Top-down Green-ups: Satellite Sensing and Deep Models to Predict
  Buffelgrass Phenology
Lucas Rosenblatt Bin Han Erin Posthumus Theresa Crimmins Bill Howe
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.