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was performed to ensure that only pixels selected from within large homogeneous forest and 
non-forest regions were used. Up to 500 forest and 500 non-forest pixels were sampled 
randomly from each of the 82 forest regions. 
For both maps the Digital Chart of the World database (DCW) (ESRI, 1993) was used to 
define urban classes that could be represented as polygons at l:lMillion scale. 
Validation of the classification results was performed by comparison with land cover 
summary statistics defined by the Statistical Office of the European Union (EUROSTAT). 
The Nomenclature of Territorial Units for Statistics (NUTS) was established to provide a 
uniform breakdown of territorial units for the production of regional statistics (EUROSTAT, 
1995). The NUTS regions use a common land use nomenclature and are hierarchically 
defined at different scales based on the institutional divisions in force in the Member States. 
METHODOLOGY 
The monthly maximum value NDVI and Ts data were classified in each region independently 
in an attempt to reduce regional climatic and biophysical variations in the NDVI and Ts data. 
Regional processing was also performed to facilitate automation of the classification 
procedure. Figure 1 shows a flow diagram of the generic classification methodology used to 
produce both the land cover and forest cover maps. The left hand side of the flow diagram 
illustrates the supervised classification procedure used to produce the forest cover map and the 
right hand side illustrates the unsupervised classification procedure used to produce the land 
cover map. The land cover map was composed of 13 ecosystem regions and the forest cover 
map was composed of 82 homogeneous forest regions. 
The supervised procedure involved the maximum likelihood classification of an optimal 
multitemporal combination of NDVI and Ts data into forest and non-forest classes. Maximum 
likelihood classification rules (James, 1985) were defined independently for each region using 
the forest and non-forest training data held in the georeferenced database. The classification 
rule was then applied to the remaining pixels in the forest region (Kennedy et al., 1995, Roy et