Nighttime light satellite images reveal uneven socioeconomic development along China's land border

Wu, Nan; Du, Yunyan; Liang, Fuyuan; Yi, Jiawei; Qian, Jia; Tu, Wenna; Huang, Sheng

doi:10.1016/j.apgeog.2023.102899

Cited by 8 publications

(5 citation statements)

References 69 publications

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“…This study used the Thiel index to measure inter‐regional differences in the GDCL level of China and its contribution to the total gap; its formula consists of three steps (Wan et al, 2023).

Theil = {Theil}_{w} + {Theil}_{b}

{Theil}_{w} = \sum_{i = 1}^{m} (\frac{n_{i}}{n} \frac{{\overset{true¯}{x}}_{i}}{\overset{x}{true}}) {Theil}_{i}

{Theil}_{b} = \sum_{i = 1}^{m} \frac{n_{i}}{n} (\frac{{\overset{x}{true}}_{i}}{\overset{true¯}{x}}) \ln (\frac{{\overset{x}{true}}_{i}}{\overset{true¯}{x}})

…”

Section: Methodsmentioning

confidence: 99%

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A framework for quantification and integration of green development of cultivated land in China: From the perspective of adaptability‐vitality‐resistance

Niu,

Lyu,

et al. 2024

Land Degrad Dev

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Exploring the green development of cultivated land (GDCL) and solving the problem of “high consumption and high pollution” caused by the traditional model are of great significance for achieving sustainable use of cultivated land and green agricultural growth. However, the existing consensus on action lacks theoretical support and quantitative analysis. To this end, the objective of this study was to develop a new multidimensional framework considering as an integrated capacity to adaptability‐vitality‐resistance (A‐V‐R) assess GDCL according to the essential requirement of green development. The analytical framework and evaluation system proposed in this study are expected to provide empirical support for the green transition of cultivated land use in China or worldwide. This study determines spatiotemporal differentiation and influencing mechanism of GDCL in China from 2000 to 2020. The results demonstrated that the GDCL level of China was low and characterized by a W‐shaped fluctuation. The regional difference in GDCL is greater than that within the regions and the evolution of different types of regions showed significant path dependence and spatiotemporal inertia. The reason behind this phenomenon is that agricultural technology and socioeconomic development factors such as agricultural mechanization level, urban–rural income equity index and extensively influenced GDCL in different regions. The interaction among driving factors forms a complex multi‐resultant force to construct a comprehensive action mechanism at the GDCL level driven by demand, economy, technology, and ecology. Therefore, this study suggests coordinating the relationship between technological progress, technological diffusion, agricultural total factor productivity, and the GDCL path, in order to effectively reduce the internal and external contradictions of GDCL.

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“…This study used the Thiel index to measure inter‐regional differences in the GDCL level of China and its contribution to the total gap; its formula consists of three steps (Wan et al, 2023).

Theil = {Theil}_{w} + {Theil}_{b}

{Theil}_{w} = \sum_{i = 1}^{m} (\frac{n_{i}}{n} \frac{{\overset{true¯}{x}}_{i}}{\overset{x}{true}}) {Theil}_{i}

{Theil}_{b} = \sum_{i = 1}^{m} \frac{n_{i}}{n} (\frac{{\overset{x}{true}}_{i}}{\overset{true¯}{x}}) \ln (\frac{{\overset{x}{true}}_{i}}{\overset{true¯}{x}})

…”

Section: Methodsmentioning

confidence: 99%

“…This study used the Thiel index to measure inter-regional differences in the GDCL level of China and its contribution to the total gap; its formula consists of three steps (Wan et al, 2023).…”

Section: Thiel Indexmentioning

confidence: 99%

A framework for quantification and integration of green development of cultivated land in China: From the perspective of adaptability‐vitality‐resistance

Niu,

Lyu,

et al. 2024

Land Degrad Dev

View full text Add to dashboard Cite

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“…The availability of resources can have a considerable impact on the amount to which AI-based DSS helps student creativity. When educational institutions have innovative technology, skilled support staff, and financial investments, AI-based DSS can boost student innovation (Wan et al, 2023). Students can benefit from innovative AI tools, personalized support, and enhanced learning experiences that stimulate creative thinking in resource-rich environments.…”

Section: Resource Availability As a Moderatormentioning

confidence: 99%

AI and big data-driven decision support for fostering student innovation in music education at private underground colleges

Liu

2023

J INFORM SYSTEMS ENG

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This study investigates the transformative impact of AI-based Decision Support Systems (DSS) and Big Data Analytics (BDA) on student innovation and employability skills in an era of rapid technological advancement, with a focus on the mediating role of technological acceptance and the moderating role of resource availability. This study, which draws on a wide range of educational contexts and data sources, gives complete knowledge of the complex links between technology adoption, student results, and contextual factors. The results of this study show how AI-based DSS and BDA have a significant impact on musical education. These technological advancements enable tailored instruction and foster students' creative thinking. In order to prepare students for a work market that is rapidly changing, they act as a catalyst for improving employability skills. The study, however, emphasizes the complicated dynamics at work. Technological Acceptance emerges as a major mediating component, underlining the significance of students and instructors freely and effectively accepting technological tools. Furthermore, as a moderating factor, Resource Availability takes center stage, emphasizing the need for equitable access to educational resources to ensure that technology-driven advantages are accessible to all. The results of this study have broad repercussions. The adoption of AI and BDA by educational institutions is encouraged as transformative technologies for enhancing the learning process. Policymakers must create regulations that support equal access to technology and promote an innovative culture in the classroom. This study highlights for students how important it is to adopt new technologies, realizing how important they are in determining both their academic and career paths.

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“…Advancements in nighttime imaging capabilities have bolstered substantial scientific interest in NTL data. Reviews of the remote sensing of NTL imaging and applications, for example [5], document the study of NTL's utility for monitoring human activities, including urbanization [6][7][8], power outages [9], national gross domestic product (GDP) and economic development [10][11][12], ecological and human health impacts (e.g., human circadian rhythm disruptions, fires, and greenhouse gas emissions) [13][14][15][16][17][18], changes in lighting sources (i.e., from high-intensity discharge (HID) to light-emitting diode (LED)), and unbiased measures of conflict [19]. Nonetheless, currently, available NTL sensors are not sufficient by themselves to address many of these research problems due to their large ground sample distance (GSD) and limited number of bands.…”

Section: Introductionmentioning

confidence: 99%

Satellite Hyperspectral Nighttime Light Observation and Identification with DESIS

Ryan,

Pagnutti,

Ryan

et al. 2024

Remote Sensing

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The satellite imagery of nighttime lights (NTLs) has been studied to understand human activities, economic development, and more recently, the ecological impact of brighter night skies. The Visible Infrared Imaging Radiometer Suite (VIIRS) Day–Night Band (DNB) offers perhaps the most advanced nighttime imaging capabilities to date, but its large pixel size and single band capture large-scale changes in NTL while missing granular but important details, such as lighting type and brightness. To better understand individual NTL sources in a region, the spectra of nighttime lights captured by the DLR Earth Sensing Imaging Spectrometer (DESIS) were extracted and compared against near-coincident VIIRS DNB imagery. The analysis shows that DESIS’s finer spatial and spectral resolutions can detect individual NTL locations and types beyond what is possible with the DNB. Extracted night light spectra, validated against ground truth measurements, demonstrate DESIS’s ability to accurately detect and identify narrow-band atomic emission lines that characterize the spectra of high-intensity discharge (HID) light sources and the broader spectral features associated with different light-emitting diode (LED) lights. These results suggest the possible application of using hyperspectral data from moderate-resolution sensors to identify lamp construction details, such as illumination source type and light quality in low-light contexts. NTL data from DESIS and other hyperspectral sensors may improve the scientific understanding of light pollution, lighting quality, and energy efficiency by identifying, evaluating, and mapping individual and small groups of light sources.

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Nighttime light satellite images reveal uneven socioeconomic development along China's land border

Cited by 8 publications

References 69 publications

A framework for quantification and integration of green development of cultivated land in China: From the perspective of adaptability‐vitality‐resistance

A framework for quantification and integration of green development of cultivated land in China: From the perspective of adaptability‐vitality‐resistance

AI and big data-driven decision support for fostering student innovation in music education at private underground colleges

Satellite Hyperspectral Nighttime Light Observation and Identification with DESIS

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