Olsson, Häkan 
  
  
2.3 Field Data 
Field data were collected as a part of the normal operations of the Swedish National Forest Inventory. Sample plots of 
7m (temporary) or 10m(permanent) radius are arranged in clusters on a regular grid. Variables are recorded by field 
teams on the sample plots according to an established protocol. Observations are made about the soil and hydrology 
conditions, ground vegetation, tree size and species composition. Several variables are derived or calculated for the plot 
level, based on sample trees. The database was queried for all plots measured within the study area during 1985 to 
1996. Due to the cyclic nature of the inventory, and the large proportion of temporary plots, very few in this set were 
actually re-measured during the period. In total 1545 plots were available in the study area after screening for clouds. 
  
  
30 600 
20 1 
  
  
  
  
  
HEIGHT S 
VOL TOT 
  
  
  
0 100 200 
AGE BA AGE BA 
Figure 2a) Stand height v.s. age shows significant variation due to site productivity. b) 
Stem volume (per hectare) as a function of age 
Unfortunately the position information for the plots is rather variable. Plots have nominal coordinates according to the 
inventory design. They may have digitized coordinates from 1:10000 scale maps, and the most recently surveyed plots 
are positioned with differential GPS. In all cases the best positional information available was used to extract pixel 
values from the image for comparison with the field data. 
3 METHODS 
3.1 Image Normalization 
Differences in atmospheric clarity during the different dates of image acquisition mean pixel values are not directly 
comparable between images in the temporal sequence. In general the atmosphere affects the recorded radiance through 
attenuation (a wavelength-dependent multiplicative effect) and path radiance (a wavelength-dependent additive effect). 
One approach to calibrating for these differences is to first convert the recorded digital counts to radiance through 
published calibration factors, then model the atmospheric radiative transfer to convert to surface reflectance factors with 
can be compared. This however requires in situ measurements of atmospheric parameters during the time of image 
acquisition. For a recent discussion of procedures, see Ouaidrari and Vermote, 1999. In the absence of in situ 
measurements, the atmospheric parameters may be inferred from radiance over dark targets such as clear lakes (Teillet 
and Fedosejevs, 1995). This approach to calibration has the advantage of being based on physical considerations, and 
results could be compared to model outputs. However with Landsat-5 at least, the main difficulty may be retrieving 
realistic radiance values from the recorded digital counts. After several years of operation, the on-board calibration of 
Landsat-5 was essentially unknown, and different receiving stations applied different calibration factors at different 
times for producing data. For a discussion of the issues, see the appendix in Teillet and Fedosejevs (1995). Without 
sensible starting values for radiance, the atmospheric correction procedures have almost no chance of producing 
  
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International Archives of Photogrammetry and Remote Sensing. Vol. XXXIII, Part B7. Amsterdam 2000.