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© 2020 by the authors; licensee Asian Online Journal Publishing Group 

Asian Review of Environmental and Earth Sciences 
Vol. 7, No. 1, 61-66, 2020 

ISSN(E) 2313-8173 / ISSN(P) 2518-0134 
DOI: 10.20448/journal.506.2020.71.61.66 

© 2020 by the authors; licensee Asian Online Journal Publishing Group 

  
 

 
 
 
Spatio-temporal Analysis of Urban Built-up Land in the Hanoi Metropolitan Area 
(Vietnam) using Remotely Sensed Images 

 
Thanh Tien Nguyen   

 

 
 
Faculty of Surveying, Mapping and Geographic Information, Hanoi University of Natural Resources and 
Environment, Hanoi, Vietnam. 

 

 
Abstract 

Rapid and unplanned urbanization leads to temperature rise, urban vegetation decrease, and built-
up land increase, forming an urban heat island. It is, therefore, the change of built-up land plays an 
important role in surface urban heat island studies. This study aims to analyze spatio-temporal 
changes of urban built-up land in the Hanoi Metropolitan Area (HMA), Vietnam, using Landsat 
remotely sensed images acquired in 1996 and 2016. Landsat time-series images were first pre-
processed preprocessed to account for sensor, solar, atmospheric, and topographic effects. Urban 
built-up land was then extracted based on an NDBI based continuous built-up index (BUc). Spatio-
temporal changes of built-up land were finally analyzed by means of Geographic Information 
System (GIS). It was found that the urban built-up land area had increased from 4063.1 hectares in 
1996 to 7163.2 hectares in 2016 which account for 13.3% and 23.4% of the total area, respectively. 
The built-up land area had increased by about 10.1% of the total area in 20 years. On average, 0.5% 
of the urban built-up area increases each year. The urban built-up land tends to expand to the west, 
southwest, and south of the HMA. These findings demonstrate the effectiveness of the proposed 

method for spatio-temporal analysis of built-up land in urban areas using remotely sensed images. 
 
Keywords: Spatio-temporal analysis, Change detection, Urban built-up land, remotely sensed data, Landsat time-series images, Urban heat 
islands; Hanoi metropolitan area (Vietnam). 

 
Citation | Thanh Tien Nguyen (2020). Spatio-temporal Analysis of 
Urban Built-up Land in the Hanoi Metropolitan Area (Vietnam) 
using Remotely Sensed Images. Asian Review of Environmental and 
Earth Sciences, 7(1): 61-66. 
History:  
Received: 17 February 2020 
Revised: 20 March 2020 
Accepted: 22 April 2020 
Published: 25 May 2020 
Licensed: This work is licensed under a Creative Commons 

Attribution 3.0 License  
Publisher:  Asian Online Journal Publishing Group 
 

Funding: This study received no specific financial support. 
Competing Interests: The author declares that there are no conflicts of interests 
regarding the publication of this paper. 
Transparency: The author confirms that the manuscript is an honest, accurate, and 
transparent account of the study was reported; that no vital features of the study have 
been omitted; and that any discrepancies from the study as planned have been 
explained. 
Ethical: This study follows all ethical practices during writing.   

 

 

Contents 
1. Introduction ...................................................................................................................................................................................... 62 
2. Study Area and Data Used ............................................................................................................................................................. 62 
3. Methodology ..................................................................................................................................................................................... 63 
4. Results and Discussion.................................................................................................................................................................... 63 
5. Conclusions ........................................................................................................................................................................................ 65 
References .............................................................................................................................................................................................. 66 
 

 
 
 
 
 
 
 
 
 
 

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Contribution of this paper to the literature 
This study provides the scientific basis for the spatio-temporal analysis of urban built-up land in 
urban areas through Landsat time-series images. In addition, this research can contribute a 
baseline for further empirical researches in the area of urban heat island studies in the Hanoi 
Metropolitan Area, Vietnam. 

 
1. Introduction  

Land covers in urban areas tend to change more drastically over a short period of time than elsewhere because of 
incessant urbanization [1]. Rapid development in urban areas has brought land cover change, especially built-up 
land urban in areas in such a developing country as Vietnam.  The replacement of vegetation cover with urban built-
up land caused by urbanization has led to negative environmental repercussions to urban areas [2], such as reduced 
precipitation [3, 4] more dryness [2] and warmer temperatures [5] which contribute greatly to atmospheric and 
urban heat islands. Built-up land in urban areas has been considered as an effective indicator of urban development 
and environmental quality. Recent studies have also shown that the increase of built-up land has resulted in serious 
negative effects on humans and environments such as the weakening of living environments and an increased 
mortality rate [6] the adverse health effects [7] and the worsening local weather and climate [8]. It is, therefore, 
the spatio-temporal analysis of urban built-up land change plays an important role in urban environmental studies.  

Traditionally, manual interpretation of aerial photography or field surveys have been commonly used for built-up 
land change detection [9, 10]. The main advantage of these conventional methods is that built-up land could be 
captured with a high accuracy [11] but they were nearly impossible to gather enough data over a large area [12, 13] 
especially for such big urban areas as megacities. The occurrences of remote sensing technology now have overcome 
most of these limitations of conventional methods. Digital remotely sensed images allow for mapping urban areas 
and detecting land-use change in a more timely, cheaper and more accurate manner, especially when using the most 
recent high-resolution satellite imagery [11]. Since remotely sensed imagery of high-resolution satellite sensors 
such as Landsat-1, 2, 3 MSS, -5 TM, -7 ETM+  and -8 OLI  started to become available, built-up land change 
detection and monitoring from space became easier. A commonly used approach for the definition of built-up land 
areas is conventional multi-spectral classification [2]. However, due to spectral confusion of the heterogeneous built-
up land class, Xu [2] concluded that this method may not produce satisfactory accuracy. Therefore, many indices 
were proposed to discriminate built-up land from non-built-up land in urban areas such as the Normalised Difference 
Vegetation Index (NDVI) [14] Normalised Difference Built-Up Index (NDBI) [1], Index-based Built-Up Index 
(IBI) [2], Urban Index (UI) [15], Enhanced Built-Up and Bareness Index (EBBI) [16], vegetation index built-up 
index (VIBI) [17], a NDBI based continuous built-up index (BUc) [18], and most recently, dry built-up index (DBI) 
[19]. The reviewed literature on spectral indices leads [19] to conclude that these indices have not entirely 
successfully addressed the confusion between impervious surfaces and bare soil. Commonly used NDBI and UI 
indices are unable to verify the distribution of built-up versus bare land areas As-syakur, et al. [16]. He, et al. [18] 
indicated that BUc is considered the most effective index for mapping built-up land. Hence, by employing the BUc, 
this study focusses on detecting the spatio-temporal change of urban built-up land in the Hanoi Metropolitan Area 
(Vietnam). 
 

2. Study Area and Data Used 
 

 
Figure-1. Study area of the HMA: 6-5-4 false color composite of Landsat-8 OLI images. 

             

The HMA is located in the center of Hanoi capital of Vietnam. Its geographic location extends latitudinally from 
20°54'34.14" to 21°6'22.69", and longitudinally from 105°42'16.97" to 105°56'21.48" Figure 1 covering an area of 
about 274 km2. Hanoi, one of the largest cities in Vietnam with an estimated population of 8.05 million and a 
population density of 2,300 people for every square kilometer in 2019, undergoes a rapid urbanization, especially 
after the Doi Moi Policy in 1986. Rapid urbanization has caused vegetation to be replaced by built-up land 
dramatically [20]. Landsat-5 and 8 time-series images (path 127, row 045/046) with 30-m spatial resolution of 



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distributed by the U.S. Geological Survey were used in this study. These images were acquired on 10 June 1996 and 
on 16 May 2016. The quality of all Landsat images was rather good (9/9) with no cloud cover over in the HMA. 
Landsat image processing was carried out in the Environment for Visualizing Images software: ENVI version 5.2. 
Maps were produced by ArcGIS 10.2 software [21].   

 

3. Methodology 
3.1. Remotely Sensed Image Pre-Processing 

The pre-processing of Landsat images includes two main steps. The conversion of the digital number data (    ) 

to top of atmosphere (ToA or at-sensor) radiance (      ) is first performed using calibration parameters provided 

in the metadata file. The     -to-       conversion for Landsat-5 TM image  and Landsat-8 OLI image [22] are 
conducted by means of Equations 1 and 2 respectively: 

       (
             

                 
)                         (1) 

where        is at-sensor spectral radiance [W/        ] at the wavelength   (µm);      is digital number 

values;          and          are minimum and maximum digital number corresponding to                  , 

respectively;        and        are at-sensor spectral radiance [W/        ]. 

                 (2) 

where        is at-sensor spectral radiance [W/(m2·sr·µm)] at the wavelength   (µm);    and    are the radiance 

multiplicative scaling factor and the radiance additive scaling factor for the bands, respectively; and      is the digital 
number.  

After the     -to-       conversion, atmospheric effects for Landsat-5 and 8 reflective bands is then carried out 
using FLAASH algorithm proposed by Adler-Golden, et al. [23]. The spectral radiance of Landsat bands in 
FLAASH model is identified based on the Equation 3  [23]:  

   










 S

A

e



1















S

B

e

e





1
  
    (3) 

where   is the surface reflectance of a pixel; e  is the average surface reflectance for the pixel and surrounding 

pixels; S is the spherical albedo of the atmosphere;   
  is the radiance back-scattered by the atmosphere; A and B are 

coefficients depending on atmospheric and geometric conditions. The values of A, B, S, and   
  are obtained based on 

MODTRAN4 model [24]. After the water retrieval, the spatially averaged reflectance e is identified using the 

following equation [23, 24]: 

   














S

BA

e

e





1

)(
  
    (4) 

where    is spatially averaged reflectance; A, B, S   
  and e  are defined as in Equation 3. 

 
3.2. Built-Up Land Extration from Landsat Images 

The NDVI is considered as an indication of the presence of vegetation and amount or condition of vegetation on 
pixel basis [6, 25], whereas, the NDBI is an index for mapping built-up land [24]. In this study, the continuous 
built-up index (BUc) proposed by He, et al. [18] which is based on NDVI and NDBI indices was employed to extract 
urban built-up land. These indices are estimated from Landsat bands using Equations 5, 6 and 7, respectively: 

     
         

         
 (5) 

     
         

         
 (6) 

              (7) 

where     ,     , and      are the ground reflectance of Landsat-5 TM band 3 of and Landsat-8 OLI band 4, 
of Landsat-5 TM band 4 and Landsat-8 OLI band 5, and of Landsat-5 TM band 5  and Landsat-8 OLI band 6, 
respectively. The BUc is the resultant binary image with only the built-up and barren pixels having positive value, 
thus allowing built-up areas to be mapped automatically [1]. 
 

3.3. Spatio-Temporal Analysis of Built-Up Land Changes 
After urban built-up land is extracted from Landsat-5 and -8 images at two-time points, the change of urban 

built-up land is detected with the help of GIS by means of ArcGIS software. In which, ArcGIS software helps to 
determine the built-up area, the percentage and spatial distribution during the period in the study area. Spatio-
temporal changes of built-up land were finally analyzed and discussed by comparing with previous findings. 
 

4. Results and Discussion 
4.1. NDBI in 1996 and 2016 

The NDBI distribution in the HMA obtained from Landsat-5 TM and -8 OLI images acquired on 10 June 1996 
and 16 May 2016 are shown in Figure 2, whereas it’s statistics are summarized in Table 1. Data from Table 1 
illustrates that it’s minimum, mean and maximum values of the NDBI computed from 1996 Landsat-5 TM image 
were -0.77, -0.07 and 0.56, respectively. Whereas, the minimum, mean and maximum values of the NDBI retrieved 
from 2016 Landsat-8 OLI were -0.45, 0.02 and 0.58, respectively. It can be seen that the NDBI values of 2016 were 
larger than those of 1996. This shows that the built-up land has increased in the period 1996-2016. Small areas of 



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high NDBI values were detected in the core urban areas of the HMA in 1996 Figure 2, left. However, these areas of 
high NDBI values had greatly expanded towards the western, north-western and south-western parts of the core 
urban HMA Figure 2, right. The rapid expansion of high NDBI value areas during the period of 1996-2016 was due 
to the high rate of urbanization after the Doi Moi policy [26]. Data from histograms in Figure 3 demonstrates that 
the distribution of the NDBI in 1996 was left-skewed with a relatively high number of low NDBI values Figure 3, 
left, however, this trend had changed in 2016. The NDBI in 2016 shows the number of pixels with NDBI values had 
increased much stronger than that of 1996. Nguyen, et al. [20] reported that the NDBI had been increasing 
dramatically in the period of 1996-2007. It can be concluded from the above-discussion that NDBI values had been 
increased in the period of 1996-2016. 
 

Table-1. Descriptive statistics of the NDBI, BUc in the HMA. 

Index Date Minimum Mean Maximum Standard deviation 

NDBI 
1996-6-10 -0.77 -0.07 0.56 0.02 
2016-5-16 -0.45 0.02 0.58 0.17 

BUc 
1996-6-10 -1.00 0.53 0.94 0.41 
2016-5-16 1.00 0.58 1.00 0.40 

               
 

 
Figure-2. NDBI in the HMA in 1996 (a) and 2016 (b). 

                                  

 

 
Figure-3. Histograms of NDBI in 1996 (a) and 2016 (b). 

                                   

4.2. BUc in 1996 and 2016 
 

 
Figure-4. Built-up index in the HMA in 1996 (a) and 2016 (b). 



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Figure-5. Histograms of built-up index in 1996 (a) and 2016 (b). 

 

The distribution of the BUc in the HMA on 10 June 1996 and 16 May 2016 are shown in Figures 4 and 5, 
whereas it’s statistics are summarized in Table 1. Data from Figure 3 shows that similar to those of the NDBI, areas 
of low BUc values were mainly detected in the western, north-western and south-western parts of the HMA at the 
two-time points, whereas, areas of high BUc values were mainly concentrated in the core urban areas of the HMA. 
Data from Table 1 demonstrates BUc ranged from -1 to 1 at the two-time points. The mean values of 0.53 in 1996 
and 0.58 in 2016 proves built-up land had decreased during this period. Data from histograms in Figure 5 
demonstrates that the distribution of the BUc in 1996 was strongly right-skewed with many low BUc values detected 
on the left of the histogram, especially for the negative BUc values Figure 5-a. However, this trend had changed in 
2016. Data in Figure 5-b demonstrates that the number of pixels with values greater than zero had increased much 
more than that of 1996. It is apparent that the values of the built-up index had increased dramatically during the 
period from 1996 to 2016. 
 

4.3. Spatio-Temporal Changes of Built-Up Land During 1996-2016 
 

Table-2. Summary of built-up land in the HMA during the period of 1996-2016. 

Land cover 
1996 2016 

Area (ha) Percent (%) Area (ha) Percent (%) 

Built-up 4063.1 13.3 7163.2 23.4 
Non-Built-up 26566.6 86.7 23466.4 76.6 

Total 30629.6 100.0 30629.6 100.0 

                      

 
Figure-6. Built-up land in the HMA in 1996 (a) and 2016 (b). 

                                           

The spatial distribution of urban built-up land in the HMA is shown in Figure 6, whereas, its statistics are 
summarized in Table 2. Data in Table 2 shows that the total urban built-up land area was 4063.1 hectares; 
accounting for 13.3% of the total area is 1996, while the non-built-up land area was 26566.6 hectares, accounting for 
86.7% of the total area. The distribution of urban built-up land was mainly concentrated in the central HMA area 
Figure 6-a. The built-up land area had increased to 7163.2 hectares in 2016 which accounts for 23.4% of the total 
area. Data in Figure 6-b shows that the built-up land tends to expand to the west, southwest, and south of the HMA. 
Thus, the urban built-up land area had increased to about 10% of the total area in 20 years. A total of 0.5% of the 
built-up area increases on average each year. This result shows that HMA had experienced a fairly fast urbanization 
rate in the period of 1996-2016. 
 

5. Conclusions 
In this study, spatio-temporal analysis of built-up land changes in the HMA, Vietnam was investigated based on 

Landsat time-series images acquired in 1996 and 2016. Landsat remotely sensed images were first pre-processed 
using inflight sensor calibration parameters and atmospherically corrected using the FLAASH algorithm. Urban 
built-up land was then extracted based on the NDBI based continuous built-up index. Spatio-temporal changes of 
built-up land were finally analyzed and statistically summarized with the help of GIS. The study results show that 
the built-up land area had increased from 4063.1 ha to 7163.2 ha in June 1996 and May 2016 which account for 13.3% 
and 23.4% of the total area, respectively. The urban built-up land area had increased about 10.1% of the total area in 



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20 years and tends to expand to the west, southwest, and south of the HMA. The results of this study demonstrate 
that spatio-temporal changes of urban built-up land can be effectively and quickly monitoring and assessed using 
remotely sensed images.  
 

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