华东师范大学学报(自然科学版) ›› 2007, Vol. 2007 ›› Issue (4): 42-48.

• 地理学 河口海岸学 • 上一篇    下一篇

长江口潮滩湿地植被光谱分析与遥感检测

张 杰，沈 芳，刘志国   

  1. 华东师范大学 河口海岸学国家重点实验室，上海 200062
  • 收稿日期:2006-06-30 修回日期:2006-09-26 出版日期:2007-07-25 发布日期:2007-07-25
  • 通讯作者: 沈 芳

Spectral Analysis and Remote Sensing Detection of Tidal Shoal′sVegetation in the Estuary of Yangtse River(Chinese)

ZHANG Jie, SHEN Fang, LIU Zhi-guo   

  1. State Key Laboratory of Estuarine and Coastal Research, East China Normal University, Shanghai 200062,China
  • Received:2006-06-30 Revised:2006-09-26 Online:2007-07-25 Published:2007-07-25
  • Contact: SHEN Fang

摘要： 通过野外实测长江口潮滩湿地主要植物的光谱特征，分析和提取了优势植被的光谱特征参数和波段.考虑到潮滩湿地植被在生长特点、季节和盖度等方面的影响因素，采用了组合光谱特征波段的植被指数对长江口潮滩湿地植被进行分类检测，以期在现有的多光谱遥感影像上提高分类精度，检测出潮滩湿地植被空间分布的变化.分别计算了实测夏季和秋季的 RVI，NDVI，SAVI和MSAVI四种植被指数，得出不同植被指数对潮滩湿地植被不同盖度和不同季节的检测方法，并将该方法应用于多光谱TM影像上，验证这几种植被指数在TM影像上的分类精度，结合实地考察，发现MSAVI应用到多光谱TM影像上对潮滩湿地植被的分类检测效果最好，但时相应选择夏季.

关键词: 潮滩, 植被指数, 光谱分析, 遥感检测, 潮滩, 植被指数, 光谱分析, 遥感检测

Abstract: This paper analyzed the spectrum character of preponderant vegetables and distilled wave band using the spectrum of the vegetation through field measure in the estuary of Yangtse River. Considering influence factors such as vegetation growth characteristic, season, vegetation cover in tidal shoal，vegetation index of combined characteristic wave bands was used to detect tide shoal's vegetation, in order that it could improve the vegetation classification precision and detect the changes of vegetation environment. The paper computed Vegetation Index of Ratio Vegetation Index（RVI）, Normalized Difference Vegetation Index（NDVI）, SoilAdjusted Vegetation Index(SAVI) and Modified SoilAdjusted Vegetation Index (MSAVI), and analyzed the advantages and disadvantages of the four vegetation indexes in different covers and seasons. Then used these indexes to detect the vegetation classification in TM image. In conclusion, combining the field measure and taking season factor into account for better classification. MSAVI wins the advantage of each vegetation index in classification.

Key words: vegetation index, spectrum analysis, remote sensing detection, tidal shoal, vegetation index, spectrum analysis, remote sensing detection