Skip to main content

Advertisement

SpringerLink
Log in

A review on spectral indices for built-up area extraction using remote sensing technology

  • Review Paper
  • Published:
Arabian Journal of Geosciences Aims and scope Submit manuscript

Abstract

The rapidly occurring urbanization is associated with urban land use change dynamics. The conversion of natural land surfaces to artificial impervious built-up surfaces in urban clusters gives rise to numerous urban environmental problems such as urban heat island. So, the expanding built-up surfaces in urban areas require necessary monitoring. The use of satellite data and further spectral indices, for the built-up area estimation by remote sensing in urban clusters, is of great significance. Thus, the current study focuses on the revision of previously developed spectral indices for the classification of built-up areas. The study also covers the algorithms and concepts and then compares the outputs of different built-up area indices derived using distinct spatiotemporal satellite data. The applications of various built-up indices in several studies have also been discussed. The study will facilitate great help to the urban planners to use appropriate spectral index according to the accuracy and suitability of other parameters for classification of the built-up areas.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

References

  • Adeyeri OE, Akinsanola AA, Ishola KA (2017) Investigating surface urban heat island characteristics over Abuja, Nigeria: relationship between land surface temperature and multiple vegetation indices. Remote Sens Appl Soc Environ 7:57–68

    Google Scholar 

  • Alibakhshi Z, Ahmadi M, Asl MF (2020) Modeling biophysical variables and land surface temperature using the GWR model: case study—Tehran and its satellite cities. J Indian Soc Remot 48:59–70

    Google Scholar 

  • As-syakur A, Adnyana I, Arthana IW, Nuarsa IW (2012) Enhanced built-up and bareness index (EBBI) for mapping built-up and bare land in an urban area. Remote Sens 4:2957–2970. https://doi.org/10.3390/rs4102957

    Article  Google Scholar 

  • Bai Y, He G, Wang G, Yang G (2020) WE-NDBI-A new index for mapping urban built-up areas from GF-1 WFV images. Remote Sens Lett 11:407–415

    Google Scholar 

  • Bala R, Prasad R, Yadav VP, Sharma J (2018) A comparative study of land surface temperature with different indices on heterogeneous land cover using Landsat 8 data. ISPRS - International Archives of the Photogrammetry. Remote Sensing and Spatial Information Sciences 425:389–394. https://doi.org/10.5194/isprs-archives-XLII-5-389-2018

    Article  Google Scholar 

  • Balçik FB (2014) Determining the impact of urban components on land surface temperature of Istanbul by using remote sensing indices. Environ Monit Assess 186:859–872

    Google Scholar 

  • Batty M, Howes D (2001) Predicting temporal patterns in urban development from remote imagery. Taylor and Francis. https://doi.org/10.4324/9780203306062_chapter_10

  • Bhatti SS, Tripathi NK (2014) Built-up area extraction using Landsat 8 OLI imagery. Gisci Remote Sens 51:445–467. https://doi.org/10.1080/15481603.2014.939539

    Article  Google Scholar 

  • Bouhennache R, Bouden T, Taleb-Ahmed A, Cheddad A (2019) A new spectral index for the extraction of built-up land features from Landsat 8 satellite imagery. Geocarto Int 34:1531–1551. https://doi.org/10.1080/10106049.2018.1497094

    Article  Google Scholar 

  • Bouzekri S, Lasbet AA, Lachehab A (2015) A new spectral index for extraction of built-up area using Landsat-8 data. J Indian Soc Remot 43:867–873. https://doi.org/10.1007/s12524-015-0460-6

    Article  Google Scholar 

  • Chen XL, Zhao HM, Li PX, Yin ZY (2006) Remote sensing image-based analysis of the relationship between urban heat island and land use/cover changes. Remote Sens Environ 104:133–146

    Google Scholar 

  • Chen L, Li M, Huang F, Xu S (2013) Relationships of LST to NDBI and NDVI in Wuhan City based on Landsat ETM+ image. 6th International Congress on Image and Signal Processing (CISP) 2:840–845

  • Chen M, Zhang H, Liu W, Zhang W (2014) The global pattern of urbanization and economic growth: evidence from the last three decades. PloS One 9:e103799

  • Cibula WG, Zetka EF, Rickman DL (1992) Response of thematic mapper bands to plant water stress. Int J Remote Sens 13:1869–1880

    Google Scholar 

  • Cleve C, Kelly M, Kearns FR, Moritz M (2008) Classification of the wildland–urban interface: a comparison of pixel-and object-based classifications using high-resolution aerial photography. Comput Environ Urban Syst 32:317–326

    Google Scholar 

  • Connors JP, Galletti CS, Chow WT (2013) Landscape configuration and urban heat island effects: assessing the relationship between landscape characteristics and land surface temperature in Phoenix, Arizona. Landscape Ecol 28:271–283

    Google Scholar 

  • Deng C, Wu C (2012) BCI: A biophysical composition index for remote sensing of urban environments. Remote Sens Environ 127:247–259. https://doi.org/10.1016/j.rse.2012.09.009

    Article  Google Scholar 

  • Deng C, Wu C (2013) A spatially adaptive spectral mixture analysis for mapping subpixel urban impervious surface distribution. Remote Sens Environ 133:62–70

    Google Scholar 

  • Deng Y, Wu C, Li M, Chen R (2015) RNDSI: a ratio normalized difference soil index for remote sensing of urban/suburban environments. Int J Appl Earth Obs Geoinf 39:40–48

    Google Scholar 

  • Dozier J (1989) Spectral signature of alpine snow cover from the Landsat Thematic Mapper. Remote Sens Environ 28:9–22

    Google Scholar 

  • Estoque RC, Murayama Y (2015) Classification and change detection of built-up lands from Landsat-7 ETM+ and Landsat-8 OLI/TIRS imageries: a comparative assessment of various spectral indices. Ecol Indic 56:205–217

    Google Scholar 

  • Ezimand K, Kakroodi AA, Kiavarz M (2018) The development of spectral indices for detecting built-up land areas and their relationship with land-surface temperature. Int J Remote Sens 39:8428–8449. https://doi.org/10.1080/01431161.2018.1488282

    Article  Google Scholar 

  • Faisal K, Shaker A, Habbani S (2016) Modeling the relationship between the gross domestic product and built-up area using remote sensing and GIS data: a case study of seven major cities in Canada. ISPRS Int J Geo Inf 5:23

    Google Scholar 

  • Faridatul MI, Wu B (2018) Automatic classification of major urban land covers based on novel spectral indices. ISPRS Int J Geo Inf 7:453

    Google Scholar 

  • Gao BC (1996) NDWI—a normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sens Environ 58:257–266

    Google Scholar 

  • Ghosh DK, Mandal AC, Majumder R, Patra P, Bhunia GS (2018) Analysis for mapping of built-up area using remotely sensed indices–a case study of Rajarhat Block in Barasat Sadar Sub-Division in West Bengal (India). J Landsc Ecol 11:67–76. https://doi.org/10.2478/jlecol-2018-0007

    Article  Google Scholar 

  • Gitelson AA, Merzlyak MN (1996) Signature analysis of leaf reflectance spectra: algorithm development for remote sensing of chlorophyll. J Plant Physiol 148:494–500

    Google Scholar 

  • Griffiths P, Hostert P, Gruebner O, van der Linden S (2010) Mapping megacity growth with multi-sensor data. Remote Sens Environ 114:426–439

    Google Scholar 

  • Guha S, Govil H, Dey A, Gill N (2018) Analytical study of land surface temperature with NDVI and NDBI using Landsat 8 OLI and TIRS data in Florence and Naples city, Italy. Eur J Remote Sens 51:667–678. https://doi.org/10.1080/22797254.2018.1474494

    Article  Google Scholar 

  • Guha S, Govil H, Diwan P (2019) Analytical study of seasonal variability in land surface temperature with normalized difference vegetation index, normalized difference water index, normalized difference built-up index, and normalized multiband drought index. J Appl Remote Sens 13(2):024518

    Google Scholar 

  • Guindon B, Zhang Y, Dillabaugh C (2004) Landsat urban mapping based on a combined spectral–spatial methodology. Remote Sens Environ 92(2):218–232

    Google Scholar 

  • He C, Shi P, Xie D, Zhao Y (2010) Improving the normalized difference built-up index to map urban built-up areas using a semiautomatic segmentation approach. Remote Sens Lett 1:213–221. https://doi.org/10.1080/01431161.2010.481681

    Article  Google Scholar 

  • Herold M, Gardner ME, Roberts DA (2003) Spectral resolution requirements for mapping urban areas. IEEE Trans Geosci Remote Sens 41(9):1907–1919

    Google Scholar 

  • Hidayati IN, Suharyadi R (2019) A comparative study of various indices for extraction urban impervious surface of Landsat 8 OLI. In Forum Geografi 33(2):162–172

  • Hu X, Weng Q (2009) Estimating impervious surfaces from medium spatial resolution imagery using the self-organizing map and multi-layer perceptron neural networks. Remote Sens Environ 113(10):2089–2210

    Google Scholar 

  • Huete AR (1988) A soil-adjusted vegetation index (SAVI). Remote Sens Environ 25:295–309. https://doi.org/10.1016/0034-4257(88)90106-X

    Article  Google Scholar 

  • Huete AR, Jackson RD (1987) Suitability of spectral indices for evaluating vegetation characteristics on arid rangelands. Remote Sens Environ 23(2):213–IN8. https://doi.org/10.1016/0034-4257(87)90038-1

  • Isa NA, Wan Mohd WMN, Salleh SA (2017) The effects of built-up and green areas on the land surface temperature of the Kuala Lumpur city. International Archives of the Photogrammetry. Remote Sensing & Spatial Information Sciences 42W5:107–112. https://doi.org/10.5194/isprs-archives-XLII-4-W5-107-2017

  • Jaeger JA, Schwick C (2014) Improving the measurement of urban sprawl: weighted Urban Proliferation (WUP) and its application to Switzerland. Ecological Indicators 38:294–308. https://doi.org/10.1016/j.ecolind.2013.11.022

    Article  Google Scholar 

  • Jamei Y, Rajagopalan P, Sun QC (2019) Spatial structure of surface urban heat island and its relationship with vegetation and built-up areas in Melbourne, Australia. Sci Total Environ 659:1335–1351

    Google Scholar 

  • Jasinski T, Bochenek A (2018) Predicting changes in spatial planning using artificial neural networks on the basis of satellite images. PLEA 2018, Hong Kong

  • Jensen JR (2007) Remote sensing of vegetation. Remote Sensing of the Environment: An Earth Resource Perspective, 2nd edn. Pearson/Prentice Hall, Upper Saddle River, NJ

    Google Scholar 

  • Jieli C, Manchun LI, Yongxue LIU, Chenglei S, Wei HU (2010) Extract residential areas automatically by new built-up index. In: 2010 18th International Conference on Geoinformatics, pp 1–5

  • Kaimaris D, Patias P (2016) Identification and area measurement of the built-up area with the Built-up Index (BUI). Int J Adv Remote Sens GIS 5(6):1844–1858

    Google Scholar 

  • Kaur R, Pandey P (2020) Monitoring and spatio-temporal analysis of UHI effect for Mansa district of Punjab. India Advances in Environmental Research 9(1):19–39

    Google Scholar 

  • Kawamura M (1996) Relation between social and environmental conditions in Colombo Sri Lanka and the urban index estimated by satellite remote sensing data. In: Proc. 51st Annual Conference of the Japan Society of Civil Engineers, pp 190–191

  • Kikon N, Singh P, Singh SK, Vyas A (2016) Assessment of urban heat islands (UHI) of Noida City, India using multi-temporal satellite data. Sustain Cities Soc 22:19–28

    Google Scholar 

  • Krishnaveni KS, Anilkumar PP (2020) Managing urban sprawl using remote sensing and GIS. The Int Arch Photogramm Remote Sens Spat Inf Sci 42:59–66

    Google Scholar 

  • Kumar D, Shekhar S (2015) Statistical analysis of land surface temperature–vegetation indexes relationship through thermal remote sensing. Ecotoxicol Environ Saf 121:39–44

    Google Scholar 

  • Kumar A, Pandey AC, Jeyaseelan AT (2012) Built-up and vegetation extraction and density mapping using WorldView-II. Geocarto Int 27:557–568

    Google Scholar 

  • Lesaignoux A, Fabre S, Briottet, X, Olioso A (2009) Influence of surface soil moisture on spectral reflectance of bare soil in the 0.4–15 μm domain. In: 6. EARSeL; Imaging spectroscopy: innivative tool for scientific and commercial environmental applications, p 6

  • Li W (2020) Mapping urban impervious surfaces by using spectral mixture analysis and spectral indices. Remote Sens 12:94

    Google Scholar 

  • Li J, Song C, Cao L, Zhu F, Meng X, Wu J (2011) Impacts of landscape structure on surface urban heat islands: a case study of Shanghai, China. Remote Sens Environ 115:3249–3263

    Google Scholar 

  • Liu C, Shao Z, Chen M, Luo H (2013) MNDISI: a multi-source composition index for impervious surface area estimation at the individual city scale. Remote Sens Lett 4:803–812. https://doi.org/10.1080/2150704x.2013.798710

    Article  Google Scholar 

  • Lu D, Weng Q (2006) Use of impervious surface in urban land-use classification. Remote Sens Environ 102:146–160

    Google Scholar 

  • Maktav D, Erbek FS (2005) Analysis of urban growth using multi-temporal satellite data in Istanbul, Turkey. Int J Remote Sens 26:797–810

    Google Scholar 

  • Malik MS, Shukla JP, Mishra S (2019) Relationship of LST, NDBI and NDVI using Landsat-8 data in Kandaihimmat watershed. Hoshangabad, India

    Google Scholar 

  • Mallick J, Kant Y, Bharath BD (2008) Estimation of land surface temperature over Delhi using Landsat-7 ETM+. J Ind Geophys Union 12:131–140

    Google Scholar 

  • Masek JG, Lindsay FE, Goward SN (2000) Dynamics of urban growth in the Washington DC metropolitan area, 1973–1996, from Landsat observations. Int J Remote Sens 21:3473–3486

    Google Scholar 

  • Mathan M, Krishnaveni M (2020) Monitoring spatio-temporal dynamics of urban and peri-urban land transitions using ensemble of remote sensing spectral indices—a case study of Chennai Metropolitan Area. India Environ Monit Assess 192(1):1–11

    Google Scholar 

  • Mathew A, Sreekumar S, Khandelwal S, Kaul N, Kumar R (2016) Prediction of surface temperatures for the assessment of urban heat island effect over Ahmedabad city using linear time series model. Energy and Buildings 128:605–616. https://doi.org/10.1016/j.enbuild.2016.07.004

    Article  Google Scholar 

  • Mohamed A, Worku H (2019) Quantification of the land use/land cover dynamics and the degree of urban growth goodness for sustainable urban land use planning in Addis Ababa and the surrounding Oromia special zone. Journal of Urban Management 8:145–158

    Google Scholar 

  • Mourya M, Kumari B, Tayyab M, Paarcha A, Rahman A (2021) Indices based assessment of built-up density and urban expansion of fast growing Surat city using multi-temporal Landsat datasets. GeoJournal 86:1607–1623

    Google Scholar 

  • Orimoloye IR, Ololade OO (2020) Spatial evaluation of land-use dynamics in gold mining area using remote sensing and GIS technology. Int J Environ Sci Te 17:4465–4480

    Google Scholar 

  • Patel N, Mukherjee R (2015) Extraction of impervious features from spectral indices using artificial neural network. Arab J Geosci 8(6):3729–3741

    Google Scholar 

  • Patra S, Sahoo S, Mishra P, Mahapatra SC (2018) Impacts of urbanization on land use/cover changes and its probable implications on local climate and groundwater level. Journal of Urban Management 7:70–84

    Google Scholar 

  • Piyoosh AK, Ghosh SK (2018) Development of a modified bare soil and urban index for Landsat 8 satellite data. Geocarto Int 33:423–442. https://doi.org/10.1080/10106049.2016.1273401

    Article  Google Scholar 

  • Prasomsup W, Piyatadsananon P, Aunphoklang W, Boonrang A (2020) Extraction Technic for Built-up Area Classification in Landsat 8 Imagery. International Journal of Environmental Science and Development 11(1):15–20

    Google Scholar 

  • Rahar PS, Pal M (2020) Comparison of various indices to differentiate built-up and bare soil with Sentinel 2 data. Applications of Geomatics in Civil Engineering. Springer, Singapore, pp 501–509

    Google Scholar 

  • Rasul A, Balzter H, Ibrahim GRF, Hameed HM, Wheeler J, Adamu B, Ibrahim SA, Najmaddin PM (2018) Applying built-up and bare-soil indices from Landsat 8 to cities in dry climates. Land 7(3):81

    Google Scholar 

  • Ray TW (1994) A FAQ on vegetation in remote sensing. Division of geological and planetary sciences, California institute of technology. Available online: http://www.yale.edu/ceo/Documentation/rsvegfaq.html

  • Richards JA (2013) Remote sensing digital image analysis: an introduction, 5th edn. Springer, Heidelberg, New York

    Google Scholar 

  • Ridd MK (1995) Exploring a VIS (vegetation-impervious surface-soil) model for urban ecosystem analysis through remote sensing: comparative anatomy for cities. Int J Remote Sens 16:2165–2185. https://doi.org/10.1080/01431169508954549

    Article  Google Scholar 

  • Rogers AS, Kearney MS (2004) Reducing signature variability in unmixing coastal marsh Thematic Mapper scenes using spectral indices. Int J Remote Sens 25:2317–2335

    Google Scholar 

  • Rouse JW, Haas RH, Schell JA, Deering DW (1973) Monitoring vegetation systems in the Great Plains with ERTS. Paper presented at the 3rd ERTS Symposium, NASA SP-351 I, 309–317

  • Santra A, Mitra SS, Sinha S, Routh S (2020) Performance testing of selected spectral indices in automated extraction of impervious built-up surface features using Resourcesat LISS-III image. Arabian Journal of Geosciences 13:1–11

    Google Scholar 

  • Sarif MO, Gupta RD (2019) Land surface temperature profiling and its relationships with land indices: a case study on Lucknow city. ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences IV-5/W2:89–96

  • Sekertekin A, Abdikan S, Marangoz AM (2018) The acquisition of impervious surface area from LANDSAT 8 satellite sensor data using urban indices: a comparative analysis. Environ Monit Assess 190:1–13

    Google Scholar 

  • Seto KC, Güneralp B, Hutyra LR (2012) Global forecasts of urban expansion to 2030 and direct impacts on biodiversity and carbon pools. Proceedings of the National Academy of Sciences 109:16083–16088. https://doi.org/10.1073/pnas.1211658109

    Article  Google Scholar 

  • Shi L, Ling F, Ge Y, Foody GM, Li X, Wang L, Du Y (2017) Impervious surface change mapping with an uncertainty-based spatial-temporal consistency model: a case study in Wuhan city using Landsat time-series datasets from 1987 to 2016. Remote Sens 9:1148

    Google Scholar 

  • Sinha P, Verma NK, Ayele E (2016) Urban built-up area extraction and change detection of Adama municipal area using time-series Landsat images. Int J Adv Rem Sens GIS 5:1886–1895. https://doi.org/10.23953/cloud.ijarsg.67

    Article  Google Scholar 

  • Small C, Lu JW (2006) Estimation and vicarious validation of urban vegetation abundance by spectral mixture analysis. Remote Sens Environ 100:441–456

    Google Scholar 

  • Somers B, Asner GP, Tits L, Coppin P (2011) Endmember variability in spectral mixture analysis: a review. Remote Sens Environ 115:1603–1616

    Google Scholar 

  • Stathakis D, Perakis K, Savin I (2012) Efficient segmentation of urban areas by the VIBI. Int J Remote Sens 33:6361–6377

    Google Scholar 

  • Sultana S, Satyanarayana ANV (2020) Assessment of urbanisation and urban heat island intensities using Landsat imageries during 2000–2018 over a sub-tropical Indian City. Sustain Cities Soc 52:101846. https://doi.org/10.1016/j.scs.2019.101846

    Article  Google Scholar 

  • Sun Q, Wu Z, Tan J (2012) The relationship between land surface temperature and land use/land cover in Guangzhou, China. Environ Earth Sci 65:1687–1694

    Google Scholar 

  • Sun Z, Wang C, Guo H, Shang R (2017) A modified normalized difference impervious surface index (MNDISI) for automatic urban mapping from Landsat imagery. Remote Sens 9:942

    Google Scholar 

  • Takeuchi W, Yasuoka Y (2005) Development of normalized vegetation, soil and water indices derived from satellite remote sensing data. J Japan Soc Photogramm Remote Sens 43:7–19

    Google Scholar 

  • Tian Y, Chen H, Song Q, Zheng K (2018) A novel index for impervious surface area mapping: development and validation. Remote Sens 10:1521

    Google Scholar 

  • Tran H, Uchihama D, Ochi S, Yasuoka Y (2006) Assessment with satellite data of the urban heat island effects in Asian mega cities. Int J Appl Earth Obs Geoinf 8:34–48

    Google Scholar 

  • Tran DX, Pla F, Latorre-Carmona P, Myint SW, Caetano M, Kieu HV (2017) Characterizing the relationship between land use land cover change and land surface temperature. ISPRS J Photogramm Remote Sens 124:119–132

    Google Scholar 

  • USGS (2013) Using the USGS Landsat 8 Product. http://landsat.usgs.gov/Landsat8 Using Product.php_

  • Varshney A, Rajesh E (2014) A comparative study of built-up index approaches for automated extraction of built-up regions from remote sensing data. J Indian Soc Remot 42:659–663. https://doi.org/10.1007/s12524-013-0333-9

    Article  Google Scholar 

  • Waqar MM, Mirza JF, Mumtaz R, Hussain E (2012) Development of new indices for extraction of built-up area & bare soil from Landsat data. Open Access Sci Rep 1:4

    Google Scholar 

  • Weng Q (ed) (2008) Remote Sensing of Impervious Surfaces: An Overview. CRC Press Taylor & Francis Group, Boca Raton

  • Xu HQ (2005) Fast information extraction of urban built-up land based on the analysis of spectral signature and normalized difference index. Geogr Res 24:311–320

    Google Scholar 

  • Xu H (2006) Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. Int J Remote Sens 27:3025–3033. https://doi.org/10.1080/01431160600589179

    Article  Google Scholar 

  • Xu H (2007) Extraction of urban built-up land features from Landsat imagery using a thematic-oriented index combination technique. Photogramm Eng Rem S 73:1381–1391

    Google Scholar 

  • Xu H (2008) A new index for delineating built-up land features in satellite imagery. Int J Remote Sens 29:4269–4276. https://doi.org/10.1080/01431160802039957

    Article  Google Scholar 

  • Xu H (2010) Analysis of impervious surface and its impact on urban heat environment using the normalized difference impervious surface index (NDISI). Photogramm Eng Rem S 76:557–565. https://doi.org/10.14358/PERS.76.5.557

    Article  Google Scholar 

  • Xu H, Wang X, Xiao G (2000) A remote sensing and GIS integrated study on urbanization with its impact on Arable Lands, Fuqing City, Fujian Province, China. Land Degrad Dev 11:301–314

    Google Scholar 

  • Yang CJ, Zhou CH (2000) Extracting residential areas on the TM imagery. J Remote Sens 4(2):146–150

    Google Scholar 

  • Zha Y, Gao J, Ni S (2003) Use of normalized difference built-up index in automatically mapping urban areas from TM imagery. Int J Remote Sens 24:583–594. https://doi.org/10.1080/01431160304987

    Article  Google Scholar 

  • Zhang Y, Odeh IO, Han C (2009) Bi-temporal characterization of land surface temperature in relation to impervious surface area, NDVI and NDBI, using a sub-pixel image analysis. Int J Appl Earth Obs Geoinf 11:256–264

    Google Scholar 

  • Zhang J, Li P, Wang J (2014) Urban built-up area extraction from Landsat TM/ETM+ images using spectral information and multivariate texture. Remote Sens 6:7339–7359

    Google Scholar 

  • Zhangyan J, Yunhao C, Jing L (2006) On urban heat island of Beijing based on Landsat TM data. Geo-Spatial Information Science 9:293–297

    Google Scholar 

  • Zhao H, Chen X (2005) Use of normalized difference bareness index in quickly mapping bare areas from TM/ETM+. In International geoscience and remote sensing symposium 3:1666–1668

  • Zhou Y, Yang G, Wang S, Wang L, Wang F, Liu X (2014) A new index for mapping built-up and bare land areas from Landsat-8 OLI data. Remote Sens Lett 5:862–871

    Google Scholar 

Download references

Funding

This review has not been supported financially by any institution or organization (public, commercial, or not-for-profit sectors). The authors acknowledge the Geoinformatics Laboratory, Department of Environmental Science and Technology, Central University of Punjab, Bathinda for providing the infrastructural and other necessary facilities to carry out the above work.

Author information

Authors and Affiliations

  1. Department of Environmental Sciences and Technology, Central University of Punjab, Ghudda, Bathinda, Punjab-151401, India

    Rajveer Kaur & Puneeta Pandey

Authors
  1. Rajveer Kaur
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Puneeta Pandey
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Puneeta Pandey.

Ethics declarations

Competing interests

The authors declare that they have no known competing financial or any other conflict of interest that could have affected the work reported in this paper.

Additional information

Responsible Editor: Biswajeet Pradhan

Highlights

• A systematic review of urban built-up index extraction algorithms.

• Majority of the studies used NDBI to extract built-up area.

• Landsat satellite data has been predominantly used for derivation of the built-up area algorithms.

• Review depicts the importance of employing built-up area spectral indices in LULC/LST and UHI effect studi

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kaur, R., Pandey, P. A review on spectral indices for built-up area extraction using remote sensing technology. Arab J Geosci 15, 391 (2022). https://doi.org/10.1007/s12517-022-09688-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12517-022-09688-x

Keywords

Search

Navigation