Estimating House Vacancy Rate in Metropolitan Areas Using NPP-VIIRS Nighttime Light Composite Data

Chen, Zuoqi; Yu, Bailang; Hu, Yingjie; Huang, Chang; Shi, Kaifang; Wu, Jianping

doi:10.1109/jstars.2015.2418201

Cited by 116 publications

(73 citation statements)

References 37 publications

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“…The outliers are probably caused by lights from the fires of oil or gas flares located in those areas. Since Beijing and Shanghai are the two most developed administrative regions in China, the pixel values of the other areas should not exceed those of the two regions theoretically [29]. The highest radiance of those two regions is 2.62ˆ10´7 W/cm 2 -sr, and other pixels whose radiance is larger than 2.625ˆ10´7 W/cm 2 -sr in the DNB composite data were smoothed by their eight neighbors.…”

Section: Dnb Noise Filtering With the Noise Masking Methodsmentioning

confidence: 99%

“…Ma et al [25] investigated correlations of DNB nighttime light radiance with GDP, population, electrical power consumption and paved road areas, and this work indicated that these parameters had a significantly positive linear relation with nighttime light radiance. The application of VIIRS DNB nighttime data, beyond correlating with socio-economic data [26][27][28][29][30][31][32], also can be used to detect social insurgency [33]. Recent work by Li et al [26] compared the capabilities of using DNB and DMSP-OLS data to model the gross regional product (GRP) in China.…”

Section: Introductionmentioning

confidence: 99%

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Comparison between the Suomi-NPP Day-Night Band and DMSP-OLS for Correlating Socio-Economic Variables at the Provincial Level in China

et al. 2015

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Abstract:Nighttime light imagery offers a unique view of the Earth's surface. In the past, the nighttime light data collected by the DMSP-OLS sensors have been used as an efficient means to correlate regional and global socio-economic activities. With the launch of the Suomi National Polar-orbiting Partnership (Suomi-NPP) satellite in 2011, the day-night band (DNB) of the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard represents a major advancement in nighttime imaging capabilities, because it surpasses its predecessor DMSP-OLS in radiometric accuracy, spatial resolution and geometric quality. In this paper, four variables (total night light, light area, average night light and log average night light) are extracted from nighttime radiance data observed by the VIIRS-DNB composite in 2013 and nighttime digital number (DN) data from the DMSP-OLS stable dataset in 2012, respectively, and correlated with 12 socio-economic parameters at the provincial level in mainland China during the corresponding period. Background noise of DNB composite data is removed using either a masking method or an optimal threshold method. In general, the correlation of these socio-economic data with the total night light and light area of VIIRS-DNB composite data is better than with the DMSP-OLS stable data. The correlations between total night light of denoised DNB composite data and built-up area, gross regional product (GRP) and power consumption are higher than 0.9 and so are the correlations between the light area of denoised DNB composite data and city and town population, built-up area, GRP, power consumption and waste water discharge. However, the correlations of socio-economic data with the average night light and log average night light of VIIRS-DNB composite data are not as good as with the DMSP-OLS stable data. To quantitatively analyze the reasons for the correlation difference, a cubic regression method is developed to correct the saturation effect of the DMSP stable data, and we artificially convert the pixel value of the DNB composite into six bits to match the DMSP stable data format. The correlation results between the processed data and socio-economic data show that the effects of saturation and quantization are two of the reasons for the correlation difference. Additionally, on this basis, we estimate the total night light ratio between saturation-corrected DMSP stable data and finite quantization DNB composite data, and it is found that the ratio is~11.28˘4.02 for China. Therefore, it appears that a different acquisition time is the other reason for the correlation difference.

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Section: Dnb Noise Filtering With the Noise Masking Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Comparison between the Suomi-NPP Day-Night Band and DMSP-OLS for Correlating Socio-Economic Variables at the Provincial Level in China

et al. 2015

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“…On one hand, previous findings had suggested strong relationships between digital number (DN) values with urban population density [16], built area density [17], house vacancy rate [18], and other socio-economic indicators [19][20][21][22][23]; On the other hand, they have a relative high temporal frequency of observation. Previous studies mainly focused on the dynamics of urbanization in general [24][25][26][27][28] or urban expansion itself in particular [29][30][31], and they ignored the influence from the non-urban lit-pixels [3,8,32,33].…”

Section: Introductionmentioning

confidence: 99%

Characterizing the Growth Patterns of 45 Major Metropolitans in Mainland China Using DMSP/OLS Data

2017

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Abstract:Understanding growth patterns at the metropolitan level is instructive for better planning and policy making on sustainable urban development. Using DMSP/OLS data from 1992 to 2013, this article aims to investigate growth patterns of major metropolitans in Mainland China from the aspects of intensification and expansion. We start by calibrating the DMSP/OLS data and selecting 45 major metropolitans. On intensification, results suggest that aggregately, metropolitans displayed cyclical pattern over time and large metropolitans tended to have higher levels of intensification than moderate or small ones. Individually, metropolitans with similar intensification over time could be clustered together using Dendrogram, and evolution pattern of the clusters exhibited similarity to the aggregated one. On expansion, results show that aggregately metropolitans displayed a decreasing trend over time, and moderate or small metropolitans tended to have higher levels of expansion than large ones. Particularly, moderate metropolitans were more likely to expand adjacently, and small ones were more likely to experience scatter or corridor expansion. Each metropolitan can be represented by a mixed expansion model over time, which might tell where and how much expansion occurred in the current year. Furthermore, intensification is highly correlated with expansion over time for small metropolitans, but they are poorly correlated for large or moderate ones. Lastly, the high correlation of intensification and expansion with the change of GDP in each year indicates the reliability of our work.

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“…Because of the improved performance, VIIRS DNB data have been used in various applications, such as extracting urban areas [23][24][25][26][27], reflecting demographic and socioeconomic conditions [25,[28][29][30][31][32][33], monitoring nocturnal surface air quality [34][35][36], and so on [26,[37][38][39][40][41].…”

Section: Introductionmentioning

confidence: 99%

An Object Similarity-Based Thresholding Method for Urban Area Mapping from Visible Infrared Imaging Radiometer Suite Day/Night Band (VIIRS DNB) Data

2018

Remote Sensing

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Nighttime light data from the Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) provides a unique data source for mapping and monitoring urban areas at regional and global scales. This study proposes an object similarity-based thresholding method using VIIRS DNB data to map urban areas. The threshold for a target potential urban object was determined by comparing its similarity with all reference urban objects with known optimal thresholds derived from Landsat data. The proposed method includes four major steps: potential urban object generation, threshold optimization for reference urban objects, object similarity comparison, and urban area mapping. The proposed method was evaluated using VIIRS DNB data of China and compared with existing mapping methods in terms of threshold estimation and urban area mapping. The results indicated that the proposed method estimated thresholds and mapped urban areas accurately and generally performed better than the cluster-based logistic regression method. The correlation coefficients between the estimated thresholds and the reference thresholds were 0.9201-0.9409 (using Euclidean distance as similarity measure) and 0.9461-0.9523 (using Mahalanobis distance as similarity measure) for the proposed method and 0.9435-0.9503 for the logistic regression method. The average Kappa Coefficients of the urban area maps were 0.58 (Euclidean distance) and 0.57 (Mahalanobis distance) for the proposed method and 0.51 for the logistic regression method. The proposed method shows potential to map urban areas at a regional scale effectively in an economic and convenient way.

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Estimating House Vacancy Rate in Metropolitan Areas Using NPP-VIIRS Nighttime Light Composite Data

Cited by 116 publications

References 37 publications

Comparison between the Suomi-NPP Day-Night Band and DMSP-OLS for Correlating Socio-Economic Variables at the Provincial Level in China

Comparison between the Suomi-NPP Day-Night Band and DMSP-OLS for Correlating Socio-Economic Variables at the Provincial Level in China

Characterizing the Growth Patterns of 45 Major Metropolitans in Mainland China Using DMSP/OLS Data

An Object Similarity-Based Thresholding Method for Urban Area Mapping from Visible Infrared Imaging Radiometer Suite Day/Night Band (VIIRS DNB) Data

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