| 基于Hyperion植被指数的干旱地区稀疏植被覆盖度估测 |
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| 引用本文： | 李晓松,李增元,高志海,白黎娜,王琫瑜,李世明.基于Hyperion植被指数的干旱地区稀疏植被覆盖度估测[J].北京林业大学学报,2010,32(3):95-100. |
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| 作者姓名： | 李晓松 李增元 高志海 白黎娜 王琫瑜 李世明 |
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| 作者单位： | 1 中国林业科学研究院资源信息研究所 2 中国科学院遥感应用研究所 |
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| 基金项目： | "863"国家高技术发展计划项目，"十一五"国家科技支撑计划项目 |
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| 摘 要： | 受稀疏植被与明亮土壤背景影响,干旱地区植被覆盖精确遥感估测难度较大。以Hyperion影像为数据源,选取甘肃省民勤绿洲-荒漠过渡带为研究区,系统比较了利用不同类型高光谱及多光谱植被指数估测干旱地区稀疏植被覆盖度的能力,以期确定干旱地区稀疏植被覆盖度估测的最佳植被指数。不同植被指数估测稀疏植被覆盖度的能力利用线性回归R2及留一交叉验证的均方根误差进行比较,结果表明:高光谱植被指数估测稀疏植被覆盖度的能力显著优于相应的多光谱植被指数,抗大气植被指数(ARVI)及抗土壤和大气植被指数(SARVI)表现明显优于归一化植被指数(NDVI)与土壤调节植被指数(SAVI),其中以基于833.3nm/640.5nm波段组合的ARVI表现最佳,R2可达0.7294,均方根误差(RMSE)仅为5.5488。
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| 关 键 词： | 稀疏植被覆盖度 多光谱植被指数 高光谱植被指数 交叉验证 |
| 收稿时间： | 1900-01-01 |
Estimation of sparse vegetation cover in arid regions based on vegetation indices derived from Hyperion data |
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| Li Xiao-song,LI Zeng-yuan,GAO Zhi-hai,BAI Li-na,WANG Beng-yu,LI Shi-ming.Estimation of sparse vegetation cover in arid regions based on vegetation indices derived from Hyperion data[J].Journal of Beijing Forestry University,2010,32(3):95-100. |
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| Authors: | Li Xiao-song LI Zeng-yuan GAO Zhi-hai BAI Li-na WANG Beng-yu LI Shi-ming |
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| Institution: | 1 Institute of Resource and Information, Chinese Academy of Forestry, Beijing, 100091, P.R.China; 2 Institute of Remote Sensing Applications, Chinese Academy of Sciences, Beijing, 100101, P.R.China. |
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| Abstract: | An accurate prediction of vegetation cover is a great challenge in arid regions, because of sparse vegetation cover and bright soil backgrounds. Taking Hyperion images as data source and the Minqin oasis-desert transitional zone as our study area, we systematically compared the ability of different vegetation indices for estimating sparse vegetation cover in arid regions, in order to find the most suitable vegetation index. The predictive performances of hyperspectral and multi-spectral vegetation indices were compared using R2 and cross-validated RMSE of linear regression models, estimating the relationships between vegetation indices and sparse vegetation cover. The results show that hyperspectral vegetation indices were significantly better than corresponding multi-spectral vegetation indices in predicting vegetation cover. Among the various hyperspectral vegetation indices, ARVI (atmospherically resistant vegetation index) and SARVI (soil-atmospherically resistant vegetation index) performed better than NDVI (normalized difference vegetaton index) and SAVI (soil adjusted vegetation index). ARVI based on a specific hyperion narrow-band (833.3 nm/640.5 nm) had the best performance given its high R2 value (0.729 4) and low cross-validated RMSE (5.548 8). |
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| Keywords: | sparse vegetation cover multi-spectral vegetation indices hyperspectral vegetation indices cross-validation |
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