Using GF-2 Imagery and the Conditional Random Field Model for Urban Forest Cover Mapping

Gaofen-2 (GF-2), a Chinese new-generation satellite launched in August 2014, is providing high-resolution imagery for Earth observation. In this study, GF-2 imagery was employed for mapping forest cover in the core area of Tongzhou district, Beijing, China. The study analysed the performance of GF-2 data for identifying urban forest using a contextual classification model conditional random field (CRF) with Gabor texture features. The results show that the proposed method outperforms the traditional maximum likelihood classifier (MLC) by improving the producer’s accuracy of conifer and hardwood forest from 86.61% to 92.41%, and 86.59% to 91.57%, respectively. Overall, 87.43% of the area classified as forest by GF-2 classification spatially corresponded to areas of the reference forest map. The mapping results suggest that GF-2 imagery in concert with an efficient classification algorithm can be recommended for urban forest monitoring.