利用Hyperion图像估算森林覆盖度  

Estimation of Vegetation Coverage Using Hyperion Image

作　　者：王新云[1] 郭艺歌[1] 

机构地区：[1]宁夏大学西北退化生态系统恢复与重建教育部重点实验室,银川750021

出　　处：《长江科学院院报》2013年第7期106-110,121,共6页Journal of Changjiang River Scientific Research Institute

基　　金：宁夏大学科学研究基金(NDZR10-12)资助

摘　　要：植被覆盖度是评价土地荒漠化最有效的指标,遥感是获取区域尺度植被覆盖度参数的一个重要手段。针对EO-1 Hyperion高光谱遥感图像成像的特点,探讨了高光谱Hyperion图像的预处理和森林覆盖度遥感估算的方法,研究中采用几何光学模型和混合像元模型等方法从高光谱EO-1 Hyperion图像估算植被覆盖度,进一步将2种方法估算的植被覆盖度进行了对比,并利用实测数据对估算结果进行验证。研究结果表明:利用几何光学模型反演的植被覆盖度(决定系数R2=0.76;均方根误差RMSE=0.06)优于混合像元模型法(R2=0.71;RMSE=0.07)。Fractional green vegetation coverage（FC） is the most effective indicator of estimating land desertification,and remote sensing is an important means to obtain regional scale vegetation coverage.The methods of preprocessing EO-1 Hyperion hyperspectral image and estimating vegetation coverage by using quantitative remote sensing are researched in this paper.We compare two different methods of estimating vegetation coverage from EO-1 Hyperion data.The first method is based on Li-Strahler Geometric-Optical model and Spectral Mixture Analysis（SMA） technique.The second method is based on mixed-pixel models.Results of vegetation coverage by the two methods are compared,and are further verified by measured data in the experimental field of Helan Mountain.The results indicate that the Li-Strahler Geometric-Optical model inversion（R2=0.76,RMSE=0.06） performs better than the mixed-pixel model inversion（R2=0.71,RMSE=0.07） for FC retrieval.

关 键 词：高光谱遥感 EO-1Hyperion图像 植被覆盖度 几何光学模型 混合像元模型 

分 类 号：S771.8[农业科学—森林工程]