Remote estimation of aquatic light environments using machine learning: A new management tool for submerged aquatic vegetation

  title={Remote estimation of aquatic light environments using machine learning: A new management tool for submerged aquatic vegetation},
  author={Ryan M. Pearson and Catherine Collier and Christopher J. Brown and Michael A. Rasheed and Jessica Bourner and Mischa P. Turschwell and Michael Sievers and Rod M. Connolly},
  journal={Science of The Total Environment},
Abstract Submerged aquatic vegetation (SAV; e.g. seagrasses, macroalgae), forms key habitats in shallow coastal systems that provide a plethora of ecosystem services, including coastal protection, climate mitigation and supporting fisheries production. Light limitation is a critical factor influencing the growth and survival of SAV, thus it is important to understand how much light SAV needs, and receives, to effectively assess the risk that light limitation poses. Light monitoring is commonly… Expand

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