Airborne Hyperspectral Features of Three Types of Typical Surface Vegetation in Central Yunnan
HU Lin1, GAN Shu1，2*, YUAN Xi-ping2，3, LI Yan1, 4, LÜ Jie1, 2, YANG Ming-long1, 2
1. School of Land and Resources Engineering, Kunming University of Science and Technology, Kunming 650093, China
2. Yunnan Institute of Engineering Research and Application of Plateau Mountain Spatial Information Surveying and Mapping Technology, Kunming 650093, China
3. West Yunnan University of Applied Sciences, Dali 671000, China
4. Research Institute of Yunnan Bureau of Science, Technology and Industry for National Defence, Kunming 650118, China
Abstract：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.
Key words：Hyperspectrum; Vegetation; First derivative; Continuum removal; Optimum band window
胡 琳，甘 淑，袁希平，李 雁，吕 杰，杨明龙. 滇中三类典型地表植被的机载高光谱特征分析[J]. 光谱学与光谱分析, 2021, 41(10): 3208-3213.
HU Lin, GAN Shu, YUAN Xi-ping, LI Yan, LÜ Jie, YANG Ming-long. Airborne Hyperspectral Features of Three Types of Typical Surface Vegetation in Central Yunnan. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(10): 3208-3213.
[1] Xu Z, Shen X, Cao L, et al. International Journal of Applied Earth Observation and Geoinformation, 2020, 92: 102.
[2] Vaglio Laurin G, Puletti N, Grotti M, et al. International Journal of Applied Earth Observation and Geoinformation, 2020, 92: 102.
[3] HUANG Hua-guo(黄华国). Journal of Beijing Forestry University(北京林业大学学报), 2019, 41(12): 1.
[4] Wan L, Lin Y, Zhang H, et al. Remote Sensing, 2020, 12(4): 656.
[5] Cao J, Leng W, Liu K, et al. Remote Sensing, 2018, 10(2): 89.
[6] ZHANG Ying, ZHANG Xiao-li, WANG Shu-han, et al(张 莹，张晓丽，王书涵, 等). Journal of Northwest A&F University(西北农林科技大学学报·自然科学版), 2016, 44(2): 83.
[7] ZI Li, XIE Fu-ming, SHU Qing-tai, et al(字 李，谢福明，舒清态, 等). Journal of Fujian Agriculture and Forestry University·Natural Science Edition(福建农林大学学报·自然科学版), 2019, 48(1): 55.
[8] ZHOU Wei, LI Hao-ran, SHI Pei-qi, et al(周 伟，李浩然，石佩琪, 等). Journal of Geo-information Science(地球信息科学学报), 2020, 22(8): 1735.
[9] Rubio-Delgado J, Pérez C J, Vega-Rodríguez M A. Precision Agriculture, 2021，22：1.
[10] Cao J, Leng W, Liu K, et al. Remote Sensing, 2018, 10(2): 89.
[11] Adão T, Hruška J, Pádua L, et al. Remote Sensing, 2017, 9(11): 1110.
[12] Cao J, Liu K, Zhu Y, et al. Remote Sensing, 2018, 10(12): 2047.
[13] DUAN Hua-chao, LI Shuang-zhi, CHA Xiao-fei, et al(段华超，李双智，茶晓飞，等). Journal of Fujian Agriculture and Forestry University·Natural Science Edition(福建农林大学学报·自然科学版), 2020, 49(3): 380.
[14] ZHANG Su-lan, QIN Ju, TANG Xiao-dong, et al(张素兰，覃 菊，唐晓东, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2019, 39(3): 865.
[15] LI Zhe, GUO Xu-dong, GU Chun, et al(李 喆, 郭旭东，古 春，等). Journal of Remote Sensing(遥感学报), 2016, 20(2): 290.
[16] LI Fen-ling, CHANG Qing-rui（李粉玲，常庆瑞）. Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2017, 48(7): 174.
