三江源区毒杂草型退化草地植被光谱特征分析

毒草型退化草地具有群落演替特点,通过高光谱遥感技术反演毒杂草分布与退化草地群落结构能对该类退化草地进行有效监测,而光谱特征分析是毒杂草与优良牧草遥感识别的基础。本文选取了三江源区毒草型退化草地的8种典型毒杂草和4种优良牧草的地面实测高光谱数据作为研究样本,经过SG平滑、包络线去除、导数变换和光谱参量化对毒杂草种和优良牧草种的光谱特征进行了分析,并通过马氏距离法提取其特征识别波段。结果表明：① 8种典型毒杂草和4种优良牧草的 “近红外峰值”差异较大,其中鹅绒萎陵菜的“近红外峰值”达到60.07%,而最小者早熟禾仅为17.53%;② 经包络线去除处理后,植被光谱曲线中吸收谷和反射峰光谱差异更加明显,且可减少环境背景对植被光谱的影响,如沼泽草甸的鹅绒委陵菜和驴蹄草,其“绿峰幅值”分别为6.46%和6.89%,经处理后其“绿峰指数”分别为0.2866和0.3671,而在2种环境下生长的同一草种（狼毒草1和狼毒草2）的峰谷特征差异不明显;③ 基于马氏距离法提取的毒杂草与优良牧草的敏感识别波段主要分布在680~750 nm和900~1000 nm波长范围内,以醉马草与矮嵩草为例,其基于反射率的敏感识别波段为713.1~737.1 nm和934.6~965.6 nm。该研究可为利用高光谱遥感进行大面积毒杂草草种识别和植被群落生长监测提供重要科学依据,对于三江源区毒杂草的监测防治和畜牧业的可持续发展具有重要意义。

Spectral Characteristics of Vegetation of Poisonous Weed Degraded Grassland in the "Three-River Headwaters" Region

The poisonous weed type degraded grassland is a type of degraded grassland in the "Three-River Headwaters" Region, which has the characteristics of community reverse succession. Its manifestation is that the grassland community with fine herbage as the dominant species evolved into the grassland community with weeds and poisonous plants as the dominant species. Therefore, the key to control this kind of grassland degradation is to monitor the spread of poisonous weeds and the change of grassland community structure. Hyperspectral remote sensing Technology can be used to identify poisonous weeds and fine herbage in a region, and spectual feature analysis is the basis of remote sensing recognition of terrain features. In this paper, the hyperspectral data of eight typical poisonous weeds and four fine herbages in the degraded grassland of toxic weed type in the Three-River Headwaters Region were selected as the research samples. The hyperspectral data in this study are all from field sampling using AvaField-2 portable hyperspectral surface object spectrometer. After Savitzky-Golay convolution smoothing, envelope removal, derivative transformation, and spectral parameterization, the spectral characteristics of poisonous weeds and fine forage species were analyzed, and the characteristic recognition bands were extracted by Mahalanobis distance method. The results showed that: (1) After data preprocessing, the spectral reflectance curves of eight poisonous weeds and four fine forages are similar, but the spectral reflectance differences can be compared through some characteristic bands and parameters; (2) NIR peak refers to the maximum reflectivity of each vegetation in the wavelength range 780~1000 nm. The NIR peak of eight typical poisonous weeds and four fine forages were significantly different. The NIR peak of Potentilla anserine reached 60.07%, while that of Poa pratensis was only 17.53%; (3) After envelope removal, the spectral difference between absorption valley and reflection peak in vegetation spectral curve is more obvious. The maximum values of Caltha and Ligulariavirgaurea with similar NIR peaks were 0.3671 and 0.2157, respectively, in the green band; (4) The sensitive recognition bands of toxic weeds and fine forages based on Mahalanobis distance are mainly distributed in the wavelength range of 680~750 nm and 900~1000 nm. This study can provide an important scientific basis for the use of hyperspectral remote sensing in the identification of large-area poisonous weeds and the monitoring of vegetation community growth. It is of great significance to the monitoring and control of poisonous weeds and the sustainable development of animal husbandry in the Three-River Headwaters Region.