  An invasive species of grass known as "buffelgrass" contributes to severe
wildfires and biodiversity loss in the Southwest United States. We tackle the
problem of predicting buffelgrass "green-ups" (i.e. readiness for herbicidal
treatment). To make our predictions, we explore temporal, visual and
multi-modal models that combine satellite sensing and deep learning. We find
that all of our neural-based approaches improve over conventional buffelgrass
green-up models, and discuss how neural model deployment promises significant
resource savings.
