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Hyperspectral imaging of disease spread in New Zealand native forests

Supervisors: David Coomes (Plant Sciences)

Reference Code: BC003

Importance of the area of research:

Introduced diseases can be highly destructive in natural forests, virtually eliminating tree species that were once common and radically altering ecosystem processes. Containing epidemics in natural forests is often impracticable, particularly in the case of wind-borne diseases, but tracking the spread is valuable for forest management particularly if patches of resistance can be identified. Airborne hyperspectral imaging is an emergent technique for tracking diseases. Hyperspectral imagers measure light energy in hundreds of narrow wavebands, making it possible to identify changes in the physico-chemical properties of canopies infected with a disease. This project will develop methods to monitor the spread of Myrtle rust (Austropuccinia psidii) in New Zealand's native forests. This non-native pathogen was first detected in northern New Zealand in 2017 and is known to infect many species of Myrtaceae, including the nectar plant used to produce manuka honey, a valuable natural product. These monitoring methods will be valuable for managers of New Zealand's forests.

Project summary:

New hyperspectral imaging approaches will be developed to map the distribution of Myrtaceous plant species in New Zealand forests and monitor the spread of Myrtle rust. We will use a spectranomic approach, developed by Asner and Martina (2009) to predict the physico-chemical properties of leaves from their spectral information, allowing us to map species and disease status remotely.

What the student will do:

Working within a team of New Zealand researchers, the student will conduct fieldwork at selected sites in the North Island. Firstly, a field spectrometer will be used to record the leaf spectra of hundreds of species of New Zealand plant, and these leaves will be analysed for 20 physico-chemical traits. Chemometric modelling will then be used to predict trait values from leaf spectra (Asner and Martin 2009). Secondly, an airborne spectrometer will be used to map forests at contrasting sites in northern New Zealand and by identifying individual tree crowns on the ground, a predictive model will be constructed to generate maps of species and disease status (see Vaughn et al 2018). Together, these two approaches should allow Myrtaceous species to be mapped and their disease status assessed. Potentially, these data could be used to parameterise a SAR epidemic model, which would be used to predict the spread of myrtle rust within New Zealand.

References:

  • Asner G.P. & Martin RE (2009) Airborne spectranomics: mapping canopy chemical and taxonomic diversity in tropical forests. Frontiers in Ecology and the Environment https://esajournals.onlinelibrary.wiley.com/doi/abs/10.1890/070152
  • Vaughn et al. (2018) An approach for high-resolution mapping of Hawaiian Metrosideros forest mortality using laser-guided imaging spectroscopy. Remote Sensing 2018, 10(4), 502; https://doi.org/10.3390/rs10040502
  • Biosecurity New Zealand website: Myrtle rust.https://www.mpi.govt.nz/protection-and-response/responding/alerts/myrtle-rust/

Follow this link to find out about applying for this project.

Please contact the lead supervisor directly for further information relating to what the successful applicant will be expected to do, training to be provided, and any specific educational background requirements.