Project description
Tracking forest stress from space
Europe’s forests are under severe pressure from frequent climate extremes. However, current monitoring tools cannot capture how trees physically respond and recover over time. Supported by the Marie Skłodowska-Curie Actions programme, the EcoSpectra project is developing an innovative approach to monitoring forest health from space using machine learning to combine high-resolution hyperspectral satellite imagery with long-term multispectral records. By merging these datasets, it will provide a clearer, more comprehensive view of how forest ecosystems are changing over time. This will produce a biweekly, high-resolution dataset of key plant traits across European forests from 2013 to 2026. Overall, the project’s goal is to support better forecasting and stronger climate and forest policies in Europe.
Objective
Europe is experiencing increasingly frequent climate extremes—heatwaves, droughts, and compound events—that threaten forest ecosystem stability. Current forest monitoring mainly relies on multispectral data, lacking spectral resolution to retrieve key plant traits and physiological stress. Spaceborne hyperspectral sensors (e.g. PRISMA, EnMAP) overcome this limitation by providing finer spectral information, improving the estimation of traits impacted by climate extremes, such as chlorophyll, carotenoids, potassium, and water content. However, their sparse temporal coverage hinders ecological change monitoring. EcoSpectra will fill this gap by producing the first high-temporal-resolution hyperspectral-estimated trait dataset and analysis for European forests. I will integrate recent hyperspectral images with long-term multispectral time series using machine learning approaches. This will generate a novel biweekly, high-resolution trait cube (2013–2026) across diverse European forest sites, representing diverse forest ecosystems. This state-of-the-art product will enable consistent monitoring of forest responses to recent climate extremes, capturing direct impacts, legacy responses, and indirect climate disturbances such as insect outbreaks. The objectives are: (i) to generate trait data cubes with a transferable fusion framework, (ii) to quantify trait dynamics and assess forest resistance and recovery under climate extremes and disturbances, beyond multispectral indices, and (iii) to explore the integration of trait-based ecosystem modelling. The fellowship will foster research independence through training at the interface of hyperspectral remote sensing, trait-based ecology, and ecosystem modelling. EcoSpectra will maximise the application of next-generation hyperspectral missions (e.g. ESA CHIME) in ecological research, advance understanding of how European forests respond to climate stress, and support EU forest and climate strategies.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
- natural sciences chemical sciences inorganic chemistry alkali metals
- natural sciences biological sciences ecology ecosystems
- natural sciences biological sciences zoology entomology
- natural sciences earth and related environmental sciences atmospheric sciences climatology climatic changes
- natural sciences computer and information sciences artificial intelligence machine learning
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Keywords
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA)
MAIN PROGRAMME
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Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships
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Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) HORIZON-MSCA-2025-PF
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Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.
78035 VERSAILLES
France
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.