Planar Block Adjustment for China’s Land Regions with LuoJia1-01 Nighttime Light Imagery

Li, Xin; Wang, Taoyang; Guo, Zhang; Jiang, Binbin; Jia, Peng; Zhang, Zhuxi; Zhao, Yuan

doi:10.3390/rs11182097

Cited by 11 publications

(11 citation statements)

References 33 publications

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“…The digital number (DN) value was converted into the radiant brightness (RB) value to participate in the subsequent calculation. The radiance conversion formula of LJ 1-01 NTL is as follows [ 44 ]:

where L represents the RB values with the unit W/(m 2 ∙sr∙μm), and DN represents the DN values for a pixel.…”

Section: Methodsmentioning

confidence: 99%

An Improved Method of Determining Human Population Distribution Based on Luojia 1-01 Nighttime Light Imagery and Road Network Data—A Case Study of the City of Shenzhen

Zhou

Zheng

et al. 2020

Sensors

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Previously published studies on population distribution were based on the provincial level, while the number of urban-level studies is more limited. In addition, the rough spatial resolution of traditional nighttime light (NTL) data has limited their fine application in current small-scale population distribution research. For the purpose of studying the spatial distribution of populations at the urban scale, we proposed a new index (i.e., the road network adjusted human settlement index, RNAHSI) by integrating Luojia 1-01 (LJ 1-01) NTL data, the enhanced vegetation index (EVI), and road network density (RND) data based on population density relationships to depict the spatial distribution of urban human settlements. The RNAHSI updated the high-resolution NTL data and combined the RND data on the basis of human settlement index (HSI) data to refine the spatial pattern of urban population distribution. The results indicated that the mean relative error (MRE) between the population estimation data based on the RNAHSI and the demographic data was 34.80%, which was lower than that in the HSI and WorldPop dataset. This index is suitable primarily for the study of urban population distribution, as the RNAHSI can clearly highlight human activities in areas with dense urban road networks and can refine the spatial heterogeneity of impervious areas. In addition, we also drew a population density map of the city of Shenzhen with a 100 m spatial resolution for 2018 based on the RNAHSI, which has great reference significance for urban management and urban resource allocation.

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where L represents the RB values with the unit W/(m 2 ∙sr∙μm), and DN represents the DN values for a pixel.…”

Section: Methodsmentioning

confidence: 99%

An Improved Method of Determining Human Population Distribution Based on Luojia 1-01 Nighttime Light Imagery and Road Network Data—A Case Study of the City of Shenzhen

Zhou

Zheng

et al. 2020

Sensors

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show abstract

“…We acquired the national Luojia-1 NTL imagery for 2018 from the Hubei Data and Application Center (http://www.hbeos.org.cn/, last accessed in July 2020). This data was produced using 275 scenes of Luojia-1 NTL images acquired between July and October that covered the land region of China [50]. Before image matching, NTL images were processed through cloud-contaminated data exclusion and stray lights removal [50].…”

Section: Luojia-1 Ntl Datamentioning

confidence: 99%

“…This data was produced using 275 scenes of Luojia-1 NTL images acquired between July and October that covered the land region of China [50]. Before image matching, NTL images were processed through cloud-contaminated data exclusion and stray lights removal [50]. The root mean square error of the tie points was 0.983 pixels and 195.491 m for independent checkpoints after the planar block adjustment [50].…”

Section: Luojia-1 Ntl Datamentioning

confidence: 99%

“…Before image matching, NTL images were processed through cloud-contaminated data exclusion and stray lights removal [50]. The root mean square error of the tie points was 0.983 pixels and 195.491 m for independent checkpoints after the planar block adjustment [50]. More information about this data can be found in [50].…”

Section: Luojia-1 Ntl Datamentioning

confidence: 99%

“…The root mean square error of the tie points was 0.983 pixels and 195.491 m for independent checkpoints after the planar block adjustment [50]. More information about this data can be found in [50]. We clipped the national Luojia-1 NTL imagery based on the administrative boundary of Guangdong Province and later uploaded it to the GEE platform.…”

Section: Luojia-1 Ntl Datamentioning

confidence: 99%

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Improved Mapping Results of 10 m Resolution Land Cover Classification in Guangdong, China Using Multisource Remote Sensing Data With Google Earth Engine

Lang

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Land cover information depicting the complex interactions between human activities and surface change is critically essential for nature conservation, social management, and sustainable development. Recent advances have shown great potentials of remote sensing data in generating high-resolution land cover maps, but it remains unclear how different models, data sources, and inclusive features affect the classification results, which hinders its applications in regional studies requiring more accurate land cover data. Informing these issues, here we developed a robust framework to improve the mapping results of 10 m resolution land cover classification in Guangdong Province, China using thousands of manually collected samples, multisource remote sensing data (Sentinel-1, Sentinel-2, and Luojia-1), machine learning algorithms, and a free cloud-based platform of Google Earth Engine. Results showed that an overall accuracy of 86.12% and a Kappa coefficient of 0.84 could be achieved for land cover classification in Guangdong for 2019. We found that Random Forest (RF) models achieved better performance than classification and regression trees (CART), minimum distance (MD), and support vector machine (SVM) models. We also found that features derived from Sentinel-1 data and Sentinel-2 spectral indices greatly contributed to the classification process, while the feature of Luojia-1 data was not as much important as other configurations. A comparison between our results and several existing land cover products in terms of classification accuracy and visual interpretation further validated the effectiveness and robustness of the proposed framework. Our experiments and findings not only systematically elucidate the role of classification methods and data sources in deriving more accurate and reliable land cover maps, but also provide certain guidelines for future land cover mapping from regional to global scales.

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Urban functional zone mapping by integrating high spatial resolution nighttime light and daytime multi-view imagery

Huang

Yang

et al. 2021

ISPRS Journal of Photogrammetry and Remote Sensing

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Planar Block Adjustment for China’s Land Regions with LuoJia1-01 Nighttime Light Imagery

Cited by 11 publications

References 33 publications

An Improved Method of Determining Human Population Distribution Based on Luojia 1-01 Nighttime Light Imagery and Road Network Data—A Case Study of the City of Shenzhen

An Improved Method of Determining Human Population Distribution Based on Luojia 1-01 Nighttime Light Imagery and Road Network Data—A Case Study of the City of Shenzhen

Improved Mapping Results of 10 m Resolution Land Cover Classification in Guangdong, China Using Multisource Remote Sensing Data With Google Earth Engine

Urban functional zone mapping by integrating high spatial resolution nighttime light and daytime multi-view imagery

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