Table 3 Mean vector coordinates for vegetation period and for both dynamic clusters separately. Values are given as 
reflectance percentage in channels 1 and 2 of the AVHRR sensor. Values in parenthesis are standard deviations of 
corresponding mean vectors. 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
Vegetation period mean vector Dynamic cluster I mean vector Dynamic cluster II mean vector 
Channel 1 Channel 2 Channel 1 Channel 2 Channel 1 Channel 2 
Snatva 7.297 23.676 8.387 26.246 6.425 21.621 
pa (1.950) (6.379) (1.933) (7.639) (1.466) (4.129) 
Cesná 9.704 25.436 12.007 27.259 8.533 24.525 
(2.184) (3.558) (2.074) (3.494) (1.014) (3.224) 
8.207 25.885 9.182 27.632 7.676 24.932 
Ilovski dol (1.352) (4.603) (1.167) (4.092) (1.132) (4.587) 
Opeke 8.833 26.308 9.368 28.889 8.068 22.621 
pe (1.494) (5.102) (1.546) (4.910) (1.007) (2.381) 
Smolov 9.576 25.907 12.149 29.420 8.504 24.443 
(2.015) (5.629) (1.293) (4.539) (1.072) (5.386) 
Conclusions Literature 
NDVI annual change and dynamic clustering 
show practical usage potential in  phenological 
phenomena monitoring for oak-woods far from existent 
phenological station network. According to currently 
available data it is possible to determine approximate 
leafing and yellowing date. For future research detailed 
data from the microlocation of observed oak-wood 
should be prepared. At the first place this concerns 
undercrown vegetation and soils. For more reliable 
interpretation of local NDVI minima during vegetation 
period meteorological data of cumulus cloudiness, 
typical for summer afternoons, should be examined as 
well. Finally, automatic algorithm for dynamic clustering 
is needed in order to avoid subjectivity and to speed up 
data processing. 
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756 International Archives of Photogrammetry and Remote Sensing. Vol. XXXII, Part 7, Budapest, 1998 
  
  
  
  
  
  
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