SCIENTIST SAYS / NGC MONTHLY SERIES / OCTOBER 2026
Elia Vangi – Expert in Forest Ecosystem Modelling
Mon 26 October 2026, NGC Communication Team
Dr Elia Vangi at the NextGenCarbon annual meeting, held in Umeå, Sweden, in September. Photo: NGC Communication Team
SCIENTIST
SAYS – OUR MONTHLY INTERVIEW SERIES Dr Elia Vangi is a researcher at University of Florence in Italy, working at the
Laboratory of Forest Geomatics (GeoLAB) with a focus on remote sensing and forest
ecosystem modelling. Elia has experience working with remote sensing data and
machine learning models and is interested in empirical and process-based forest
ecosystem modelling. As part of NextGenCarbon, he will be working on the Large-Scale
Spatial Analysis for Monitoring Key Terrestrial Ecosystems team, mapping the
forest carbon cycle and forest dynamics.
Hi Elia, tell us a little about yourself and your background!
Hi everyone, I'm Elia Vangi. I'm from the University of Florence in Italy, specializing in forest modelling and remote sensing. I have always loved forests, mountains and the outdoor environment in general. My interest in this field strengthened during my university studies, driven by the pressing challenges of climate adaptation and how technology and geospatial data can help us understand these complex natural systems. I was particularly drawn to how satellite imaging allows us to track environmental change, especially considering the huge advancement in machine learning and artificial intelligence technology. Currently, my research focuses on developing predictive models for forest biomass and carbon stocks changes. Through this work, I hope to provide usable tools to better understand the climate change impact on our forest and ultimately to mitigate its impacts and support sustainable forest management.
What will you be working on as part of the NextGenCarbon project?
Within the work package on
We are currently using multiple approaches to track and study forest changes and post-changes, such as automatic temporal segmentation algorithms, able to process huge amount of remote sensed data across Europe and provide us with precise information on where, when and how a forest disturbance happens. Such information can be coupled with process-based modelling of post disturbance dynamics to estimate and predict the carbon balance after disturbances and how this balance is changing over years.
Overall, our team has ambitious goals to improve our understanding of forest behaviour, particularly in relation to forest management and natural disturbances. In this context, our aim to deepen our understanding of forest dynamics through large-scale spatial modelling plays an important role.
By translating large-scale remote sensing data into predictive insights, this modeling empowers forest managers to proactively mitigate disturbance risks, prioritize restoration efforts, and implement climate-smart management plans.
Furthermore, high-resolution spatial maps pinpoint dense, high-vulnerability forest stands to prioritize selective thinning and fuel-load management before extreme weather hits, reducing fire and hazard risk.
Who do you think would be most interested in these findings? Which stakeholders are likely to find them relevant?
I think there will be different levels of interests from governments and administrations, of course, in the large-scale monitoring systems that we will provide. But also, private forest owners and managers could have a better understanding of their forests and thus be able to implement better management options both for their own benefit and the benefit of the environment.
Precise spatial-explicit information on disturbance and post-disturbance dynamics will allow for targeted restoration, avoiding waste of resources and money, favouring more adapted and climate resilience species. Furthermore, high-resolution spatial maps pinpoint dense, high-vulnerability forest stands to prioritize selective thinning and fuel-load management before extreme weather hits, reducing fire and hazard risk. Finally, high quality and near real time data provides transparent, data-driven verification for carbon credit markets, helping land managers monetize climate-smart forestry practices and qualify for conservation incentives.
What expectation do you have from a societal point of view?
This
is a really hard question. We hope that the output of this project can really
help us to better manage our forests, which are a key component of our ecosystems.
And this is especially important in this period of climate change, when we hear
these terms everywhere. So, this project could
What do you like most about the NextGenCarbon project?
It's a very huge project. There are a lot of people involved, from all over the Europe, but also from all over the world. So, it's really interesting network to be part of. That is probably the most exciting aspect for me.
Where can our readers find you and your work?
You can
find me on Research Gate at
