Correlation analysis between light distribution area of SDGSAT-1 low-light images and impervious area at township scale

doi:10.7523/j.ucas.2023.074

Abstract

Abstract: Taking Bozhou City, Anhui Province as the research area, the differences in the light intensity of several buildings with different uses in two phases were shown in a visualized way. The light area and the impervious area were analyzed by linear regression and IoU (intersection over union) to obtain the differences and changes in light distribution in urban and rural areas. The results show that: 1) Different building types have different light intensities at different times. 2) Due to the high spatial resolution, SDGSAT-1 low-light images can partially distinguish between the main road lights and building lights in urban areas. 3) The linear regression of the light area and the area of the impermeable layer can produce a good fitting effect in the urban area, but the light in the township area can not be well fitted with the area of the impermeable layer because it is not easy to be received by the sensor. 4) The IoU of light area and impervious area in the towns of Bozhou is related to GDP.

Key words: SDGSAT-1, low-light images, township scale, linear regression, IoU

CLC Number:

TP79

WANG Zhao, YAN Dongmei, TANG Ping, ZHANG Zheng. Correlation analysis between light distribution area of SDGSAT-1 low-light images and impervious area at township scale[J]. Journal of University of Chinese Academy of Sciences, 2025, 42(4): 528-537.

References

[1] 李德仁, 李熙. 论夜光遥感数据挖掘[J]. 测绘学报, 2015, 44(6):591-601. DOI: 10.11947/j.AGCS.2015.20150149.
[2] Forbes D J. Multi-scale analysis of the relationship between economic statistics and DMSP-OLS night light images[J]. GIScience & Remote Sensing, 2013, 50(5): 483-499. DOI: 10.1080/15481603.2013.823732.
[3] Doll C N H, Muller J P, Morley J G. Mapping regional economic activity from night-time light satellite imagery[J]. Ecological Economics, 2006, 57(1): 75-92. DOI: 10.1016/j.ecolecon.2005.03.007.
[4] 陈晋, 卓莉, 史培军, 等. 基于DMSP/OLS数据的中国城市化过程研究: 反映区域城市化水平的灯光指数的构建[J]. 遥感学报, 2003, 7(3): 168-175, T001. DOI: 10.3321/j.issn: 1007-4619.2003.03.002.
[5] Li X, Li D. Can night-time light images play a role in evaluating the Syrian Crisis?[J]. International Journal of Remote Sensing, 2014, 35(18): 6648-6661. DOI: 10.1080/01431161.2014.971469.
[6] 李峰, 米晓楠, 刘军,等.基于NPP-VIIRS夜间灯光数据的北京市GDP空间化方法[J]. 国土资源遥感, 2016, 28(3):19-24. DOI: 10.6046/gtzyyg.2016.03.04.
[7] 李翔. 基于夜光遥感数据的中国2005—2015年居民收入时空变化与驱动力研究[D]. 南京:南京大学, 2018.
[8] Li D R, Zhang G, Shen X, et al. Design and processing night light remote sensing of LJ-1 01 satellite[J]. National Remote Sensing Bulletin, 2019, 23(6): 1011-1022. DOI: 10.11834/jrs.20199327.
[9] 欧晓华, 梁保平.“珞珈一号”夜光数据在漓江流域城镇信息提取中的应用[J].测绘与空间地理信息, 2022, 45(10):18-22. DOI: 10.3969/j.issn.1672-5867.2022.10.007.
[10] 娄格, 陈秋晓. 基于“珞珈一号”夜光遥感数据融合的乡村人口空间化方法研究[J].建筑与文化, 2021(1):74-75. DOI: 10.19875/j.cnki.jzywh.2021.01.021.
[11] Guo H D, Dou C Y, Chen H Y, et al. SDGSAT-1: the world’s first scientific satellite for sustainable development goals[J]. Science Bulletin, 2023, 68(1): 34-38. DOI: 10.1016/j.scib.2022.12.014.
[12] Zhao M, Cheng W M, Zhou C H, et al. GDP spatialization and economic differences in South China based on NPP-VIIRS nighttime light imagery[J]. Remote Sensing, 2017, 9(7): 673. DOI: 10.3390/rs9070673.
[13] Zhang X, Liu L Y, Zhao T T, et al. GISD30: global 30 m impervious-surface dynamic dataset from 1985 to 2020 using time-series Landsat imagery on the Google Earth Engine platform[J]. Earth System Science Data, 2022, 14(4): 1831-1856. DOI: 10.5194/essd-14-1831-2022.