Abstract

Several built-up indices have been proposed in the literature in order to extract the urban sprawl from satellite data. Given their relative simplicity and easy implementation, such methods have been widely adopted for urban growth monitoring. Previous research has shown that built-up indices are sensitive to different factors related to image resolution, seasonality, and study area location. Also, most of them confuse urban surfaces with bare soil and barren land covers. By gathering the existing built-up indices, the aim of this paper is to discuss some of their advantages, difficulties, and limitations. In order to illustrate our study, we provide some application examples using Sentinel 2A data.

© 2017 Optical Society of America

Full Article  |  PDF Article
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References

  • View by:

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    [Crossref]
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    [Crossref]
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    [Crossref]
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    [Crossref]
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    [Crossref]
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    [Crossref]
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    [Crossref]
  39. E. Angiuli and G. Trianni, “Urban mapping in Landsat images based on normalized difference spectral vector,” IEEE Geosci. Remote Sens. Lett. 11, 661–665 (2014).
    [Crossref]
  40. M. Pesaresi, A. Gerhardinger, and F. Kayitakire, “A robust built-up area presence index by anisotropic rotation-invariant textural measure,” IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 1, 180–192 (2008).
    [Crossref]
  41. M. Pesaresi and A. Gerhardinger, “Improved textural built-up presence index for automatic recognition of human settlements in arid regions with scattered vegetation,” IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 4, 16–26 (2011).
    [Crossref]
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2017 (1)

T. Grippa, M. Lennert, B. Beaumont, S. Vanhuysse, N. Stephennen, and E. Wolff, “An open-source semi-automated processing chain for urban object-based classification,” Remote Sens. 9, 358 (2017).
[Crossref]

2016 (1)

G. Sun, X. Chen, X. Jia, Y. Yao, and Z. Wang, “Combinational build-up index (CBI) for effective impervious surface mapping in urban areas,” IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 9, 2081–2092 (2016).
[Crossref]

2015 (1)

S. Bouzekri, A. Aziz-Lasbet, and A. Lachehab, “A new spectral index for extraction of built-up area using Landsat-8 data,” J. Indian Soc. Remote Sens. 43, 867–873 (2015).
[Crossref]

2014 (4)

E. Angiuli and G. Trianni, “Urban mapping in Landsat images based on normalized difference spectral vector,” IEEE Geosci. Remote Sens. Lett. 11, 661–665 (2014).
[Crossref]

A. Varshney and E. Rajesh, “A comparative study of built-up index approaches for automated extraction of built-up regions from remote sensing data,” J. Indian. Soc. Remote Sens. 42, 659–663 (2014).
[Crossref]

Z. Shao, Y. Tian, and X. Shen, “BASI: a new index to extract built-up areas from high-resolution remote sensing images by visual attention model,” Remote Sens. Lett. 5, 305–314 (2014).
[Crossref]

Y. Liu, J. Chen, W. Cheng, C. Sun, S. Zhao, and Y. Pu, “Spatiotemporal dynamics of the urban sprawl in a typical urban agglomeration: a case study on Southern Jiansgu, China,” Front. Earth Sci. 8, 490–504 (2014).
[Crossref]

2012 (4)

D. Stathakis, K. Perakis, and I. Savin, “Efficient segmentation of urban areas by the VIBI,” Int. J. Remote Sens. 33, 6361–6377 (2012).
[Crossref]

M. M. Waqar, J. F. Mirza, R. Mumtaz, and E. Hussain, “Development of new indices for extraction of built-up area & bare soil from Landsat data,” Open Access Sci. Rep. 1, 2–4 (2012).
[Crossref]

C. Deng and C. Wu, “BCI: a biophysical composition index for remote sensing of urban environments,” Remote Sens. Environ. 127, 247–259 (2012).
[Crossref]

C. Y. Sung and M. H. Li, “Considering plant phenology for improving the accuracy of urban impervious surface mapping in a subtropical climate regions,” Int. J. Remote Sens. 33, 261–275 (2012).
[Crossref]

2011 (3)

D. Lu, E. Moran, and S. Hetrick, “Detection of impervious surface change with multitemporal Landsat images in an urban-rural frontier,” ISPRS J. Photogramm. Remote Sens. 66, 298–306 (2011).
[Crossref]

X. Bian, T. Zhang, and X. Zhang, “Combining clustering and classification for remote-sensing images using unlabeled data,” Chin. Opt. Lett. 9, 011002 (2011).
[Crossref]

M. Pesaresi and A. Gerhardinger, “Improved textural built-up presence index for automatic recognition of human settlements in arid regions with scattered vegetation,” IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 4, 16–26 (2011).
[Crossref]

2010 (3)

H. Taubenbock, T. Esch, M. Wurm, A. Roth, and S. Dech, “Object based feature extraction using high spatial resolution satellite data of urban areas,” J. Spat. Sci. 55, 117–132 (2010).
[Crossref]

H. Xu, “Analysis of impervious surface and its impact on urban heat environment using the normalized difference impervious surface index (NDISI),” Photogramm. Eng. Remote Sens. 76, 557–565 (2010).
[Crossref]

C. He, P. Shi, D. Xie, and Y. Zhao, “Improving the normalized difference built-up index to map urban built-up areas using a semiautomatic segmentation approach,” Remote Sens. Lett. 1, 213–221 (2010).
[Crossref]

2009 (2)

X. Hu and Q. Weng, “Estimating impervious surfaces from medium spatial resolution imagery using the self-organizing map and multi-layer perceptron neural networks,” Remote Sens. Environ. 113, 2089–2102 (2009).
[Crossref]

R. B. Thapa and Y. Murayama, “Urban mapping, accuracy, and image classification: a comparison of multiple approaches in Tsukuba city, Japan,” Appl. Geogr. 29, 135–144 (2009).
[Crossref]

2008 (2)

H. Xu, “A new index for delineating built-up land features in satellite imagery,” Int. J. Remote Sens. 29, 4269–4276 (2008).
[Crossref]

M. Pesaresi, A. Gerhardinger, and F. Kayitakire, “A robust built-up area presence index by anisotropic rotation-invariant textural measure,” IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 1, 180–192 (2008).
[Crossref]

2007 (3)

U. Heiden, K. Segl, S. Roessner, and H. Kaufmann, “Determination of robust spectral features for identification of urban surface materials in hyperspectral remote sensing data,” Remote Sens. Environ. 111, 537–552 (2007).
[Crossref]

R. B. Xiao, Z. Y. Ouyang, H. Zheng, W. F. Li, E. W. Schienke, and X. K. Wang, “Spatial pattern of impervious surfaces and their impacts on land surface temperature in Beijing, China,” J. Environ. Sci. 19, 250–256 (2007).
[Crossref]

M. Zoran and C. Weber, “Use of multi-temporal and multispectral satellite data for urban change detection analysis,” J. Optoelectron. Adv. Mater. 9, 1926–1932 (2007).

2006 (2)

D. Lu and Q. Weng, “Spectral mixture analysis of ASTER images for examining the relationship between urban thermal features and biophysical descriptors in Indianapolis, Indiana, USA,” Remote Sens. Environ. 104, 157–167 (2006).
[Crossref]

L. Gomez-Chova, D. Fernandez-Prieto, J. Calpe, E. Soria, J. Vila, and G. Camps-Valls, “Urban monitoring using multi-temporal SAR and multi-spectral data,” Pattern Recogn. Lett. 27, 234–243 (2006).
[Crossref]

2005 (1)

D. Maktav, F. S. Erbek, and C. Jurgens, “Remote sensing of urban areas,” Int. J. Remote Sens. 26, 655–659 (2005).
[Crossref]

2003 (4)

P. E. Dennison and D. A. Roberts, “Endmember selection for multiple endmember spectral mixture analysis using endmember average RMSE,” Remote Sens. Environ. 87, 123–135 (2003).
[Crossref]

S. J. Goetz, R. K. Wright, A. J. Smith, E. Zinecker, and E. Schaub, “IKONOS imagery for resource management: tree cover, impervious surfaces, and riparian buffer analyses in the mid-Atlantic region,” Remote Sens. Environ. 88, 195–208 (2003).
[Crossref]

A. K. Shackelford and C. H. Davis, “A combined fuzzy-pixel based and object based approach for classification of high resolution multi-spectral data over urban areas,” IEEE Trans. Geosci. Remote Sens. 41, 2354–2364 (2003).
[Crossref]

Y. Zha, J. Gao, and S. Ni, “Use of normalized difference built-up index in automatically mapping urban areas from TM imagery,” Int. J. Remote Sens. 24, 583–594 (2003).
[Crossref]

2002 (1)

M. Herold, J. Scepan, and K. C. Clarke, “The use of remote sensing and landscape metrics to describe structures and changes in urban land uses,” Environ. Plann. A 34, 1443–1458 (2002).

2001 (2)

Q. Weng, “A remote sensing-GIS evaluation of urban expansion and its impact on surface temperature in the Zhujiang Delta, China,” Int. J. Remote Sens. 22, 1999–2014 (2001).
[Crossref]

J. Zhang and G. M. Foody, “Fully-fuzzy supervised classification of sub-urban land cover from remotely sensed imagery: statistical and artificial neural network approaches,” Int. J. Remote Sens. 22, 615–628 (2001).
[Crossref]

2000 (1)

D. Ward, S. R. Phinn, and A. T. Murray, “Monitoring growth in rapidly urbanizing areas using remotely sensed data,” Prof. Geogr. 52, 371–386 (2000).
[Crossref]

1996 (1)

S. K. McFeeters, “The use of the normalized difference water index (NDWI) in the delineation of open water features,” Int. J. Remote Sens. 17, 1425–1432 (1996).
[Crossref]

1988 (1)

A. R. Huete, “A soil-adjusted vegetation index (SAVI),” Remote Sens. Environ. 25, 295–309 (1988).
[Crossref]

1981 (1)

C. J. Tucker, B. N. Holben, J. H. Elgin, and J. E. McMurtry, “Remote sensing of total dry matter accumulation in winter wheat,” Remote Sens. Environ. 11, 171–189 (1981).
[Crossref]

Angiuli, E.

E. Angiuli and G. Trianni, “Urban mapping in Landsat images based on normalized difference spectral vector,” IEEE Geosci. Remote Sens. Lett. 11, 661–665 (2014).
[Crossref]

Aziz-Lasbet, A.

S. Bouzekri, A. Aziz-Lasbet, and A. Lachehab, “A new spectral index for extraction of built-up area using Landsat-8 data,” J. Indian Soc. Remote Sens. 43, 867–873 (2015).
[Crossref]

Beaumont, B.

T. Grippa, M. Lennert, B. Beaumont, S. Vanhuysse, N. Stephennen, and E. Wolff, “An open-source semi-automated processing chain for urban object-based classification,” Remote Sens. 9, 358 (2017).
[Crossref]

Bian, X.

Bouzekri, S.

S. Bouzekri, A. Aziz-Lasbet, and A. Lachehab, “A new spectral index for extraction of built-up area using Landsat-8 data,” J. Indian Soc. Remote Sens. 43, 867–873 (2015).
[Crossref]

Calpe, J.

L. Gomez-Chova, D. Fernandez-Prieto, J. Calpe, E. Soria, J. Vila, and G. Camps-Valls, “Urban monitoring using multi-temporal SAR and multi-spectral data,” Pattern Recogn. Lett. 27, 234–243 (2006).
[Crossref]

Camps-Valls, G.

L. Gomez-Chova, D. Fernandez-Prieto, J. Calpe, E. Soria, J. Vila, and G. Camps-Valls, “Urban monitoring using multi-temporal SAR and multi-spectral data,” Pattern Recogn. Lett. 27, 234–243 (2006).
[Crossref]

Chen, J.

Y. Liu, J. Chen, W. Cheng, C. Sun, S. Zhao, and Y. Pu, “Spatiotemporal dynamics of the urban sprawl in a typical urban agglomeration: a case study on Southern Jiansgu, China,” Front. Earth Sci. 8, 490–504 (2014).
[Crossref]

Chen, X.

G. Sun, X. Chen, X. Jia, Y. Yao, and Z. Wang, “Combinational build-up index (CBI) for effective impervious surface mapping in urban areas,” IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 9, 2081–2092 (2016).
[Crossref]

Cheng, W.

Y. Liu, J. Chen, W. Cheng, C. Sun, S. Zhao, and Y. Pu, “Spatiotemporal dynamics of the urban sprawl in a typical urban agglomeration: a case study on Southern Jiansgu, China,” Front. Earth Sci. 8, 490–504 (2014).
[Crossref]

Chenglei, S.

C. Jieli, L. Manchun, L. Yongxue, and S. Chenglei, “Extract residential areas automatically by New Built-up Index,” in 18th International Conference on Geoinformatics (CPGIS, 2010), pp. 1–5.

Civco, D. L.

M. Flanagan and D. L. Civco, “Subpixel impervious surface mapping,” in ASPRS Annual Convention (2001), pp. 13–25.

Clarke, K. C.

M. Herold, J. Scepan, and K. C. Clarke, “The use of remote sensing and landscape metrics to describe structures and changes in urban land uses,” Environ. Plann. A 34, 1443–1458 (2002).

Davis, C. H.

A. K. Shackelford and C. H. Davis, “A combined fuzzy-pixel based and object based approach for classification of high resolution multi-spectral data over urban areas,” IEEE Trans. Geosci. Remote Sens. 41, 2354–2364 (2003).
[Crossref]

Dech, S.

H. Taubenbock, T. Esch, M. Wurm, A. Roth, and S. Dech, “Object based feature extraction using high spatial resolution satellite data of urban areas,” J. Spat. Sci. 55, 117–132 (2010).
[Crossref]

Deering, D. W.

J. W. Rouse, R. S. Haas, J. A. Schell, and D. W. Deering, “Monitoring vegetation systems in the Great Plains with ERTS,” in 3rd ERTS Symposium (1973), pp. 48–62.

Deng, C.

C. Deng and C. Wu, “BCI: a biophysical composition index for remote sensing of urban environments,” Remote Sens. Environ. 127, 247–259 (2012).
[Crossref]

Dennison, P. E.

P. E. Dennison and D. A. Roberts, “Endmember selection for multiple endmember spectral mixture analysis using endmember average RMSE,” Remote Sens. Environ. 87, 123–135 (2003).
[Crossref]

Elgin, J. H.

C. J. Tucker, B. N. Holben, J. H. Elgin, and J. E. McMurtry, “Remote sensing of total dry matter accumulation in winter wheat,” Remote Sens. Environ. 11, 171–189 (1981).
[Crossref]

Erbek, F. S.

D. Maktav, F. S. Erbek, and C. Jurgens, “Remote sensing of urban areas,” Int. J. Remote Sens. 26, 655–659 (2005).
[Crossref]

Esch, T.

H. Taubenbock, T. Esch, M. Wurm, A. Roth, and S. Dech, “Object based feature extraction using high spatial resolution satellite data of urban areas,” J. Spat. Sci. 55, 117–132 (2010).
[Crossref]

Fernandez-Prieto, D.

L. Gomez-Chova, D. Fernandez-Prieto, J. Calpe, E. Soria, J. Vila, and G. Camps-Valls, “Urban monitoring using multi-temporal SAR and multi-spectral data,” Pattern Recogn. Lett. 27, 234–243 (2006).
[Crossref]

Flanagan, M.

M. Flanagan and D. L. Civco, “Subpixel impervious surface mapping,” in ASPRS Annual Convention (2001), pp. 13–25.

Foody, G. M.

J. Zhang and G. M. Foody, “Fully-fuzzy supervised classification of sub-urban land cover from remotely sensed imagery: statistical and artificial neural network approaches,” Int. J. Remote Sens. 22, 615–628 (2001).
[Crossref]

Gao, J.

Y. Zha, J. Gao, and S. Ni, “Use of normalized difference built-up index in automatically mapping urban areas from TM imagery,” Int. J. Remote Sens. 24, 583–594 (2003).
[Crossref]

Gerhardinger, A.

M. Pesaresi and A. Gerhardinger, “Improved textural built-up presence index for automatic recognition of human settlements in arid regions with scattered vegetation,” IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 4, 16–26 (2011).
[Crossref]

M. Pesaresi, A. Gerhardinger, and F. Kayitakire, “A robust built-up area presence index by anisotropic rotation-invariant textural measure,” IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 1, 180–192 (2008).
[Crossref]

Goetz, S. J.

S. J. Goetz, R. K. Wright, A. J. Smith, E. Zinecker, and E. Schaub, “IKONOS imagery for resource management: tree cover, impervious surfaces, and riparian buffer analyses in the mid-Atlantic region,” Remote Sens. Environ. 88, 195–208 (2003).
[Crossref]

Gomez-Chova, L.

L. Gomez-Chova, D. Fernandez-Prieto, J. Calpe, E. Soria, J. Vila, and G. Camps-Valls, “Urban monitoring using multi-temporal SAR and multi-spectral data,” Pattern Recogn. Lett. 27, 234–243 (2006).
[Crossref]

Grippa, T.

T. Grippa, M. Lennert, B. Beaumont, S. Vanhuysse, N. Stephennen, and E. Wolff, “An open-source semi-automated processing chain for urban object-based classification,” Remote Sens. 9, 358 (2017).
[Crossref]

Haas, R. S.

J. W. Rouse, R. S. Haas, J. A. Schell, and D. W. Deering, “Monitoring vegetation systems in the Great Plains with ERTS,” in 3rd ERTS Symposium (1973), pp. 48–62.

He, C.

C. He, P. Shi, D. Xie, and Y. Zhao, “Improving the normalized difference built-up index to map urban built-up areas using a semiautomatic segmentation approach,” Remote Sens. Lett. 1, 213–221 (2010).
[Crossref]

Heiden, U.

U. Heiden, K. Segl, S. Roessner, and H. Kaufmann, “Determination of robust spectral features for identification of urban surface materials in hyperspectral remote sensing data,” Remote Sens. Environ. 111, 537–552 (2007).
[Crossref]

Herold, M.

M. Herold, J. Scepan, and K. C. Clarke, “The use of remote sensing and landscape metrics to describe structures and changes in urban land uses,” Environ. Plann. A 34, 1443–1458 (2002).

Hetrick, S.

D. Lu, E. Moran, and S. Hetrick, “Detection of impervious surface change with multitemporal Landsat images in an urban-rural frontier,” ISPRS J. Photogramm. Remote Sens. 66, 298–306 (2011).
[Crossref]

Holben, B. N.

C. J. Tucker, B. N. Holben, J. H. Elgin, and J. E. McMurtry, “Remote sensing of total dry matter accumulation in winter wheat,” Remote Sens. Environ. 11, 171–189 (1981).
[Crossref]

Hu, X.

X. Hu and Q. Weng, “Estimating impervious surfaces from medium spatial resolution imagery using the self-organizing map and multi-layer perceptron neural networks,” Remote Sens. Environ. 113, 2089–2102 (2009).
[Crossref]

Huete, A. R.

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M. M. Waqar, J. F. Mirza, R. Mumtaz, and E. Hussain, “Development of new indices for extraction of built-up area & bare soil from Landsat data,” Open Access Sci. Rep. 1, 2–4 (2012).
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Jia, X.

G. Sun, X. Chen, X. Jia, Y. Yao, and Z. Wang, “Combinational build-up index (CBI) for effective impervious surface mapping in urban areas,” IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 9, 2081–2092 (2016).
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C. Jieli, L. Manchun, L. Yongxue, and S. Chenglei, “Extract residential areas automatically by New Built-up Index,” in 18th International Conference on Geoinformatics (CPGIS, 2010), pp. 1–5.

Jurgens, C.

D. Maktav, F. S. Erbek, and C. Jurgens, “Remote sensing of urban areas,” Int. J. Remote Sens. 26, 655–659 (2005).
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U. Heiden, K. Segl, S. Roessner, and H. Kaufmann, “Determination of robust spectral features for identification of urban surface materials in hyperspectral remote sensing data,” Remote Sens. Environ. 111, 537–552 (2007).
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M. Pesaresi, A. Gerhardinger, and F. Kayitakire, “A robust built-up area presence index by anisotropic rotation-invariant textural measure,” IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 1, 180–192 (2008).
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S. Bouzekri, A. Aziz-Lasbet, and A. Lachehab, “A new spectral index for extraction of built-up area using Landsat-8 data,” J. Indian Soc. Remote Sens. 43, 867–873 (2015).
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T. Grippa, M. Lennert, B. Beaumont, S. Vanhuysse, N. Stephennen, and E. Wolff, “An open-source semi-automated processing chain for urban object-based classification,” Remote Sens. 9, 358 (2017).
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C. Y. Sung and M. H. Li, “Considering plant phenology for improving the accuracy of urban impervious surface mapping in a subtropical climate regions,” Int. J. Remote Sens. 33, 261–275 (2012).
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Li, W. F.

R. B. Xiao, Z. Y. Ouyang, H. Zheng, W. F. Li, E. W. Schienke, and X. K. Wang, “Spatial pattern of impervious surfaces and their impacts on land surface temperature in Beijing, China,” J. Environ. Sci. 19, 250–256 (2007).
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Y. Liu, J. Chen, W. Cheng, C. Sun, S. Zhao, and Y. Pu, “Spatiotemporal dynamics of the urban sprawl in a typical urban agglomeration: a case study on Southern Jiansgu, China,” Front. Earth Sci. 8, 490–504 (2014).
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Lu, D.

D. Lu, E. Moran, and S. Hetrick, “Detection of impervious surface change with multitemporal Landsat images in an urban-rural frontier,” ISPRS J. Photogramm. Remote Sens. 66, 298–306 (2011).
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D. Lu and Q. Weng, “Spectral mixture analysis of ASTER images for examining the relationship between urban thermal features and biophysical descriptors in Indianapolis, Indiana, USA,” Remote Sens. Environ. 104, 157–167 (2006).
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D. Maktav, F. S. Erbek, and C. Jurgens, “Remote sensing of urban areas,” Int. J. Remote Sens. 26, 655–659 (2005).
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C. Jieli, L. Manchun, L. Yongxue, and S. Chenglei, “Extract residential areas automatically by New Built-up Index,” in 18th International Conference on Geoinformatics (CPGIS, 2010), pp. 1–5.

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S. K. McFeeters, “The use of the normalized difference water index (NDWI) in the delineation of open water features,” Int. J. Remote Sens. 17, 1425–1432 (1996).
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McMurtry, J. E.

C. J. Tucker, B. N. Holben, J. H. Elgin, and J. E. McMurtry, “Remote sensing of total dry matter accumulation in winter wheat,” Remote Sens. Environ. 11, 171–189 (1981).
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M. M. Waqar, J. F. Mirza, R. Mumtaz, and E. Hussain, “Development of new indices for extraction of built-up area & bare soil from Landsat data,” Open Access Sci. Rep. 1, 2–4 (2012).
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D. Lu, E. Moran, and S. Hetrick, “Detection of impervious surface change with multitemporal Landsat images in an urban-rural frontier,” ISPRS J. Photogramm. Remote Sens. 66, 298–306 (2011).
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M. M. Waqar, J. F. Mirza, R. Mumtaz, and E. Hussain, “Development of new indices for extraction of built-up area & bare soil from Landsat data,” Open Access Sci. Rep. 1, 2–4 (2012).
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D. Ward, S. R. Phinn, and A. T. Murray, “Monitoring growth in rapidly urbanizing areas using remotely sensed data,” Prof. Geogr. 52, 371–386 (2000).
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M. Pesaresi and A. Gerhardinger, “Improved textural built-up presence index for automatic recognition of human settlements in arid regions with scattered vegetation,” IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 4, 16–26 (2011).
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M. Pesaresi, A. Gerhardinger, and F. Kayitakire, “A robust built-up area presence index by anisotropic rotation-invariant textural measure,” IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 1, 180–192 (2008).
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D. Ward, S. R. Phinn, and A. T. Murray, “Monitoring growth in rapidly urbanizing areas using remotely sensed data,” Prof. Geogr. 52, 371–386 (2000).
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Y. Liu, J. Chen, W. Cheng, C. Sun, S. Zhao, and Y. Pu, “Spatiotemporal dynamics of the urban sprawl in a typical urban agglomeration: a case study on Southern Jiansgu, China,” Front. Earth Sci. 8, 490–504 (2014).
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U. Heiden, K. Segl, S. Roessner, and H. Kaufmann, “Determination of robust spectral features for identification of urban surface materials in hyperspectral remote sensing data,” Remote Sens. Environ. 111, 537–552 (2007).
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H. Taubenbock, T. Esch, M. Wurm, A. Roth, and S. Dech, “Object based feature extraction using high spatial resolution satellite data of urban areas,” J. Spat. Sci. 55, 117–132 (2010).
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D. Stathakis, K. Perakis, and I. Savin, “Efficient segmentation of urban areas by the VIBI,” Int. J. Remote Sens. 33, 6361–6377 (2012).
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M. Herold, J. Scepan, and K. C. Clarke, “The use of remote sensing and landscape metrics to describe structures and changes in urban land uses,” Environ. Plann. A 34, 1443–1458 (2002).

Schaub, E.

S. J. Goetz, R. K. Wright, A. J. Smith, E. Zinecker, and E. Schaub, “IKONOS imagery for resource management: tree cover, impervious surfaces, and riparian buffer analyses in the mid-Atlantic region,” Remote Sens. Environ. 88, 195–208 (2003).
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J. W. Rouse, R. S. Haas, J. A. Schell, and D. W. Deering, “Monitoring vegetation systems in the Great Plains with ERTS,” in 3rd ERTS Symposium (1973), pp. 48–62.

Schienke, E. W.

R. B. Xiao, Z. Y. Ouyang, H. Zheng, W. F. Li, E. W. Schienke, and X. K. Wang, “Spatial pattern of impervious surfaces and their impacts on land surface temperature in Beijing, China,” J. Environ. Sci. 19, 250–256 (2007).
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A. K. Shackelford and C. H. Davis, “A combined fuzzy-pixel based and object based approach for classification of high resolution multi-spectral data over urban areas,” IEEE Trans. Geosci. Remote Sens. 41, 2354–2364 (2003).
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Z. Shao, Y. Tian, and X. Shen, “BASI: a new index to extract built-up areas from high-resolution remote sensing images by visual attention model,” Remote Sens. Lett. 5, 305–314 (2014).
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C. He, P. Shi, D. Xie, and Y. Zhao, “Improving the normalized difference built-up index to map urban built-up areas using a semiautomatic segmentation approach,” Remote Sens. Lett. 1, 213–221 (2010).
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Smith, A. J.

S. J. Goetz, R. K. Wright, A. J. Smith, E. Zinecker, and E. Schaub, “IKONOS imagery for resource management: tree cover, impervious surfaces, and riparian buffer analyses in the mid-Atlantic region,” Remote Sens. Environ. 88, 195–208 (2003).
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L. Gomez-Chova, D. Fernandez-Prieto, J. Calpe, E. Soria, J. Vila, and G. Camps-Valls, “Urban monitoring using multi-temporal SAR and multi-spectral data,” Pattern Recogn. Lett. 27, 234–243 (2006).
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D. Stathakis, K. Perakis, and I. Savin, “Efficient segmentation of urban areas by the VIBI,” Int. J. Remote Sens. 33, 6361–6377 (2012).
[Crossref]

Stephennen, N.

T. Grippa, M. Lennert, B. Beaumont, S. Vanhuysse, N. Stephennen, and E. Wolff, “An open-source semi-automated processing chain for urban object-based classification,” Remote Sens. 9, 358 (2017).
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Sun, C.

Y. Liu, J. Chen, W. Cheng, C. Sun, S. Zhao, and Y. Pu, “Spatiotemporal dynamics of the urban sprawl in a typical urban agglomeration: a case study on Southern Jiansgu, China,” Front. Earth Sci. 8, 490–504 (2014).
[Crossref]

Sun, G.

G. Sun, X. Chen, X. Jia, Y. Yao, and Z. Wang, “Combinational build-up index (CBI) for effective impervious surface mapping in urban areas,” IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 9, 2081–2092 (2016).
[Crossref]

Sung, C. Y.

C. Y. Sung and M. H. Li, “Considering plant phenology for improving the accuracy of urban impervious surface mapping in a subtropical climate regions,” Int. J. Remote Sens. 33, 261–275 (2012).
[Crossref]

Taubenbock, H.

H. Taubenbock, T. Esch, M. Wurm, A. Roth, and S. Dech, “Object based feature extraction using high spatial resolution satellite data of urban areas,” J. Spat. Sci. 55, 117–132 (2010).
[Crossref]

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R. B. Thapa and Y. Murayama, “Urban mapping, accuracy, and image classification: a comparison of multiple approaches in Tsukuba city, Japan,” Appl. Geogr. 29, 135–144 (2009).
[Crossref]

Tian, Y.

Z. Shao, Y. Tian, and X. Shen, “BASI: a new index to extract built-up areas from high-resolution remote sensing images by visual attention model,” Remote Sens. Lett. 5, 305–314 (2014).
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E. Angiuli and G. Trianni, “Urban mapping in Landsat images based on normalized difference spectral vector,” IEEE Geosci. Remote Sens. Lett. 11, 661–665 (2014).
[Crossref]

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C. J. Tucker, B. N. Holben, J. H. Elgin, and J. E. McMurtry, “Remote sensing of total dry matter accumulation in winter wheat,” Remote Sens. Environ. 11, 171–189 (1981).
[Crossref]

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T. Grippa, M. Lennert, B. Beaumont, S. Vanhuysse, N. Stephennen, and E. Wolff, “An open-source semi-automated processing chain for urban object-based classification,” Remote Sens. 9, 358 (2017).
[Crossref]

Varshney, A.

A. Varshney and E. Rajesh, “A comparative study of built-up index approaches for automated extraction of built-up regions from remote sensing data,” J. Indian. Soc. Remote Sens. 42, 659–663 (2014).
[Crossref]

Vila, J.

L. Gomez-Chova, D. Fernandez-Prieto, J. Calpe, E. Soria, J. Vila, and G. Camps-Valls, “Urban monitoring using multi-temporal SAR and multi-spectral data,” Pattern Recogn. Lett. 27, 234–243 (2006).
[Crossref]

Wang, X. K.

R. B. Xiao, Z. Y. Ouyang, H. Zheng, W. F. Li, E. W. Schienke, and X. K. Wang, “Spatial pattern of impervious surfaces and their impacts on land surface temperature in Beijing, China,” J. Environ. Sci. 19, 250–256 (2007).
[Crossref]

Wang, Z.

G. Sun, X. Chen, X. Jia, Y. Yao, and Z. Wang, “Combinational build-up index (CBI) for effective impervious surface mapping in urban areas,” IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 9, 2081–2092 (2016).
[Crossref]

Waqar, M. M.

M. M. Waqar, J. F. Mirza, R. Mumtaz, and E. Hussain, “Development of new indices for extraction of built-up area & bare soil from Landsat data,” Open Access Sci. Rep. 1, 2–4 (2012).
[Crossref]

Ward, D.

D. Ward, S. R. Phinn, and A. T. Murray, “Monitoring growth in rapidly urbanizing areas using remotely sensed data,” Prof. Geogr. 52, 371–386 (2000).
[Crossref]

Weber, C.

M. Zoran and C. Weber, “Use of multi-temporal and multispectral satellite data for urban change detection analysis,” J. Optoelectron. Adv. Mater. 9, 1926–1932 (2007).

Weng, Q.

X. Hu and Q. Weng, “Estimating impervious surfaces from medium spatial resolution imagery using the self-organizing map and multi-layer perceptron neural networks,” Remote Sens. Environ. 113, 2089–2102 (2009).
[Crossref]

D. Lu and Q. Weng, “Spectral mixture analysis of ASTER images for examining the relationship between urban thermal features and biophysical descriptors in Indianapolis, Indiana, USA,” Remote Sens. Environ. 104, 157–167 (2006).
[Crossref]

Q. Weng, “A remote sensing-GIS evaluation of urban expansion and its impact on surface temperature in the Zhujiang Delta, China,” Int. J. Remote Sens. 22, 1999–2014 (2001).
[Crossref]

Wolff, E.

T. Grippa, M. Lennert, B. Beaumont, S. Vanhuysse, N. Stephennen, and E. Wolff, “An open-source semi-automated processing chain for urban object-based classification,” Remote Sens. 9, 358 (2017).
[Crossref]

Wright, R. K.

S. J. Goetz, R. K. Wright, A. J. Smith, E. Zinecker, and E. Schaub, “IKONOS imagery for resource management: tree cover, impervious surfaces, and riparian buffer analyses in the mid-Atlantic region,” Remote Sens. Environ. 88, 195–208 (2003).
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Wu, C.

C. Deng and C. Wu, “BCI: a biophysical composition index for remote sensing of urban environments,” Remote Sens. Environ. 127, 247–259 (2012).
[Crossref]

Wurm, M.

H. Taubenbock, T. Esch, M. Wurm, A. Roth, and S. Dech, “Object based feature extraction using high spatial resolution satellite data of urban areas,” J. Spat. Sci. 55, 117–132 (2010).
[Crossref]

Xiao, R. B.

R. B. Xiao, Z. Y. Ouyang, H. Zheng, W. F. Li, E. W. Schienke, and X. K. Wang, “Spatial pattern of impervious surfaces and their impacts on land surface temperature in Beijing, China,” J. Environ. Sci. 19, 250–256 (2007).
[Crossref]

Xie, D.

C. He, P. Shi, D. Xie, and Y. Zhao, “Improving the normalized difference built-up index to map urban built-up areas using a semiautomatic segmentation approach,” Remote Sens. Lett. 1, 213–221 (2010).
[Crossref]

Xu, H.

H. Xu, “Analysis of impervious surface and its impact on urban heat environment using the normalized difference impervious surface index (NDISI),” Photogramm. Eng. Remote Sens. 76, 557–565 (2010).
[Crossref]

H. Xu, “A new index for delineating built-up land features in satellite imagery,” Int. J. Remote Sens. 29, 4269–4276 (2008).
[Crossref]

Yao, Y.

G. Sun, X. Chen, X. Jia, Y. Yao, and Z. Wang, “Combinational build-up index (CBI) for effective impervious surface mapping in urban areas,” IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 9, 2081–2092 (2016).
[Crossref]

Yongxue, L.

C. Jieli, L. Manchun, L. Yongxue, and S. Chenglei, “Extract residential areas automatically by New Built-up Index,” in 18th International Conference on Geoinformatics (CPGIS, 2010), pp. 1–5.

Zha, Y.

Y. Zha, J. Gao, and S. Ni, “Use of normalized difference built-up index in automatically mapping urban areas from TM imagery,” Int. J. Remote Sens. 24, 583–594 (2003).
[Crossref]

Zhang, J.

J. Zhang and G. M. Foody, “Fully-fuzzy supervised classification of sub-urban land cover from remotely sensed imagery: statistical and artificial neural network approaches,” Int. J. Remote Sens. 22, 615–628 (2001).
[Crossref]

Zhang, T.

Zhang, X.

Zhao, S.

Y. Liu, J. Chen, W. Cheng, C. Sun, S. Zhao, and Y. Pu, “Spatiotemporal dynamics of the urban sprawl in a typical urban agglomeration: a case study on Southern Jiansgu, China,” Front. Earth Sci. 8, 490–504 (2014).
[Crossref]

Zhao, Y.

C. He, P. Shi, D. Xie, and Y. Zhao, “Improving the normalized difference built-up index to map urban built-up areas using a semiautomatic segmentation approach,” Remote Sens. Lett. 1, 213–221 (2010).
[Crossref]

Zheng, H.

R. B. Xiao, Z. Y. Ouyang, H. Zheng, W. F. Li, E. W. Schienke, and X. K. Wang, “Spatial pattern of impervious surfaces and their impacts on land surface temperature in Beijing, China,” J. Environ. Sci. 19, 250–256 (2007).
[Crossref]

Zinecker, E.

S. J. Goetz, R. K. Wright, A. J. Smith, E. Zinecker, and E. Schaub, “IKONOS imagery for resource management: tree cover, impervious surfaces, and riparian buffer analyses in the mid-Atlantic region,” Remote Sens. Environ. 88, 195–208 (2003).
[Crossref]

Zoran, M.

M. Zoran and C. Weber, “Use of multi-temporal and multispectral satellite data for urban change detection analysis,” J. Optoelectron. Adv. Mater. 9, 1926–1932 (2007).

Appl. Geogr. (1)

R. B. Thapa and Y. Murayama, “Urban mapping, accuracy, and image classification: a comparison of multiple approaches in Tsukuba city, Japan,” Appl. Geogr. 29, 135–144 (2009).
[Crossref]

Chin. Opt. Lett. (1)

Environ. Plann. A (1)

M. Herold, J. Scepan, and K. C. Clarke, “The use of remote sensing and landscape metrics to describe structures and changes in urban land uses,” Environ. Plann. A 34, 1443–1458 (2002).

Front. Earth Sci. (1)

Y. Liu, J. Chen, W. Cheng, C. Sun, S. Zhao, and Y. Pu, “Spatiotemporal dynamics of the urban sprawl in a typical urban agglomeration: a case study on Southern Jiansgu, China,” Front. Earth Sci. 8, 490–504 (2014).
[Crossref]

IEEE Geosci. Remote Sens. Lett. (1)

E. Angiuli and G. Trianni, “Urban mapping in Landsat images based on normalized difference spectral vector,” IEEE Geosci. Remote Sens. Lett. 11, 661–665 (2014).
[Crossref]

IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. (3)

M. Pesaresi, A. Gerhardinger, and F. Kayitakire, “A robust built-up area presence index by anisotropic rotation-invariant textural measure,” IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 1, 180–192 (2008).
[Crossref]

M. Pesaresi and A. Gerhardinger, “Improved textural built-up presence index for automatic recognition of human settlements in arid regions with scattered vegetation,” IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 4, 16–26 (2011).
[Crossref]

G. Sun, X. Chen, X. Jia, Y. Yao, and Z. Wang, “Combinational build-up index (CBI) for effective impervious surface mapping in urban areas,” IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 9, 2081–2092 (2016).
[Crossref]

IEEE Trans. Geosci. Remote Sens. (1)

A. K. Shackelford and C. H. Davis, “A combined fuzzy-pixel based and object based approach for classification of high resolution multi-spectral data over urban areas,” IEEE Trans. Geosci. Remote Sens. 41, 2354–2364 (2003).
[Crossref]

Int. J. Remote Sens. (8)

S. K. McFeeters, “The use of the normalized difference water index (NDWI) in the delineation of open water features,” Int. J. Remote Sens. 17, 1425–1432 (1996).
[Crossref]

Y. Zha, J. Gao, and S. Ni, “Use of normalized difference built-up index in automatically mapping urban areas from TM imagery,” Int. J. Remote Sens. 24, 583–594 (2003).
[Crossref]

H. Xu, “A new index for delineating built-up land features in satellite imagery,” Int. J. Remote Sens. 29, 4269–4276 (2008).
[Crossref]

D. Maktav, F. S. Erbek, and C. Jurgens, “Remote sensing of urban areas,” Int. J. Remote Sens. 26, 655–659 (2005).
[Crossref]

Q. Weng, “A remote sensing-GIS evaluation of urban expansion and its impact on surface temperature in the Zhujiang Delta, China,” Int. J. Remote Sens. 22, 1999–2014 (2001).
[Crossref]

C. Y. Sung and M. H. Li, “Considering plant phenology for improving the accuracy of urban impervious surface mapping in a subtropical climate regions,” Int. J. Remote Sens. 33, 261–275 (2012).
[Crossref]

J. Zhang and G. M. Foody, “Fully-fuzzy supervised classification of sub-urban land cover from remotely sensed imagery: statistical and artificial neural network approaches,” Int. J. Remote Sens. 22, 615–628 (2001).
[Crossref]

D. Stathakis, K. Perakis, and I. Savin, “Efficient segmentation of urban areas by the VIBI,” Int. J. Remote Sens. 33, 6361–6377 (2012).
[Crossref]

ISPRS J. Photogramm. Remote Sens. (1)

D. Lu, E. Moran, and S. Hetrick, “Detection of impervious surface change with multitemporal Landsat images in an urban-rural frontier,” ISPRS J. Photogramm. Remote Sens. 66, 298–306 (2011).
[Crossref]

J. Environ. Sci. (1)

R. B. Xiao, Z. Y. Ouyang, H. Zheng, W. F. Li, E. W. Schienke, and X. K. Wang, “Spatial pattern of impervious surfaces and their impacts on land surface temperature in Beijing, China,” J. Environ. Sci. 19, 250–256 (2007).
[Crossref]

J. Indian Soc. Remote Sens. (1)

S. Bouzekri, A. Aziz-Lasbet, and A. Lachehab, “A new spectral index for extraction of built-up area using Landsat-8 data,” J. Indian Soc. Remote Sens. 43, 867–873 (2015).
[Crossref]

J. Indian. Soc. Remote Sens. (1)

A. Varshney and E. Rajesh, “A comparative study of built-up index approaches for automated extraction of built-up regions from remote sensing data,” J. Indian. Soc. Remote Sens. 42, 659–663 (2014).
[Crossref]

J. Optoelectron. Adv. Mater. (1)

M. Zoran and C. Weber, “Use of multi-temporal and multispectral satellite data for urban change detection analysis,” J. Optoelectron. Adv. Mater. 9, 1926–1932 (2007).

J. Spat. Sci. (1)

H. Taubenbock, T. Esch, M. Wurm, A. Roth, and S. Dech, “Object based feature extraction using high spatial resolution satellite data of urban areas,” J. Spat. Sci. 55, 117–132 (2010).
[Crossref]

Open Access Sci. Rep. (1)

M. M. Waqar, J. F. Mirza, R. Mumtaz, and E. Hussain, “Development of new indices for extraction of built-up area & bare soil from Landsat data,” Open Access Sci. Rep. 1, 2–4 (2012).
[Crossref]

Pattern Recogn. Lett. (1)

L. Gomez-Chova, D. Fernandez-Prieto, J. Calpe, E. Soria, J. Vila, and G. Camps-Valls, “Urban monitoring using multi-temporal SAR and multi-spectral data,” Pattern Recogn. Lett. 27, 234–243 (2006).
[Crossref]

Photogramm. Eng. Remote Sens. (1)

H. Xu, “Analysis of impervious surface and its impact on urban heat environment using the normalized difference impervious surface index (NDISI),” Photogramm. Eng. Remote Sens. 76, 557–565 (2010).
[Crossref]

Prof. Geogr. (1)

D. Ward, S. R. Phinn, and A. T. Murray, “Monitoring growth in rapidly urbanizing areas using remotely sensed data,” Prof. Geogr. 52, 371–386 (2000).
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Remote Sens. (1)

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Figures (9)

Fig. 1.
Fig. 1. (a) False color image of Guadalajara city obtained by combination of bands 12, 7, and 3 of Sentinel 2A (the marked area within the white rectangle is characterized by bare soil and farmland covers). (b) Mean spectral value per land cover type for five bands of Sentinel 2A.
Fig. 2.
Fig. 2. Built-up index values obtained for specific land cover types from Sentinel 2A data, registered over Guadalajara city. The graphs have been normalized to the range [1,1]; the labels indicate bare soil (Bar), urban areas (Urb), roads (Roa), water (Wat), forest (For), gardens (Gar), and farmlands (Far).
Fig. 3.
Fig. 3. Built-up indices computed from Sentinel 2A bands registered over Guadalajara city: (a) NDBI, (b) IBI, (c) NBI, (d) BRBA, (e) NBAI, (f) BCI, (g) MBI, (h) BAEI, and (i) CBI. Blue circles denote impervious surfaces and orange circles indicate bare soil areas.
Fig. 4.
Fig. 4. Normalized difference spectral vectors extracted from urban and bare soil pixels, respectively; (a) vectors in the rainy season (October 2016), (b) vectors in the dry season (May 2016).
Fig. 5.
Fig. 5. Histograms of urban areas and bare soil for the corresponding built-up indices: (a) NDBI, (b) IBI, (c) NBI, (d) BRBA, (e) NBAI, (f) BCI, (g) MBI, (h) BAEI, and (i) CBI.
Fig. 6.
Fig. 6. Performance of NBI [images (a) and (b)] and NBAI [images (c) and (d)] computed from datasets registered in February and May, respectively, over Guadalajara city.
Fig. 7.
Fig. 7. Histograms of urban areas and bare soil computed from a dry month dataset (May 19, 2016): (a) NDBI, (b) IBI, (c) NBI, (d) BRBA, (e) NBAI, (f) BCI, (g) MBI, (h) BAEI, and (i) CBI.
Fig. 8.
Fig. 8. Urban areas extraction of Monterrey city using different indices: (a) False color image, (b) BRBA, (c) NBAI, and (d) BCI.
Fig. 9.
Fig. 9. Urban areas extraction of Ensenada city using different indices: (a) False color image, (b) NBAI, (c) BCI, and (d) CBI.

Tables (3)

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Table 1. Overall Accuracy and Study Areas for the Proposed Built-Up Indicesa

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Table 2. Sentinel 2A Spectral Bands Employed to Compute Built-Up Indices

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Table 3. Spectral Discrimination Index (SDI) between Bare Soil and Urban Surfaces for the Studied Built-Up Indices

Equations (15)

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NDVI=bnirbredbnir+bred.
SAVI=(bnirbred)(1+l)(bnir+bred+l).
NDWI=bgreenbnirbgreen+bnir.
NDBI=bswirbnirbswir+bnir.
IBI=NDBI(SAVI+MNDWI)/2NDBI+(SAVI+MNDWI)/2.
NBI=bred·bswirbnir.
NDISI=btir[WI+bnir+bswir]/3btir+[WI+bnir+bswir]/3,
BRBA=bredbswir,
NBAI=bswirbswir2/bgreenbswir+bswir2/bgreen,
BCI=(TC1+TC3)/2TC2(TC1+TC3)/2+TC2,
MBI=bswir2·bredbnir2bred+bnir+bswir2.
BAEI=bred+Lbgreen+bswir.
NDSV=[f(b1,b2),,f(b1,b6),,f(b5,b6)]T,
CBI=(PC1+NDWI)/2SAVI(PC1+NDWI)/2+SAVI.
SDI=|μ1μ2|/(σ1+σ2).

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