利用Hyperion图像估算森林覆盖度

王新云; 郭艺歌

doi:10.3969/j.issn.1001-5485.2013.07.021

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长江科学院院报 ›› 2013, Vol. 30 ›› Issue (7) : 106-110. DOI: 10.3969/j.issn.1001-5485.2013.07.021

信息技术应用

利用Hyperion图像估算森林覆盖度

王新云,郭艺歌

作者信息 +

Estimation of Vegetation Coverage using Hyperion Image

WANG Xin-yun, GUO Yi-ge

Author information +

文章历史 +

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)。

Abstract

Fractional green vegetation coverage (FC) is the most effective indicator of land desertification, and remote sensing is an important means to obtain regional scale vegetation coverage. The methods of processing EO-1 Hyperion hyperspectral image and estimating vegetation coverage 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.

导出引用

王新云,郭艺歌. 利用Hyperion图像估算森林覆盖度[J]. 长江科学院院报. 2013, 30(7): 106-110 https://doi.org/10.3969/j.issn.1001-5485.2013.07.021

WANG Xin-yun, GUO Yi-ge. Estimation of Vegetation Coverage using Hyperion Image[J]. Journal of Changjiang River Scientific Research Institute. 2013, 30(7): 106-110 https://doi.org/10.3969/j.issn.1001-5485.2013.07.021

中图分类号： TP701

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