东祁连山高寒灌丛六种灌木植物的光谱特征分析

高寒灌丛是青藏高原生态系统的重要组成部分，研究高寒灌丛对青藏高原生态系统的系统研究具有重要的意义。但是长期以来，由于地处偏远而交通欠发达、加之生长条件严酷，造成青藏高原高寒灌丛相关研究较为困难。遥感探测技术，可以克服地理及环境造成的困难，而且可以进行大面积、无损的探测，因此，可以采用遥感探测技术进行青藏高原的高寒灌丛研究。传统的高分辨率遥感探测技术，由于常常采用的是RGB三个波段，对不同植物的辨别精度低，对应植物的NDVI指数和RVI指数差异性较小，不能有效区分各类植被。同时，高光谱反射率曲线和辐照度曲线，蕴含上千波段的光谱信息，若选择某一单一波段来进行植被探测，则光谱信息损失非常大，反应出来的灌丛特征不明显，结果置信度低。为了区别高寒灌丛植被，利用高光谱技术对灌丛开展光谱特征分析，为青藏高原灌丛的遥感探测提供理论支持。本研究借助美国FieldSpec4高分辨率地物光谱仪，在东祁连山马牙雪山景区内采集头花杜鹃（Rhododendron capitatum Maxim.）、鬼见愁（Caraganajubata (Pall.) Poir.）、金露梅（Potentillafruticosa L.）、高山柳（Salix cupularis）、甘肃瑞香（Daphne tangutica Maxim.）和鲜黄小檗（Berberisdiaphana）六种典型灌木植物的室内光谱数据，通过反射率（REF）、吸收率（ABS）及其一阶微分（GREF和GABS）的变换，进一步提高灌木植物光谱曲线间的可辨析度，分析并筛选出敏感波段，而后通过各个波段之间的相互组合计算NDVI′值和RVI′值，并且以TM设置波段计算的NDVI值和RVI值作为参考，筛选出优于TM波段且差值最大的波段组合确定为最优模型。结果表明：(1）灌木植物对太阳辐射吸收形成的光谱特征曲线与大多数植物相似，但与草本植物相比，灌木植物的第一个波谷发生了左移现象；(2）灌木植物在某些敏感波段中反映出独有的光谱特征，通过REF，ABS，GREF和GABS变换，可以进一步扩大，利用这一特点可以筛选出敏感波段，进行灌丛分类和识别；(3）六种灌木植物光谱值差异较大，且数值相对较为稳定的波段有550～680，860～1 075，1 375～1 600和1 900～2 400 nm，因此可选取这四个波段为敏感区进行灌木植物识别；(4）利用575～673和874～920 nm敏感波段的REF均值或者685～765，556～590，635～671和1 117～1 164 nm敏感波段的GABS面积，计算的NDVI值和RVI值可以有效辨别六种灌木植物。

Spectral Characteristics Analysis on Six Shrubs in Different Alpine Brushlands of Eastern Qilian Mountains

As an very important part of the Qinghai-Tibet Plateau ecosystem, it is of great significance to study alpine shrubs. But for a long time, due to the remote location and underdeveloped transportation, as well as the harsh growing conditions, the alpine shrub on the Qinghai-Tibet Plateau has been less studied. Remote sensing detection technology can overcome the difficulties caused by geography and environment and can be used to detect large areas and non-destructive. Therefore, remote sensing detection technology can be used to study alpine shrubs in Qinghai-Tibet Plateau. As the traditional high-resolution remote sensing detection technology is often adopted with three bands of RGB, the discrimination accuracy of different plants is low, and the difference of NDVI′ index and RVI′ index of corresponding plants is small, which cannot effectively distinguish various types of vegetation. At the same time, hyperspectral reflectance curve and irradiance curve contain spectral information of thousands of bands. If a single band is selected for plants detection, the loss of spectral information is very large, and the characteristics of thickets reflected are not obvious, resulting in low confidence. In order to distinguish the alpine shrub vegetation, this paper uses hyperspectral technology to carry out spectral characteristic analysis of the shrub, providing theoretical support for remote sensing detection of the shrub on the Qinghai-TibetPlateau. The research draws support from FieldSpec4 high resolution spectrometer of the America. It was used to identify 6 shrubs (Rhododendron capitatum, Caraganajubata, Potentillafruticosa, Salix cupularis, Daphne tangutica and Berberisdiaphana) grown in the eastern Qilian Mountains through measuring the reflectance rate and absorption rate, calculating the first order differential of absorption rate (GREF and GABS) to enlarge the resolution of spectral curve, screening the sensitive wavelength, and then identifying different shrubs by calculating their values with NDVI and RVI. The result indicated that (1) the absorption spectral curves of shrubs were similar with most plants, but their first absorption valley shifted to left; (2) the shrubs performed unique spectral features in some sensitive wavelengths, and these features could be used to improve the resolution by REF, ABS, GREF and GABS transformation to identify the shrublands; (3) The spectral values of the 6 shrubs are different, and the relatively stable wavelengths are 550～680, 860～1 075, 1 375～1 600 and 1 900～2 400 nm. Therefore, these 4 wavelengths can be selected as sensitive areas to identify shrub plants; (4) NDVI and RVIcalculated with the REF average value of sensitive wavelengths of 575～673 and 874～920 nm and/or the area value of sensitive wavelengths of 685～765, 556～590, 635～671 and 1 117～1 164 nm could effectively identify 6 shrubs.