  Studies on rapid change detection of large area urgently need to be extended
from 2D image to digital elevation model (DEM) due to the challenge of changes
caused by disasters. This research investigates positional uncertainty of
digital elevation change detection (DECD) caused by different degrees of DEM
complexity and DEM misregistration. Unfortunately, using three-sigma rule
(3{\sigma}R) for DECD is disturbed by accuracy of parameter estimation, which
is affected by the outliers (i.e., varied DEM) from DEM differencing samples.
Hence, to reduce the aforementioned uncertainty of DECD, we propose a new
strategy of quality assurance, adaptively censored three-sigma rule
(AC3{\sigma}R), in which with the samples censored, outliers of global DEM
differencing samples outside the standard deviations of the mean calculated by
moment estimation are iteratively removed. Compared with the 3{\sigma}R and
censored three-sigma rule (C3{\sigma}R) that is similar to AC3{\sigma}R but
without iteration for both simulation and real-world data experiments, the
proposed global AC3{\sigma}R method always exhibits the highest accuracies of
DECD in terms of both the overall accuracies 0.99967, 0.98740 and kappa
coefficients 0.99598, 0.81803 respectively, and the strongest robustness with a
large convergence interval [0, 0.30010] under the simulated maximum
registration error and most complex terrain complexity conditions.
