Modeling the Spatiotemporal Dynamics of Gross Domestic Product in China Using Extended Temporal Coverage Nighttime Light Data

Zhu, Xiaobo; Ma, Mingguo; Yang, Hong; Ge, Wei

doi:10.3390/rs9060626

Cited by 59 publications

(44 citation statements)

References 52 publications

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“…These data can be obtained from NOAA's National Centers for Environmental Information (NOAA/NCEI) website [28]. However, as it is a preliminary product, these data are not filtered to remove detected light associated with gas flares, fires, volcanoes, or aurorae, and the dataset has not been processed to remove background noise [29]. In addition, the VIIRS annual night-time light data are being discontinued by NOAA, and only annual data from 2015 and 2016 are supported [30].…”

Section: Npp−viirs Night-time Light Datamentioning

confidence: 99%

Assessing the Distribution of Heavy Industrial Heat Sources in India between 2012 and 2018

Niu

et al. 2019

IJGI

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The heavy industry in India has witnessed rapid development in the past decades. This has increased the pressures and load on the Indian environment, and has also had a great impact on the world economy. In this study, the Preparatory Project Visible Infrared Imaging Radiometer (NPP VIIRS) 375-m active fire product (VNP14IMG) and night-time light (NTL) data were used to study the spatiotemporal patterns of heavy industrial development in India. We employed an improved adaptive K-means algorithm to realize the spatial segmentation of long-term VNP14IMG data and artificial heat-source objects. Next, the initial heavy industry heat sources were distinguished from normal heat sources using a threshold recognition model. Finally, the maximum night-time light data were used to delineate the final heavy industry heat sources. The results suggest, that this modified method is a much more accurate and effective way of monitoring heavy industrial heat sources, and the accuracy of this detection model was higher than 92.7%. The number of main findings were concluded from the study: (1) the heavy industry heat sources are mainly concentrated in the north-east Assam state, east-central Jharkhand state, north Chhattisgarh and Odisha states, and the coastal areas of Gujarat and Maharashtra. Many heavy industrial heat sources were also found around a line from Kolkata on the Eastern Indian Ocean to Mumbai on the Western Indian Ocean. (2) The number of working heavy industry heat sources (NWH) and, particularly, the total number of fire hotspots for each working heavy industry heat source area (NFHWH) are continuing to increase in India. These trends mirror those for the Gross Domestic Product (GDP) and total population of India between 2012 and 2017. (3) The largest values of NWH and NFHWH were in Jharkhand, Chhattisgarh, and Odisha whereas the smallest negative values, the S l o p e _ N W H in Jharkhand and Chhattisgarh were also the two largest values in the whole country. The smallest negative values of S l o p e _ N W H and S l o p e _ N F H W H were in Haryana. The S l o p e _ N F H W H in the mainland Gujarat had the second most negative value, while the value of the S l o p e _ N W H was the third-highest positive value.

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Section: Npp−viirs Night-time Light Datamentioning

confidence: 99%

Assessing the Distribution of Heavy Industrial Heat Sources in India between 2012 and 2018

Niu

et al. 2019

IJGI

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“…The low-light sources at night are lamps from streets, block buildings, auto headlights, public service areas, and industrial areas. These nocturnal images have been widely used for various purposes, such as socio-economic condition evaluation [35][36][37][38][39][40], GDP estimation [41][42][43][44][45], energy use and electricity consumption estimation [46][47][48][49][50], urbanization mapping [51][52][53], dynamic monitoring [54][55][56], impervious surface measurement [57][58][59], density simulation and population estimation [60][61][62][63][64][65], biogeochemical cycles and CO 2 emissions simulation [66][67][68], ecological environmental impact assessment [69,70], light pollution estimation [71,72], fishery lighting detection [73][74][75][76], and fire and flammable areas mapping [77,78]. It should be noted that Ge et al recently estimated the ghost city rate at the national scal...…”

Section: Introductionmentioning

confidence: 99%

Modeling the Census Tract Level Housing Vacancy Rate with the Jilin1-03 Satellite and Other Geospatial Data

Wang

Zou

et al. 2018

Remote Sensing

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The vacant house is an essential phenomenon of urban decay and population loss. Exploration of the correlations between housing vacancy and some socio-environmental factors is conducive to understanding the mechanism of urban shrinking and revitalization. In recent years, rapidly developing night-time remote sensing, which has the ability to detect artificial lights, has been widely applied in applications associated with human activities. Current night-time remote sensing studies on housing vacancy rates are limited by the coarse spatial resolution of data. The launch of the Jilin1-03 satellite, which carried a high spatial resolution (HSR) night-time imaging camera, provides a new supportive data source. In this paper, we examined this new high spatial resolution night-time light dataset in housing vacancy rate estimation. Specifically, a stepwise multivariable linear regression model was engaged to estimate the housing vacancy rate at a very fine scale, the census tract level. Three types of variables derived from geospatial data and night-time image represent the physical environment, landuse (LU) structure, and human activities, respectively. The linear regression models were constructed and analyzed. The analysis results show that (1) the HVRs estimating model using the Jilin1-03 satellite and other ancillary geospatial data fits well with the Census statistical data (adjusted R2 = 0.656, predicted R2 = 0.603, RMSE = 0.046) and thus is a valid estimation model; (2) the Jilin1-03 satellite night-time data contributed a 28% (from 0.510 to 0.656) fitting accuracy increase and a 68% (from 0.359 to 0.603) predicting accuracy increase in the estimate model of the housing vacancy rate. Reflecting socio-economic conditions, the luminous intensity of commercial areas derived from the Jilin1-03 satellite is the most influential variable to housing vacancy. Land use structure indirectly and partially demonstrated that the social environment factors in the community have strong correlations with residential vacancy. Moreover, the physical environment factor, which depicts vegetation conditions in the residential areas, is also a significant indicator of housing vacancy. In conclusion, the emergence of HSR night light data opens a new door to future microscopic scale study within cities.

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“…This question is important, because remote sensing applications using visible band data taken at night are frequently focused on analyzing changes in artificial light. For example, night lights data have been used to monitor changes in urban footprints [2], gross domestic product [3], and infrastructure damage due to war [4][5][6]. Changes in both spatial positions and intensities of emitted visible light over time are of interest to those studying light pollution, for example to examine light encroachment into protected areas [7,8], light emission from individual bright sources [9], as well as global and national rates of change [10].…”

Section: Introductionmentioning

confidence: 99%

Variation of Individual Location Radiance in VIIRS DNB Monthly Composite Images

Coesfeld¹,

Anderson²,

Baugh³

et al. 2018

Preprint

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With the growing size and use of night lights time series from the Visible Infrared Imaging Radiometer Suite Day/Night Band (DNB), it is important to understand the stability of the dataset. All satellites observe differences in pixel values during repeat observations. In the case of night lights data, these changes can be due to both environmental effects and changes in light emission. Here we examine the stability of individual locations of particular large scale light sources (e.g. airports, prisons) in the monthly composites of DNB data from April 2012 to September 2017. The radiances for individual pixels of most large light emitters are approximately normally distributed, with a standard deviation of typically 15-20% of the mean. We observe geospatial autocorrelation in the monthly variations for nearby sites, while the correlation for sites separated by large distances is small. This suggests that local factors contribute most to the variation in the pixel radiances, and furthermore that averaging radiances over large areas will reduce the total variation. A better understanding of the causes of temporal variation would improve the sensitivity of DNB to lighting changes.

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Modeling the Spatiotemporal Dynamics of Gross Domestic Product in China Using Extended Temporal Coverage Nighttime Light Data

Cited by 59 publications

References 52 publications

Assessing the Distribution of Heavy Industrial Heat Sources in India between 2012 and 2018

Assessing the Distribution of Heavy Industrial Heat Sources in India between 2012 and 2018

Modeling the Census Tract Level Housing Vacancy Rate with the Jilin1-03 Satellite and Other Geospatial Data

Variation of Individual Location Radiance in VIIRS DNB Monthly Composite Images

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