88
splitting the 0 - 100 % coverage range in two classes (empty or occupied). Induced by the incremental discrete
integer data representation in most raster image processing systems, the splitting can only be done, if the 50 %
coverage level of a pixel does not appear. Otherwise those pixels must be added either to the empty class or to the
occupied class. It is impossible to resize exactly a 10 m binary forest groundtruth into 20 m or 100 m. To show the
resulting effects the 10 m forest groundtruth, derived from the vector base, was resized to different output levels.
For each level the 50-100 % (and 51-100 % , if a binarization was not possible) forest coverage classes were cal
culated and compared to the vector forest area ( figure - 2 top). Additionally the compactness values are reported
below.
resized resolution In m
resized resolution In m
THEMATIC
RESAMPLING
RESAMPLING
RESAMPLING
50% UP LEVEL
51%UP LEVEL
EXACT
LEVEL
Figure - 2: Area (top) and compactness (bottom) comparison of resized forest groundtruth relative to original
vector data set. (‘POLYGRID’: vector to specific raster size conversion. ‘THEMATIC’: 10 m
raster to specific raster size conversion)
In cases where a 50 % binarization level was possible, the forest groundtruth differs only by 1 % relative to
vector data. Otherwise a significant area difference in 10 m to 20 m resizing of over 5% is detected. This effect
must be traced back to the natural 5-10 m boundary undulation of forest in our testsite, which can be represented
quite good by 10 m pixels. With raising pixel size all curves are nearly congruent.
3.3. Conclusions for Thematic Scale Change
Correct transformation of binary groundtruth data, as forest / non-forest data, from vector format into raster repre
sentation, using ARC/INFO’s polygrid task, depends on object shapes. Only if the selected output grid resolution
is adequate to the objects boundary scale variations (10 m for forest in testsite ‘Beckenried’) almost no area differ
ences have to be expected. Larger grid cell sizes may lead to differences of ± 6 %, which will reduce the
groundtruth comparison capabilities to classifications significantly.