A novel method for urban area extraction from VIIRS DNB and MODIS NDVI data: a case study of Chinese cities

ABSTRACT Mapping urban areas at the regional and global scales has been used in ecology, environment, sociology, and other subjects. Recently, it has become increasingly popular to extract urban areas from night-time light remote-sensing data. In this article, we reported an alternative method to extract information of urban areas from VIIRS Day/Night Band (DNB) and MODIS normalized differential vegetation index (NDVI) data based on the adaptive mutation particle swarm optimization (AMPSO) algorithm and the Support Vector Machine (SVM) classification algorithm. This method was validated using the urban areas of nine Chinese cities classified from Landsat Enhanced Thematic Mapper (ETM+) images by object-oriented image classification technology. We demonstrated that this new method for urban area extraction had a good classification coherency with the Landsat8 OLI result. In addition, it is more robust than other classification methods, and can be used to characterize the inter-urban texture as well.

[1]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[2]  Fang Chuanglin,et al.  THE URBANIZATION AND URBAN DEVELOPMENT IN CHINA AFTER THE REFORM AND OPENING-UP , 2009 .

[3]  C. Elvidge,et al.  A Technique for Using Composite DMSP/OLS "City Lights"Satellite Data to Map Urban Area , 1997 .

[4]  Y. Zeng,et al.  Mapping and evaluation the process, pattern and potential of urban growth in China , 2016 .

[5]  Peng Wenfu,et al.  Dynamical Detection on Urban Sprawl Based on EO Data , 2009, 2009 International Forum on Information Technology and Applications.

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

[7]  Chengquan Huang,et al.  Detecting 2014 Northern Iraq Insurgency using night-time light imagery , 2015 .

[8]  K. Baugh,et al.  Mapping urban structure and spatial connectivity with VIIRS and OLS night light imagery , 2013, Joint Urban Remote Sensing Event 2013.

[9]  Russell G. Congalton,et al.  A review of assessing the accuracy of classifications of remotely sensed data , 1991 .

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

[11]  Li Xi,et al.  An Overview on Data Mining of Nighttime Light Remote Sensing , 2015 .

[12]  Yansui Liu,et al.  Urban boundary extraction and sprawl analysis using Landsat images: A case study in Wuhan, China , 2015 .

[13]  Yuke Zhou,et al.  A new approach to the application of DMSP/OLS nighttime light data to urbanization assessment , 2012, IGARSS.

[14]  Hongyi Pan,et al.  Dynamical Detection on Urban Sprawl Based on EO Data , 2009, IFITA.

[15]  Lu Liang,et al.  China’s urban expansion from 1990 to 2010 determined with satellite remote sensing , 2012 .

[16]  S. Running,et al.  Assessing the impact of urban land development on net primary productivity in the southeastern United States , 2003 .

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

[18]  C. Elvidge,et al.  Spatial analysis of global urban extent from DMSP-OLS night lights , 2005 .

[19]  Stephen P. Mills,et al.  Suomi satellite brings to light a unique frontier of nighttime environmental sensing capabilities , 2012, Proceedings of the National Academy of Sciences.

[20]  S. Carpenter,et al.  Global Consequences of Land Use , 2005, Science.

[21]  Jungho Im,et al.  ISPRS Journal of Photogrammetry and Remote Sensing , 2022 .

[22]  P. Gong,et al.  Validation of urban boundaries derived from global night-time satellite imagery , 2003 .

[23]  Ramakrishna R. Nemani,et al.  The Nightsat mission concept , 2007, International Journal of Remote Sensing.

[24]  Osamu Higashi,et al.  A SVM-based method to extract urban areas from DMSP-OLS and SPOT VGT data , 2009 .

[25]  Nicholas C. Coops,et al.  Regional assessment of pan-Pacific urban environments over 25 years using annual gap free Landsat data , 2016, Int. J. Appl. Earth Obs. Geoinformation.

[26]  N. Grimm,et al.  Global Change and the Ecology of Cities , 2008, Science.

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

[28]  Cristina Milesi,et al.  Assessing the environmental impacts of human settlements using satellite data , 2003 .

[29]  A. Thomson,et al.  A cluster-based method to map urban area from DMSP/OLS nightlights , 2014 .

[30]  Bai Zhang,et al.  A novel optimization parameters of support vector machines model for the land use/ cover classification , 2012 .

[31]  P. Shi,et al.  Restoring urbanization process in China in the 1990s by using non-radiance-calibrated DMSP/OLS nighttime light imagery and statistical data , 2006 .

[32]  Yaping Yang,et al.  Mapping Urban Areas with Integration of DMSP/OLS Nighttime Light and MODIS Data Using Machine Learning Techniques , 2015, Remote. Sens..