Can Night-Time Light Data Identify Typologies of Urbanization? A Global Assessment of Successes and Failures

Zhang, Qian; Seto, Karen C.

doi:10.3390/rs5073476

Cited by 119 publications

(81 citation statements)

References 37 publications

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“…In addition, the SNLD was frequently used to generate the urban area across different years [44,45]. However, the SNLD data has a blooming effect; the urban area extracted from SNLD was usually larger than the real urban area [44,47]. Figures 2c and 3c showed that the urban areas at WH extracted from SNLD contained the Yangtze River and some lakes, which should be removed in SUHI's study [3,10,48].…”

Section: Extraction Of Urban Areamentioning

confidence: 99%

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Urbanization Effects on Vegetation and Surface Urban Heat Islands in China’s Yangtze River Basin

et al. 2017

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Abstract:In the context of rapid urbanization, systematic research about temporal trends of urbanization effects (UEs) on urban environment is needed. In this study, MODIS (Moderate Resolution Imaging Spectroradiometer) land surface temperature (LST) data and enhanced vegetation index (EVI) data were used to analyze the temporal trends of UEs on vegetation and surface urban heat islands (SUHIs) at 10 big cities in Yangtze River Basin (YRB), China during 2001-2016. The urban and rural areas in each city were derived from MODIS land cover data and nighttime light data. It was found that the UEs on vegetation and SUHIs were increasingly significant in YRB, China. The ∆EVI (the UEs on vegetation, urban EVI minus rural EVI) decreased significantly (p < 0.05) in 9, 7 and 5 out of 10 cities for annual, summer and winter, respectively. The annual daytime and nighttime SUHI intensity (SUHII; urban LST minus rural LST) increased significantly (p < 0.05) in 10 and 4 out of 10 cities, respectively. The increasing rate of daytime SUHII and the decreasing rate of ∆EVI in old urban areas were much less than the whole urban area (0.034 • C/year vs. 0.077 • C/year for annual daytime SUHII; 0.00209/year vs. 0.00329/year for ∆EVI). The correlation analyses indicated that the annual and summer daytime SUHII were significantly negatively correlated with ∆EVI in most cities. The decreasing ∆EVI may also contribute to the increasing nighttime SUHII. In addition, the significant negative correlations (r < −0.5, p < 0.1) between inter-annual linear slope of ∆EVI and SUHII were observed, which suggested that the cities with higher decreasing rates of ∆EVI may show higher increasing rates of SUHII.

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Section: Extraction Of Urban Areamentioning

confidence: 99%

“…2017, 9, 540 5 of 17 was usually larger than the real urban area [44,47]. Figures 2c and 3c showed that the urban areas at WH extracted from SNLD contained the Yangtze River and some lakes, which should be removed in SUHI's study [3,10,48].…”

Section: Extraction Of Urban Areamentioning

confidence: 99%

Urbanization Effects on Vegetation and Surface Urban Heat Islands in China’s Yangtze River Basin

et al. 2017

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“…Finally, substantial quantities of impervious surfaces can exist within rural areas, including rural roads, highways, and farmhouses, which might not all be well lit and thus can be missed by the NDUI (Figure 14). Some urban areas are in darkness due to the lack of electricity and humanitarian disasters [63][64][65] and non-urban bare land can be influenced by night lights, which will also lead to underestimation or overestimation accordingly.…”

Section: Discussionmentioning

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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“…While lights appear in rural human settlements, which are altered landscapes that are less urban; there is a higher brightness factor in urban areas (Figure 2). DMSP OLS data more accurately identify urbanization in developed countries, but are less accurate in developing countries [14]. One challenge in mapping the urban extent is misclassification between urban and non-urban surfaces.…”

Section: Urban Extentmentioning

confidence: 99%

Supporting Global Environmental Change Research: A Review of Trends and Knowledge Gaps in Urban Remote Sensing

et al. 2014

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This paper reviews how remotely sensed data have been used to understand the impact of urbanization on global environmental change. We describe how these studies can support the policy and science communities' increasing need for detailed and up-to-date information on the multiple dimensions of cities, including their social, biological, physical, and infrastructural characteristics. Because the interactions between urban and surrounding areas are complex, a synoptic and spatial view offered from remote sensing is integral to measuring, modeling, and understanding these relationships. Here we focus on OPEN ACCESSRemote Sens. 2014, 6 3880 three themes in urban remote sensing science: mapping, indices, and modeling. For mapping we describe the data sources, methods, and limitations of mapping urban boundaries, land use and land cover, population, temperature, and air quality. Second, we described how spectral information is manipulated to create comparative biophysical, social, and spatial indices of the urban environment. Finally, we focus how the mapped information and indices are used as inputs or parameters in models that measure changes in climate, hydrology, land use, and economics.

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Can Night-Time Light Data Identify Typologies of Urbanization? A Global Assessment of Successes and Failures

Cited by 119 publications

References 37 publications

Urbanization Effects on Vegetation and Surface Urban Heat Islands in China’s Yangtze River Basin

Urbanization Effects on Vegetation and Surface Urban Heat Islands in China’s Yangtze River Basin

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

Supporting Global Environmental Change Research: A Review of Trends and Knowledge Gaps in Urban Remote Sensing

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