Forest monitoring for carbon stock estimation

In the context of climate change and biodiversity loss, forest monitoring plays an important role not only in mitigating the effects of human activity and protecting biodiversity, but also in better understanding its composition. The world's forests are major sinks that absorb and store carbon from the atmosphere. On a global scale, they can be seen as a means of mitigating climate change.
In this panel discussion, we will introduce current research topics and applicative works on forest monitoring and carbon stock estimation. We will try to understand if forest restoration projects can be scaled to large areas and to what extent these projects have the potential to actually increase forest carbon sinks. Given that new forest conservation and restoration projects are currently linked to the carbon market, we will also address the transparency of these projects. As these projects attempt to estimate forest carbon stocks on a large scale, often with remote sensing data, we will make the link with the statistical and machine learning tools used for this purpose. Since machine learning models are sometimes considered to be 'black boxes', we will also examine their robustness and the way in which their uncertainty can be quantified, as well as the next research opportunities in this area.
Speakers: Dr. Elias Ayrey (Chief science officer at Renoster), Martha Morrissey (Machine learning engineer at Pachama) and Dr. Leland Werden (Restoration ecologist at ETH Zurich, Crowther Lab)
To ask your questions for the Q&A session, Slido link: app.sli.do/event/uaPXxyZavQHw...

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