An Algorithm and a New Vegetation Index for ADEOS-II/GLI Data Analysis
An Algorithm and a New Vegetation Index for ADEOS-II/GLI Data Analysis
复制标题
ADEOS-II/GLI 数据分析的算法和新植被指数
DOI：
10.11440/rssj1981.18.126
复制
发表时间：
1998
影响因子：
--
通讯作者：
M. Daigo
中科院分区：
文献类型：
--
作者：
Ayami Hayashi;K. Muramatsu;S. Furumi;Yumiko Shiono;N. Fujiwara;M. Daigo
我们研究了一种基于模式分解方法的 ADEOS-II/GLI 数据分析算法和新的植被指数。 1）为了模拟 GLI 传感器的光谱响应模式，使用覆盖 GLI 传感器光谱范围的光谱仪在现场测量了约 450 个样本的反射率。十九维GLI数据中大约96%的信息成功地转化为三种模式分解系数。2)通过光谱仪实验证实了从模式分解系数可以正确估计像素内的土地覆盖率。此外，250m和1km空间分辨率的GLI数据是根据30m空间分辨率的LANDSAT/TM数据模拟的。利用该数据表明，根据模式分解系数估计的GLI像元中的土地覆盖率几乎等于GLI像元相应区域的TM像元的土地覆盖率。3）提出了一种新的植被指数VIPD（基于模式分解的植被指数）。 VIPD利用了全部十九维GLI数据，反映了植被数量和植被活力程度。该指数比 NDVI 对植被覆盖率、植被垂直厚度以及阔叶和针叶等植被类型更敏感。从这些结果可以看出，基于模式分解方法的算法足以分析超多维GLI数据，并且新的植被指数在植被研究中很有用。
We have studied an algorithm and a new vegetation index for analyses of ADEOS-II/GLI data, based on the pattern decomposition method.1) To simulate spectral response patterns of the GLI sensor, reflectances of about 450 samples were measured in the field with a spectrometer covering the spectral range of the GLI sensor. About 96% of the information of the nineteen-dimensional GLI data was successfully transformed into three pattern decomposition coefficients.2) It was confirmed .by the experiment using the spectrometer that land cover ratios in a pixel are estimated from the pattern decomposition coefficients correctly. Furthermore, GLI data with 250m and 1km spatial resolutions were simulated from LANDSAT/TM data with 30m spatial resolution. Using the data, it was shown that land cover ratios in the GLI pixel estimated from the pattern decomposition coefficients are nearly equal to those of TM pixels in corresponding areas of the GLI pixel.3) A new vegetation index, VIPD (Vegetation Index based on Pattern Decomposition) was developed. VIPD utilizes all the nineteen-dimensional GLI data, and reflects the amount of vegetation and the degree of vegetation vigor. The index is more sensitive for vegetation cover ratio, for the vertical thickness of vegetation, and for vegetation type, such as broad leaves and needle leaves, than is NDVI. From these results, it became evident that the algorithm based on the pattern decomposition method is sufficiently able for analyzing hyper-multidimensional GLI data, and the new vegetation index is useful in the study of vegetation.