Examining the utility of visible near-infrared and optical remote sensing for the early detection of rapid 'ohi'a death

Authors: PERROY, RYAN - University Of Hawaii; HUGHES, MARC - University Of Hawaii; Keith, Lisa; COLLIER, ESZTER - University Of Hawaii; SULLIVAN, TIMO - University Of Hawaii; LOW, GABRIEL - University Of Alaska

Submitted to: Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/4/2020
Publication Date: 6/7/2020
Citation: Perroy, R., Hughes, M., Keith, L.M., Collier, E., Sullivan, T., Low, G. 2020. Examining the utility of visible near-infrared and optical remote sensing for the early detection of rapid 'ohi'a death. Remote Sensing. 12(11):1846. https://doi.org/10.3390/rs12111846.
DOI: https://doi.org/10.3390/rs12111846

Interpretive Summary: Early detection of plant pathogens at the landscape scale holds great promise for better managing forest ecosystem threats. In Hawai‘i, two recently described fungal species (Ceratocystis lukuohia and C. huliohia) are responsible for increasingly widespread mortality in 'ohi'a (Metrosideros polymorpha), a foundational tree species in Hawaiian native forests. Here we share work from repeat laboratory and field measurements to determine if visible near-infrared and optical remote sensing can be used to detect pre-symptomatic trees infected with these pathogens.

Technical Abstract: Early detection of plant pathogens at the landscape scale holds great promise for better managing forest ecosystem threats. In Hawai‘i, two recently described fungal species (Ceratocystis lukuohia and C. huliohia) are responsible for increasingly widespread mortality in 'ohi'a (Metrosideros polymorpha), a foundational tree species in Hawaiian native forests. Here we share work from repeat laboratory and field measurements to determine if visible near-infrared and optical remote sensing can be used to detect pre-symptomatic trees infected with these pathogens. After generating a dense time-series of laboratory spectral reflectance data and RGB images for inoculated 'ohi'a seedlings, seedlings subjected to extreme drought, and control plants, we found no few obvious spectral indicators that could be used for reliable pre-symptomatic detection in the inoculated seedlings, which quickly experienced complete and total wilting following stress onset. In the field, we found similar results when we collected repeat multispectral and RGB imagery over inoculated mature trees (sudden onset of symptoms with little advance warning), though our field measurements lacked the spectral resolution of the laboratory trials. We found selected vegetation indices to be reliable indicators for detecting non-specific stress in ‘ohi‘a trees, but never providing more than five days prior warning relative to visual detection in the laboratory trials. Finally, we generated a sequence of linear support vector machine (SVM) classification models from the laboratory reflectance data and RGB image samples at time steps ranging from pre-treatment to late-stage stress. Overall classification accuracies increased with stress stage maturity, but model performance prior to stress onset (maximum overall accuracy of 66.6% for a two-class hyperspectral model) and the sudden full-canopy onset of symptoms in infected trees suggest that early detection of rapid ‘ohi‘a death over timescales helpful for land managers remains a challenge.