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  • 标题:OBSERVATION OF FOREST ENVIRONMENT CHANGES IN SIBERIA
  • 本地全文:下载
  • 作者:H. Sawada ; Y. Sawada ; Y. Matsuura
  • 期刊名称:ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
  • 印刷版ISSN:2194-9042
  • 电子版ISSN:2194-9050
  • 出版年度:2010
  • 卷号:XXXVIII - Part 8
  • 页码:605-608
  • 出版社:Copernicus Publications
  • 摘要:Basic forest information of Siberia in global scale has been corrected as GIS datasets from the IIASA, IGBP and other organizat ions as well as satellite image and field survey data. Thermal data as well as vegetation index is considered useful to evaluate environmental conditions taking into account the impact of global warming in Siberia. The NOAA Pathfinder data from 1981 to 2001 were used and the large fluctuations originated from noises have been removed by either method of LMF and LMF-KF. As the land surface temperature (LST) data, the Channel 4 data of NOAA-AVHRR were used. By applying the LMF-KF to NOAA Pathfinder data, we could get cloud-free and noise-free images with 10 days interval for both NDVI and LST. The LST shows highe r than station temperature in summer time and the relation becomes inverted in winter time. The LST data is useful for understand ing environmental condition of the terrain. One of such examples is the satellite warm index (the summation of temperature when it is higher than 5 degree C), which is considered useful for zoning the eco-region. The images of various indices of the world were created. The maximum NDVI of each year, for example, is a good indicator for annual changes of vegetation condition as each NDVI shows the seasonal condition of vegetation coverage. The trend of annual maximum LST for 20 years shows the average temperature changes of the surface, which are related to the impact of global environmental changes to the ground. The NPP in Russia in late 1990s was estimated as 7.8 million tC. The NPP in the whole Russia was increasing but it was decreasing in European Russia because of deforestation
  • 关键词:Forest environment; Siberia; Time-series model; NOAA
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