  Accurate information on the distribution of vegetation species is used as a
proxy for the health of an ecosystem, a currency of international environmental
treaties, and a necessary planning tool for forest preservation and
rehabilitation, to name just a few of its applications. However, direct,
extensive observation of vegetation across large geographic regions can be very
expensive. The extensive coverage and high temporal resolution of remote
sensing data collected by satellites like the European Space Agency's
Sentinel-2 system could be a critical component of a solution to this problem.
  We propose a hierarchical model for predicting vegetation cover that
incorporates high resolution satellite imagery, landscape characteristics such
as elevation and slope, and direct observation of vegetation cover. Besides
providing model-based predictions of vegetation cover with accompanying
uncertainty quantification, our proposed model offers inference about the
effects of landscape characteristics on vegetation type. Implementation of the
model is computationally challenging due to the volume and spatial extent of
data involved. Thus, we propose an efficient, approximate method for model
fitting that is able to make use of all available observations. We demonstrate
our approach with an application to the distribution of three post-fire
resprouting deciduous species in the Jemez Mountains of New Mexico.
