本文中作者提出分段Morlet小波变换的方法从遥感数据中识别出地表物候.地表物候是人类了解地球生态系统的必要参数,也是动植物保护、农耕等活动的重要依据.研究发现已有的方法存在物候识别不准确、去除噪声效果差等缺陷,而Morlet小波在周期识别、去除噪声方面表现非常好,因此本文使用Morlet小波变换的方法处理青海湖流域2003-2014年的NDVI数据,发现该方法存在变换后的曲线与原NDVI数据不贴合或物候周期偏移的情况.因此作者提出进一步的改进方法:分段Morlet小波变换,原理是根据NDVI最大值将每个NDVI周期划分成两段,对左右两段分别进行Morlet小波变换并自动选取合适的参数,使物候识别更加合理、准确.作者通过分段Morlet小波变换和最大斜率法提取青海湖流域LSP参数,分析LSP参数的时间变化、空间变化、特别年份等,揭示了青海湖流域物候变化的特点,同时证明基于分段Morlet小波变换的植被物候遥感识别方法在准确性与高效性上都有所提高.%In this paper, the author proposes to identify Land Surface Phenology from remote sensing data by using segmented Morlet wavelet transform. Land Surface Phenology is a necessary parameter for human understanding the Earth's ecological system, and the essential basis for protecting animals and plants, farming and other activities. The author finds that there are some defects in the existing methods, such as inaccurate in identifying phenology, poor at removing noise, while Morlet wavelet performs very good in cycle identification and noise removal. Therefore, the Morlet wavelet transform is used to deal with NDVI of Qinghai Lake Basin from 2003 to 2014. Then it is found that there is a case where the transformed curve is not fit with the original NDVI or the phenological period is shifted. So the author proposes an improvement method: segmenting Morlet wavelet transform, which means to divide each NDVI cycle into two sections according to the NDVI maximum, and then use Morlet wavelet transform on two segments respectively, and finally select appropriate parameter automatically. With this method, the phenology identification will be more reasonable and accurate. The authors extract the LSP parameters of Qinghai Lake Basin by segmenting Morlet wavelet transform and maximum slope, analysis of LSP parameters on time, space, and special year scales, and reveal the characteristics of Land Surface Phenology in the Qinghai Lake Basin. At the same time, it is proved that the Land Surface Phenology remote sensing recognition method based on segmented Morlet wavelet transform has improved both its accuracy and efficiency.
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