时序NDVI数据集重建方法评价与实例研究

时序NDVI数据集已经成功地应用于全球与区域环境变化、植被动态变化、土地覆盖变化和植物生物物理量参数反演等方面的研究。受到大气条件和传感器自身因素的制约,虽然经过严格的预处理,时序NDVI数据集仍包含很多噪声,影响其进一步的应用。首先介绍了近几年来普遍使用的6种时序NDVI数据集的重建方法：改进的最佳指数斜率提取法、均值迭代滤波法、Savitzky-Golay滤波法、傅立叶变换法、非对称高斯函数拟合法和时间序列谐波分析法 |然后采用这几种方法对张掖地区2007年和2008年10 d最大值合成的SPOT/VEGETATION的时序NDVI数据进行了重建,对重建结果进行了比较和评价 |最后对人为的噪声序列进行重建,对重建结果的优缺点进行评价。

Reconstruction of Temporal NDVI Dataset:Evaluation and Case Study

NDVI time series datasets have been successfully applied to global and regional environmental change research, vegetation dynamic, land cover change and extraction of surface biophysical parameters. Due to the limitation of atmospheric conditions and sensors,although temporal NDVI dataset has been pre-processed rigorously,there is still so much noise within the NDVI time series datasets which would affect further analysis and applications. This study introduced six common methods for reconstructing high-quality NDVI time series dataset in recent years: Mean-value iteration filter,the modified best index slope extraction, Fourier Transform,Savitzky-Golay filter, Asymmetric Gaussian function fitting and Harmonic a-nalysis of time series. Then,the six methods mentioned above were applied to reconstruct the SPOT VEGETATION 10-days-maximum-NDVI composite data in 2007 and 2008 in Zhangye. The reconstructing results are compared and evaluated separately. Finally,a temporal profile with artificial noise was also reconstructed with the six methods respectivly in order to evaluating the merits and demerits of every method.