%A Kun JIA, Jingcan LIU, Yixuan TU, Qiangzi LI, Zhiwei SUN, Xiangqin WEI, Yunjun YAO, Xiaotong ZHANG %T Land use and land cover classification using Chinese GF-2 multispectral data in a region of the North China Plain %0 Journal Article %D 2019 %J Front. Earth Sci. %J Frontiers of Earth Science %@ 2095-0195 %R 10.1007/s11707-018-0734-8 %P 327-335 %V 13 %N 2 %U {https://journal.hep.com.cn/fesci/EN/10.1007/s11707-018-0734-8 %8 2019-06-15 %X

The newly launched GF-2 satellite is now the most advanced civil satellite in China to collect high spatial resolution remote sensing data. This study investigated the capability and strategy of GF-2 multispectral data for land use and land cover (LULC) classification in a region of the North China Plain. The pixel-based and object-based classifications using maximum likelihood (MLC) and support vector machine (SVM) classifiers were evaluated to determine the classification strategy that was suitable for GF-2 multispectral data. The validation results indicated that GF-2 multispectral data achieved satisfactory LULC classification performance, and object-based classification using the SVM classifier achieved the best classification accuracy with an overall classification accuracy of 94.33% and kappa coefficient of 0.911. Therefore, considering the LULC classification performance and data characteristics, GF-2 satellite data could serve as a valuable and reliable high-resolution data source for land surface monitoring. Future works should focus on improving LULC classification accuracy by exploring more classification features and exploring the potential applications of GF-2 data in related applications.