Evaluation of Luojia 1–01 Nighttime Light Imagery for Built-Up Urban Area Extraction: A Case Study of 16 Cities in China

Zhai, Weixin; Han, Bing; Cheng, Chengqi

doi:10.1109/lgrs.2019.2955496

Cited by 17 publications

(11 citation statements)

References 26 publications

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“…Landsat 8 is one of the longest observing Earth satellites, with a large amount of Earth observation data, and it can usually be applied directly without requiring geometric corrections. To use Landsat 8 remote sensing data rich in beam spectral information and to best retain the information in the original spectrum, radiometric calibration and image cropping must be performed [35][36][37]. royalsocietypublishing.org/journal/rsos R. Soc.…”

Section: Landsat 8 Operational Land Imager Remote Sensing Datamentioning

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.

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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.

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Section: Landsat 8 Operational Land Imager Remote Sensing Datamentioning

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.

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“…LJ1-01 nighttime light remote sensing images are currently high-resolution and radiometrically calibrated nighttime light data [38,39]. It has great application potential in the recognition of builtup areas and the modeling of social-economic parameters [40][41][42][43][44]. This article adopts the radiance equation provided by the Hubei Data and Application Center of the High-Resolution Earth Observation System to convert the radiance.…”

Section: Data Sourcesmentioning

confidence: 99%

Estimation and Analysis of the Nighttime PM2.5 Concentration Based on LJ1-01 Images: A Case Study in the Pearl River Delta Urban Agglomeration of China

Wang

Huang

et al. 2021

Remote Sensing

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At present, fine particulate matter (PM2.5) has become an important pollutant in regard to air pollution and has seriously harmed the ecological environment and human health. In the face of increasingly serious PM2.5 air pollution problems, feasible large-scale continuous spatial PM2.5 concentration monitoring provides great practical value and potential. Based on radiative transfer theory, a correlation model of the nighttime light radiance and ground PM2.5 concentration is established. A multiple linear regression model is proposed with the light radiance, meteorological elements (temperature, relative humidity, and wind speed) and terrain elements (elevation, slope, and terrain relief) as variables to estimate the ground PM2.5 concentration at 56 air quality monitoring stations in the Pearl River Delta (PRD) urban agglomeration from 2018 to 2019, and the accuracy of model estimation is tested. The results indicate that the R2 value between the model-estimated and measured values is 0.82 in the PRD region, and the model attains a high estimation accuracy. Moreover, the estimation accuracy of the model exhibits notable temporal and spatial heterogeneity. This study, to a certain extent, mitigates the shortcomings of traditional ground PM2.5 concentration monitoring methods with a high cost and low spatial resolution and complements satellite remote sensing technology. This study extends the use of LJ1-01 nighttime light remote sensing images to estimate nighttime PM2.5 concentrations. This yields a certain practical value and potential in nighttime ground PM2.5 concentration inversion.

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“…Therefore, it can be concluded that Luojia-01 data have changed the study emphasis of NTL data from focusing on urban agglomerations and metropolitan areas to focusing on a single city [40,41]. However, although NTL data could relatively represent the urban internal structure, there is a light spill phenomenon [42,43], which means that the areas with nighttime light are larger than actual urban areas [44,45]. Therefore, studies that aim to improve the extraction accuracy of NTL data have been conducted [46].…”

Section: Introductionmentioning

confidence: 99%

Using POI Data and Baidu Migration Big Data to Modify Nighttime Light Data to Identify Urban and Rural Area

Chen

Deng

2022

IEEE Access

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The spatial difference between urban and rural areas is the direct result of urban-rural relations. Accurate identification of urban-rural area is helpful to judge the urban-rural mechanism and promote the integration development of urban-rural area. Previous studies only used single nighttime light (NTL) data to identify urban and rural areas, which is likely to have an impact on the identification results due to the large brightness difference of lights. Therefore, based on NTL data and combine with data level fusion algorithm, this study separately fuses point of interest (POI) data that representing the quantity distribution of urban infrastructure and Baidu migration big (BM)data that representing the change relationship of regional population mobility to identify urban and rural areas by using deep learning method. The results show that the highest accuracy of urban-rural spatial identification with single NTL data is 84.32% and kappa is 0.6952, while the highest accuracy identified by data fusion is 95.02% and kappa is 0.8259. It can be seen that the differences caused by light brightness are effectively corrected after data fusion, which greatly improves the accuracy of urban and rural spatial identification. By comparing the results of NTL data modified by different big data, this study analyzes and identifies the accuracy of urban and rural area by using deep learning method, which not only enriches the study of data fusion in urban area, but also provides a basis for analyzing regional urban-rural relations and urban-rural development. Therefore, this study is believed to have important practical value for the coordinated development of urban and rural areas.

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Evaluation of Luojia 1–01 Nighttime Light Imagery for Built-Up Urban Area Extraction: A Case Study of 16 Cities in China

Cited by 17 publications

References 26 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

Estimation and Analysis of the Nighttime PM2.5 Concentration Based on LJ1-01 Images: A Case Study in the Pearl River Delta Urban Agglomeration of China

Using POI Data and Baidu Migration Big Data to Modify Nighttime Light Data to Identify Urban and Rural Area

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