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

Zha, Yong; Gao, Jay; Song, Ning

doi:10.1080/01431160304987

Cited by 1,967 publications

(1,048 citation statements)

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“…We calculate the distributions of the NDUI, BCI, NDVI, and the Normalized Difference Built Up Index (NDBI) [62] within each of the barren land classes and the Developed classes in the two evaluation sites in the Salt Lake City region ( Figure 9D 1−2). The Developed classes are well separated from the barren land class by the NDUI, with the majority of barren land class having NDUI values around −1, while the majority of Developed classes have NDUI values larger than 0 ( Figure 13A).…”

Section: Comparison With Other Indices In Terms Of Separating Urban Amentioning

confidence: 99%

Building a Better Urban Picture: Combining Day and Night Remote Sensing Imagery

Zhang

Thau

et al. 2015

Remote Sensing

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Urban areas play a very important role in global climate change. There is increasing need to understand global urban areas with sufficient spatial details for global climate change mitigation. Remote sensing imagery, such as medium resolution Landsat daytime multispectral imagery and coarse resolution Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) nighttime light imagery, has provided a powerful tool for characterizing and mapping cities, with advantages and disadvantages. Here we propose a framework to merge cloud and cloud shadow-free Landsat Normalized Difference Vegetation Index (NDVI) composite and DMSP/OLS Night Time Light (NTL) to characterize global urban areas at a 30 m resolution, through a Normalized Difference Urban Index (NDUI) to make full use of them while minimizing their limitations. We modify the maximum NDVI value multi-date image compositing method to generate the cloud and cloud shadow-free Landsat NDVI composite, which is critical for generating a global NDUI. Evaluation results show the NDUI can effectively increase the separability between urban areas and bare lands as well as farmland, capturing large scale urban extents and, at the same time, providing sufficient spatial details inside urban areas. With advanced

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Section: Comparison With Other Indices In Terms Of Separating Urban Amentioning

confidence: 99%

Building a Better Urban Picture: Combining Day and Night Remote Sensing Imagery

Zhang

Thau

et al. 2015

Remote Sensing

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“…To demonstrate the potential application value of NDSTI-Red, this study examined and compared the performance of the state-of-the-art approaches, i.e., the urban index (UI), the normalized difference built-up index (NDBI), the visible green-based built-up indices (VgNIR-BI), and the visible red-based built-up indices (VrNIR-BI). The NDBI was used to map urban areas by exploiting the spectral response difference of built-up lands in the NIR and SWIR1 portions of the electromagnetic spectrum (Equation (4)) [46]. Based on the same principle, the UI was designed to detect updated information about the conditions of urban areas from satellite images in the NIR and SWIR2 channels (Equation (5)) [47].…”

Section: Comparison With Other Methodologiesmentioning

confidence: 99%

A Strategy of Rapid Extraction of Built-Up Area Using Multi-Seasonal Landsat-8 Thermal Infrared Band 10 Images

et al. 2017

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Abstract:Recently, studies have focused more attention on surface feature extraction using thermal infrared remote sensing (TIRS) as supplementary materials. Innovatively, in this paper, using three-date (winter, early spring, and end of spring) TIRS Band 10 images of Landsat-8, we proposed an empirical normalized difference of a seasonal brightness temperature index (NDSTI) for enhancing a built-up area based on the contrast heat emission seasonal response of a built-up area to solar radiation, and adopted a decision tree classification method for the rapidly accurate extraction of the built-up area. Four study areas, including one major experimental study area (Tangshan) and three verification areas (Minqin, Laizhou, and Yugan) in different climate zones, respectively, were used to empirically establish the overall strategy system, then we specified constrained conditions of this strategy. Moreover, we compared the NDSTI to the current built-up indices, respectively, for extracting the built-up area. The results showed that (1) the new index (NDSTI) exploited the seasonal thermal characteristic variation between the built-up area and other covers in the time series analysis, helping achieve more accurate built-up area extraction than other spectral indices; (2) this strategy could effectively realize rapid built-up area extraction with generally satisfied overall accuracy (over 80%), and was especially excellent in Tangshan and Laizhou; however, (3) it may be constrained by climate patterns and other surface characteristics, which need to be improved from the view of the results of Minqin and Yugan. In summary, the method developed in this study has the potential and advantage to extract the built-up area rapidly from the multi-seasonal thermal infrared remote sensing data. It could be an operative tool for long-term monitoring of built-up areas efficiently and for more applications of thermal infrared images in the future.

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“…For the coastal erosion detection, we have used the combination of Normalized Difference Water Index (NDWI) and Normalized Difference Urban Index (UI) -or also called the build-up index (Zha et al, 2003). Furthermore, for the needs of urbanisation growth detection through time CVA we applied Normalized Difference Vegetation Index (NDVI) in combination with UI indexes.…”

Section: Cva Change Detectionmentioning

confidence: 99%

Coastal changes and movements in the wider vlora (Albania) area

Kanjir

Bon

2016

GEOBIA 2016: Solutions and Synergies

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ABSTRACT:This paper explores the environmental change monitoring on the Albanian coastline (Vlora area) by analysing optical remote sensing data. Changes along this coastline are driven by coastal erosion and abusive urban construction of mostly tourist complexes and other man-made processes and pressures (e.g. sandpits, poor wastewater management, etc.). The change detection is based on yearly Landsat data of the period between 1984 and 2015. On the basis of mentioned images, each year's Normalized Difference Water Index (NDWI), Normalized Difference Build-up Index (NDBI) and Normalized Difference Vegetation Index (NDVI) are extracted. With the use of change vector analysis technique the combination of these indexes were then compared between each other. The quantitative extent of coastal changes was defined using geographic object-based image analysis (GEOBIA). By obtaining GEOBIA post-classification results we compared both change detection approaches of pairs of one period (2010)(2011)(2012)(2013)(2014)(2015). The coastline and coastal change maps produced at 30 m resolution, applying both pixel-and object-based approaches, showed that CVA approach is a preferred technique when analysing Landsat data for coastal change detection. Apart from identifying spatial changes through time, the objective of this study is to show that these landscape changes correspond with the population movements and migrations. This study argues that movements of both people and landscape strongly impact each other and form an intertwining and corresponding relationship.

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

Cited by 1,967 publications

References 0 publications

Building a Better Urban Picture: Combining Day and Night Remote Sensing Imagery

Building a Better Urban Picture: Combining Day and Night Remote Sensing Imagery

A Strategy of Rapid Extraction of Built-Up Area Using Multi-Seasonal Landsat-8 Thermal Infrared Band 10 Images

Coastal changes and movements in the wider vlora (Albania) area

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