  Monitoring plant and animal populations is an important goal for both
academic research and management of natural resources. Successful management of
populations often depends on obtaining estimates of their mean or total over a
region. The basic problem considered in this paper is the estimation of a total
from a sample of plots containing count data, but the plot placements are
spatially irregular and non randomized. Our application had counts from
thousands of irregularly-spaced aerial photo images. We used change-of-support
methods to model counts in images as a realization of an inhomogeneous Poisson
process that used spatial basis functions to model the spatial intensity
surface. The method was very fast and took only a few seconds for thousands of
images. The fitted intensity surface was integrated to provide an estimate from
all unsampled areas, which is added to the observed counts. The proposed method
also provides a finite area correction factor to variance estimation. The
intensity surface from an inhomogeneous Poisson process tends to be too smooth
for locally clustered points, typical of animal distributions, so we introduce
several new overdispersion estimators due to poor performance of the classic
one. We used simulated data to examine estimation bias and to investigate
several variance estimators with overdispersion. A real example is given of
harbor seal counts from aerial surveys in an Alaskan glacial fjord.
