Abstract:In the present paper, the slope gradient, aspect, detection zenith angle and plant types were analyzed. In order to strengthen the theoretical discussion, the research was under laboratory condition, and modeled uniform slope for slope plant. Through experiments we found that these factors indeed have influence on plant hyperspectral remote sensing. When choosing slope gradient as the variate, the blade reflection first increases and then decreases as the slope gradient changes from 0° to 36°; When keeping other factors constant, and only detection zenith angle increasing from 0° to 60°, the spectral characteristic of slope plants do not change significantly in visible light band, but decreases gradually in near infrared band; With only slope aspect changing, when the dome meets the light direction, the blade reflectance gets maximum, and when the dome meets the backlit direction, the blade reflectance gets minimum, furthermore, setting the line of vertical intersection of incidence plane and the dome as an axis, the reflectance on the axis’s both sides shows symmetric distribution; In addition, spectral curves of different plant types have a lot differences between each other, which means that the plant types also affect hyperspectral remote sensing results of slope plants. This research breaks through the limitations of the traditional vertical remote sensing data collection and uses the multi-angle and hyperspectral information to analyze spectral characteristics of slope plants. So this research has theoretical significance to the development of quantitative remote sensing, and has application value to the plant remote sensing monitoring.
Key words:Remote sensing;Spectroscopy;Ratio of reflectance;Slope plant
[1] ZHAO Yun-sheng, HUANG Fang, JIN Lun, et al(赵云升,黄 方,金 伦,等). Journal of Remote Sensing(遥感学报), 2000, 4(2):131. [2] Huang K Y, Lo N J, Chang W I. Predicting Cinnamomum Randaientse Habitat Using Multivariate Statistical Methods with GIS. Acrs2009, 2009, 2: 663. [3] YAO Chen, HUANG Wei, LI Xian-hua(姚 晨,黄 微,李先华). Remote Sensing Technology and Application(遥感技术与应用), 2009, 24(4): 496. [4] ZHANG Da, ZHENG Yu-quan(张 达, 郑玉权). Optics & Optoelectronic Technology(光学与光电技术), 2013, 11(3): 67. [5] JIANG Hong, WANG Xiao-qin, WU Bo, et al (江 洪,汪小钦,吴 波,等). Journal of Fuzhou University(Natural Science)(福州大学学报·自然科学版), 2010, 38(4): 527. [6] Achard V, Lenot X. Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, 2009. WHISPERS’09. First Workshop on. IEEE, 2009. 1. [7] Bhogal A S, Goodenough D G, Gougeon F, et al. Geoscience and Remote Sensing Symposium, 2000. Proceedings. IGARSS 2000. IEEE 2000 International. IEEE, 2000, 4: 1388. [8] WANG Ya-chao, ZHAO Hui-jie, JIA Guo-rui(王亚超,赵慧洁,贾国瑞). Joumal of Beijing University of Aeronautics and Astronautics(北京航空航天大学学报), 2010, 36(9): 1131. [9] Schaaf C B, Li X, Strahler A H. IEEE Transactions on Geoscience and Remote Sensing, 1994, 32(6): 1186. [10] TANG Guo-an, SONG Jia(汤国安,宋 佳). Journal of Soil and Water Conservation(水土保持学报), 2006, 20(2): 157. [11] DENG Jun-yuan(邓钧元). MS Thesis. Northeast Normal University, 2012.