中国农业气象

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 典型滩涂植被米草和芦苇叶片高光谱特征分析

 杜正朕,王琳,包云轩,周晓   

  1.  南京信息工程大学气象灾害预报和评估协同创新中心/江苏省农业气象重点实验室,南京 210044
  • 收稿日期:2019-11-22 出版日期:2020-06-20 发布日期:2020-06-18
  • 作者简介:杜正朕,E-mail:dzznuist123@163.com
  • 基金资助:
     国家自然科学基金(31601221)

 Analysis of Hyperspectral Characteristics of Phragmites australis and Spartina alterniflora Leaves in Typical Beach

 DU Zheng-zhen,WANG Lin, BAO Yun-xuan, ZHOU Xiao   

  1.  Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology/Jiangsu Key Laboratory of Agricultural Meteorology, Nanjing 210044
  • Received:2019-11-22 Online:2020-06-20 Published:2020-06-18
  • Supported by:
     

摘要:  芦苇(Phragmites australis)和互花米草(Spartina alterniflora)是中国东南沿海滩涂典型的植被类型。芦苇是本土植物,在沿海湿地发挥着重要的生态功能,而互花米草是外来入侵物种,近几十年来不断扩散,对当地生态系统和生物多样性构成严重威胁。利用遥感技术监测互花米草的时空动态已成为关注的热点,但它与芦苇在叶片形状、颜色等方面都具有很大相似性,传统的遥感信息难以区分。为了准确识别不同生长阶段的芦苇和互花米草,以江苏盐城湿地珍禽国家级自然保护区沿海滩涂内的芦苇和互花米草为研究对象,对它们的原始反射率光谱曲线、经连续统去除后的反射率光谱曲线和一阶导数光谱曲线进行了研究对比。结果表明:在可见光波段范围内,分析连续统去除后的光谱曲线可以有效区分夏季芦苇与米草,芦苇反射率显著低于米草(P<0.05),在350-1000nm波段范围内,原始光谱曲线更容易区分秋季芦苇与米草,芦苇反射率显著高于米草(P<0.05)。在350-540nm波段和近红外波段范围内,芦苇原始光谱曲线容易区分植被所处的不同生长阶段,其中夏季芦苇光谱反射率显著高于秋季(P<0.05),而在520-700nm波段范围内,连续统去除法更易区分两者,其中秋季芦苇光谱反射率显著高于夏季(P<0.05)。在350-1000nm整个波段范围内,分析米草原始光谱曲线可以直观区分植被所处的不同生长阶段,其中夏季米草光谱反射率显著高于秋季(P<0.05)。一阶导数光谱无法直接区分芦苇和米草,但根据其红边参数以及植被光谱特征参数的变化可以识别两者不同的生长阶段。

关键词:  , 芦苇, 米草, 高光谱遥感, 光谱特征, 连续统去除法

Abstract:  The coastal beach has abundant resources and diverse ecological functions, and plays a crucial role in regulating climate, purifying water quality and protecting biodiversity.In recent years, frequent human activities have continued to affect the wetland ecosystem, which has been causing the wetland area to shrink gradually and destroying the living environment. As a result, the ecological function of the wetland is limited. Phragmites australis and Spartina alterniflora are typical vegetation types in coastal beaches of southeast China. Phragmites australisisa native plant which plays important ecological roles in coastal wetlands.Spartina alterniflora is an invasive species, which hinders the growth of local plants and changes the spatial distribution of original vegetation. It has been spreading continuously in recent decades and posing a serious threat to the local ecosystem and biodiversity. Traditional vegetation monitoring methods mainly rely on manual sampling, which takes a lot of time and effort and may damage the wetland. It has become a hot topic to use remote sensing technology to monitor Spartina alterniflora. However, it is very similar to Phragmites australis in leaf shape, color and other aspects. And the traditional remote sensing information is difficult to distinguish them. In the present study,Phragmites australis and Spartina alterniflorainthe coastal beach of Jiangsu Yancheng Wetland National Nature Reserve Rare Birds were selected as the research objects. The original hyperspectral reflectance curves, the reflectance spectral curves after continuum removed, and the first derivative spectral curves were utilized to accurately identify Phragmites australis and Spartina alterniflora in different growing periods, in order to realize fast and accurate identification of different vegetation in wetland and to provide references for dynamic monitoring of the wetland vegetation and protection of the wetland ecosystem. Continuum removal could effectively highlight the absorption and reflection characteristics of spectral curves, which could be used to extract characteristic bands for classification and recognition. It was shown that the original spectral curves of Phragmites australis and Spartina alterniflorawere basically the same insummer. Both of them showed a similar reflection peak near 550nm in the green band and a similar absorption valley near 670nm in the red band. Analyzing the spectral curves after continuum removed in the visible bands could effectively distinguish Phragmites australis from Spartina alterniflora in summer and the reflectance of Phragmites australis was lower than that of Spartina alterniflora(P<0.05). However, the original spectral curves in autumn showed that the reflection peak and absorption valley of Phragmites australis were higher than those of Spartina alterniflora. The analysis of the original spectral curves in the whole range of 350-1000nm made it easier to distinguish Phragmites australis from Spartina alterniflora in autumn, and Phragmites australis had higher reflectance values than Spartina alterniflora(P<0.05). The analysis of the original spectral curves of Phragmites australis in the range of 350-540nm and near-infrared bands could easily distinguish its growing periods and the spectral reflectance of Phragmites australis in summer was higher than that in autumn(P<0.05). Meanwhile, the continuum removal method was easier to distinguish them in the range of 520-700nm, and the spectral reflectance of Phragmites australis in autumn was higher than that in summer(P<0.05). In the range of 350-1000nm, the analysis of the original spectral curves of Spartina alternifloracould intuitively distinguish different growing periods and the spectral reflectance of Spartina alterniflora in summer was significantly higher than that in autumn(P<0.05). The first derivative spectra curve could not directly distinguish Phragmites australis from Spartina alterniflora, but their different growth periods could be identified according to the changes of their red edge and spectral characteristic parameters. The research could provide a basis for the identification of vegetation types and provide effective information for the protection and planning of the wetland ecosystem.

Key words:  Phragmites australis, Spartina alterniflora, Hyperspectral remote sensing, Spectral signature, Continuum removal

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