HiPOC, Vertical farming and optical sensors (2023-2024)
In this project we conduct experimental research to develop and test optical sensor technology that is deployable on vertical farms. The project is building towards commercialization.
Here is an old pitch that explains the concept.
CREDIBLE, Soil carbon farming, EC-Horizon, Co-PI (2023-2026)
Project description
CREDIBLE: Building momentum and trust to achieve credible soil carbon farming in the EU. Our role in CREDIBLE is to form a focus group and collate and report information on proximal sensing and digitilization in carbon farming. If you work in this area, or have an interest in carbon farming, we would love to hear from you, please get in touch jon.athertonAThelsinki.fi.
MONOCLE Academy of Finland Research Fellow (2022-2027)
Project description
What is happening to Finland's forests under climate change? Satellite observations point to large-scale greening as we cross the Arctic circle. These decadal timescale structural changes have been detected from space using measurements of greenness such as the NDVI. Further south, warm dry summers coupled with a lack of winter snow are starting to cause concern. Shorter timescale stress-related changes in forests are detectable by emerging satellite techniques such as chlorophyll fluorescence. However, and regardless of the method, satellite data only shows half the picture. To really understand the mechanisms of forest change, satellite data needs to be supplemented by detailed field measurements and models. In this project I use state of the art drone data, long term measurements and advanced 3D modelling to unravel the mechanisms behind the satellite measurements of change.
Hyperspectral imaging of pine seedlings
In MONOCLE, we will use drone based hyperspectral imaging. This summer we have tested out our new hyperspectral camera in the greenhouse, sans drone. Here is a PRI image, hypothetically sensitive to carotenoids, from drought stressed (labelled D) pine seedlings:
PRI seems to do a good job at selecting the drought, but so do chlorophyll related indices (not shown). The spectral resolution of the camera is a little coarse compared to a regular field spectrometer, so more work is needed to be make sure we are actually viewing carotenoid pigments here.
Canofi: panoramic imagery for canopy structural parameters
Project description and web app
Canofi investigates the use of panoramic imagery to retrieve vegetation stuctural parameters such as the leaf area index (LAI).
The initial goal was to apply LAI theory and software developed for fish eye camera systems to panoramic imagery collected using small drones & flown within forest canopies.
To do this requires a method to convert panoramic imagery to a fish-eye projection. One such method is implemented in the Canofi web app here and accompanying website here.
