Here are the definitions of the variable importance measures. The
first measure is computed from permuting OOB data: For
each tree, the prediction error on the out-of-bag portion of the
data is recorded (error rate for classification, MSE for regression).
Then the same is done after permuting each predictor variable. The
difference between the two are then averaged over all trees, and
normalized by the standard deviation of the differences. If the
standard deviation of the differences is equal to 0 for a variable,
the division is not done (but the average is almost always equal to 0
in that case).

The second measure is the total decrease in node impurities from
splitting on the variable, averaged over all trees. For
classification, the node impurity is measured by the Gini index.
For regression, it is measured by residual sum of squares.