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The mapping accuracy is assessed on one intermediate flood stage, showing a precision in excess of 90%.
The mapping accuracy is relatively high at high elevations, compared to the mid-slope and low-lying areas.
The mapping accuracy is highest for training areas with a size of 7.5 10% of the total area.
The influence of sequence divergence on mapping accuracy is conceptually more difficult to handle.
Therefore, in those locations where the selection of cloudy Landsat imagery was unavoidable, settlement mapping accuracy is likely to be lower (especially in rugged areas), though sufficient verification data was unavailable to test this.
The trade-off between the number of detected variants and mapping accuracy is important.
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Fire mapping accuracy was assessed by the spatial coincidence between mapped fires and ground-truthed information using a decision-tree approach and by testing the hypothesis that various calculated accuracy components were equal within an ANOVA design.
However, adequate training sample collection and mapping accuracy are two hindering factors over large regions.
Mapping accuracy was performed using landscape metrics and the Kappa coefficient to compare the topographical and geometric similarity between the giant reed patches obtained with the OBIA map and with a validation map derived from on-screen digitizing.
Overall, we found that the mapping accuracy was high.
Even with 9% mutations, only a very limited effect on the mapping accuracy was noted.
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