Assessment of urbanization and urban heat islands in Ho Chi Minh City, Vietnam using Landsat data

Accepted Manuscript Title: Assessment of urbanization and urban heat islands in Ho Chi Minh City, Vietnam using Landsat data Authors: Nguyen-Thanh Son, Chi-Farn Chen, Cheng-Ru Chen, Bui-Xuan Thanh, Tran Hau Vuong PII: DOI: Reference:

S2210-6707(16)30580-7 http://dx.doi.org/doi:10.1016/j.scs.2017.01.009 SCS 568

To appear in: Received date: Revised date: Accepted date:

6-11-2016 20-1-2017 20-1-2017

Please cite this article as: Son, Nguyen-Thanh., Chen, Chi-Farn., Chen, Cheng-Ru., Thanh, Bui-Xuan., & Vuong, Tran Hau., Assessment of urbanization and urban heat islands in Ho Chi Minh City, Vietnam using Landsat data.Sustainable Cities and Society http://dx.doi.org/10.1016/j.scs.2017.01.009 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Assessment of urbanization and urban heat islands in Ho Chi Minh City, Vietnam using Landsat data Nguyen-Thanh Son a,*, Chi-Farn Chena, Cheng-Ru Chen a, Bui-Xuan Thanh b,c, Tran Hau Vuong d a

Center for Space and Remote Sensing Research, National Central University, Jhongli District, Taoyuan City 32001, Taiwan Faculty of Environmental Engineering, Ho Chi Minh City University of Technology, 268 Ly Thuong Kiet, District. 10, Ho Chi Minh City, Vietnam c Institute of Research and Development, Duy Tan University, 182 Nguyen Van Linh, Da Nang City, Vietnam d Faculty of Meteorological Hydrology, Ho Chi Minh City University of Natural Resources and Environment, 236 B Le Van Sy, Tan Binh District, Ho Chi Minh City, Vietnam b

*

Corresponding author. E-mail addresses: [email protected] (N.T. Son)

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Highlights 

Study developed an approach for decadal monitoring of urbanization and UHI effects.



The city was significantly urbanized into multiple directions during 1996−2016.



There was a strong correlation between LST and newly developed index (NCDI).



LST remarkably increased during 1996−2016 owing to changes of urban landscapes.

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ABSTRACT Urbanization has a great impact on the local climate of a city, thereby triggering the urban heat island (UHI) effect and affecting the quality of human life. Information on past and present changes of urban landscapes and temperature could be of interest to urban planners in an attempt to shape the urban planning process and mitigate effects of UHI. This study aimed to develop an approach for assessing the urbanization and UHI effects in Ho Chi Minh City (HCMC), Vietnam using Landsat data during the periods 1996–2007–2016. The data were processed through four main steps: (1) data preprocessing, (2) impervious surface (IS) extraction, (3) land surface temperature (LST) retrieval, and (4) accuracy assessment. The results of IS extraction compared with the ground reference data indicated the overall accuracies and Kappa coefficients generally higher than 86.1% and 0.72, in all cases. During 1996 to 2016, the city was drastically urbanized into multiple directions, with the IS area increasing from 10,488.5 ha (1996) to 27,142.7 ha (2016). The results of retrieved LST revealed the radiant temperature for 1996 ranging from 22.4–35.8°C, while that for 2016 remarkably higher from 25.3–40.4°C. The relationship between LST and a newly developed normalized difference composite index (NDCI) was also examined to investigate the genesis of temperature due to decadal urbanization, indicating a strong correlation with the correlation coefficients (r) of higher than 0.71 (p-value <0.001), in all cases. To further understand UHI impacts due to the urbanization, the proportion of IS and averaged LST over IS areas for each district was calculated. The results showed that only few central districts located in the city higher than 30 °C in 1996, while most of districts were heavily influenced by urban surface structures with the LST higher than 31 °C in 2007 and 32 °C in 2016. Keywords: Landsat data, urbanization, urban heat island, Ho Chi Minh City.

1. Introduction Around 54% of the world’s human population lives in urban areas and the proportion is expected to increase to 70% by 2030 as urban agglomerations emerge and population migration from rural to urban or suburban areas continues (UN, 2014). Rapid population growth and aggregation of people in urban areas are creating societal issues for policymakers and the public. Over the past 60 years, the urban population in Asia had grown from 16.3% (of its total population) in 1950 to 42.5% in 2010. The urban population in this region is estimated to reach 49.9% in 2025 and 64.6% in 2050 (UN, 2014). The urbanization has not only brought benefits to people, but also triggered severe impacts on the environment at local, regional, and global scales, including loss of agricultural land (López et al., 2001), habitat destruction (Alphan, 2003; Shalaby et al., 2004), effects of urban heat island (UHI) (Shi et al., 2015; Souza et al., 2016; Voogt and Oke, 2003), and contamination of air, soil, and water (El Araby, 2002). The UHI, which is defined as the phenomenon of higher temperatures in urban areas than surrounding areas mainly attributed to changes in biophysical properties of the land surface, is one of the most critical factors affecting the quality of human life (Cao et al., 2016; Grimmond, 2007; Mirzaei, 2015; Wong and Nichol, 2013). The increase of UHI intensity triggers heat-related diseases such as heart stroke and cardiorespiratory, cardiovascular stress, thermal exhaustion, and premature deaths in the cities (Basu and Samet, 2002; Doyon et al., 2008; Kleerekoper et al., 2012; Rydin et al., 2012). Hence, assessment of UHI effects linking to the urbanization to shape urban planning strategies for mitigating negative impacts of UHI has become increasingly important for government agencies and researchers from many affected countries around the world. This phenomenon can be extrapolated to Vietnam, where the urban population noticeably grew from only 3.2 million people or 11.6% of the country’s population in 1950 to 9 million people (18.9%) in 1975 and 32 million people (33.6%) in 2015 (GSO, 2015; UN, 2014). Ho Chi Minh City (HCMC) formerly referred to as Saigon is the most populated and industrial city in Vietnam. The population of this city was approximately 8.9% of the country’s total population (91.7 million people), with the density of 3,937 people/km2 (GSO, 2015). The city, contributing more than 22% of the country’s total gross domestic product (GDP) (GSO, 2015), has remarkably expanded in both spatial extent and human population owing to the economic development and process of industrialisation following the economic reforms in 1987. Many industrial zones and residential areas have been established; thus, 3

accelerating the urbanization, economic transformation, and development. Consequently, an increasing number of people moved from rural areas to the city to search for better job opportunities and living conditions, thus triggering rapid urbanization and environmental issues. The city population had significantly increased from approximately 3.8 million people in 1986 to more than 10 million people (i.e., 8.2 million people added at least two unregistered million people from other regions) in 2015. The economic growth due to the urbanization not only improved people’s standard of living and had positive impacts on economic development, but also created environmental problems (e.g., UHI, air and water pollution, and flood hazards) and made the infrastructure overloaded. Because the city has been facing environmental problems, but relatively little has been known about negative effects of UHI associated with the urbanization, understanding of UHI effects and changes in temperature linking to the urbanization process from a spatial perspective would be crucial to the process of urban development strategies. Remote sensing technology has been widely recognized as an indispensable tool for environmental assessment and urban planning because it can be used to derive spatiotemporal data essential for modelling environmental impacts, population growth, and urbanization. Satellite-based assessment of urbanization and environmental impacts has attracted increasing interests of scientists worldwide (Minh et al., 2015; Son et al., 2012; Son et al., 2016; Tran et al., 2006; Wu et al., 2006). The Landsat data revealed advantages for a long-term monitoring of urbanization and UHI effects due to the availability of its free historical archives since the 1970s. A number of studies used Landsat data for urbanization and UHI monitoring (Buyantuyev et al., 2007; Gao et al., 2012; Lougeay et al., 1996; Lu and Weng, 2006; Y et al., 2007; Zhao et al., 2016). Supervised algorithms have been developed for mapping urban areas from Landsat data, including the maximum likelihood, support vector machines, artificial neural networks, and linear unmixing model (Dewan and Yamaguchi, 2009; Dingle Robertson and King, 2011; Kavzoglu and Colkesen, 2009; Lee and Lathrop, 2005; Lu et al., 2011; Myint, 2006; Pakhale and Gupta, 2010; Powell et al., 2007; Sangbum and Lathrop, 2006; Sun et al., 2011). The use of these supervised methods for image classification revealed disadvantages because they require much effort to construct consistent training samples for multi-year classification of urban areas owing to the fact that land-cover types are diverse and changing over time. In this study, we developed an approach using a new normalized difference composite index (NDCI) for automatically extracting IS areas from Landsat images. This index was constructed based on a definition that a mixed pixel of urban area is composed of IS, vegetation, and exposed soils; and thus the fraction of IS can be estimated from NCDI. The purpose of this study was to develop an approach for automatically mapping urban (IS) areas associated with the rapid urbanization in HCMC, and then assess impacts of such changes on the intensity and spatial pattern of UHI effects using a suit of Landsat images acquired for the periods 1996–2007–2016. The results achived from this study could be useful for urban planners and policymakers to devise proper plannning strategies for assessing environemental issues associated with the urbanization and UHI effects in the city. 3. Study area HCMC is the largest city in Vietnam, covering approximately 2,095 km2 and lying between 10°10’– 10°38’N and 106°2’–106°54’E (Fig. 1). The population was approximately 8.2 million people in 2015 with a density of 3,809 persons/km2 (GSO, 2015). The real population was assumed to add approximately two million additional people who are unregistered to the city. The city has urbanized at unprecedented rates after the launch of a reformed economic policy in 1987. From 1986 to 2015, the city’s population was more than doubled from approximately 3.8 to 8.2 million people (GSO, 2015). The transformation of agricultural land to built-up land increased the total urban area in the city from approximately 142 to 494 km2 during 1997 to 2008 (Du and Fukushima, 2009; Storch and Downes, 4

2011). Over the last decades, the city has been remarkably urbanized into multiple directions linking with neighboring centers of other provinces and the population is expected to reach more than 10 million people by 2020 to become a mega-urban city. The rapid urbanization without a proper urban planning has created severe impacts on the environment including UHI effects due to the sealing of surfaces and increasing building volumes with less ventilation (Van, 2008). The city’s annual and summer mean temperature had increased at least 0.5 °C during 1996–2000 (Fig. 2). The increase in temperature has triggered deterioration of air and water quality, consequently affecting human health and lessening the liveability of the city. Insert Figs. 1 and 2 here

4. Data collection 4.1 Satellite data A suit of Landsat images used in this study was acquired from the U.S. Geological Survey, including two Landsat TM images (21 February 1996 and 3 February 2007), and a Landsat OLI image (28 February 2016). The Landsat TM data have seven spectral bands, with a spatial resolution of 30 m for bands 1−5 and 7. The TM band 6 (thermal infrared) is acquired at 120 m resolution, but is resampled to 30 m pixels. The Landsat OLI data have nine spectral bands with a spatial resolution of 30 m for bands 1−7 and 9, 1 panchromatic band with spatial resolution of 15 m for band 8, and 2 thermal infrared bands with a spatial resolution of 100 m for bands 10 and 11. In this study, six spectral bands (i.e., bands 1–5 and 7 for Landsat TM, and bands 2–7 for Landsat OLI from the surface reflectance climate data record product) for each set of data were used for developing an approach for IS mapping, while the thermal bands (band 6 for Landsat TM and band 10 for Landsat OLI) were used for derivation of LST used for the UHI assessment. The spectral bands are generally between the visible and short-wavelength-infrared regions, except for band 9, which has a cirrus wavelength between 1.36 and 1.38 µm. The Landsat TM and Landsat OLI data have been geometrically and radiometically corrected to account for positional errors and atmospheric inferences (Feng et al., 2013; Masek et al., 2006; Min et al., 2012; Roy et al., 2014). The 2000 and 2006 land-use/cover (LUC) maps collected from the Vietnam Academy of Science and Technology (VAST) were used as reference data for preparing the ground reference data used for accuracy assessment of the IS mapping results (Fig. 3). Multiyear population data of the study area were also collected and used for analysing drivers of the urbanization process. Other ancillary datasets such as road and river networks and administrative boundaries were collected and used as reference layers for geometric corrections of the satellite images. Insert Fig. 3 here

5. Methods 5.1 Data pre-processing The data pre-processing was carried out to account for geometric distortions and atmospheric effects. The geometric correction for Landsat TM images using Landsat OLI (28 February 2016) as a reference image was processed using at least 20 ground control points (GCPs) selected from distinct features throughout the study region. The results of geometric corrections yielded the root mean squared error (RMSE) value smaller than half of the size of a Landsat pixel. The data were then registered to the Universal Transverse Mercator system (zone 48N) and subset over the study region. 5

The Landsat TM and OLI images have been radiometrically corrected to the level-1 standard. The data products stored as digital numbers (DNs) were converted to the reflectance by multiplying with a scaling factor of 0.0001. For Landsat OLI data, the DN values were converted to reflectance (   ) using rescaling coefficients provided in the product metadata file, calculated using the following equation:

  

' , sin  SE

(1)

where ' is the TOA planetary reflectance, with correction for solar angle, and  SE is the local sun elevation angle. We also performed the reflectance normalization for Landsat TM images using the Landsat OLI image as a reference base. The histogram matching algorithm was applied to make the distribution of brightness values as close as possible to the reference image (Richards and Jia, 2006). Details about this method can be found in the text of Remote Sensing Digital Image Analysis (Richards and Jia, 2006). 5.2 Impervious surface extraction An urban area is basically composed of three components: IS, vegetation, and exposed soils, while ignoring water (Ridd, 1995). The spectral profiles derived from averaging pixel samples of the 2016 Landsat OLI data for each component were illustrated in Fig. 4. In general, these spectral profiles showed the separability between the components. The profile of IS showing the range of DN values in spectral bands was especially differentiated from those of vegetation and exposed soils in the visible-toNIR and SWIR regions. The spectral values of vegetation were distinctly higher than those of IS and exposed soils in the NIR region. The profile of exposed soils showed apparent differences among other components for all spectral bands. Overall, the pixel samples extracted from the Landsat OLI image indicated that these components appeared distinct and thus suggested that they would be suitable for classification. In this study, the permanent water bodies were masked out from the analysis using the normalized difference water index (NDWI) (Gao, 1996; Jackson et al., 2004) if its value was greater than 0.2. This process was to limit our analysis to IS-related areas. Because water is also an important component as it is retained in soils and plants, the moisture also has an effect on the spectral properties of satellite data. Thus, three indices, normalized difference built-up index (NDBI) (Zha et al., 2003), soil adjusted vegetation index (SAVI) (Huete, 1988), and NDWI associated with these components (i.e., IS, vegetation, and exposed soils) were taken into account for developing a new composite index (NDCI) to map IS areas in the study region, calculated as follows:

NDCI 

( fNDBI  fNDWI ) / 2  fSAVI ( fNDBI  fNDWI ) / 2  fSAVI

(2)

where NDBI, SAVI, and NDWI were calculated using the following equations: SWIR  NIR SWIR  NIR

(3)

( NIR  RED )(1  L) ( NIR  RED  L)

(4)

NDBI 

SAVI 

6

GREEN  SWIR (5) GREEN  SWIR where NIR is near infrared band (i.e., Landsat TM band 4 and Landsat OLI band 5), SWIR is short-wave near-infrared band (i.e., Landsat TM band 5 and Landsat OLI band 6), and GREEN is band 2 (Landsat TM and band 3 Landsat OLI). The NDWI is highly correlated with moisture content in the soil and the vegetation canopy (Gao, 1996; Jackson et al., 2004), while the NDBI is sensitive to IS typically having higher reflectance in the SWIR region, compared to that of the NIR region (Zha et al., 2003). The SAVI is structured similar to the normalized difference vegetation index (NDVI), but with the addition of a soil brightness correction factor to account for first-order, nonlinear, differential NIR and red radiative transfer through a canopy; thus overcoming the soil ―noise‖ inherent in NDVI as well as minimizing soil brightness variations and eliminating the need for additional calibration for different soils (Huete and Liu, 1994; Miura et al., 2000). The NDCI developed based on the composition of these indices could be used to delineate IS-related and non-IS pixels in the region. Because the size of a Landsat pixel (30×30 m) was generally larger than that of a typical house (4×14 m) in residential areas of the study area, we assumed that an urban pixel of Landsat image was generally a mixture of IS, vegetation, and water in terms of soil moisture. Thus, the fraction of this pixel derived by scaling NDCI could be estimated using the following equation (Carlson and Ripley, 1997; Gutman and Ignatov, 1998). NDWI 

 NDCI  NDCI min  fNDCI     NDCI max  NDCI min 

2

(6)

where and NDCImax is the pixels with the highest NDCI of built-up area, and NDCImin is the pixels with the lowest NDCI of vegetation. Insert Fig. 4 here

The fNDCI image has values from 0−1, determining the abundance fractions of IS materials in each pixel. A hardening process using a threshold value was then applied to the image to convert a mixed pixel to a pure pixel with respect to the two desired classes of IS and non-IS using the fuzzy c-mean clustering algorithm (Bezdek, 1981; Dunn, 1973). This method assigns the membership to each pixel corresponding to a cluster center based on the distance between clusters and the pixel through an updating iteration process, for threshold derivation. The threshold values for the hardening processing achieved for 1996, 2007, and 2016 were 0.517, 0.383, and 0.510, respectively. 5.3 Land surface temperature retrieval The Landsat TM TIR (band 6) and Landsat OLI TIR (band 10) data were used to retrieve LST for the study region. The DNs of Landsat TIR data was converted to at-sensor radiance, using the following equation: L 

( Lmax  Lmin )  ( DN  QCALmin )  Lmin , (QCALmax  QCALmin )

(7)

where Lλ is at-sensor spectral radiance (W*m−2 sr−1 μm−1); Lmin is the TOA radiances for band 6 (Landsat TM) and band 10 (Landsat OLI) at DNs; QCALmax = 255 and QCALmin = 0 are the maximum and minimum quantized calibrated values, respectively; and Lmax and Lmin are the spectral radiance scaled to QCALmax and QCALmin (W*m−2 sr−1 μm−1), respectively. The data were subsequently converted to at-satellite brightness temperature as follows: 7

TB 

K2  k   ln  1  1  L 

(8)

where TB is the brightness temperature (in Kelvin), and K1 and K2 are coefficients determined by effective wavelength of a satellite sensor, and ε is emissivity (typically 0.95). The brightness temperature values obtained above are referenced to a black body whose properties are relatively different from those of real objects. Thus, corrections for spectral emissivity (ε) according to the nature of the land cover are necessary. The emissivity-corrected land surface temperatures (Ts) were computed as follows: Ts 

TB  273.15 (1    TB /  )  ln 

(9)

where Ts is the LST (in Celsius); λ is the wavelength of emitted radiance, which is 11.5 μm (Landsat TM) (Markham and Barker, 1985) and 10.9 μm (Landsat OLI); α = h*c/b (1.438×10−2 mK); h is Planck’s constant (6.626 × 10−34 Js); c is the velocity of light (2.998 × 108 m/s), and b is Boltzmann’s constant (1.38 × 10−23 J/K), and ε is surface emissivity that can be alternatively estimated using the NDVI fraction method because this index is correlated with vegetation cover (Van De Griend and Owe, 1993). The surface emissivity (ε) for Landsat TM and OLI was thus derived the following empirical equations (10) and (11) (Sobrino et al., 2004), respectively.

 TM  0.004  Pv  0.986

(10)

 OLI  0.00149  Pv  0.986481

(11)

where Pv is the vegetation fraction (Carlson and Ripley, 1997) obtained using the following equation:  NDVI  NDVI min  Pv     NDVI max  NDVI min 

2

(12)

where NDVImax = 0.5 and NDVImin = 0.2. 5.4 Accuracy assessment The majority filter (using a 3×3 moving window size) was first applied to eliminate ―salt-andpepper‖ effects in the classification maps obtained from extracting IS areas for 1996, 2007, and 2016. The mapping results were then assessed in a way that 4,000 (i.e., 2,000 pixels for each class of IS and non-IS) randomly extracted from the ground reference data (Fig. 3) were compared with those pixels synchronized from the classification maps. The Kappa coefficient and other parameters, including overall accuracy, ommission error, and commission error were calculated to measure the overall and perclass mapping accuracy. 6. Results and discussion 6.1 Accuracy of impervious surface mapping The results of achieved from the classification of Landsat images indicated that IS areas were more concentrated in the city center in 1996 (Fig. 5a), but remarkably expanded into different directions in 2007 and 2016 (Figs. 5b and c). The classification results were assessed using the ground reference data 8

(Fig. 3). A total of 4,000 reference pixels (i.e., 2,000 pixels for each class of IS and non-IS) randomly extracted from the ground reference map were compared with those pixels synchronized from the classification maps. The comparison results revealed a good consistency between these two datasets (Table 1). The overall accuracies achieved for the 1996, 2007, and 2016 data were 86.1%, 93.6%, and 90.2%, respectively. The Kappa coefficient, which measures the difference between the actual agreement and the agreement expected by chance, also confirmed strong agreement between these two datasets with the values of 0.72 for 1996, 0.87 for 2007, and 0.8 for 2016, respectively. In general, the mapping results achieved for 1996 were slightly less accurate than those obtained for 2007 and 2016. Of 4,000 reference pixels used to check the per-class mapping accuracy, the larger omission error (i.e., pixels that belong to the truth class but fail to be classified into the proper class) was observed for 1996 (27.5%) compared to the error values for 2007 (11%), and 2016 (9.7%), respectively. The IS areas in 1996 were relatively scattered across the study region and generally mixed with especially bare land or exposed soils, dry vegetation, and cropland without green crops during the fallow period. This could be particularly observed in the southern and south-western parts of the study region. The spectral confusion and mixed-pixel problems caused difficulties to accurately map IS areas, and thus led to the increased mapping error in the classification assessment results. The larger commission error (i.e., pixels that belong to another class but are labelled as belonging to the class) was especially observed for 2016, mainly attributed to extensive LUC changes in the region due to rapid urbanization. The IS areas newly constructed houses and infrastructures were generally fragmented and spatially scattered throughout the study region, causing spectral confusion in separating IS areas from other LUC types, especially in areas occupied by dry vegetation and agricultural land, subsequently contributing to the lower results of the mapping accuracy assessment. Insert Fig. 5 here Insert Table 1 here

6.2 Spatiotemporal urbanization during periods 1996–2007–2016 The IS classification maps for 1996, 2007, and 2016 were compared to examine the decadal urbanization in the study region during this 20-year period. The results indicated that the area of IS in 1996 was approximately 10,488.5 ha, mostly concentrated in the city center; but it had been remarkably increased to approximately 22,346.5 ha in 2007, and 27,142.7 ha in 2016, respectively (Figs. 6 and 7). The city had been drastically urbanized into multiple directions in 2007 and 2016, especially to the north, northeast, and west territories of the city, owing to advantages of infrastructures and the areas planned for residential, industrial, and commercial zones located near main roads. These expansions linked HCMC with its neighboring centers of Bien Hoa City of Dong Nai province and Thu Dau Mot City of Binh Duong province to form new satellite urban centers. This linkage was one of the most important driving factors for socioeconomic development in the region. The rapid urbanization in the region was generally driven by several factors, including economic development and rapid population growth. During the last 15 years, the city’s total nominal GDP had increased more than 10-fold, from approximately US$ 3.4 billion in 2000 to US$ 44.3 billion in 2015 (GSO, 2015). This achievement was reflected by a remarkable increase of the city population from approximately 3.5 million people in 1996 to 5.6 million people in 2007 and 8.2 million people in 2015 (GSO, 2015). The real population was assumed to add at least two million additional people unregistered to the city commonly owing to the migration of people from other regions to the city with respect to searching for job opportunities to improve the quality of life. Insert Figs. 6 and 7 here

9

6.2 Relationship between LST and NDCI The results of LST retrieval from Landsat data were categorized into eight classes using the natural break (Jenks) method to characterized spatial distributions and temporal changes of LST in the study region during the periods 1996–2007–2016 (Fig. 8). The radiant temperature in 1996 ranged from 22.4– 35.8°C, while the values were remarkably higher in 2007 (23.8–39.8°C) and 2016 (25.3–40.4°C). These results showed an increasing trend consistent with the annual and summer temperatures measured at the metrological station of Tan Son Hoa during the last 20 years from 1996 to 2016 (Fig. 2). In general, the LST values were relatively higher in the built-up areas than in the nearby suburbs. Hotspots of UHI were apparently more intensive in the central part of the city, where dense residential areas were constructed. There were also many smaller hotpots of UHI along main roads connected to the city center. In 1996, the LST in most of the study region was lower than 30.5°C. Only 4.5% and around 0.8% of the region revealed the LST values in ranges of 30.5–31.7°C and 30.5–40.4°C, respectively (Table 2). The proportions of LST in 2007 had significantly increased to 24.9% (30.5–31.7°C), 16% (31.7–33.1 °C), and around 6.4% (33.1–40.4°C), respectively. Obviously, the most intensive LST was observed for 2016, with the LST proportions of 11.9% (30.5–31.7°C), 28.6% (31.7–33.1 °C), 23.2% (33.1–34.6°C), and 4.9% (34.6–40.4°C), respectively. Because the unavailability of clear-sky Landsat images, this study used images acquired at slightly different dates during the summer season (i.e., 21 February 1996, 3 February 2007, and 28 February 2016) to investigate LST. The time difference between these images might cause some biases when comparing LST proportions between these dates. Although the most influential factor controlling UHI effects was the distribution of urban surface characteristics (e.g., building materials, geometry, and density) that exhibited a unique radiative, thermal, moisture, and aerodynamic properties, and related to their surrounding site environment, the urbanization had clearly effected on the heat energy balance in the study region. The rapid urban development brought up higher LST values by replacing natural environment (e.g., forests, water, and agricultural land) with nonevaporating, non-transpiring surfaces, such as stone, metal, and concrete. To further understand the mechanism for the genesis of UHI effects and changes in climate due to decadal urbanization, the relationship between LST and IS properties was investigated through the pixelby-pixel correlation analysis. The basis underlying this LST–NBCI analysis was that the NBCI representing the amount of IS generally inferred built-up area conditions, while the LST has been documented to correlate closely with UHI effects. Because the urbanization considered as a root cause of LUC changes generally caused the increased spatial variability of LST, the NDCI was thus assumed to show a strongly positive correlation with LST, indicating that the more IS abundance a land cover has, the higher LST value was. The results of NDCI–LST regression analysis indicated close correlation between these two datasets. The correlation coefficients (r) achieved for 1996, 2007, and 2016 were respectively 0.74 (F-statistic = 23,691.3), 0.71 (F-statistic = 24,889.3), and 0.74 (F-statistic = 25,384.6) with p-value <0.001, indicating that the relationship was significant at 95% confidence limit (Fig. 9). The Durbin-Watson statistics were smaller than 2, in all cases, confirming that there was an autocorrelation in the residuals or no significant correlation due to sequence of variable input in the analysis. It was thus suggested that the NDCI was a good indicator of surface radiant temperatures and could be for urbanization and UHI monitoring studies. Insert Figs. 8 and 9 here

6.3 Relating LST to impervious surface at district level To improve understanding of UHI impacts due to the rapid urbanization in the study region, we calculated the proportion of IS and averaged LST over IS areas for each district. Obviously, the LST 10

values in 1996 for each district corresponding to the proportion of IS were generally lower than 30 °C (Fig. 10). There were only few districts, for example, 4, 6, 11, and Tan Binh, exhibiting the LST higher than 30 °C. Because of the rapid urbanization by converting vegetated and agricultural areas to IS materials (e.g., concrete, tars, stone, and asphalt) reflected by the increased proportion of IS within each district in 2007 and 2016, the values of LST values reached higher than 32 °C for 2007 and 33 °C for 2016, respectively. Many districts in the central part of the city (e.g., 4, 6, 11, Tan Binh, Tan Phu, and Go Vap) revealed the LST higher than 31 °C in 2007. The city was heavily influenced by urban surface structures after 9-year development from 2007 to 2016. Most of districts in the region had the LST higher than 32 °C in 2016. Although urban temperature changes were driven by a number of factors, including changes in physical characteristics of the surface mainly attributed to the conversion of vegetation to built-up areas, decrease of surface moisture, changes in the radiative fluxes due to the geometry of streets and buildings, and anthropogenic heat, and anthropogenic heat emissions (Dousset and Gourmelon, 2003), changing characteristics of urban landscapes due to the urbanization was the most significant factor, causing the local air and surface temperatures to rise several degrees higher than the simultaneous temperatures of the surrounding areas (Streutker, 2002; Streutker, 2003). As the current population of the city was 8.2 million (population density of 3,937/km2 and growing at a rate of 1.1%) added at least two million people unregistered to the city (GSO, 2015) and the city was still underurbanized at a fast rate, the elevated temperatures from UHI effects particularly during the summer could affect the city community’s environment and quality of life. Geographic understanding of past and future changes due to urbanization could be of interest to city planners, who could shape the process of sustainable urban planning in an attempt to monitor population density and mitigate the effects of UHI. Insert Fig. 10 here

7. Conclusions In this study, we investigated the urbanization and UHI effects using a suit of Landsat images acquired for 1996, 2007, and 2016. The research findings confirmed the validity of our approach for automatically delineating spatiotemporal urbanization in HCMC from Landsat data to produce satisfactory results. The IS mapping results compared with the ground reference data revealed the overall accuracies and Kappa coefficients generally higher than 86.1% and 0.72, respectively. During 1996 to 2016, the city had been drastically urbanized into multiple directions, with the urban areas increasing from approximately 10,488.5 ha in 1996 to 22,346.5 ha in 2007, and 27,142.7 ha in 2016, respectively, mainly attributed to the increasing population and demands for economic development. The LST retrieved from Landsat data also indicated the increasing LST from 22.4–35.8 °C in 1996 to 23.8–39.8 °C in 2007, and 25.3–40.4°C in 2016 due to the urbanization commonly owing to LUC changes in the urban landscape. There were a strong correlation between LST and NCDI, with the correlation coefficients (r) higher than 0.71 (p-value <0.001), in all cases. When examining the percentage of IS and the mean LST over IS areas for each district to improve the geographic understanding of UHI impacts due to the urbanization process, there were only few central districts exhibiting the LST higher than 30 °C in 1996, while most of districts had been heavily influenced by urban surface structures with the LST higher than 31 °C in 2007 and 32 °C in 2016. It was evidenced that that urban development was contributing to the formation of UHI. Interventions of implementing green building codes and considering natural ventilation in city planning were thus recommended to reduce heat stress. The methods used in this study could provide quantitatively spatiotemporal information of urbanization and UHI in HCMC during the periods 1996–2007–2016, which was useful for urban planners with to form strategies to address environmental issues associated with urbanization and UHI effects. The proposed methods can thus be transferable to other cities around the world for urbanization and UHI monitoring. 11

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Fig. 1. Map showing the location of the study area with reference to the administrative boundaries of districts and geography of HCMC. Landsat OLI (28 February 2016) showing the false-color composite (RGB = bands 5, 4, 3).

Fig. 2. Annual and summer temperature (January–April) at Tan Son Hoa station collected from HCMC department of meteorology showed the increasing trends of temperature during 1996 to 2015.

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Fig. 3. Land-use/cover map of the study area. Ground reference pixels (dots) were used for accuracy assessment of the urban mapping results.

Fig. 4 Mean spectral profiles of three components of IS, vegetation, and exposed soils derived from averaging pixel samples selected from the 2016 Landsat OLI data. The standard deviations were also plotted.

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(a)

(b)

(c)

Fig. 5. Spatial distributions of IS areas in the study area: (a) 1996, (b) 2007, and (c) 2016.

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Fig. 6. Area of IS for 1996, 2007, and 2016.

Fig. 7. Results of change detection between 1996 and 2016.

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(a)

(b)

(c)

Fig. 8. Spatial distributions of LST retrieved from Landsat data: (a) 1996, (b) 2007, and (c) 2016.

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(a)

(b)

F-statistic = 23,691.3 y = 11x + 25 p-value < 0.01 DW statistic = 1.5 r = 0.74

(c)

F-statistic = 25,384.6 y = 8.3x + 27 p-value < 0.01 r = 0.74 DW statistic = 1.6

Fig. 9. Correlation between NDCI and LST: (a) 1996, (b) 2007, and (c) 2016.

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F-statistic = 24,889.3 y = 12x + 27 p-value < 0.01 DW statistic = 1.3 r = 0.71

Fig. 10. Percentage of impervious surface in each district and LST calculated for IS within each district. Table 1. Results of accuracy assessment of the IS mapping results for 1996, 2007, and 2016. Mapping accuracy (%)

1996 27.5 0.5 86.1 0.72

Omission error Commission error Overall accuracy Kappa coefficient

Year 2007 11.0 1.9 93.6 0.87

2016 9.7 9.9 90.2 0.80

Table 2. Proportion of LST corresponding to each LST interval categorized based on the natural breaks for 1996, 2007, and 2016. LST (°C) 22.4–26.5 26.5–27.6 27.6–29.1 29.1–30.5 30.5–31.7 31.7–33.1 33.1–34.6 34.6–40.4

1996 Area (ha) 27,814.0 8,321.8 11,785.2 9,167.7 2,717.6 432.9 41.22 8.64

% 46.1 13.8 19.5 15.2 4.5 0.7 0.1 0.0

2007 Area (ha) 8,998.5 6,686.7 7,317.1 8,728.4 15,015.3 9,658.5 3,147.3 7,37.2

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% 14.9 11.1 12.1 14.5 24.9 16.0 5.2 1.2

2016 Area (ha) 1,760.8 4,356.6 7,108.2 5,704.4 7,152.8 17257.1 13971.4 2977.7

% 2.9 7.2 11.8 9.5 11.9 28.6 23.2 4.9