Hyperspectral remote sensing technology has the advantages of map integration. And compared with the traditional multispectral remote sensing technology, it can realize the accurate identification of the target. Therefore, it is gradually applied to the detection of surface vegetation. In this paper, the three typical surface vegetations are bamboo forest, armand pine and spinney in central Yunnan, which were taken as the research objects. In order to get the hyperspectral features of three typical surface vegetation types in central Yunnan, based on the airborne hyperspectral image data, the original high spectrum, first-order differential treatment spectra and the continuum removal spectra were compared and analyzed. Results showed the following: (1) Based on the analysis of the original spectral features, the optimal band window of the original hyperspectral of the three typical surface vegetations appeared in 690~946 nm, and the spectral reflectance characteristics in this band range were bamboo forest>armand pine>spinney; (2) The analysis of spectral features by first-order differential processing shows that the spectral difference of vegetation can be enhanced by spectral differential transformation. After the first-order differential treatment, the optimal band window of the spectrum appeared in the range of 670~774 nm, and the first-order differential coefficient is bamboo forest>armand pine>spinney. Moreover, it was found that 718 nm was the sensitive band of the three types of vegetation, and the characteristic sensitive band of 718 nm could be used to distinguish the three types of vegetation. In addition, three types of vegetation types can be distinguished by comprehensively applying the characteristic parameters of the first-order differential spectrum, including the blue edge amplitude, the yellow edge amplitude, the red edge amplitude, the blue edge area, the yellow edge area and the red edge area. (3) Finally, based on the analysis of the spectral features of the continuum removal treatment, it is concluded that the continuum removal method can effectively enhance the spectral curve reflection and absorption features of vegetation. After the continuum removal, the optimal band window of the three typical vegetations was between 458~554 and 570~690 nm. In the range of these two bands, the first-order differential coefficient is bamboo forest>armand pine>spinney. Moreover, it was found that 502 and 674 nm were sensitive bands of the three types of vegetation, and this feature could be used to distinguish the three types of vegetation comprehensively. The research results of this paper are helpful to provide technical methods for the fine discrimination of forest vegetation in central Yunnan. At the same time, it will provide technical support for the future development of integrated remote sensing vegetation fine classification of space-ground-air hyperspectral image data.