Please wait a minute...
 
Remote Sensing for Land & Resources    2020, Vol. 32 Issue (3) : 39-48     DOI: 10.6046/gtzyyg.2020.03.06
|
Extraction method of urban built-up area based on light brightness combination value of DMSP/OLS
YANG Yi(), SUN Wenbin(), HAN Yahui
Faculty of Earth Sciences and Surveying Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China
Download: PDF(7771 KB)   HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks    
Abstract  

The saturation and overflow effects of DMSP/OLS data tend to misclassified the boundary pixels between urban built-up and non-urban built-up areas as urban built-up areas, which will affect the extraction accuracy of urban built-up areas. This paper proposed an urban built-up area extraction method based on the combination value of light brightness. By determining the accurate boundary location between built-up and non-built-up area, the authors tried to improve the extraction accuracy of built-up area. First, the combination of the brightness of the light is constructed to highlight the difference between the urban built-up and the non-urban built-up area. Second, the Mann-Kendall non-parametric test method is used to determine the change-point of the combination value of light brightness of adjacent pixels, which is used as edge pixel to identify urban built-up area. The relevant experiments were carried out in Guangzhou, Shenzhen, Wuhan and Nanjing. The results show that, the average area error is only 10.89%, the average coverage of spatial overlap is 84.93%, the kappa coefficient is 0.863 0, and the overall accuracy is 91.68%. Compared with threshold method, NSA and feature combination method, this method can reach the highest extraction accuracy, and hence the authors provide an effective and reliable method for extracting urban built-up area based on nighttime light data.
Keywords DMSP/OLS data      urban built-up area      combination value of light brightness      boundary pixels     
:  TP79  
Corresponding Authors: SUN Wenbin     E-mail: yangyi520rs@gmail.com;swb@cumtb.edu.cn
Issue Date: 09 October 2020
Service
E-mail this article
E-mail Alert
RSS
Articles by authors
Yi YANG
Wenbin SUN
Yahui HAN
Cite this article:   
Yi YANG,Wenbin SUN,Yahui HAN. Extraction method of urban built-up area based on light brightness combination value of DMSP/OLS[J]. Remote Sensing for Land & Resources, 2020, 32(3): 39-48.
URL:  
https://www.gtzyyg.com/EN/10.6046/gtzyyg.2020.03.06     OR     https://www.gtzyyg.com/EN/Y2020/V32/I3/39
Fig.1  Light brightness and lighting fluctuations
Fig.2  Technical roadmap
Fig.3  Comparison of original image, fluctuation of lighting intensity and light brightness combination image of Guangzhou City
Fig.4  Profile image of light brightness combined value of the horizontal and vertical direction
Fig.5  Image of Mann-Kendall mutation test for the horizontal and vertical direction
Fig.6  Extraction results of built-up area
Fig.7  Overlay map of sample point and space overlap coverage
指标 广州市 深圳市 武汉市 南京市 平均值
采样点准确率 91.25 92.23 86.36 89.10 89.74
空间重叠覆盖率 88.68 86.90 83.79 80.36 84.93
Tab.1  Accuracy of spatial distribution of urban built-up and non-urban built-up area(%)
Fig.8  Comparison of the extraction results of the four methods
城市 统计数据 阈值法 NSA 特征组合值法 本文方法
提取面积 面积误差 提取面积 面积误差 提取面积 面积误差 提取面积 面积误差
广州市 952.04 1 090 137.96 1 198 245.96 1 148 195.96 897 -55.04
深圳市 661 754 93 778 117 761 100 708 47
武汉市 500 651 151 704 204 689 189 597 97
南京市 513 583 70 636 123 614 101 571 58
Tab.2  Error analysis of extraction in urban built-up areas(km2)
城市 方法 总体精度/% Kappa系数 城市 方法 总体精度/% Kappa系数
广州市 阈值法 86.400 1 0.727 4 武汉市 阈值法 90.697 7 0.807 7
NSA 80.800 0 0.719 2 NSA 86.046 5 0.776 7
特征组合值法 77.610 3 0.643 2 特征组合值法 87.127 0 0.790 2
本文方法 91.401 2 0.827 4 本文方法 93.385 6 0.875 6
深圳市 阈值法 88.123 3 0.908 0 南京市 阈值法 86.719 3 0.769 5
NSA 79.767 6 0.715 4 NSA 80.473 7 0.730 3
特征组合值法 83.202 1 0.886 7 特征组合值法 84.324 5 0.679 5
本文方法 91.030 1 0.936 7 本文方法 90.899 2 0.812 1
Tab.3  Confusion matrix evaluation the results of different methods for urban built-up areas extraction
Fig.9  Grouped histogram of landscape index
方法 LSI平均误差 PARA平均误差 CONTIG平均误差
阈值法 0.648 1 1.324 4 0.028 3
NSA 0.823 4 2.062 7 0.030 9
特征组合值法 0.713 8 2.777 3 0.043 2
本文方法 0.239 0 1.075 5 0.028 1
Tab.4  Average error of landscape index for different methods
[1] 孙凌蔚. 基于Landsat数据和DMSP/OLS夜间灯光数据的山东省城市扩张探讨[D]. 重庆:重庆师范大学, 2017.
[1] Sun L W. Urban expansion in Shandong Province based on Landsat data and DMSP/OLS night lighting data[D]. Chongqing:Chongqing Normal University, 2017.
[2] Xu T, Coco G, Gao J. Extraction of urban built-up areas from nighttime lights using artificial neural network[J]. Geocarto International, 2018: 1-18.
[3] 卓莉, 张晓帆, 郑璟, 等. 基于EVI指数的DMSP/OLS夜间灯光数据去饱和方法[J]. 地理学报, 2015,70(8):1339-1350.
doi: 10.11821/dlxb201508012 url: http://www.geog.com.cn/CN/abstract/abstract36745.shtml
[3] Zhuo L, Zhang X F, Zheng J, et al. An EVI-based method to reduce saturation of DMSP/OLS nighttime light data[J]. Journal of Geography, 2015,70(8):1339-1350.
[4] Elvidge C D, Imhoff M L, Baugh K E, et al. Night-time lights of the world:1994—1995[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2001,56(2):81-99.
doi: 10.1016/S0924-2716(01)00040-5 url: https://linkinghub.elsevier.com/retrieve/pii/S0924271601000405
[5] 邹进贵, 陈艳华, 田径, 等. 基于ArcGIS的DMSP/OLS夜间灯光影像校正模型的构建[J]. 测绘地理信息, 2014,39(4):33-37.
[5] Zou J G, Chen Y H, Tian J, et al. Construction of DMSP/OLS nighttime light image correction model based on ArcGIS[J]. Mapping Geographic Information, 2014,39(4):33-37.
[6] 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 url: https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1365-2486.1997.00115.x
[7] Elvidge C D, Baugh K E, Anderson S J, et al. The night light development index (NLDI):A spatially explicit measure of human development from satellite data[J]. Social Geography & Discussions, 2012,7(1):23-35.
doi: 10.5194/sg-7-23-2012 url: http://www.soc-geogr.net/7/23/2012/
[8] Small C, Pozzi F, Elvidge C D. Spatial analysis of global urban extent from DMSP-OLS night lights[J]. Remote Sensing of Environment, 2005,96(34):277-291.
doi: 10.1016/j.rse.2005.02.002 url: https://linkinghub.elsevier.com/retrieve/pii/S0034425705000696
[9] Liu Z, He C, Zhang Q, et al. Extracting the dynamics of urban expansion in China using DMSP-OLS nighttime light data from 1992 to 2008[J]. Landscape and Urban Planning, 2012,106(1):62-72.
doi: 10.1016/j.landurbplan.2012.02.013 url: http://dx.doi.org/10.1016/j.landurbplan.2012.02.013
[10] Zhou Y, Smith S J, Elvidge C D, et al. A cluster-based method to map urban area from DMSP/OLS nightlights[J]. Remote Sensing of Environment, 2014,147:173-185.
doi: 10.1016/j.rse.2014.03.004 url: http://dx.doi.org/10.1016/j.rse.2014.03.004
[11] Zhang Q, Seto K C. Mapping urbanization dynamics at regional and global scales using multi-temporal DMSP/OLS nighttime light data[J]. Remote Sensing of Environment, 2011,115(9):2320-2329.
doi: 10.1016/j.rse.2011.04.032 url: http://dx.doi.org/10.1016/j.rse.2011.04.032
[12] Xie Y, Weng Q. Updating urban extents with nighttime light imagery by using an object-based thresholding method[J]. Remote Sensing of Environment, 2016,187:1-13.
doi: 10.1016/j.rse.2016.10.002 url: https://linkinghub.elsevier.com/retrieve/pii/S0034425716303741
[13] Ma T, Zhou C, Pei T, et al. Quantitative estimation of urbanization dynamics using time series of DMSP/OLS nighttime light data:A comparative case study from China’s cities[J]. Remote Sensing of Environment, 2012,124:99-107.
doi: 10.1016/j.rse.2012.04.018 url: http://dx.doi.org/10.1016/j.rse.2012.04.018
[14] Liu Y, Delahunty T, Zhao N, et al. These lit areas are undeveloped:Delimiting China’s urban extents from thresholded nighttime light imagery[J]. International Journal of Applied Earth Observation and Geoinformation, 2016,50:39-50.
doi: 10.1016/j.jag.2016.02.011 url: https://linkinghub.elsevier.com/retrieve/pii/S0303243416300228
[15] 舒松, 余柏蒗, 吴健平, 等. 基于夜间灯光数据的城市建成区提取方法评价与应用[J]. 遥感技术与应用, 2011,26(2):169-176.
url: http://www.rsta.ac.cn/CN/abstract/abstract484.shtml
[15] Shu S, Yu B L, Wu J P, et al. Evaluation and application of urban built-up area extraction method based on night lighting data[J]. Remote Sensing Technology and Applications, 2011,26(2):169-176.
[16] 李靖业, 龚健, 杨建新, 等. 利用夜间灯光数据的武汉城市空间格局演化[J].遥感信息, 2017(3):133-141.
[16] Li J Y, Gong J, Yang J X, et al. Evolution of urban spatial pattern in Wuhan using night light data[J].Remote Sensing Information 2017(3):133-141.
[17] Huang X M, Annemarie Schneider, Mark AFriedl, et al. Mapping sub-pixel urban expansion in China using MODIS and DMSP/OLS nighttime lights[J]. Remote Sensing of Environment, 2016,175:92-108.
doi: 10.1016/j.rse.2015.12.042 url: https://linkinghub.elsevier.com/retrieve/pii/S0034425715302637
[18] Ma X L, Tong X H, Liu S C, et al. Optimized sample selection in SVM classification by combining with DMSP-OLS,Landsat NDVI and GlobeLand30 products for extracting urban built-up areas[J]. Remote Sensing, 2017,9(3):236-251.
doi: 10.3390/rs9030236 url: http://www.mdpi.com/2072-4292/9/3/236
[19] 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 url: https://linkinghub.elsevier.com/retrieve/pii/S0034425797000461
[20] Lu D, Tian H, Zhou G, et al. Regional mapping of human settlements in Southeastern China with multi-sensor remotely sensed data[J]. Remote Sensing of Environment, 2008,112(9):3668-3679.
doi: 10.1016/j.rse.2008.05.009 url: https://linkinghub.elsevier.com/retrieve/pii/S0034425708001776
[21] Zhao N, Zhou Y, Samson E L. Correcting incompatible DN values and geometric errors in nighttime lights time-series images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015,53(4):2039-2049.
doi: 10.1109/TGRS.2014.2352598 url: http://ieeexplore.ieee.org/document/6905742/
[22] 曹子阳, 吴志峰, 匡耀求, 等. DMSP/OLS夜间灯光影像中国区域的校正及应用[J]. 地球信息科学学报, 2015,17(9):1092-1102.
doi: 10.3724/SP.J.1047.2015.01092 url: http://www.dqxxkx.cn/CN/abstract/abstract36978.shtml
[22] Cao Z Y, Wu Z F, Kuang Y Q, et al. Correction and application of DMSP/OLS night lighting image in China[J]. Journal of Geo-Information Science, 2015,17(9):1092-1102.
[23] Zhao M, Cheng W M, Zhou C H, et al. Assessing spatiotemporal characteristics of urbanization dynamics in Southeast Asia using time series of DMSP/OLS nighttime light data[J]. Remote Sensing, 2018,10(2):47-66.
doi: 10.3390/rs10010047 url: http://www.mdpi.com/2072-4292/10/1/47
[24] 张志刚, 张安明, 郭欢欢. 基于DMSP/OLS夜间灯光数据的城乡结合部空间识别研究——以重庆市主城区为例[J].地理与地理信息科学, 2016(6):37-42.
[24] Zhang Z G, Zhang A M, Guo H H. Research on spatial recognition of urban-rural junction based on DMSP/OLS night light data:Take the main urban area of Chongqing as an example[J].Geographic and Geographic Information Science 2016(6):37-42.
[25] Su Y X, Chen X Z, Wang C Y, et al. A new method for extracting built-up urban areas using DMSP-OLS nighttime stable lights:A case study in the Pearl River Delta,southern China[J]. GIScience & Remote Sensing, 2015,52(2):218-238.
[26] 张盛霖, 邓高燕, 黄勇奇. Mann-Kendall检验法在Excel中的实现与应用[EB/OL].北京:中国科技论文在线[2014-06-27]. http://www.paper.edu.cn/releasepaper/content/201406-448.
url: http://www.paper.edu.cn/releasepaper/content/201406-448
[26] Zhang S L, Deng G Y, Huang Y Q. Mann-Kendall implementation and application of test method in Excel[EB/OL].Beijing:China Science and Technology Paper Online[2014-06-27]. http://www.paper.edu.cn/releasepaper/content/201406-448.
url: http://www.paper.edu.cn/releasepaper/content/201406-448
[27] 汪攀. 时间序列趋势分析及其在预防性维修中的应用[D]. 武汉:武汉科技大学, 2015.
[27] Wang P. Time series trend analysis and its application in preventive maintenance[D]. Wuhan:Wuhan University of Science and Technology, 2015.
[28] 宋金超, 李新虎, 吝涛, 等. 基于夜晚灯光数据和Google Earth的城市建成区提取分析[J]. 地球信息科学学报, 2015,17(6):750-756.
doi: 10.3724/SP.J.1047.2015.00750 url: http://www.dqxxkx.cn/CN/abstract/abstract36903.shtml
[28] Song J C, Li X H, Wei T, et al. Extraction and analysis of urban built-up areas based on night lighting data and Google Earth[J]. Journal of Earth Information Science, 2015,17(6):750-756.
[29] 何春阳, 史培军, 李景刚, 等. 基于DMSP/OLS夜间灯光数据和统计数据的中国大陆20世纪90年代城市化空间过程重建研究[J]. 科学通报, 2006,51(7):856-861.
doi: 10.1360/972004-300 url: http://csb.scichina.com:8080/kxtb/CN/abstract/abstract370053.shtml
[29] He C Y, Shi P J, Li J G, et al. Research on the spatial process reconstruction of urbanization in mainland China in 1990s based on DMSP/OLS night lighting data and statistical data[J]. Scientific Bulletin, 2006,51(7):856-861.
[1] LIU Zhao, ZHAO Tong, LIAO Feifan, LI Shuai, LI Haiyang. Research and comparative analysis on urban built-up area extraction methods from high-resolution remote sensing image based on semantic segmentation network[J]. Remote Sensing for Land & Resources, 2021, 33(1): 45-53.
[2] ZHENG Bohong, WANG Zhiyuan, WANG Hua. A study of the evolution characteristics of built-up areas in Chang-Zhu-Tan urban agglomeration based on DMSP/OLS data[J]. Remote Sensing for Land & Resources, 2020, 32(4): 172-181.
[3] Zhili LIU, Qibin ZHANG, Depeng YUE, Yuguang HAO, Kai SU. Extraction of urban built-up areas based on Sentinel-2Aand NPP-VIIRS nighttime light data[J]. Remote Sensing for Land & Resources, 2019, 31(4): 227-234.
[4] GAO Ying-Chun, WANG Li-Xiang, TONG Lian-Jun, YIN Jun. An Analysis of Land Use Change in Urban Boroler Areas of Shijiazhuang City[J]. REMOTE SENSING FOR LAND & RESOURCES, 2010, 22(1): 107-111.
[5] ZHAO Xiao-Li, LIU Bin, YI Ling, ZHANG Zeng-Xiang. AN ANALYSIS OF CHARACTERISTICS AND DRIVING FORCES OF URBAN SPATIAL EXPANSION IN ZHENGZHOU[J]. REMOTE SENSING FOR LAND & RESOURCES, 2009, 21(4): 90-95.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
京ICP备05055290号-2
Copyright © 2017 Remote Sensing for Natural Resources
Support by Beijing Magtech