Built-up index methods and their applications for urban extraction from Sentinel 2A satellite data: discussion

Juan C. Valdiviezo-N; Alejandro Téllez-Quiñones; Adan Salazar-Garibay; Alejandra A. López-Caloca

doi:10.1364/JOSAA.35.000035

Journal of the Optical Society of America A
Vol. 35,
Issue 1,
pp. 35-44
(2018)
•https://doi.org/10.1364/JOSAA.35.000035

Built-up index methods and their applications for urban extraction from Sentinel 2A satellite data: discussion

Juan C. Valdiviezo-N, Alejandro Téllez-Quiñones, Adan Salazar-Garibay, and Alejandra A. López-Caloca

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Juan C. Valdiviezo-N, Alejandro Téllez-Quiñones, Adan Salazar-Garibay, and Alejandra A. López-Caloca, "Built-up index methods and their applications for urban extraction from Sentinel 2A satellite data: discussion," J. Opt. Soc. Am. A 35, 35-44 (2018)

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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.

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Index	Study Area	Sensor	Accuracy
NDBI	Nanjing, China	Landsat TM	92.6%
IBI	Fuzhou, China	Landsat ETM+	96.77%
NBI	Changzhou, China	Landsat TM	90%
NDISI	Fuzhou, China	Landsat ETM+	90.7%
BRBA	Islamabad, Pakistan	Landsat TM	85.5%
NBAI			86.8%
BCI	Grafton, WI, USA	Landsat ETM+
		IKONOS	NR
	State of Wisconsin	MODIS
MBI	Jiangsu, China	Landsat MSS, TM, and ETM+	87.7%
BAEI	Djelfa, Algeria	Landsat-8 OLI	92.66%
NDSV	Tripoli, Libya	Landsat TM	97.9%
	Lagos, Nigeria		97.49%
CBI	Qingdao, China	Landsat-8 OLI
	Okinawa, Japan	WorldView-2	NR
	Fushun, China	SPOT-5
	Berlin, Germany	HyMap

Band	Wavelength (nm)	Specification
3	560	$b_{green}$
4	665	$b_{red}$
8A	865	$b_{nir}$
11	1610	$b_{{swir}_{2}}$
12	2190	$b_{swir}$

Built-Up Index	SDI
NDBI	0.7830
IBI	0.3037
NBI	0.3137
BRBA	1.4712
NBAI	1.2652
BCI	1.2033
MBI	0.0336
BAEI	0.1562
CBI	1.0093

Index	Study Area	Sensor	Accuracy
NDBI	Nanjing, China	Landsat TM	92.6%
IBI	Fuzhou, China	Landsat ETM+	96.77%
NBI	Changzhou, China	Landsat TM	90%
NDISI	Fuzhou, China	Landsat ETM+	90.7%
BRBA	Islamabad, Pakistan	Landsat TM	85.5%
NBAI			86.8%
BCI	Grafton, WI, USA	Landsat ETM+
		IKONOS	NR
	State of Wisconsin	MODIS
MBI	Jiangsu, China	Landsat MSS, TM, and ETM+	87.7%
BAEI	Djelfa, Algeria	Landsat-8 OLI	92.66%
NDSV	Tripoli, Libya	Landsat TM	97.9%
	Lagos, Nigeria		97.49%
CBI	Qingdao, China	Landsat-8 OLI
	Okinawa, Japan	WorldView-2	NR
	Fushun, China	SPOT-5
	Berlin, Germany	HyMap

Band	Wavelength (nm)	Specification
3	560	$b_{green}$
4	665	$b_{red}$
8A	865	$b_{nir}$
11	1610	$b_{{swir}_{2}}$
12	2190	$b_{swir}$

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