Top-down Green-ups: Satellite Sensing and Deep Models to Predict Buffelgrass Phenology
This work addresses invasive species management for environmental conservation in the Southwest United States, but it is incremental as it applies existing deep learning techniques to a specific domain.
The researchers tackled the problem of predicting buffelgrass green-ups for herbicidal treatment using satellite sensing and deep learning models, finding that neural-based approaches improved over conventional methods and promised significant resource savings.
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.