In an exciting advancement towards proactive water management for forests, new research shows that early signs of water stress in Kauri can be detected as early as two weeks from the start of drought conditions, and two weeks before signs of stress are usually detected using a conventional approach.

These results were published in the Remote Sensing Journal in January.

This is the first time that hyperspectral remote sensing has been used to detect water stress in kauri.

Lead author, Jayson Felix, and the team used hyperspectral spectroscopy combined with machine learning to measure water content and compared this with conventional, plant physiological measurements.

Different plant traits (chlorophyll, water content, carotenoids) were included in the modelling to further improve detection.

Why this research is important

As climate models project more frequent and severe drought events in different places around the world, including New Zealand, this study showed that early detection of water stress in Kauri is possible, which will allow us to carry out mitigation actions before trees are heavily impacted.

The next step following from leaf-level research is to scale it up to the canopy level, in mature stands of Kauri with hyperspectral cameras on UAVs or aircraft. The team has already made great progress at scaling this up and the preliminary results are very promising. This research is ongoing and will be published soon.

Canopy level detection of water stress will enable faster, large-scale monitoring and early detection of water stress in Kauri and potentially other indigenous and exotic species in NZ.

Connecting expertise and technology

Here at Scion, we are equipped with the advanced technological resources (instruments, computers, sensors, imagers, drones, controlled environment) along with strong partnerships with our stakeholders, both of which are necessary to carry out this type of research and extend it beyond the lab.

We have the scientists and researchers with proven track records and expertise capable of conducting advanced remote sensing, artificial intelligence, and big data analysis for high-throughput phenotyping.

This work comes from the research area led by Scion's Russell Main in the Transforming Tree Phenotyping Programme.

Find the full text article here.