Who ever said that SDG research should be boring? Enjoy the presentation that Samuli Junttila, from University of Helsinki, Finland, made about his research on health condition of trees in the context of climate change.
Four winners shared the awards in the four categories (social sciences, biology, chemistry, physics) and they are all worth to be seen.
But here we have a small bias toward the physics winner. Samuli Junttila, who got his PhD from Helsinki University, Finland, presents in a hip hop style a novel technology to measure the health condition of trees. An important challenge in the context of climate change.
If you want to know more, you can read the summary posted on the Youtube page for the video:
The World’s forests are facing novel stress due to climate change. Pest insects and pathogens are shifting towards new latitudes and heat stress is resulting in increased tree mortality and more frequent forest fires globally. Remote sensing methods are required to monitor forest decline and to estimate tree mortality but detecting subtle changes in tree canopies is challenging, especially at the early stages of decline. Multispectral lidar is a novel remote sensing technology that is capable of measuring the 3D structure and spectral properties of trees simultaneously. The scanner instrument sends laser light pulses over 300 000 times per second that are reflected from the target and detected then by the instrument, which is capable of calculating the distance for each pulse using the time difference. The scanner also records how strong the reflection of the laser light was at the used wavelength. This information can be used to estimate various leaf properties, such as leaf water content, by utilizing shortwave infrared light that is longer than the visible light wavelenghts that our eyes are able to observe. Leaf water content is sensitive to numerous different stress agents and can be used as a proxy to detect tree decline. We developed novel methods to measure leaf water content remotely using multispectral lidar and showed how multispectral lidar can be used to detect tree decline that is caused by a variety of different tree stressors, such as drought, blue-stain pathogens and pest insects. With our method, we were able to detect early signs of tree decline during a bark beetle infestation at an initial stage when the tree canopy showed no symptoms.