Hot spots tracking of nighttime light data application in research of urbanization and its resource and environmental effects

doi:10.7523/j.ucas.2021.0010

Abstract

Abstract: Being closely related to human socioeconomic activity and its footprints, nighttime light (NTL) data shows great advantages in urbanization and socioeconomic development research, especially in densely populated cities. Based on CiteSpace software and the core databases of CNKI (China National Knowledge Infrastructure) and WOS (Web of Science), this paper tracked the hot spots of NTL data in the study of urbanization and related resource consumption and environmental effects from 2000 to 2019. The main results are as follows. 1) Urbanization was the main focus of the application of NTL data, but the researches on the resource consumption and environmental effects caused by urbanization were slightly weak, which was more obvious in Chinese literature. 2) Researches of urban expansion and urban form evolution focused on process of land expansion based on different features of NTL datasets, while in researches of population, socioeconomic development, electricity consumption and carbon emissions, NTL data usually played the role as a supporting tool to explore spatiotemporal characteristics and mechanism. 3) In regards of air pollution and urban heat island induced by urbanization, NTL datasets were usually used to represent factors related to human activities and their impacts. 4) Urbanization process and its impacts on resource and environment are complex, the improved spatial resolution and integrated multi-source data, along with new methods as machine learning, will make the urbanization related research be more precise and scientific. Finally, the paper summarizes the possible new directions of the application of NTL data in urban geography.

Key words: nighttime light data, bibliometric analysis, resource and environmental effects, urbanization

CLC Number:

ZHANG Xiaoping, GAO Shanshan, CHEN Mingxing, ZHAO Yanyan. Hot spots tracking of nighttime light data application in research of urbanization and its resource and environmental effects[J]. Journal of University of Chinese Academy of Sciences, 2022, 39(4): 490-501.

References

[1] 国家统计局.中国统计摘要2020[M].北京:中国统计出版社,2020.
[2] 王鹤饶,郑新奇,袁涛. DMSP/OLS数据应用研究综述[J].地理科学进展, 2012, 31(1):11-19.
[3] 杨眉,王世新,周艺,等. DMSP/OLS夜间灯光数据应用研究综述[J].遥感技术与应用, 2011, 26(1):45-51.
[4] Huang Q X, Yang X, Gao B, et al. Application of DMSP/OLS nighttime light images:a meta-analysis and a systematic literature review[J]. Remote Sensing, 2014, 6(8):6844-6866.DOI:10.3390/rs6086844.
[5] Li X C, Zhou Y Y. Urban mapping using DMSP/OLS stable night-time light:a review[J]. International Journal of Remote Sensing, 2017, 38(21):6030-6046.DOI:10.1080/01431161.2016.1274451.
[6] Bennett M M, Smith L C. Advances in using multitemporal night-time lights satellite imagery to detect, estimate, and monitor socioeconomic dynamics[J]. Remote Sensing of Environment, 2017, 192:176-197.DOI:10.1016/j.rse.2017.01.005.
[7] 陈颖彪,郑子豪,吴志峰,等.夜间灯光遥感数据应用综述和展望[J].地理科学进展, 2019, 38(2):205-223.
[8] 赵敏,程维明.基于DMSP/OLS夜间灯光数据的城市空间扩展研究综述[J].测绘与空间地理信息, 2015, 38(3):64-68.DOI:10.3969/j.issn.1672-5867.2015.03.021.
[9] 肖东升,杨松.基于夜间灯光数据的人口空间分布研究综述[J].国土资源遥感, 2019, 31(3):10-19.DOI:10.6046/gtzyyg.2019.03.02.
[10] Zhang G, Li L T, Jiang Y H, et al. On-orbit relative radiometric calibration of the night-time sensor of the LuoJia1-01 satellite[J]. Sensors, 2018, 18(12):4225.DOI:10.3390/s18124225.
[11] 陈明星.城市化领域的研究进展和科学问题[J].地理研究, 2015, 34(4):614-630.DOI:10.11821/dlyj201504002.
[12] Elvidge C, Baugh K, Hobson V, et al. Satellite inventory of human settlements using nocturnal radiation emissions:a contribution for the global toolchest[J]. Global Change Biology, 1997, 3(5):387-395.DOI:10.1046/j.1365-2486.1997.00115.x.
[13] Henderson M, Yeh E T, Gong P, et al. Validation of urban boundaries derived from global night-time satellite imagery[J]. International Journal of Remote Sensing, 2003, 24(3):595-609.DOI:10.1080/01431160304982.
[14] Imhoff M L, Lawrence W T, Stutzer D C, et al. A technique for using composite DMSP/OLS "city lights" satellite data to map urban area[J]. Remote Sensing of Environment, 1997, 61(3):361-370.DOI:10.1016/s0034-4257(97)00046-1.
[15] Shao Z F, Liu C. The integrated use of DMSP-OLS nighttime light and MODIS data for monitoring large-scale impervious surface dynamics:a case study in the Yangtze River Delta[J]. Remote Sensing, 2014, 6(10):9359-9378.DOI:10.3390/rs6109359.
[16] Sutton P. Modeling population density with night-time satellite imagery and GIS[J]. Computers, Environment and Urban Systems, 1997, 21(3/4):227-244.DOI:10.1016/s0198-9715(97)01005-3.
[17] Elvidge C D, Tuttle B T, Sutton P C, et al. Global distribution and density of constructed impervious surfaces[J]. Sensors (Basel), 2007, 7(9):1962-1979.DOI:10.3390/s7091962.
[18] Sutton P C. A scale-adjusted measure of "urban sprawl" using nighttime satellite imagery[J]. Remote Sensing of Environment, 2003, 86(3):353-369.DOI:10.1016/s0034-4257(03)00078-6.
[19] Zhai W X, Han B, Cheng C Q. Evaluation of Luojia 1-01 nighttime light imagery for built-up urban area extraction:a case study of 16 cities in China[J]. IEEE Geoscience and Remote Sensing Letters, 2020, 17(10):1802-1806.DOI:10.1109/LGRS.2019.2955496.
[20] Wang C X, Chen Z Q, Yang C S, et al. Analyzing parcel-level relationships between Luojia 1-01 nighttime light intensity and artificial surface features across Shanghai, China:a comparison with NPP-VIIRS data[J]. International Journal of Applied Earth Observation and Geoinformation, 2020, 85:101989.DOI:10.1016/j.jag.2019.101989.
[21] Ou J P, Liu X P, Liu P H, et al. Evaluation of Luojia 1-01 nighttime light imagery for impervious surface detection:a comparison with NPP-VIIRS nighttime light data[J]. International Journal of Applied Earth Observation and Geoinformation, 2019, 81:1-12.DOI:10.1016/j.jag.2019.04.017.
[22] Xiao P F, Wang X H, Feng X Z, et al. Detecting China's urban expansion over the past three decades using nighttime light data[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(10):4095-4106.DOI:10.1109/JSTARS.2014.2302855.
[23] Zhang Q, Wang P, Chen H, et al. A novel method for urban area extraction from VIIRS DNB and MODIS NDVI data:a case study of Chinese cities[J]. International Journal of Remote Sensing, 2017, 38(21):6094-6109.DOI:10.1080/01431161.2017.1339927.
[24] Dou Y Y, Liu Z F, He C Y, et al. Urban land extraction using VIIRS nighttime light data:an evaluation of three popular methods[J]. Remote Sensing, 2017, 9(2):175.DOI:10.3390/rs9020175.
[25] Cao K, Guo H, Zhang Y. Comparison of approaches for urban functional zones classification based on multi-source geospatial data:a case study in Yuzhong district, Chongqing, China[J]. Sustainability, 2019, 11(3):660.DOI:10.3390/su11030660.
[26] Chen Z Q, Yu B L, Zhou Y Y, et al. Mapping global urban areas from 2000 to 2012 using time-series nighttime light data and MODIS products[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2019, 12(4):1143-1153.DOI:10.1109/JSTARS.2019.2900457.
[27] 何春阳,李景刚,陈晋,等.基于夜间灯光数据的环渤海地区城市化过程[J].地理学报, 2005, 60(3):409-417.DOI:10.3321/j.issn:0375-5444.2005.03.007.
[28] Lu H M, Zhang M L, Sun W W, et al. Expansion analysis of Yangtze River Delta urban agglomeration using DMSP/OLS nighttime light imagery for 1993 to 2012[J]. ISPRS International Journal of Geo-Information, 2018, 7(2):52.DOI:10.3390/ijgi7020052.
[29] Chen Z Q, Yu B L, Song W, et al. A new approach for detecting urban centers and their spatial structure with nighttime light remote sensing[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(11):6305-6319.DOI:10.1109/TGRS.2017.2725917.
[30] Cai J X, Huang B, Song Y M. Using multi-source geospatial big data to identify the structure of polycentric cities[J]. Remote Sensing of Environment, 2017, 202:210-221.DOI:10.1016/j.rse.2017.06.039.
[31] Zhao X, Li X, Zhou Y Y, et al. Analyzing urban spatial connectivity using night light observations:a case study of three representative urban agglomerations in China[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020, 13:1097-1108.DOI:10.1109/JSTARS.2020.2980514.
[32] 陈昕,彭建,刘焱序,等.基于DMSP/OLS夜间灯光数据的京津冀地区城市空间扩展与空间关联测度[J].地理研究, 2018, 37(5):898-909.DOI:10.11821/dlyj201805004.
[33] 廖兵,魏康霞,宋巍巍. DMSP/OLS夜间灯光数据在城镇体系空间格局研究中的应用与评价:以近16年江西省间城镇空间格局为例[J].长江流域资源与环境, 2012, 21(11):1295-1300.
[34] 范俊甫,马廷,周成虎,等. 1992-2010年基于DMSP-OLS图像的环渤海城市群空间格局变化分析[J].地球信息科学学报, 2013, 15(2):280-288.DOI:10.3724/SP.J.1047.2013.00280.
[35] Sutton P, Roberts D, Elvidge C, et al. A comparison of nighttime satellite imagery and population density for the continental United States[J]. Photogrammetric Engineering and Remote Sensing, 1997, 63(11):1303-1313.
[36] Briggs D J, Gulliver J, Fecht D, et al. Dasymetric modelling of small-area population distribution using land cover and light emissions data[J]. Remote Sensing of Environment, 2007, 108(4):451-466.DOI:10.1016/j.rse.2006.11.020.
[37] Zhao S, Liu Y X, Zhang R, et al. China's population spatialization based on three machine learning models[J]. Journal of Cleaner Production, 2020, 256:120644.DOI:10.1016/j.jclepro.2020.120644.
[38] Yang J S, Liu S, Wang Y D, et al. The assessment and analysis of the phenomenon of vacancy within Wuhan city using multi-source datasets[J]. Journal of Geo-Information Science, 2020, 22(5):997-1007.
[39] Tan Z R, Wei D L, Yin Z X. Housing vacancy rate in major cities in China:perspectives from nighttime light data[J]. Complexity, 2020:5104578.DOI:10.1155/2020/5104578.
[40] Liu X C, Sweeney J. Modelling the impact of urban form on household energy demand and related CO₂ emissions in the Greater Dublin Region[J]. Energy Policy, 2012, 46:359-369.DOI:10.1016/j.enpol.2012.03.070.
[41] Li X M, Zhou W Q. Dasymetric mapping of urban population in China based on radiance corrected DMSP-OLS nighttime light and land cover data[J]. Science of the Total Environment, 2018, 643:1248-1256.DOI:10.1016/j.scitotenv.2018.06.244.
[42] Pavía J M, Cantarino I. Can dasymetric mapping significantly improve population data reallocation in a dense urban area?[J]. Geographical Analysis, 2017, 49(2):155-174.DOI:10.1111/gean.12112.
[43] Sun W C, Zhang X, Wang N, et al. Estimating population density using DMSP-OLS night-time imagery and land cover data[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017, 10(6):2674-2684.DOI:10.1109/JSTARS.2017.2703878.
[44] 卓莉,陈晋,史培军,等.基于夜间灯光数据的中国人口密度模拟[J].地理学报, 2005, 60(2):266-276.DOI:10.3321/j.issn:0375-5444.2005.02.010.
[45] Elvidge C D, Baugh K E, Kihn E A, et al. Relation between satellite observed visible-near infrared emissions, population, economic activity and electric power consumption[J]. International Journal of Remote Sensing, 1997, 18(6):1373-1379.DOI:10.1080/014311697218485.
[46] Henderson J V, Storeygard A, Weil D N. Measuring economic growth from outer space[J]. The American Economic Review, 2012, 102(2):994-1028.DOI:10.1257/aer.102.2.994.
[47] Sun J, Di L P, Sun Z H, et al. Estimation of GDP using deep learning with NPP-VIIRS imagery and land cover data at the County level in CONUS[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020, 13:1400-1415.DOI:10.1109/JSTARS.2020.2983331.
[48] Li G E, Chang L Y, Liu X J, et al. Monitoring the spatiotemporal dynamics of poor counties in China:Implications for global sustainable development goals[J]. Journal of Cleaner Production, 2019, 227:392-404.DOI:10.1016/j.jclepro.2019.04.135
[49] 李兵,龚昭,刘思勤,等."一带一路"国家投资适宜性研究:基于夜间灯光数据的视角[J].世界经济文汇, 2019(3):18-37.
[50] Lo C P. Modeling the population of China using DMSP operational linescan system nighttime data[J]. Photogrammetric Engineering and Remote Sensing, 2001, 67(9):1037-1047.
[51] 吴健生,牛妍,彭建,等.基于DMSP/OLS夜间灯光数据的1995-2009年中国地级市能源消费动态[J].地理研究, 2014, 33(4):625-634.
[52] Li S Y, Cheng L, Liu X Q, et al. City type-oriented modeling electric power consumption in China using NPP-VIIRS nighttime stable light data[J]. Energy, 2019, 189:116040.DOI:10.1016/j.energy.2019.116040.
[53] 李熙,薛翔宇.基于波士顿矩阵的夜光遥感电力消费估算方法[J].武汉大学学报.信息科学版, 2018, 43(12):1994-2002.DOI:10.13203/j.whugis20180334.
[54] Jasiński T. Modeling electricity consumption using nighttime light images and artificial neural networks[J]. Energy, 2019, 179:831-842.DOI:10.1016/j.energy.2019.04.221
[55] Zhao J C, Chen Y L, Ji G X, et al. Residential carbon dioxide emissions at the urban scale for county-level cities in China:a comparative study of nighttime light data[J]. Journal of Cleaner Production, 2018, 180:198-209.DOI:10.1016/j.jclepro.2018.01.131
[56] Ghosh T, Elvidge C D, Sutton P C, et al. Creating a global grid of distributed fossil fuel CO₂ emissions from nighttime satellite imagery[J]. Energies, 2010, 3(12):1895-1913.DOI:10.3390/en3121895.
[57] Oda T, Maksyutov S. A very high-resolution (1km×1km) global fossil fuel CO₂ emission inventory derived using a point source database and satellite observations of nighttime lights[J]. Atmospheric Chemistry and Physics, 2011, 11(2):543-556.DOI:10.5194/acp-11-543-2011.
[58] Lyu Q, Liu H B, Wang J T, et al. Multiscale analysis on spatiotemporal dynamics of energy consumption CO₂ emissions in China:utilizing the integrated of DMSP-OLS and NPP-VIIRS nighttime light datasets[J]. Science of the Total Environment, 2020, 703:134394.DOI:10.1016/j.scitotenv.2019.134394.
[59] 赵笑然,石汉青,杨平吕,等. NPP卫星VIIRS微光资料反演夜间PM_2.5质量浓度[J].遥感学报, 2017, 21(2):291-299.
[60] Wang J, Aegerter C, Xu X G, et al. Potential application of VIIRS day/night band for monitoring nighttime surface PM_2.5 air quality from space[J]. Atmospheric Environment, 2016, 124:55-63.DOI:10.1016/j.atmosenv.2015.11.013.
[61] Lin G, Fu J Y, Jiang D, et al. Spatio-temporal variation of PM_2.5 concentrations and their relationship with geographic and socioeconomic factors in China[J]. International Journal of Environmental Research and Public Health, 2014, 11(1):173-186.DOI:10.3390/ijerph110100173.
[62] Shi K F, Shen J W, Wang L, et al. A multiscale analysis of the effect of urban expansion on PM_2.5 concentrations in China:evidence from multisource remote sensing and statistical data[J]. Building and Environment, 2020, 174:106778.DOI:10.1016/j.buildenv.2020.106778.
[63] 祝芸依,郭亮,张秋圆.基于DMSP/OLS数据湖南省城市发展与PM_2.5时空分布研究//2019中国城市规划年会论文集.北京:中国建筑工业出版社, 2019:293-304.
[64] Shi K F, Li Y, Chen Y, et al. How does the urban form-PM_2.5 concentration relationship change seasonally in Chinese cities?A comparative analysis between national and urban agglomeration scales[J]. Journal of Cleaner Production, 2019, 239:118088.DOI:10.1016/j.jclepro.2019.118088.
[65] Ji G X, Tian L, Zhao J C, et al. Detecting spatiotemporal dynamics of PM_2.5 emission data in China using DMSP-OLS nighttime stable light data[J]. Journal of Cleaner Production, 2019, 209:363-370.DOI:10.1016/j.jclepro.2018.10.285.
[66] Ji G X, Zhao J C, Yang X, et al. Exploring China's 21-year PM₁₀ emissions spatiotemporal variations by DMSP-OLS nighttime stable light data[J]. Atmospheric Environment, 2018, 191:132-141.DOI:10.1016/j.atmosenv.2018.07.045.
[67] 罗鑫玥,陈明星.城镇化对气候变化影响的研究进展[J].地球科学进展, 2019, 34(9):984-997.
[68] Li J F, Wang F F, Fu Y C, et al. A novel SUHI referenced estimation method for multicenters urban agglomeration using DMSP/OLS nighttime light data[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020, 13:1416-1425.DOI:10.1109/JSTARS.2020.2981285.
[69] 谢志清,杜银,曾燕,等.长江三角洲城市带扩展对区域温度变化的影响[J].地理学报, 2007, 62(7):717-727.
[70] 刘勇洪,房小怡,张硕,等.京津冀城市群热岛定量评估[J].生态学报, 2017, 37(17):5818-5835.
[71] 杨续超,陈锋,祝炜平,等.城市化对浙江省夏季极端高温事件的影响[J].热带气象学报, 2014, 30(4):719-726.DOI:10.3969/j.issn.1004-4965.2014.04.012.
[72] Yang J, Wang Y C, Xiu C L, et al. Optimizing local climate zones to mitigate urban heat island effect in human settlements[J]. Journal of Cleaner Production, 2020, 275:123767.DOI:10.1016/j.jclepro.2020.123767.
[73] Hu K J, Yang X C, Zhong J M, et al. Spatially explicit mapping of heat health risk utilizing environmental and socioeconomic data[J]. Environmental Science&Technology, 2017, 51(3):1498-1507.DOI:10.1021/acs.est.6b04355.
[74] 杨智威,陈颖彪,吴志峰,等.粤港澳大湾区城市热岛空间格局及影响因子多元建模[J].资源科学, 2019, 41(6):1154-1166.
[75] Hu D Y, Chen S S, Duan F Z. Estimation of the anthropogenic heat flux distribution in Beijing-Tianjin-Hebei region based on Suomi-NPP/VIIRS nighttime light image//2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). July 23-28, 2017, Fort Worth, TX, USA. IEEE, 2017:4012-4015.DOI:10.1109/IGARSS.2017.8127880.
[76] Sun Y R, Wang S H, Wang Y. Estimating local-scale urban heat island intensity using nighttime light satellite imageries[J]. Sustainable Cities and Society, 2020, 57:102125.DOI:10.1016/j.scs.2020.102125.
[77] 向炀,周志翔.基于地理探测器的城市热岛驱动因素分析:以武汉市为例[J].长江流域资源与环境, 2020, 29(8):1768-1779.