Multi-temporal Pacific madrone leaf blight assessment with unoccupied aircraft systems


  • Matthew I. Barker
  • Jonathan D. Burnett
  • Tanya Haddad
  • William Hirsch
  • Dae Kun Kang
  • Kale’a Pawlak-Kjolhaug
  • Michael G. Wing



Pacifica madrone, unoccupied aircraft systems,


: Pacific madrone leaf blight (PMLB) is a contributing agent to the declineof Pacific madrone (Arbutus menziesii) trees. Multiple fungal pathogens causePMLB, resulting in leaf spotting that can eventually kill leaves, increasing stress inindividuals, and leaving them more susceptible to deadly cankers. Spores transmit viaair and water droplets, particularly during wet Spring months. Unoccupied aircraftsystems (UAS) technologies are in their relative infancy, but UAS are becomingmore affordable and accessible. UAS promise increased efficiency in forest healthmonitoring applications, providing a safer aerial data collection method at arelatively-low cost when compared to occupied aircraft. In this study, we developand present a UAS methodology to detect PMLB with a multispectral sensor. Thismethodology combines orthomosaic products derived from high-resolution (~4 cm)multirotor platform UAS multispectral imagery with machine learning and groundassessment of PMLB to classify visual presence of blight at the individual treelevel during multiple site revisits. The resulting model detected PMLB infectionstatus of 29 field surveyed madrone trees with a kappa coefficient of , a balancedaccuracy of 0.85, and a true positive rate of 0.92. The method presented here can bereadily scaled to efficiently cover a much larger extent with a beyond-line-of-sitecapable UAS and minimal field sampling. The increased efficiency of this approachmay be critical to characterizing PMLB in the near future as it is anticipatedthat PMLB prevalence will continue to increase as a result of climate change.


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