文献类型：期刊文献

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

作者：Wang, Hao[1] Wang, Chengbo[1] Wu, Honggan[2]

第一作者：Wang, Hao

通信作者：Wang, CB[1]

机构：[1]Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Beijing, Peoples R China;[2]Chinese Acad Forestry, Res Inst Forest Resources Informat Tech, Beijing, Peoples R China

年份：2016

卷号：7

期号：4

起止页码：378-387

外文期刊名：REMOTE SENSING LETTERS

收录：;EI(收录号：20161802323810);Scopus(收录号：2-s2.0-84964529884);WOS:【SCI-EXPANDED(收录号:WOS:000394261900007)】；

基金：This work was supported by the National Science and Technology Major Projects of China [grant number 21-Y30B05-9001-13/15].

语种：英文

外文关键词：Mapping - Maximum likelihood - Random processes - Satellite imagery - Textures

摘要：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.