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

On June 2, 2018, the Luojia 1–01 (LJ1-01) nighttime light satellite was launched from China with a spatial resolution of 130 m at nadir, which is a significant improvement over the resolutions of previous nighttime light satellites. However, few studies have focused on the applications of LJ1-01 imagery. This letter aimed to evaluate the potential of utilizing LJ1-01 data to extract built-up urban areas in comparison with Visible Infrared Imaging Radiometer Suite (VIIRS) day–night band (DNB) data by conducting a case study of 16 cities involved in the Belt and Road Initiative in China. The built-up urban areas of the 16 cities were extracted by thresholding segmentation in reference to administrative statistical data, and 30-m-resolution artificial impervious data were adopted as the benchmark (ground truth). Qualitative and quantitative assessments were implemented for an evaluation, revealing that the built-up urban areas extracted by LJ1-01 data outperformed the areas extracted by VIIRS DNB data. We argue that these improvements originate from the superior spatial resolution and image quality of LJ1-01 over its predecessors. The LJ1-01 data presented excellent suitability for the extraction of built-up urban areas at the city scale, and hence, these data may be further applied to other related investigations.

[1]  Xiaoping Liu,et al.  High-resolution multi-temporal mapping of global urban land using Landsat images based on the Google Earth Engine Platform , 2018 .

[2]  Joan S. Weszka,et al.  A survey of threshold selection techniques , 1978 .

[3]  Jay Lee,et al.  A brightness–darkness–greenness model for monitoring urban landscape evolution in a developing country – A case study of Shanghai , 2014 .

[4]  Jianping Wu,et al.  Evaluation of NPP-VIIRS night-time light composite data for extracting built-up urban areas , 2014 .

[5]  Deren Li,et al.  Intercalibration between DMSP/OLS and VIIRS night-time light images to evaluate city light dynamics of Syria’s major human settlement during Syrian Civil War , 2017 .

[6]  Rui Liu,et al.  An evaluation of Suomi NPP-VIIRS data for surface water detection , 2015 .

[7]  K. Seto,et al.  Mapping urbanization dynamics at regional and global scales using multi-temporal DMSP/OLS nighttime light data , 2011 .

[8]  K. Gaston,et al.  The nature of the diffuse light near cities detected in nighttime satellite imagery , 2019, Scientific Reports.

[9]  Masanao Hara,et al.  A Saturated Light Correction Method for DMSP/OLS Nighttime Satellite Imagery , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[10]  Zhe Zhu,et al.  Understanding an urbanizing planet: Strategic directions for remote sensing , 2019, Remote Sensing of Environment.

[11]  K. Seto,et al.  It's Time for an Urbanization Science , 2013 .

[12]  Ruizhi Chen,et al.  Initial Assessment of the LEO Based Navigation Signal Augmentation System from Luojia-1A Satellite , 2018, Sensors.

[13]  Michael Jendryke,et al.  A preliminary investigation of Luojia-1 night-time light imagery , 2019, Remote Sensing Letters.

[14]  Bin Chen,et al.  Stable classification with limited sample: transferring a 30-m resolution sample set collected in 2015 to mapping 10-m resolution global land cover in 2017. , 2019, Science bulletin.

[15]  Monika Kuffer,et al.  The continuous built-up area extracted from ISS night-time lights to compare the amount of urban green areas across European cities , 2019, European Journal of Remote Sensing.

[16]  Zhifeng Liu,et al.  Extracting the dynamics of urban expansion in China using DMSP-OLS nighttime light data from 1992 to 2008 , 2012 .

[17]  C. Elvidge,et al.  Why VIIRS data are superior to DMSP for mapping nighttime lights , 2013 .

[18]  N. Otsu A threshold selection method from gray level histograms , 1979 .

[19]  Guojin He,et al.  Potentiality of Using Luojia 1-01 Nighttime Light Imagery to Investigate Artificial Light Pollution , 2018, Sensors.

[20]  Chenghu Zhou,et al.  Applications of Satellite Remote Sensing of Nighttime Light Observations: Advances, Challenges, and Perspectives , 2019, Remote. Sens..

[21]  J. Eom,et al.  Projecting Global Urban Area Growth Through 2100 Based on Historical Time Series Data and Future Shared Socioeconomic Pathways , 2019, Earth's Future.

[22]  Xue Li,et al.  Assessing the Ability of Luojia 1-01 Imagery to Detect Feeble Nighttime Lights , 2019, Sensors.

[23]  Shunping Zhou,et al.  Mapping Regional Urban Extent Using NPP-VIIRS DNB and MODIS NDVI Data , 2017, Remote. Sens..

[24]  Ying Long,et al.  Understanding uneven urban expansion with natural cities using open data , 2017, Landscape and Urban Planning.

[25]  K. Seto,et al.  Urban land teleconnections and sustainability , 2012, Proceedings of the National Academy of Sciences.

[26]  Xi Li,et al.  Mapping Urban Extent Using Luojia 1-01 Nighttime Light Imagery , 2018, Sensors.

[27]  Wei Zhang,et al.  40-Year (1978-2017) human settlement changes in China reflected by impervious surfaces from satellite remote sensing. , 2019, Science bulletin.