Top-down Green-ups: Satellite Sensing and Deep Models to Predict Buffelgrass Phenology

2023/10/01 by Lucas Rosenblatt, Bin Han, Rosenblatt, Lucas +7
Environmental Science · #Computer Vision and Pattern Recognition (cs.CV) #Computers and Society (cs.CY) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Rangeland and Wildlife Management #Remote Sensing in Agriculture #Species Distribution and Climate Change

paper · pdf · doi:10.48550/arxiv.2310.00740

openalex publication_date 2023/10/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

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