单春芝,王娟,孙乐成,刘旭东.黄河口典型植被光谱及 NDVI 变化对比分析[J].海洋通报,2021,(1):
黄河口典型植被光谱及 NDVI 变化对比分析
Comparison analysis of typical vegetation spectrum and NDVI changes in the Yellow River Estuary
投稿时间:2020-06-21  修订日期:2020-09-30
DOI:
中文关键词:  黄河口  芦苇  互花米草  植被群落  NDVI  识别
英文关键词:Yellow River Estuary  Phragmites australis  Spartina alterniflora  vegetation community  NDVI  recognition
基金项目:国家重点研发计划(SQ2018YFC140023-05)
作者单位E-mail
单春芝 国家海洋局北海环境监测中心山东 青岛 266033 zzdream456@126.com 
王娟 国家海洋局北海环境监测中心山东 青岛 266033 wangjuan@ncs.mnr.gov.cn 
孙乐成 国家海洋局北海环境监测中心山东 青岛 266033  
刘旭东 国家海洋局北海环境监测中心山东 青岛 266033  
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中文摘要:
      作为黄河口地区两种典型的植被群落类型,芦苇群落 (Comm. Phragmites australis) 与互花米草群落 (Comm. Spartinaalterniflora) 在遥感影像的表征较为相似,利用单时相遥感影像识别时,易存在混淆区域。工作中发现,两种植被处于不同生长期时,其遥感影像具有不同的表征。为了提高黄河口地区芦苇、互花米草两类植被群落遥感识别结果的可靠性,本文通过获取多时相遥感影像中芦苇、互花米草群落纯净像元的光谱值,分析植被“红边”光谱曲线随时间的变化特征,进一步计算研究区多期遥感影像 NDVI (Normalized Difference Vegetation Index),提取纯净像元的 NDVI 值,对比分析这两种植被 NDVI 指数在时间序列上的变化特征,发现芦苇群落与互花米草群落的 NDVI 值在时间序列上存在明显区别。结果表明,利用 5 月的影像能够较好地识别芦苇群落的分布范围,利用 11 月影像能够较好地识别互花米草群落的分布范围。本研究结果可为今后黄河口地区这两类典型植被群落的遥感识别方法研究提供可靠依据。
英文摘要:
      As two typical vegetation communities in the Yellow River Estuary, Comm. Phragmites australis and Comm.Spartina alterniflora are similar in the representation of remote sensing images. They can be easily confused in the process of single-phase remote sensing image recognition. It is found in the work that the two vegetation communities are different in different growth periods. Therefore, in order to improve the reliability of the results of remote sensing recognition of these two typical vegetation, the spectral values of pure pixels were extracted to analyze the change characteristics of "red edge" with time. Further, multi-phase remote sensing image NDVI in the study area was calculated and the value of pure pixels was extracted. By comparing and analyzing the NDVI change characteristics of the two vegetations in the time series, it is found that the NDVI values are significantly different. Experiments prove that the distribution of P. australis can be better identified using the May image and the distribution range of S. alterniflora can be identified using the November image. The conclusion can be used for remote sensing recognition of these two vegetations in the Yellow River Estuary and it is reliable.
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