AJA | Environmental sensors for real-time forest ecosystem monitoring

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AJA | Environmental sensors for real-time forest ecosystem monitoring

DE
EN
Forest health solution built upon an innovative sensor technology for real-time ecosystem monitoring

The startup foldAI has developed sensors to screen health status of forests providing forest managers with a rich understanding of their forest ecosystems, and a decision toolbox to deploy immediate mitigating actions. The team’s solution, Aja, used in the sensors is  a framework for ecosystem management based on deep technology. By harnessing state-of-art Machine Learning on precise, real-time sensor data, Aja can not only detect forest threats as they happen, but even predict their arising and forecast their unfolding. Aja improves forest health, resilience and bioeconomical performance by introducing lean processes to a broad ecosystem management community. It helps reducing greenhouse emissions by scaling high resolution forest management through a fully automated and affordable solution for more than 30 Million forest owners in Europe, Russia and North America. The solution builds on embedded Machine Learning, and biochemical and environmental signal processing on high-dimensional data. Use cases comprise the assessment of environmental impacts enabling greater accuracy in the evaluation of the environmental consequences of a strategy or policy, risks assessment including alerts to threats, biodiversity quantification and ecosystem health tracking. Aja’s significant carbon reduction impact has been independently certified by The Climate Impact Forecast.

Print PDF
yes
Watch video
Main website
Region of origin
2861876
Sustainability Potential
Sustainable forest management is strongly supported by real time data on the forest's health status
Impact on environment & biodiversity

The solution helps to monitor ecosystem functions of forests and biodiversity, thereby improving risk management 

Domain
Inventory, monitoring
Forest management, ecosystem, resilience
Forest disturbances, risks
Keywords
forest monitoring; sensors; machine learning; biodiversity
Challenge addressed
1.- Improve forest resilience and adaption to climate change
Type of solution
Sensors, measurement equipment
Digital solution
Yes
Innovation
Yes
Country of origin
Germany
Region of origin
2861876
Scale of application
Cross-border/multi-lateral (several countries)
Start and end year
2019 -
Contact data
Owner or author
foldAI
Dr. Friedrich Förster
hello@fold.ai
https://fold.ai
Reporter
Dr. Marie-Charlotte Hoffmann
marie-charlotte.hoffmann@wald-und-holz.nrw.de
References and Resources
Main website
https://fold.ai
Post date
Thursday, 16 December, 2021 - 16:01
Project under which this factsheet has been created
Rosewood 4.0