002 for the spatial distribution of LST to be a normal distribution. An important thing about Global

Fig. 6. Global Moran Index of LSTs on acquisition dates.

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Moran index value is about the negativity or positivity of its value. In this case, Fig. 6 demonstrates that the Global Moran Index value for LST in Melbourne for all acquisition dates is positive. A concurrent analysis on this index with significant value (threshold) indicates that all of them are valuable at the level of a = 0.01. In conclusion, the LST in Melbourne has a spatial structure and is distributed in cluster form, which is confirmed by Fig. 6. 3.4.2. Detailed trends of LST spatial distribution An optimized hotspot analysis is applied to identify the high and low values of the clustering parts in Melbourne. The hotspot analysis statistic returned for each LST value in the dataset is a z-score. For significant positive z-scores, the larger the z-score is, the more intense the clustering of high LST values will be. For significant negative z-scores, a smaller z-score result in more intense clustering of low values. Figs. 15 and 16 in appendices and Fig. 7 presents the values, mean p-value and z-score and proportion of hot, cold, and non-significant spots of LST in Melbourne during the acquisition dates. The total proportion of hotspot based on Fig. 15 on 6th February 2014, 6th January 2017, and 7th February 2017 is significant among the acquisition dates, which ranges from 30% to 38%. Additionally, an essential relationship is observed between the high proportion of the hotspots in mentioned days and high Global Moran index values. Another essential thing to consider in Figs. 15 and 7 are the nonsignificant spots of Melbourne and their proportion compared with

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hot and cold spots. Considering the irrelevant spots on acquisition dates, a remarkable part of the city has a random LST distribution. Fig. 15 shows that the proportion of “non-significant spots” is important on 17th February 2018 and 24th January 2015, which are approximately 34% and 68%, respectively. The comparison of hot–cold spot maps indicates that the pattern of LST tends to be a cluster on the west side on most of the dates. In addition, the comparison between hot and cold spot maps shows that the LST expanded towards the north-west and south-east parts of the city. However, considerable accumulation of hotspots observed on the west compared with the east. Also, the north, east and south-east parts of Melbourne are mostly situated in the non-significant and cold spot category due to the presence of coastal areas, seaside, and mountains. Subsequent comparisons between LST and hotspot maps indicate that LST and hotspot pattern is inaccurately matched. This condition is because the LST values are not numerically close to each other mostly on the inner part of Melbourne. In summary, the hotspot analysis identifies the parts of the city where LST spatially tends to aggregate or create a cluster. 4. Discussion Understanding the factors that minimize the adverse effects of UHI is important. On this basis, considerable studies have attempted to understand the relationship and spatial distribution of LST, NDBI, and NDVI in

Fig. 7. LST hot and cold spots on acquisition dates.

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various metropolitan areas worldwide (Zhou et al., 2016; Vargo et al., 2016; Sodoudi et al., 2014; Dos Santos et al., 2017; Chang, 2016; Fujibe, 2011). The results of this study show that the LST deals with the anomalies in different days of summer from 2014 to 2018, which is consistent with the findings of (McAlpine et al., 2007; Nicholls, 2006; Haynes et al., 2018). This study considers multiple days as acquisition dates in the summer season for LST analysis. In addition, the differences are approximately between 3 and 4 °C, as shown in Fig. 17 in the appendix, by comparing the LST and air temperature acquired from the BoM weather station, which is consistent with findings of Coutts et al. (2007). The effects of NDVI and NDBI on LST and their interactions are commonly evaluated in a temporal and dynamic manner (Alhawitti and M, 2016; Chen et al., 2013; Guha et al., 2018; Rajeshwari and Mani, 2014; Sharifi et al., 2017; Nakata-Osaki et al., 2018). However, this study presents an innovative method in terms of NDVI studies. The method proposed by Avdan and Jovanovska (2016) did not consider the alterations of NDVI classes during the acquisition dates. The modified method in this study provides a better understanding of the amount of vegetation and built-up areas and their alterations by calculating the NDVI index and classes for each day individually. Specifically, this can help to identify critical areas for tree planting and to control the urban growth in local government areas, which lead to efficient UHI mitigation plans. Also, the findings from the interactions of NDBI, NDVI, and LST will impact on the policy-making process in urban design and planning projects, such as land use zone planning and designing public open spaces. Considering the Tables 2 and 1 in the appendix, maximum daytime temperature range of 31 °C was calculated on 24th January 2015 while the vegetation and soil classes covered 60% of NDVI index in Melbourne. Furthermore, the most significant increase in mean LST happened at Maribyrnong during 6th to 22th January 2017, from 19.61 °C to 27.86 °C. Meanwhile, the decrease of mean LST is considerable from 14th January to 30th January 2014 in most of the local government areas. The correlation coefficients value between built-up areas with LST obtains its most considerable amount in Tullamarine-Broadmeadows (0.6). Green areas and NDVI have a strong association with the LST at Yarra Ranges with a correlation coefficient of (−0.8). One of the limitations of this study is neglecting the exact temperature of the associated land due to the limited spatial and temporal resolutions of Landsat 8 images. Thus, this study applies the zonal statistics and considers the mean LST for each local government area. For the second part of this study, the integration of LST, NDVI, and NDBI and the consideration of the local value as well as heterogeneity of LST for its detailed spatial distribution (e.g., high or low clustering and dispersed spatial distribution pattern) lead to better steps toward proper urban development and reduce the adverse effects of UHI (Aflaki et al., 2017; Deilami et al., 2018; Salata et al., 2017; Yang and Santamouris, 2018; Mavrakou et al., 2018). One application of detailed spatial distribution analysis of LST could be found in the study of Coutts et al. (2016) that proposed urban greening for targeted thermal hotspots in local Melbourne municipalities. Ren et al. (2016) determined that the strong relationship among dominant species and elevation is prevalent in hot or cold spots areas in different years in Xiamen City, China from 1996 to 2006. Furthermore, the findings of Zhao et al. (2018) showed that the spatial patterns of LST are statistically significant in hot spot zones in the center of the study area and partly extend to the western and southern industrial areas, which indicate that the intensity of UHI has a remarkable spatial cluster in Zhengzhou City. Moreover, this analysis is important due to its application on various topics, such as environmental health (Wang and Hu, 2012), heat risk (Ho et al., 2015) energy (Tyralis et al., 2017), water (Liu et al., 2016), and their relationship to the increase or decrease in temperature.

For the Melbourne metropolitan area, Plan Melbourne document and its strategic framework advised about the incompatible development and encroachment to rural areas when their management is not comprehensive (DELWP, 2018a). Future urban growth is guided by directions, policies, and Victorian Planning scheme for local government areas. For the Plan Melbourne document, the reduction of UHI mentioned as one of the significant direction “A sustainable and resilient city” (DELWP, 2018a). By considering the results of LST, identifying the hotspots and its spatial distribution in Melbourne and minimizing the UHI effect are achievable, which are in line with Plan Melbourne directions and policies. Some of the examples are making Melbourne cool and green and reducing sprawl to maintain a permanent urban growth boundary around to create a consolidated, sustainable city (DELWP, 2018a). In addition, these results could provide critical strategies specified within the local area and city council's direction apart from other documents, such as (Block et al., 2012; DELWP, 2018b; MCC, 2016). The available RS satellite data and stable methodology in this study could be implemented in other cities around the world. Therefore, the results could specify the important guidelines, strategies, decision, and actions in terms of future development and related UHI challenges.

5. Conclusion This study analyzed the spatial distribution of LST in Melbourne using an image processing approach by applying a hot spot index. On this basis, LSTs were calculated and analyzed using the Landsat 8 images. Subsequently, NDVI and NDBI were calculated, and the results were analyzed to understand the factors that influence SUHI. The analysis indicated a high correlation between NDVI and LST. As expected, green areas and vegetation result in the decrease of LST, and the increase in the proportion of built-up areas has a negative impact on LST. SUHI in Melbourne was identified in two spatial forms, namely, focal and linear, based on detailed analysis. Focal SUHI was mainly developed in parts of Melbourne that are considered to be nonresidential land use with a compact form and residential areas that coincide with the inner and older texture of the city. Linear type mainly reflects the asphalt-coated surfaces and transport lanes. The results of spatial distribution analysis based on Global Moran's I index indicated the rejection of the hypothesis due to the lack of spatial structures for LST. In fact, the LST values have a clustering structure and distributed cluster. In summary, the high and low values of LST tend to agglomerate and create a cluster form. In addition, the clustering pattern of LST expanded toward north-west (LGAs such as Hume, Wyndham and Melton) and south-east (LGAs such as Knox, Monash and Greater Dandenong) during the period of the study. On the basis of the mapping of spatial structures of LST, the cold spots are more dominant compared with the hot spots. However, their spatial formation tendencies and their spatial ratios during each day were found to be different. The main technical contribution of this study is the modifications in the LST calculation method in terms of analyzing the NDVI value for each acquisition dates in ERDAS and ENVI Software. In comparison to previous studies, this study particularly focused on the interactions of LST-vegetation-built-up areas from a practical perspective. The spatial structure of LST and hot spot maps has provided information to target local government areas. The ultimate purpose is to assist government decision-makers in strategic planning process at a local scale. Therefore, by identifying the hot spots, decision-makers could increase urban vegetation and allocate resources in critical areas at local scale. Future studies could use the results of this study for predicting LST, NDVI, and NDBI Also, future studies can combine various methods to calculate LST, NDBI, and NDVI and make their spatial and temporal resolution better and more accurate. Furthermore, night-time SUHI and its relationship with NDBI and NDVI as well as various types of land cover could be investigated.

Y. Jamei et al. / Science of the Total Environment 659 (2019) 1335–1351

Appendices

Fig. 8. Study area with acquisition dates

Fig. 9. Statistical indicators of surface temperature in the Melbourne metropolitan area (1).

Fig. 10. Statistical indicators of surface temperature in the Melbourne metropolitan area (2).

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Table 1 Mean daytime LST of local government areas during acquisition dates. LGA's name

ID 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40

Banyule Bayside Boroondara Brimbank Brunswick - Coburg Cardinia Casey - North Casey - South Dandenong Darebin - North Darebin - South Essendon Frankston Glen Eira Hobsons Bay Keilor Kingston Knox Macedon Ranges Manningham - East Manningham - West Maribyrnong Maroondah Melbourne City Melton - Bacchus Marsh Monash Moreland - North Mornington Peninsula Nillumbik - Kinglake Port Phillip Stonnington - East Stonnington - West Sunbury Tullamarine - Broadmeadows Whitehorse - East Whitehorse - West Whittlesea - Wallan Wyndham Yarra Yarra Ranges

Mean LST on acquisition dates (centigrade degree) 14/01/2014

30/01/2014

6/02/2014

24/01/2015

9/02/2015

5/02/2016

12/02/2016

21/02/2016

6/01/2017

22/01/2017

29/01/2017

7/02/2017

16/01/2018

17/02/2018

35.38

25.67

28.58

27.46

23.96

25.74

26.12

24.18

28.66

26.81

27.74

23.22

25.42

22.15

34.32

24.96

27.06

25.42

22.62

26.18

24.90

23.69

27.56

26.61

26.49

22.69

25.08

20.91

34.57

24.72

27.52

26.85

22.38

25.39

25.57

23.32

28.49

26.89

27.17

22.86

25.22

21.30

**

**

27.98

17.45

25.42

**

27.93

**

**

**

30.02

**

27.73

25.87

34.79

26.11

26.31

19.29

23.56

26.53

26.54

24.70

29.45

28.31

28.57

23.91

26.72

23.36

31.75

**

**

**

**

**

**

**

**

24.34

**

**

**

**

33.81

25.69

**

**

**

26.16

**

24.20

26.92

26.07

**

**

**

**

33.31

26.76

**

**

**

26.31

**

25.86

27.43

25.95

**

16.17

**

**

34.62

26.32

28.22

29.10

24.00

27.23

27.32

25.53

28.75

27.14

28.13

22.31

24.30

24.21

35.09

26.54

27.08

28.26

24.68

26.45

26.93

24.88

25.43

27.89

28.66

23.84

26.53

24.44

34.49

25.71

26.58

25.44

23.24

25.89

26.21

24.07

28.68

27.45

28.04

23.42

26.03

23.32

35.32

26.25

27.41

22.74

23.89

26.92

26.61

25.05

29.11

28.59

28.56

23.99

26.63

23.07

34.14

25.47

26.91

26.56

23.32

25.96

26.34

24.29

27.26

25.34

26.72

20.57

23.15

22.26

35.05

25.80

28.48

28.04

23.03

26.80

26.20

24.29

29.15

28.03

27.92

23.28

25.98

22.40

35.46

26.56

27.81

22.58

24.04

26.75

26.32

25.07

27.61

27.57

28.16

22.76

26.63

22.75

35.49

26.88

27.33

20.53

25.14

27.53

27.65

25.87

25.87

28.57

29.54

23.63

26.83

25.44

34.49

26.23

26.62

27.80

23.86

27.07

26.59

24.99

28.47

27.25

27.90

22.90

25.00

22.99

33.62

24.32

28.06

**

22.68

25.72

25.54

23.05

27.76

26.30

26.69

22.00

24.17

24.13

**

**

**

**

**

**

**

**

**

**

**

**

**

**

33.82

23.41

27.66

20.42

22.62

23.37

23.69

22.27

26.71

23.70

25.12

21.02

22.30

22.46

35.33

25.38

28.88

24.44

23.75

25.92

26.32

24.21

28.89

26.81

27.77

23.22

25.42

21.29

35.36

26.52

27.74

21.14

24.14

27.44

26.83

25.46

19.61

28.76

28.94

23.58

26.72

23.77

34.17

24.35

28.11

24.23

23.35

24.92

25.11

22.90

28.68

26.38

26.98

23.19

24.90

24.28

34.42

25.34

25.40

21.86

22.85

26.09

25.58

24.11

28.22

27.71

27.25

23.35

26.08

21.76

**

**

28.33

26.78

28.02

**

31.40

**

**

**

30.96

**

30.00

27.77

35.12

25.39

28.82

27.66

22.97

26.46

26.24

23.90

28.85

27.63

27.76

22.84

25.41

23.65

35.41

26.91

27.60

17.57

24.91

27.39

27.49

25.68

28.49

28.46

29.13

24.15

27.08

24.63

32.19

**

**

**

**

**

**

**

**

24.50

**

**

**

**

31.20

23.32

**

**

**

21.63

**

21.56

24.83

23.00

**

18.54

**

**

34.03

25.26

25.63

21.06

22.38

25.95

25.00

23.88

27.13

26.87

26.55

22.96

25.86

21.54

34.68

25.05

28.01

27.34

22.28

25.97

25.61

23.57

28.77

27.34

27.39

22.88

25.28

21.48

34.00

24.66

25.54

26.02

21.87

25.43

24.90

22.94

27.98

26.96

26.70

22.55

25.00

21.78

**

**

26.24

21.81

28.64

**

32.64

**

**

**

30.23

**

29.57

29.49

**

**

27.26

20.08

27.02

**

30.45

**

**

**

29.46

**

27.12

27.84

35.09

24.77

28.50

22.06

22.72

25.73

26.02

23.36

28.70

27.27

27.46

22.97

25.59

24.08

34.98

24.82

28.49

25.96

22.75

25.76

25.93

23.40

28.79

27.27

27.51

22.97

25.53

21.33

34.06

26.86

24.88

23.11

24.98

26.15

27.73

26.15

27.82

26.34

28.16

21.13

24.00

23.73

**

**

28.75

29.92

26.46

**

25.31

**

**

**

29.48

**

27.68

25.80

34.20

25.19

25.75

23.94

22.72

25.59

25.71

23.74

28.76

27.28

27.54

23.12

25.67

22.44

28.64

20.70

**

**

**

20.52

**

19.64

23.82

21.11

**

17.92

**

**

**

Data not provided Maximum mean LST of LGA during acquisition dates Minimum mean LST of LGA during acquisition dates

Top 3 of mean LST on each acquisition date between LGA's Bottom 3 of mean LST on each acquisition date between LGA's

Fig. 11. Alteration of NDVI classes during acquisition dates.

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Table 2 LST range comparison regarding the vegetation and soil classes of NDVI. Variables/dates

14/01/ 2014

30/01/ 2014

6/02/ 2014

24/01/ 2015

9/02/ 2015

5/02/ 2016

12/02/ 2016

21/02/ 2016

6/01/ 2017

22/01/ 2017

29/01/ 2017

7/02/ 2017

16/01/ 2018

17/02/ 2018

NDVI (VEG AND SOIL) Related LST RANGE

66 19–45

67 18–38

66 17–38

60 09–40

65 11–37

74 13–39

63 13–42

63 12–38

69 12–39

70 13–38

65 18–40

62 7–33

73 12–38

65 7–37

Fig. 12. NDBI histograms during acquisition dates

Fig. 13. LST–NDBI–NDVI correlation and covariance value on data acquisition dates.

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Fig. 14. LST–NDVI–NDBI density slice scatter plot.

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Fig. 15. Variation of hot and cold spots during the acquisition dates.

Fig. 16. Mean p-value and z-score of hot–cold spot maps during the acquisition dates.

Fig. 17. LST and air temperature during acquisition dates based on the BoM database of Melbourne weather stations.

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