地球信息科学学报 ›› 2011, Vol. 13 ›› Issue (5): 656-664.doi: 10.3724/SP.J.1047.2011.00656

• 遥感技术与应用 • 上一篇    下一篇

卫星影像数据构建山地植被指数与应用分析

吴志杰1,2, 徐涵秋2   

  1. 1. 龙岩学院资源工程系, 龙岩 364012;
    2. 福州大学环境与资源学院, 福州 350108
  • 收稿日期:2011-01-12 修回日期:2011-08-08 出版日期:2011-10-25 发布日期:2011-10-25
  • 作者简介:吴志杰(1971-),男,硕士,副教授,主要从事环境与资源遥感的教学与研究。E-mail:wuzhijiefj@163.com
  • 基金资助:

    福建省教育厅重点项目(JK2009004)资助。

A New Index for Vegetation Enhancements of Mountainous Regions Based on Satellite Image Data

WU Zhijie1,2, XU Hanqiu2   

  1. 1. Department of Resources Engineering, Longyan University, Longyan 364012, China;
    2. College of Environment and Resources, Fuzhou University, Fuzhou 350108, China
  • Received:2011-01-12 Revised:2011-08-08 Online:2011-10-25 Published:2011-10-25

摘要: 本研究以Landsat影像为数据源,在分析复杂地形山地植被在阳坡和阴坡反射率差异特征的基础上,提出一种归一化差值山地植被指数NDMVI (Normalized Difference Mountain Vegetation Index)。该指数模型无需辅助数据(如DEM)的支持,通过同时降低近红外波段(TM4)和红光波段(TM3)反射率的方法来消除或抑制地形的影响,具有较强的可操作性。研究表明:NDMVI与太阳入射角余弦值(cos i)的相关性相当小,对地形起伏变化表现不敏感,可有效消除或抑制地形的影响;比NDVI值动态变化范围更宽,对地物有更强的遥感识别能力;该模型抑制地形影响的效果比用C校正模型的效果更佳,不会出现过度校正的问题。

关键词: 遥感, 山地, 地形校正, 植被指数, NDVI, NDMVI

Abstract: In this study, a normalized difference mountain vegetation index (NDMVI) is proposed using Landsat TM/ETM+ images. The new index aims to correct terrain influence on the vegetation measurements of the NDVI (Normalized Difference Vegetation Index). Through analyzing the different spectral reflectance characteristics of vegetation between sunny and shaded slopes in the complex mountainous region, we developed a new index to introduce two parameters to adjust values of the near-infrared and red bands of the Landsat TM/ETM+ image. The newly developed index has advantages over the NDVI in correcting terrain impact and over the other similar terrain correction vegetation indices in that it does not need other supplement data such as DEM for the model. The study shows that the NDMVI has nearly no significant correlation with cosine value of solar incidence angle and is not sensitive to the terrain changes. Therefore, this new index can more effectively suppress the influence to vegetation observations due to terrain difference better than does the NDVI. In addition, the NDMVI has a wider dynamic range than the NDVI does, and thus, could distinguish different land cove types more effectively than the NDVI. We concluded that, compared with the C-correction model, the new index can achieve a better performance because it has avoided the over-correction bias.

Key words: remote sensing, mountainous region, terrain correction, vegetation index, NDMVI, NDVI