Urban vegetation is an important component of an urban ecosystem, which provides important ecological service including purifying urban air, regulating urban climate, and beautifying urban landscape. Different types of vegetation provide different ecological service. To date, there have been many studies on characterizing urban green space landscape pattern and analyzing its spatiotemporal dynamics. However, the green space landscape patterns of different vegetation types have not been well quantified. In this study, we took Beijing Vice-City Center as the study area and focused on different types of urban vegetation (broad-leaved trees, conifers, farmland, grasslands, and shrublands). Due to the small size of urban vegetation patches and their high spatial heterogeneity, the object-oriented classification method was adopted in this study. Specifically, the image objects using multi-resolution segmentation were generated first. Then, combing the field sampling data, we applied random forest in feature selection and classification using GF-2 images in summer and winter. Based on the classified vegetation map with detailed information, landscape metrics and moving window approach were used to quantify the landscape pattern of urban green space at functional scale and grid scale, respectively. Our results show that, for GF-2 images, the spectral and textural features of image objects after multi-resolution segmentation can effectively improve the extraction of different vegetation types. Multi-temporal images can also provide phenological information of different vegetation types. Compared with the classification results using either summer images or winter images, our classification accuracy was improved to 87.7%. In our study area, the fragmentation of shrublands was the highest. The area proportion of conifers was the smallest, but its patch shape was the most complex. While the distribution of broad-leaved trees, grasslands, and farmland was spatially centralized and contiguous with a regular shape. We found that the green space landscape pattern in different functional areas were quite different. The urban green center had the most abundant vegetation. While the commercial area had less green space that was also fragmentized. The landscape diversity and the distribution of different vegetation types showed a spatial heterogeneity in the Beijing Vice-City Center. At present, the landscape pattern of green space in Beijing Vice-City Center has formed the basic outline of planning, but the construction of urban green center and the public green space in city center are still insufficient. Our study evaluates the current situation of green space patten in Beijing Vice-City Center and proves that GF-2 can be applied in urban ecological environment monitoring, which provides a useful reference for the future monitoring and optimization of urban green space.