Use of normalized difference built-up index in automatically mapping urban areas from TM imagery

By Y. Zha, Jay Gao, S. Ni. 2003

2,610 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change, Media Technology

View Paper: https://www.semanticscholar.org/paper/1ca7bbe601d1ca3b90cd5a6182cbf13ea86996ba

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Citations in the corpus

Simulating Land Cover Changes and Their Impacts on Land Surface Temperature in Dhaka, Bangladesh (2013, methodology): “It has been recently used as an indicator to represent the extent of built-up areas [5,33].”
Enhanced Built-Up and Bareness Index (EBBI) for Mapping Built-Up and Bare Land in an Urban Area (2012, methodology): “Nevertheless, these two indices are unable to verify the distribution of built-up versus bare land areas [7,10,11].”
Mapping Urban Land Use by Using Landsat Images and Open Social Data (2016, background): “The index was useful for detecting impervious surfaces [51].”
Assessing the robustness of Random Forests to map land cover with high resolution satellite image time series over large areas (2016, background): “For instance, NDWI (McFeeters, 1996) for water detection, NDBI (Zha et al., 2003) for building detection, and the tasseled cap coefficients with Landsat images for the description of agricultural crops (Kauth and Thomas, 1976; Crist and Cicone, 1984; Huang et al., 2002; Baig et al., 2014).”
Applying Built-Up and Bare-Soil Indices from Landsat 8 to Cities in Dry Climates (2018, methodology): “Generally, NDBI is more efficient in places where the NDVI value is greater than 0 [1].”
Random Forest Classification for Mangrove Land Cover Mapping Using Landsat 5 TM and Alos Palsar Imageries (2015, methodology): “The method proposed in the study consisted of four steps: (i) Data preparation, including pre-processing of Landsat 5 TM, such as atmospheric correction [8], ALOS PALSAR backscattering calibration [9], noise reduction [10], spectral transformation of Landsat 5 TM data, NDVI [11], NDWI [12], and NDBI”
Effects of landscape composition and pattern on land surface temperature: An urban heat island study in the megacities of Southeast Asia. (2017, background): “, 1996) and the normalized difference built-up index (NDBI) (Zha et al., 2003), the VrNIR-BI exploits the red and NIR channels, the same portions of the electromagnetic spectrum used for deriving the NDVI.”
Disproportionately higher exposure to urban heat in lower-income neighborhoods: a multi-city perspective (2019, background): “The normalized difference built-up index (NDBI) is a proxy for the degree of urbanization, capturing the fact that urban areas reflect more in the shortwave infrared band than in the near infrared (Zha et al 2003).”
Land cover and land use classification performance of machine learning algorithms in a boreal landscape using Sentinel-2 data (2019, methodology): “The normalized di ﬀ erence built-up index (NDBI) uses the SWIR band to identify arti ﬁ cial surfaces because of their strong re ﬂ ectivity in that portion of the EMS while using the NIR to suppress the in ﬂ uence of vegetated surfaces (Zha, Gao, and Ni 2003).”