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

• Published in: International journal of remote sensing Vol. 38; no. 21; pp. 6094 - 6109
• Main Authors: Zhang, Qiao, Wang, Ping, Chen, Hui, Huang, Qinglun, Jiang, Hongbing, Zhang, Zijian, Zhang, Yanmei, Luo, Xiang, Sun, Shujuan
• Format: Journal Article
• Language: English
• Published: London Taylor & Francis 02.11.2017; Taylor & Francis Ltd
• Subjects: Adaptive algorithms; Algorithms; Case studies; cities; Classification; Data; Ecology; Extraction; Image classification; Image enhancement; Landsat; Landsat satellites; Mathematical models; Methods; moderate resolution imaging spectroradiometer; MODIS; Mutation; Night; normalized difference vegetation index; Object oriented programming; Particle swarm optimization; Remote sensing; Satellite imagery; Sociology; Support vector machines; texture; thematic maps; Urban areas; Vegetation; Vegetation index
• ISSN: 0143-1161; 1366-5901; 1366-5901
• DOI: 10.1080/01431161.2017.1339927

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.