Extraction of urban built-up area based on the fusion of night-time light data and point of interest data

He, Xianghuo; Zhang, Zhiming; Yang, Zi‐Jiang

doi:10.1098/rsos.210838

Cited by 22 publications

(12 citation statements)

References 71 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Currently, scholars have started to fuse the POI data and night-time light data as a method to extract the urban built-up areas [ 28 ], but night-time light data has light spillovers and noise, which leads to fragmented boundaries and scattered patches of built-up areas; and the built-up areas in the low-light and no-light areas cannot be effectively extracted. Starting from the fusion of multi-source heterogeneous spatio-temporal data, to improve the extraction accuracy of urban built-up areas, an FCM-STP comprehensive index that integrates the spatio-temporal features of night-time lights and POIs is proposed, which can effectively use the spatio-temporal features of the night-time lights and POI data.…”

Section: Methodsmentioning

confidence: 99%

An STP-HSI index method for urban built-up area extraction based on multi-source remote sensing data

Dai

Tu³

et al. 2022

R. Soc. open sci.

View full text Add to dashboard Cite

A change in an urban built-up area can reflect the process of urbanization and the development of a city. At present, multi-source remote sensing data extraction of built-up areas based on the human settlement index (HSI) has achieved relatively good results but the existence of noise, such as light spillover in the night-time light remote sensing data, seriously affects the accuracy of the HSI. In this paper, a high-precision human settlement index (STP-HSI) method based on spatio-temporal remote sensing and point-of-interest (POI) data is presented to improve the classification accuracy in urban built-up areas extractions. First, to correct light spillover, a new night-time light index the fuzzy c-means spatio-temporal point (FCM-STP) based on fuzzy c-means clustering is proposed, which integrates the spatio-temporal characteristics and uses night light video imaging data from Luojia-1 and POI data. Then, based on the FCM-STP index, the HSI is updated to the STP-HSI index. Finally, a random forest algorithm is used to extract the urban built-up areas, and the random forest feature database is composed of normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI) and STP-HSI index features and texture features. To develop and evaluate the accuracy of the new method for built-up areas extraction with multi-source data, three test sites located in the cities of China (Guangzhou, Xiamen and Nanjing) are used. The experimental results show that our method outperforms the single-source multi-spectral (Landsat 8) data extraction results, the overall accuracy is improved by up to 7.52%, and the kappa coefficient is improved by up to 14%. Compared with the HSI index, the maximum contribution rates of the STP-HSI increased by 25.74%. These experimental results show that the method in this paper is feasible.

show abstract

Section: Methodsmentioning

confidence: 99%

An STP-HSI index method for urban built-up area extraction based on multi-source remote sensing data

Dai

Tu³

et al. 2022

R. Soc. open sci.

View full text Add to dashboard Cite

show abstract

“…The spatial distribution of artificial light sources is strongly correlated with urbanized spaces, connective roadways, infrastructures (factories, ports and airports, ...) and any other areas where human activity is assumed or expected at nighttime. This enables using remote sensing of nighttime lights as a proxy indicator for urbanization [74] and economic activity [75]. The widespread mesh of artificial light sources directly pollutes its immediate surroundings, altering substantially their natural darkness and being a relevant factor for habitat fragmentation via phototactic effects that disrupt nighttime ecological corridors [76][77][78].…”

Section: Spatial Distribution and Temporal Dynamicsmentioning

confidence: 99%

Artificial light at night: a global disruptor of the night-time environment

Bará,

Falchi

2023

Phil. Trans. R. Soc. B

View full text Add to dashboard Cite

Light pollution is the alteration of the natural levels of darkness by an increased concentration of light particles in the night-time environment, resulting from human activity. Light pollution is profoundly changing the night-time environmental conditions across wide areas of the planet, and is a relevant stressor whose effects on life are being unveiled by a compelling body of research. In this paper, we briefly review the basic aspects of artificial light at night as a pollutant, describing its character, magnitude and extent, its worldwide distribution, its temporal and spectral change trends, as well as its dependence on current light production technologies and prevailing social uses of light. It is shown that the overall effects of light pollution are not restricted to local disturbances, but give rise to a global, multiscale disruption of the night-time environment. This article is part of the theme issue ‘Light pollution in complex ecological systems’.

show abstract

“…The areas with greater intensity of (brighter) night lighting show more frequent human production (intensity of economic behavior) and living (concentration of population) activities, and higher quality of urbanization development in general [53,54]. Now the night light data are widely used for the identification of urban built-up areas [55] and urban centers [56,57], the extraction of urban impervious surfaces [58], and the evaluation of urban spatial expansion and growth trend [59,60], land use and cover changes [61,62], Spatial estimation of socio-economic indicators [63], major events [64], ecological environment [65] and other fields [66], which are still in their infancy in the urbanization process research.…”

Section: Indicator Selectionmentioning

confidence: 99%

Spatio-Temporal Evolution and Driving Mechanism of Urbanization in Small Cities: Case Study from Guangxi

Zhao

Wang

et al. 2022

Land

View full text Add to dashboard Cite

Urbanization has an abundant connotation in dimensions such as population, economy, land, and society and is an important sign to measure regional economic development and social progress. The use of Night Light Data from remote sensing satellites as a proxy variable can significantly improve the accuracy and comprehensiveness of the measurement of urbanization development dynamics. Based on the Night Light Data and statistical data from 2015 to 2019, this paper quantitatively analyzes the spatio-temporal evolution pattern of urbanization in Guangxi and its driving mechanism using exploratory time-space data analysis, GeoDetector and Matrix: Boston Consulting Group, providing an important basis for sustainable urban development planning and scientific decision-making by the government. The findings show that (1) there is a high level of spatial heterogeneity and spatial autocorrelation of urbanization in Guangxi, with the Gini index of urban night light index and urban night light expansion vitality index always greater than 0.5, the global Moran’s I greater than 0.17, the spatial differentiation converging but the spatial correlation increasing. (2) The spatial pattern of urbanization in Guangxi has long been solidified, but there is a differentiation in urban development trend, with the coexistence of urban expansion and shrinkage, requiring differentiated policy design for urban governance. (3) The development and evolution of urbanization in Guangxi present a complex intertwined dynamic mechanism of action, with interaction effects of bifactor enhancement and non-linear enhancement among factors. It should be noted that the influence of factors varies greatly, with the added value of the tertiary industry, gross domestic product, total retail sales of social consumer goods having the strongest direct effect on the urban night light index, while the added value of secondary industry, per capita GDP, gross domestic product having the strongest direct effect on the urban night light expansion vitality index. All of them are key factors, followed by some significant influence factors such as government revenue, population urbanization rate, per government revenue, population urbanization rate, per capita disposable income of urban and rural residents that should not be ignored, and the rest that play indirect roles mainly by interaction.

show abstract

Extraction of urban built-up area based on the fusion of night-time light data and point of interest data

Cited by 22 publications

References 71 publications

An STP-HSI index method for urban built-up area extraction based on multi-source remote sensing data

An STP-HSI index method for urban built-up area extraction based on multi-source remote sensing data

Artificial light at night: a global disruptor of the night-time environment

Spatio-Temporal Evolution and Driving Mechanism of Urbanization in Small Cities: Case Study from Guangxi

Contact Info

Product

Resources

About