Imagine having a high-tech health tracker for nature that combines satellite photos, ground sensors, and reports from regular people. It's like using a GPS and a medical check-up together to see exactly where forests or rivers are struggling. This helps us predict future damage before it happens so we can fix it in time.
Companies and policymakers lack a reliable, unified way to measure biodiversity loss. Current data is fragmented, making it hard to prove compliance with environmental laws or predict ecosystem collapse.
A set of data workflows and ecological models that merge satellite imagery with ground-level sensor data and citizen reports.
If you are an ESG reporting firm dealing with inaccurate biodiversity data for clients — this project developed integrated EO and in-situ data streams that provide measurable evidence of ecosystem change. This allows for more precise reporting on nature restoration targets.
If you are a tech provider dealing with the impact of farming on local freshwater ecosystems — this project developed next-generation ecological models that account for uncertainty. This helps farmers adjust practices to meet the Nature Restoration Law requirements.
If you are an insurer dealing with unpredictable ecosystem collapse risks — this project developed a system to monitor and predict biodiversity change and its drivers. This enables better risk pricing based on real-time terrestrial and freshwater data.
Based on available project data, no pricing or commercial cost structure is mentioned as this is a Horizon RIA research project.
The project aims to create scalable observation systems and workflows that integrate multi-source data across multiple scales to meet EU-wide commitments.
Based on available project data, the project follows FAIR and open science practices, suggesting a focus on shared research outputs rather than restrictive licensing.
It is specifically designed to provide evidence for the EU Biodiversity Strategy for 2030 and the Nature Restoration Law.
The project period runs from 2024-01-01 to 2027-12-31, with final results expected by the end of 2027.
The project builds workflows that combine remote sensing, in-situ monitoring, and citizen science data into decision-support systems.
The consortium is well-balanced for a research-to-application project, consisting of 11 partners across 9 countries. With a 27% industry ratio (3 industrial partners and 4 SMEs), there is a significant commercial presence to ensure the tools are practical. The mix of 3 universities and 5 research organizations provides the necessary scientific depth to handle complex Earth Observation data.
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