Research output per year
Research output per year
Research output: Contribution to journal › Article › peer-review
At the near-surface, ozone (O3) is a toxic pollutant which has reached dangerously high concentrations across the world and is predicted to continue to rise. O3 reduces the growth, productivity and resilience of trees but the extent of O3 damage to forests is uncertain. To develop a high throughput method of monitoring O3 damage to forests, we pioneer hyperspectral monitoring of O3 damage in adult oak trees across a range of naturally occurring O3 concentrations. Using a machine learning approach, we demonstrate accurate prediction of O3 exposure of trees from hyperspectral leaf reflectance alone. This method could be used for forest level assessments of O3 damage. Vegetation indices characterising green reflectance and red-edge track O3 induced changes in leaf reflectance. Vegetation indices have the potential to scale up O3 damage monitoring across spatial scales. As O3 concentrations continue to rise globally, understanding the extent of O3 damage to forests is crucial to effectively harness the carbon sequestration potential of forests. We demonstrate the exciting potential of spectral monitoring of O3 damage in mature trees under natural conditions.
| Original language | English |
|---|---|
| Article number | 113263 |
| Number of pages | 11 |
| Journal | Ecological Indicators |
| Volume | 172 |
| Early online date | 22 Feb 2025 |
| DOIs | |
| Publication status | Published - Mar 2025 |
Research output: Contribution to journal › Article › peer-review
MacKenzie, R. (Principal Investigator) & Jackson, R. (Co-Investigator)
1/08/24 → 1/12/34
Project: Research