摘要
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多方面的研究。时序NDVI数据集受到云和气溶胶等大气条件和传感器自身等因素的影响包含很多噪声,影响了其进一步的应用。基于对近几年来普遍使用的5种重建方法的对比分析结果,发展了基于标准差权重和噪声点性质的两种综合方法。以黑河流域2009年16 d最大值合成的MODIS NDVI数据为例,对比了两种综合方法与5种重建方法的效果;并用2009年5月下旬至8月上旬的地面实测NDVI数据验证了两种综合方法的重建效果。结果表明这两种综合方法的效果都优于对比的5种重建方法,它们既保留了原始数据中大部分的点,又最大限度地修正了噪声点,所生产的时序NDVI数据集,可以更好地用来开展全球与区域土地覆盖和植被动态变化监测等研究。
Time-series of Normalized Difference Vegetation Index（NDVI） datasets have been used in detecting the long-term vegetation cover changes in regional,continental or global scales.They are also successfully applied to extract the biophysical parameters of vegetation cover.Normally,there are quite frequently fluctuations because of atmospheric condition and sensor effect in the NDVI dataset.According to the comparative analysis of five widely used NDVI reconstruction algorithms,two integrated approaches were developed based on standard deviation weight and characteristics of noise points respectively.The reconstructed results were validated and assessed by using some in-suit NDVI measurements carried out during late May to early August,2009.The result shows that these two integrated methods are better than the five separate methods above.They do not only retain most of the original data,but also modify the noise to the utmost extent.NDVI time series datasets produced by these two approaches can be better applied in the researches on global and regional environmental change,vegetation dynamic,and so on.
出处
《遥感技术与应用》
CSCD
北大核心
2010年第6期891-896,共6页
Remote Sensing Technology and Application
基金
国家973计划项目(2009CB421305)
中国科学院西部行动计划(二期)项目(KZCX2-XB2-09-03)和中国科学院"西部之光"人才培养计划项目(CACXO728501001)联合资助
