Modeling the direction and magnitude of angular effects in nighttime light remote sensing

The cargo handling capacity of a port is the most basic and important indicator of port size. Based on the Defense Meteorological Satellite Program-Operational Linescan System (DMSP-OLS) nighttime light data and panel model, this study attempts to estimate the cargo handling capacity of 28 coastal ports in China using satellite remote sensing. The study confirmed that there is a very close correlation between DMSP-OLS nighttime light data and the cargo handling capacity of the ports. Based on this correlation, the panel data model was established for remote sensing-based estimation of cargo handling capacity at the port and port group scales. The test results confirm that the nighttime light data can be used to accurately estimate the cargo handling capacity of Chinese ports, especially for the Yangtze River Delta Port Group, Pearl River Delta Port Group, Southeast Coastal Port Group, and Southwest Coastal Port Group that possess huge cargo handling capacities. The high accuracy of the model reveals that the remote sensing analysis method can make up for the lack of statistical data to a certain extent, which helps to scientifically analyze the spatiotemporal dynamic changes of coastal ports, provides a strong basis for decision-making regarding port development, and more importantly provides a convenient estimation method for areas that have long lacked statistical data on cargo handling capacity.

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Study on the distribution and variation of fishing vessels in East China Sea and Yellow Sea based on the nighttime light remote sensing data from SNPP/VIIRS

Journal of Fishery Sciences of China ◽

10.3724/sp.j.1118.2019.18272 ◽

2019 ◽

Vol 26 (2) ◽

pp. 221

Author(s):

Jiawen WU ◽

Wenjiang GUAN

Keyword(s):

Remote Sensing ◽

East China Sea ◽

Yellow Sea ◽

Remote Sensing Data ◽

East China ◽

Sensing Data ◽

China Sea ◽

Fishing Vessels ◽

Nighttime Light

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Characterizing Light Pollution Trends across Protected Areas in China Using Nighttime Light Remote Sensing Data

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi7070243 ◽

2018 ◽

Vol 7 (7) ◽

pp. 243 ◽

Cited By ~ 7

Author(s):

Wei Jiang ◽

Guojin He ◽

Wanchun Leng ◽

Tengfei Long ◽

Guizhou Wang ◽

...

Keyword(s):

Remote Sensing ◽

Protected Areas ◽

Remote Sensing Data ◽

Light Pollution ◽

Sensing Data ◽

Nighttime Light

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Detection of County Economic Development Using LJ1-01 Nighttime Light Imagery: A Comparison with NPP-VIIRS Data

Sensors ◽

10.3390/s20226633 ◽

2020 ◽

Vol 20 (22) ◽

pp. 6633

Author(s):

Hongliang Liu ◽

Nianxue Luo ◽

Chunchun Hu

Keyword(s):

Remote Sensing ◽

Economic Development ◽

Remote Sensing Data ◽

County Level ◽

Socioeconomic Indicators ◽

Regression Methods ◽

Public Resources ◽

The Status ◽

Nighttime Light ◽

Nighttime Light Imagery

Nighttime light (NTL) remote sensing data have been widely used to derive socioeconomic indicators at the national and regional scales to study regional economic development. However, most previous studies only chose a single measurement indicator (such as GDP) and adopted simple regression methods to investigate the economic development of a certain area based on DMSP-OLS or NPP-VIIRS stable NTL data. The status quo shows the problems of using a single evaluation index—it has a low evaluation precision. The LJ1-01 satellite is the first dedicated NTL remote sensing satellite in the world, launched in July 2018. The data provided by LJ1-01 have a higher spatial resolution and fewer blooming phenomena. In this paper, we compared the accuracy of the LJ1-01 data and NPP-VIIRS data in detecting county-level multidimensional economic development. In three provinces in China, namely, Hubei, Hunan and Jiangxi, 20 socioeconomic parameters were selected from the following five perspectives: economic conditions, people’s livelihood, social development, public resources and natural vulnerability. Then, a County-level Economic Index (CEI) was constructed to evaluate the level of multidimensional economic development, with the spatial pattern of the multidimensional economic development also identified across the study area. The present study adopted the random forest (RF) and linear regression (LR) algorithms to establish the regression model individually, and the results were evaluated by cross-validation. The results show that the RF algorithm greatly improves the accuracy of the model compared with the LR algorithm, and thus is suitable for the study of NTL data. In addition, a better determinate coefficient (R2) based on the LJ1-01 data (0.8168) was obtained than that from the NPP-VIIRS data (0.7245) in the RF model, which reflects that the LJ1-01 data offer better potential in the evaluation of socioeconomic parameters and can be used to identify, both accurately and efficiently, multidimensional economic development at the county level.

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Monitoring and assessing “ghost cities” in Northeast China from the view of nighttime light remote sensing data

Habitat International ◽

10.1016/j.habitatint.2017.10.005 ◽

2017 ◽

Vol 70 ◽

pp. 34-42 ◽

Cited By ~ 16

Author(s):

Qiming Zheng ◽

Jingsong Deng ◽

Ruowei Jiang ◽

Ke Wang ◽

Xingyu Xue ◽

...

Keyword(s):

Remote Sensing ◽

Northeast China ◽

Remote Sensing Data ◽

Sensing Data ◽

Nighttime Light

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Where Does Nighttime Light Come From? Insights from Source Detection and Error Attribution

Remote Sensing ◽

10.3390/rs12121922 ◽

2020 ◽

Vol 12 (12) ◽

pp. 1922 ◽

Cited By ~ 1

Author(s):

Zhehao Ren ◽

Yufu Liu ◽

Bin Chen ◽

Bing Xu

Keyword(s):

Remote Sensing ◽

Land Use ◽

Method Development ◽

Infrared Imaging ◽

Urban Land Use ◽

Light Sources ◽

Socioeconomic Indicators ◽

Linear Unmixing ◽

Nighttime Light ◽

Urban Planning And Management

Nighttime light remote sensing has aroused great popularity because of its advantage in estimating socioeconomic indicators and quantifying human activities in response to the changing world. Despite many advances that have been made in method development and implementation of nighttime light remote sensing over the past decades, limited studies have dived into answering the question: Where does nighttime light come from? This hinders our capability of identifying specific sources of nighttime light in urbanized regions. Addressing this shortcoming, here we proposed a parcel-oriented temporal linear unmixing method (POTLUM) to identify specific nighttime light sources with the integration of land use data. Ratio of root mean square error was used as the measure to assess the unmixing accuracy, and parcel purity index and source sufficiency index were proposed to attribute unmixing errors. Using the Visible Infrared Imaging Radiometer Suite (VIIRS) nighttime light dataset from the Suomi National Polar-Orbiting Partnership (NPP) satellite and the newly released Essential Urban Land Use Categories in China (EULUC-China) product, we applied the proposed method and conducted experiments in two China cities with different sizes, Shanghai and Quzhou. Results of the POTLUM showed its relatively robust applicability of detecting specific nighttime light sources, achieving an rRMSE of 3.38% and 1.04% in Shanghai and Quzhou, respectively. The major unmixing errors resulted from using impure land parcels as endmembers (i.e., parcel purity index for Shanghai and Quzhou: 54.48%, 64.09%, respectively), but it also showed that predefined light sources are sufficient (i.e., source sufficiency index for Shanghai and Quzhou: 96.53%, 99.55%, respectively). The method presented in this study makes it possible to identify specific sources of nighttime light and is expected to enrich the estimation of structural socioeconomic indicators, as well as better support various applications in urban planning and management.

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A Relative Radiation Normalization Method of ISS Nighttime Light Images Based on Pseudo Invariant Features

Remote Sensing ◽

10.3390/rs12203349 ◽

2020 ◽

Vol 12 (20) ◽

pp. 3349

Author(s):

Shengrong Wei ◽

Weili Jiao ◽

Tengfei Long ◽

Huichan Liu ◽

Lu Bi ◽

...

Keyword(s):

Remote Sensing ◽

Space Station ◽

Remote Sensing Data ◽

Normalization Method ◽

Small Scale ◽

Free Access ◽

Invariant Features ◽

Planning Optimization ◽

Composite Product ◽

Nighttime Light

The International Space Station (ISS) offers a unique view from space that provides nighttime light (NTL) images of many parts of the globe. Compared with other NTL remote sensing data, ISS NTL multispectral images taken by astronauts with commercial digital single-lens reflex (DSLR) cameras have the characteristics of free access, high spatial resolution, abundant data and no light saturation, so it plays a unique advantage in the research of small-scale urban planning, optimization of lighting resource allocation and blue light pollution. In order to improve the radiation consistency of ISS NTL images, a relative radiation normalization method of ISS NTL images is proposed in this paper. Pseudo invariant features (PIF) were identified in the cloud-free Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) time series NTL remote sensing annual composite product, and then they were used to derive the relative radiation normalization model of ISS NTL images. The results show that the radiation brightness of ISS NTL images in different regions is normalized to the same gray level with that of DMSP/OLS NTL remote sensing images in the same year, which improves the radiation brightness comparability between different regions of ISS NTL images. This method is universally applicable to all ISS NTL images, which is beneficial to the NTL comparability of ISS NTL image in the regional horizontal and temporal vertical.

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