  Post-stratification is frequently used to improve the precision of survey
estimators when categorical auxiliary information is available from sources
outside the survey. In natural resource surveys, such information is often
obtained from remote sensing data, classified into categories and displayed as
pixel-based maps. These maps may be constructed based on classification models
fitted to the sample data. Post-stratification of the sample data based on
categories derived from the sample data (``endogenous post-stratification'')
violates the standard post-stratification assumptions that observations are
classified without error into post-strata, and post-stratum population counts
are known. Properties of the endogenous post-stratification estimator are
derived for the case of a sample-fitted generalized linear model, from which
the post-strata are constructed by dividing the range of the model predictions
into predetermined intervals. Design consistency of the endogenous
post-stratification estimator is established under mild conditions. Under a
superpopulation model, consistency and asymptotic normality of the endogenous
post-stratification estimator are established, showing that it has the same
asymptotic variance as the traditional post-stratified estimator with fixed
strata. Simulation experiments demonstrate that the practical effect of first
fitting a model to the survey data before post-stratifying is small, even for
relatively small sample sizes.
