Mapping urban expansion using night-time light images from Luojia1-01 and International Space Station

Night-time light (NTL) images have been proved as a type of reliable data source to map urban expansion. In this paper, to investigate the potential of using multi-source NTL images at near 100 m resolution to detect urban expansion, we use a Luojia1-01 (LJ1-01) image in 2018 and an International Space Station (ISS) night-time image in 2010 in Wuhan city as experiment images. Based on the multiple linear robust regression model, a process of intercalibration between LJ1-01 imagery and ISS imagery is proposed to build a comparable dataset. To detect urban expansion, using the above images at 130 m resolution, Jeffries-Matusita distance is used as an indicator to select the feature combination with the largest class separability. Among all the candidate combinations, the combination of the LJ1-01 image, the simulated LJ1-01 image, and their ratio best meets our requirements for classification. With this feature combination, a multi-temporal classification method based on Support Vector Machines and Back Propagating (BP) - Neural Network, respectively, is utilized to classify the study area into stable non-urban class, stable urban class, and expanding the urban class. The results of the multi-temporal classification show that the overall accuracy is around 90%, and the Kappa coefficients are larger than 0.84. For each class, the user's accuracy is larger than 87%, and the producer's accuracy is larger than 83%. The results of this study indicate that it is feasible to detect urban expansion by using multi-source NTL images at near 100 m resolution.