International Journal of Environmental Science and Development, Vol. 11, No.

1, January 2020

Extraction Technic for Built-up Area Classification in

Landsat 8 Imagery
W. Prasomsup, P. Piyatadsananon, W. Aunphoklang, and A. Boonrang

 particularly with the large area. Based on the digital data

Abstract—Built-up areas play a significant type of land use emerging in the image, computer-assisted digital processing
associated with the urbanization. Classifying the built-up area plays a significant role in land use and land cover
by using satellite image, is also a high demand for a relevant classification due to the fast processing and high accuracy.
organization to investigate the urban sprawl. This study aims to
develop an index used to classify the built-up areas accurately.
Many authors explored indices conducted to classify
In order to achieve the aim of the study, two objectives were land-use and landcover by using remote sensing data. It
intensively studied to compare the results from several indices appeared a standard city classification method using a
used to appropriately classify the built-up areas from satellite Built-up Index (BUI) and assessed its performance [1]. The
data of Landsat 8. The second objective is to develop a built-up images were classified according to the critical value of the
area index which is suitable for classifying the built-up areas. BUI based on the values of the selected samples. It was
This study considers the two study areas, Bangkachao and
Bangkok International Airport, which encounter the rapid
concluded that Normalized Difference Built-up Index (NDBI)
urbanization. This study employs a satellite image of Landsat 8 can be defined as the linear combination of the near-infrared
OLI (Operational Land Imager (OLI). The Normalized band (0.76 ~ 0.90 µm) and the middle infrared (MIR) band
Difference Vegetation Index (NDVI), Normalized Difference (1.55 ~ 1.75 µm), used for extraction of urban built-up land
Built-up Index (NDBI) were intensively examined to present a [2]. As a study, it showed the NDBI (Normalized Difference
BUI map correspondingly. On the other hand, Modified Built-up Index) on the footprints of the NDVI using Landsat
Normalized Difference Water Index (MNDWI) was tested to
Thematic Mapper (TM) near-infrared (NIR) band 4 (low
separate the water areas and wetlands from the built-up areas
distinctively. Furthermore, the Modified Built-Up Index (MBUI) reflectance in the built-up area) and mid-infrared (MIR) band
were developed based on the integration between the MNDWI 5 (high reflectance in the built-up area) [3]. The output NDBI
and BUI map. As a result, it is clearly shown that the MBUI was further refined by removing vegetation noise using
provides more accurate results of built-up area classification NDVI (Normalized Difference Vegetation Index).
than the BUI. Also, the MBUI presents 82-83% accuracy of Additionally, NDBI and NDVI were modified the
both study areas, which are higher than the BUI map. It is to
approaches by employing continuous images of both indices
say that MBUI can be employed to classify the built-up areas of
the study areas accurately. [3] and [4]. The output was a continuous raster in which the
pixels with higher values indicated a higher probability of
Index Terms—Built-up index, modified built-up index, GIS, them to represent built-up areas. It was found that the
remote sensing. extraction of urban built land can be automatically done with
NDBI [5]. However, the weakness of NDBI is that it cannot
distinguish built up and bare land. For this reason, formulated
I. INTRODUCTION a new urban index called Index-Based Built-up Index (IBI),
Satellite image has been used to investigate the derived from three other indices, SAVI, MNDWI, and NDBI,
environmental phenomena over the world. Satellite remote to map urban areas were created [6]. Also, a new index for
sensing has a number of potential applications across a broad mapping built-up and bare land called Enhanced Built-up and
range of environmental disciplines. Recently, Landsat 8 Bareness Index (EBBI), compared with IBI showed a high
(OLI), the downloadable data has been intensively studied in overall accuracy of EBBI in mapping built-up and bare land,
a broad range of land use applications. Land use planning and while the IBI presented more accuracy in mapping built-up
change are the other crucial missions derived from the areas [7]. The study used three indices; Normalized
investigation of Landsat data. In order to plan for the change Difference Built-up Index (NDBI), Modified Normalized
of the land use and land cover, classification technics have Difference Water Index (MNDWI), and Soil Adjusted
been studied continuously. Satellite images are usually Vegetation Index (SAVI) to reduce the seven bands Landsat
converted into useful information such as land cover maps TM7 image into three thematic-oriented bands [8].
using two conventional methods: manual interpretation and This study aims to develop the indices used in classifying
computer-assisted digital processing. Image interpretation built-up areas from agricultural areas. In order to achieve the
can be done visually which is a time-consuming procedure, aim of the study, two objectives were intensively studied to
compare the results from several indices used to
Manuscript received July 13, 2019; revised November 12, 2019. appropriately classify the built-up areas from satellite data of
W. Prasomsup is with Institute of Survey Engineering, Faculty of Landsat 8. The second objective is to develop a built-up area
Engineering and Architecture, Rajamangala University of Technology Isan, index which is suitable for classifying the built-up areas. Two
Nakhon Ratchasima Province, Thailand (e-mail: wilawan.pa@rmuti.ac.th).
P. Piyatadsananon, W. Aunphoklang, and A. Boonrang are with School of study-areas where have been encountering the rapid
Geoinformatics, Institute of Science, Suranaree University of Technology, urbanization were selected for this study. The processes of
Nakhon Ratchasima Province, Thailand.

doi: 10.18178/ijesd.2020.11.1.1219 15
 International Journal of Environmental Science and Development, Vol. 11, No. 1, January 2020

developing indices were addressed with provided results of BUI = NDBI – NDVI (3)
the validation.
4) Modified Normalized Difference Water Index (MNDWI).
It was proposed that the Normalized Difference Water
II. METHOD AND STUDY AREA Index (NDWI) would delineate open water features,
The study areas present the urban areas, Bangkok which is expressed as follows [9]:
International Airport (Fig. 1 (a)), and Bangkachao (Fig. 1 (b)).
Formerly, these areas were agricultural lands, whereas the NDWI = (GREEN – NIR) / (GREEN + NIR) (4)
development in terms of infrastructures and facility
transforms these areas towards urbanization. For this reason, where GREEN is a green band such as Landsat 8 data band 3,
the mixed land use between built-up areas and agricultural and NIR is a near infrared band such as Landsat 8 data band 5.
areas is a distinctive reason for this study. This also This index maximizes reflectance of water by using green
challenges to examine the classification technics to light wavelengths and minimizes low reflectance of NIR by
discriminate the built-up area separated from the agriculture water features. Therefore, water features are enhanced with
lands. positive values and vegetation and soil are presented as zero
In order to achieve the aim of the study, satellite image, or negative values.
Landsat 8 OLI data, was used to generate several indices to
classify the built-up areas from the following workflow of the
methodology (Fig. 2). All indices were recoded and
calculated the response to the algorithms. As expected,
Built-Up Index (BUI) was calculated by considering two
significant indices, NDBI and NDVI. Simultaneously,
enhancing the accuracy of built-up areas regardless of the
water areas in the data, the MNDWI was generated to extract
the water areas from others. Therefore, the Modified
Built-Up Index was generated by the integration between
MNDWI and BUI to extract the built-up areas from the
agricultural areas precisely.
A. Thematic Oriented Index
In this study considers the following indices used to extract
the built-up areas, which are
1) Normalized Difference Vegetation Index (NDVI). This is
primarily due to the internal structure of plant leaves.
High reflectance in NIR and high absorption in Red
spectrum, these two bands are used to calculate NDVI. So,
the following formula gives Normalized Difference
(a)
Vegetation Index (NDVI).

NDVI = (NIR – Red) / (NIR + Red) (1)

The NDVI value varies from -1 to 1. Higher the value of

NDVI reflects high Near Infrared (NIR), means dense
greenery.
2) Normalized difference built-up index (NDBI). It was
proposed that the NDBI method and Landsat TM data
were used to determine the urban area [3]. For Landsat
OLI data, NDBI can be calculated using the following
equation:

NDBI = (MIR - NIR) / (MIR + NIR) (2)

NDBI value lies between -1 to +1. A negative value of

NDBI represents water bodies whereas higher value
represents build-up areas. NDBI value for vegetation is low.
3) Built-up Index (BUI). Build-up Index (BUI) is the index
for analysis of urban pattern using NDBI and NDVI. BUI
is the binary image with only higher positive value (b)
indicates built-up and barren thus, allows BUI to map the Fig. 1. Study areas, (a.) Bankachao and (b.) Bangkok International Airport.
built-up area automatically.

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 International Journal of Environmental Science and Development, Vol. 11, No. 1, January 2020

LANDSAT 8 OLI DATA the built-up area. Therefore, this study considers the BUI and
(Band 3,4,5,and 6) MNDWI to enhance the contrast between built-up land and
Preprocessing
water to extract the built-up areas separately from other
classes effectively as the following equation.
NDBI NDVI MNDWI

MBUI = BUI – MNDWI (6)

Re-coded index Re-code index Re-code index
Visual validation
Google Earth
NDBI NDVI MNDWI or
(recoding) (recoding) (recoding)

Accuracy assessment 1 Built-up index (BUI)

MBUI = (NDVI - NDBI) – MNDWI (7)
Accuracy assessment 2 Modified Built-up index (MBUI)

Fig. 2. The workflow of the methodology. D. Accuracy Assessment

To compare the extraction accuracy between BUI and
However, the extracted water information in these regions
MBUI maps with Google Earth images. This study examines
was often mixed up with built-up land noise because many
the extraction accuracy of BUI (overall accuracy are 0.70 and
built-up lands also have positive values in the NDWI image.
0.80 in Table I) and MBUI maps (overall accuracy are 0.82
To remedy this problem, it is able to modify the NDWI by
and 0.83 in Table I) by selecting 210 points, Fig. 3, from
using a middle infrared (MIR) band such as Landsat 8 data
systematic random over the study areas.
band 5 to substitute the NIR band in the NDWI [10]. The
It is clearly shown that this MBUI provides high accuracy
MNDWI is expressed as follows:
of built-up area classification of both two study areas.
MNDWI = (GREEN – MIR) / (GREEN + MIR) (5)

B. Arithmetic Manipulation of Recoded Indices

Regarding the above indices, the NDVI, NDBI, and
MNDWI, were transformed into Binary index, which
represents as 0 and 254 by considering two criteria before
transforming as followed.
If “Mean value of index” > 0
If “Index value” >= Mean value of index
Recode = 254
Else Recode = 0
Else
If “Index value” >= (Mean value of index)5 +(-0.02)
Recode = 254
Else Recode = 0
If the mean value of the index shown as a positive value,
the DN (Digital Number) values, where are greater than the
mean value must be transformed to 254, while the DN value (a)
is smaller than the mean value presenting as zero.
If the mean value of the index shown as a negative value,
the DN values, where are greater than the mean value to the
fifth power and added -0.02 to be 254, while the DN value is
smaller than zero.
The derived NDVI image was recoded with 254 for all
pixels having positive indices (vegetation) and 0 for all
remaining pixels of negative indices. Derived NDBI image
was recoded with 254 for all pixels having positive indices
(built-up area) and 0 for all remaining pixels of negative
indices. Finally, the derived MNDWI image was recoded
with 254 for all pixels having positive indices (Water) and 0
for all remaining pixels of negative indices.
C. Modified Built-up Index (MBUI)
Considering the recoded indices, they can be integrated to
enhance the quality of classification in built-up areas. This
study proposes a developed technic, Modified Built-up Index
(b)
(MBUI), which is integrated between BUI and MNDWI. Fig. 3. Sampling points for accuracy assessment of study areas, (a.)
According to the wetlands, these areas emerge shatteringly in Bankachao and (b.) Bangkok International Airport.

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 International Journal of Environmental Science and Development, Vol. 11, No. 1, January 2020

TABLE I: THE OVERALL ACCURACY OF CLASSIFYING THE BUILT-UP AREAS III. RESULTS
IN THE STUDY AREAS
Overall Accuracy A. Index Results and Recoded Indices
BUI MBUI The descriptive statistical results were listed in Table II
Area1 0.70 0.82 response to the NDVI, NDBI, and MNDWI. These statistical
Area2 0.80 0.83 data were recorded by considering the equation 6 to 7.

TABLE II: THE STATISTICS SUMMARY OF EACH INDEX TO USE FOR RECODED INDEX.
Statistic NDVI NDBI MNDWI
Area1 Area2 Area1 Area2 Area1 Area2
Min -0.0844 -0.0911 -0.3458 -0.3660 -0.4881 -0.4561
Max 0.4588 0.4594 0.3053 0.2979 0.2665 0.2924
Mean 0.1325 0.1662 -0.0485 -0.0795 -0.0583 -0.0613
Std dev. 0.1011 0.0995 0.0949 0.0828 0.1068 0.1071

BUI map of Bangkachao MBUI map of Bangkachao RGB map of Bangkachao

BUI map of Bangkok International MBUI map of Bangkok International RGB map of Bangkok International
Airport Airport Airport
Fig. 4. Results of built-up areas extraction from BUI and MBUI methods.

the built-up land, it reflects the MIR radiation higher than the
B. Comparison of Built-up Areas Extraction between BUI
NIR radiation. Calculating the MNDWI, the built-up land
and MBUI
should have negative values while keeping the water values
Considering the images present in Fig. 4, it is clearly positive. Accordingly, the enhanced water features will no
shown that the MBUI map of both two study areas provides a longer have built-up land noise in an MNDWI image. This
better-classified result comparing with the real image of RGB substitution has no impact on vegetation, as vegetation still
map of the study areas. has negative value when calculated using the above
equations. Therefore, employing the MNDWI instead of
NDWI to enhance water features in the built-up
IV. CONCLUSION AND DISCUSSION land-dominated urban area obviously. Consequently, the BUI
This modified index, it considers the major land-use types map provides high accuracy of both study areas. The
of image for extraction. Applying the MNDWI map to integration between MNDWI and BUI as MBUI can enhance
enhance the contrast between built-up land and water the built-up areas distinctively. However, this technic is
presents a better result than using the NDWI. According to appropriate to the image which the digital number values

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 International Journal of Environmental Science and Development, Vol. 11, No. 1, January 2020

(DN values) show the normal distribution histogram. It is to Wilawan Prasomsup was born in Kanchanaburi
province Thailand in 1985.
say that it provides very little values (-0.02) of the difference In 2017, she got the Ph.D. (geoinformatics) from
between the median value and the mean value of the image Suranaree University of Technology, Thailand; in
data. Therefore, the MBUI would be suitable for an image 2011, she got the M.Sc. (environmental science) from
where the digital number values perform as a normal Silpakorn University, Thailand; in 2008, she got the
B.Sc. (computer science) from Sripatum University,
distribution. Thailand.
She is a lecturer in Rajamangala University of
APPENDIX Technology Isan, Nakhonratchasima, Thailand. She is interested in
Environmental observation using Remote Sensing Technology. She has
No appendix. published eight academic articles, some example are listed below;
[1] W Prasomsup, P Athiwat, T Wwatchlakorn, S Ratchanon, W nakarin and
K Pimprapai “Vertical Accuracy Calibration Technic of Digital Elevation
CONFLICT OF INTEREST Model from Shuttle Radar Topography Mission by using Linear Regression”
The authors declare no conflict of interest. The 24th National Convention on Civil Engineering. July 10-12, 2019,
Udonthani, Thailand.
[2] S. Ongsomwang, S. Dasananda, and W. Prasomsup “Spatio-Temporal
AUTHOR CONTRIBUTIONS Urban Heat Island Phenomena Assessment using Landsat Imagery: A Case
Study of Bangkok Metropolitan and its Vicinity, Thailand” Environment and
W Prasomsup conducted the research; A Boonrang
Natural Resources Journal 2018; 16(2): 29-44.
analyzed the data; W Aunphoklang summarized the results; P [3] P. Littidej, S. Sarapirome, W. Aunphoklang, S. Tanang and W. Prasomsu.
Piyatadsananon and W Prasomsup wrote the paper; all “Frequency of violencemapping of air pollution using mathematical model
authors had approved the final version. and geographic information system” Proceedings of the 34th Asian
Conference on Remote Sensing 2013 (ACRS 2013); 3: 2207-2215, 2013
Dr. Prasomsup also received a certificate of participated in the educational
REFERENCES activity (English Research Programme) from Caritas Institute of Higher
[1] J. A. Lee, S. S. Lee, and K. H. Chi, “Development of an urban Education, Hong Kong, 2011.
classification method using a built-up index,” in Proc. 6th. WSEAS Int.
Conf. on Remote Sensing, 2010, pp. 39-43. Pantip Piyatadsananon was born in
[2] Y. Zha, S. Ni, and S. Yang. (2003). An effective approach to Nakhonpathom province Thailand in 1972.
automatically extract urban landuse from TM imagery. J. Remote Sens. In 2014, she got the Ph.D. (geoinformatics) from
[Online]. 7(1). pp. 37–40. Available: University of Salford, UK; in 1986, she got the
http://en.cnki.com.cn/Article_en/CJFDTOTAL-YGXB200301006.ht M.Sc. (urban and regional planning) from King
m Mongkut‟s Institute of Technology Ladkrabang,
[3] Y. Zha, J. Gao, and S. Ni. (2003). Use of normalized difference built-up Thailand; in 1981she got the B.Sc. (geography) from
index in automatically mapping urban areas from TM imagery. Int. J. Srinakarinwirot University, Thailand.
Remote Sens. [Online]. 24(3). pp. 583-594. Available: She is an assistant professor working at Suranaree
https://doi.org/10.1080/01431160304987 University of Technology, Nakhonratchasima, Thailand. She is interested in
[4] C. He, P. Shi, D. Xie, and Y. Zhao. (2010). Improving the normalized photogrammetry and GIS for generating and analysing high-resolution image
difference built-up index to map urban built-up areas using a and data. She has published more than 10 academic articles, which some
semiautomatic segmentation approach. Remote Sens. Lett. [Online]. examples are listed below;
1(4). pp. 213–221. Available: https://doi.org/10.1080/01431161. [1] PIYATADSANANON P. (2016) Spatial factors Consideration in site
2010.481681 selection of ground-mounted PV Power plants. Energy Procedia 100 (2016),
[5] I. N. Hidayati, R. Suharyadi, and P. Danoedoro. (2018). Developing an pp. 78-85. In: 3rd International Conference on Power and Energy Systems
extraction method of urban built-up area based on remote sensing Engineering, CPESE 2016, 8-10 September 2016, Kitakyushu, Japan.
imagery transformation index. Indones. J. Spat. Reg. Anal. (For. Geo.) [2] HTUT Z. K., PIYATADSANANON P., RATANAVARAHA V. (2016)
[Online]. 32(1). pp. 96-108. Available: Identifying the Spatial Clustering of Road Traffic Accidents on
https://doi.org/10.23917/forgeo.v32i1.5907 Naypyitaw-Mandalay Expressway. In: 9th ATRANS symposium: Young
[6] H. Xu. (2008). A new index for delineating built-upland features in researcher‟s forum 2016, 19 August 2016, Bangkok, Thailand.
satellite imagery. Int. J. Remote Sens. [Online]. 29(14). pp. 4269-4276. [3] KERAMINIYAGE K. and PIYATADSANANON P. (2013) „Achieving
Available: https://doi.org/10.1080/01431160802039957 success in post-disaster resettlement programmes through better
[7] A. R. As-Syakur, I. W. S. Adnyana, I. W. Arthana, and I. W. Nuarsa. coordination between spatial and socio-economic/cultural factors‟,
(2012). Enhanced built-up and bareness index (EBBI) for mapping International Journal of Disaster Resilience in the Built Environment, 4 (3),
built-up and bareland in an urban area. J. Remote Sens. [Online]. 4(10). pp. 352-372.
pp. 2957-2970. Available: https://doi.org/10.3390/rs4102957 Asst. Prof. Dr. Piyatadsananon received a scholarship of Royal Thai
[8] F. Mwakapuja, E. Liwa, and J. Kashaigili. (2013). Usage of indices for Government in 2007. She also obtained some government funding to
extraction of built-up areas and vegetation features from landsat TM research in Crime, Agricultural, and Urban studies using Geoinformatics
Image: A case of Dar Es Salaam and Kisarawe Peri-Urban areas, technology.
Tanzania. Int. J. Agric. For. [Online]. 3(7). pp. 273-283. Available:
https://bit.ly/2GlriwN Apinya Boonrang was born in Phayao Province
[9] S. K. McFeeters. (1996). The use of normalized difference water index Thailand in 1990.
(NDWI) in the delineation of open water features. Int. J. Remote Sens. In 2019, she is Ph.D. candidate (geoinformatics)
[Online]. 17(7). pp. 1425–1432. Available: from Suranaree University of Technology,
https://doi.org/10.1080/01431169608948714 Thailand; in 2016, she got the M.Sc. (applied
[10] H. Xu. (2005). A study on information extraction of water body with physics)) from Chiang Mai University, Thailand; in
the Modified Normalized Difference Water Index (MNDWI). J. 2013, she got the B.Sc. (physics) from Chiang Mai
Remote Sens. [Online]. 9(5). pp. 511–517. Available: University, Thailand.
http://en.cnki.com.cn/Article_en/CJFDTotal-YGXB200505011.htm She is a Ph.D. in School of Geoinformatics
Institute of Science Suranaree University of Technology, Nakhonratchasima,
Copyright © 2020 by the authors. This is an open access article distributed Thailand. She is interested in image processing and image classification
under the Creative Commons Attribution License which permits unrestricted using remote sensing data. She has published five academic articles, some
use, distribution, and reproduction in any medium, provided the original example are listed below;
work is properly cited (CC BY 4.0). [1] A. Boonrang, P Piyatadsananon, T Machikowa, S Wonprasaid
“Surveying and Classification of Weeds in Cassava Field from UAV-borne
High-resolution Map for Precision Management” The 24th National
Convention on Civil Engineering. July 10-12, 2019, Udonthani, Thailand.

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[2] A. Boontang and S. Kamontum “Application of Spatial Regression on geographic information system. She has published six academic articles,
Eucalyptus Tree Age Estimating Using Diameter at Breath Height” Journal some example are listed below;
of Remote Sensing and GIS Association of Thailand 2018, 19(2). [1] W. Aunphoklang and S. Sarapirome. “Plot-level urban land-use planning
[3] A Boonrang and C. Chaiwong. “Nitrogen Doping of Graphene by Ion using Genetic algorithm and Multi-objective optimization: case study areas
Implantation” Proceedings of the 7th National Science Reseach Conference. in Nakhon Ratchasima town” The International Conference on Science and
March 30-31 Phitsanulok, Thailand. Technology 2017 (Oral Presentation). December 7–8, 2017, Rajamangala
Boonrang has received a Science Achievement Scholarship of Thailand in University of Technology Thanyaburi, Thailand.
2009 to present. [2] W. Aunphoklang and S. Sarapirome. “Determination of potential areas
for types of rapid urban growth planning” The 41st Congress on Science and
Warunee Aunphoklang was born in Nakhon Technology of Thailand (Oral Presentation). November 6-8, 2015, Suranaree
Ratchasima Province Thailand in 1986. University of Technology, Nakhon Ratchasima, Thailand.
In 2019, she is the Ph.D. candidate (geoinformatics) [3] W. Aunphoklang, S. Sarapirome, and P. Littidej. “Comparison on
from Suranaree University of Technology, Thailand; different clustering of origins for sugarcane transportation using Network
in 2012, she got the M.Sc. (geoinformatics) from Analysis and Linear Programming” Proceedings of the 32nd Asian
Suranaree University of Technology, Thailand; in Conference on Remote Sensing 2011 (ACRS 2011). October 3-7, 2011,
2007, she got the B.I.S (management information Taipei, Taiwan.
systems) from: Suranaree University of Technology, Miss Aunphoklang received a SUT outstanding academic performance
Thailand. scholarship from Suranaree University of Technology Thailand, 2013.
She is a Ph.D. in School of Geoinformatics Institute of Science Suranaree
University of Technology, Nakhonratchasima, Thailand. She is interested in
environmental management using remote sensing technology and

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