植物フェノロジー観測における時系列NDVIデータの雑音除去手法

摘要

Vegetation phenology is closely related to seasonal dynamics of the lower atmosphere, and important elements in global models and vegetation monitoring. Time-series NDVI data based on imagery from AVHRR or MODIS are suitable for phenological monitoring, because these sensors provide data with a high temporal frequency. In order to reduce noises caused by cloud contamination or atmospheric variability, Maximum Value Composite (MVC) is applied to the data. However, composite data have undesirable noises due to remained cloud contamination and inequality of observation intervals. Though MVC technique is applied, these noises disturb phenological monitoring. This paper proposed new noise reduction algorithm which integrates Best Index Slope Extraction (BISE) and Maximum Value Interpolated (MVI) algorithms. Integrated algorithm was applied to time -series NDVI data consists of NOAA AVHRR composite images. This algorithm worked well in areas dominated by vegetation such as cropland including double cropping area, deciduous broadleaf forest and evergreen needleleaf forest. We confirmed that the algorithm reduced effects of cloud contamination, and equalized each observation interval. Therefore, applying developed algorithm to time-series NDVI data allows us to phenological monitoring more precisely.%展葉や落葉など季節特有の植物活動を意味する植物 フユノロジーは下層大気の季節変動と密接に関係して おり，炭素循環や水循環，熱輸送等における季節変動 をもたらすため，地球科学や大気科学における重要な 要素である（Running et al.，1991）。