New technologies for remote biodiversity monitoring
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New technologies for remote biodiversity monitoring

03/10/2022 Pensoft Publishers

Unbiased, integrated and regularly updated biodiversity and ecosystem service data is necessary for the creation of comprehensive EU policies. Despite this, efforts to monitor animals and plants remain spatially and temporally fragmented. This lack of integration regarding data and methods creates a gap in biodiversity monitoring, which can negatively impact policy-making. Today modern technologies such as drones, artificial intelligence algorithms, or remote sensing are still not widely used in biodiversity monitoring.

MAMBO project (Modern Approaches to the Monitoring of BiOdiversity) recognises this need and aims to develop, test, and implement enabling tools for monitoring conservation status and ecological requirements of species and habitats for which knowledge gaps still exist. To do so, MAMBO will implement a multi-disciplinary approach by utilising technical expertise in the fields of computer science, remote sensing, and social science expertise on human-technology interactions, environmental economy, and citizen science. This will be combined with knowledge on species, ecology, and conservation biology.

MAMBO aims to integrate new technologies with existing research infrastructures to create new methods of biodiversity monitoring across the EU and beyond. The project’s strong stakeholder engagement component will help identify user and policy needs in the realm of biodiversity monitoring and assess its tools at carefully selected demonstration sites across Europe. This will culminate in improved and more cost-effective monitoring schemes for species and habitats using novel technologies.

The project kickstarted with a consortium meeting on 15-16 September in Aarhus, Denmark. The kick-off meeting welcomed representatives from 10 partnering institutions from 7 EU countries as well as the United Kingdom.

“Classification algorithms have matured to an extent where it is possible to identify organisms automatically from digital data such as images or sound,” comments project coordinator Prof. Toke T. Høye from Aarhus University. “Technical breakthroughs in the realm of high spatial resolution remote sensing set the future of ecological monitoring and can greatly enrich traditional approaches to biodiversity monitoring.”

“You do not dance the MAMBO alone,” says work package leader Prof. Dr. Koos Biesmeijer. “Our tools will link to the diverse landscape of EU and national projects and infrastructures so that anybody interested in biodiversity monitoring can benefit from them.”

In relation to this, Project Officer Colombe Warin shares that she is looking forward to “seeing MAMBO address strategically the biodiversity decline in liaison with the Green Deal and the Biodiversity Strategy for 2030.”

MAMBO plans to develop, evaluate, and integrate image and sound recognition-based artificial intelligence solutions for EU biodiversity monitoring from species to habitats and deliver high spatial resolution habitat extent maps. At its core the project aims to co-design novel ecological monitoring tools with researchers, policy makers, citizens, and other stakeholders. The cost-benefit analysis and testing of the tools will showcase the possibility of upscaling MAMBO’s approach and making it widely available across the EU and beyond.

Stay tuned for more information on the new MAMBO website, which is coming soon: www.mambo-project.eu

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This project receives funding from the EU Horizon Europe Research and Innovation Action Grant agreement No. 101060639.

Views and opinions expressed are those of the author(s) only and do not necessarily reflect those of the European Union or the European Commission. Neither the EU nor the EC can be held responsible for them.

Attached files
  • ntitled20design202.png
  • MAMBO Kick-Off Meeting
03/10/2022 Pensoft Publishers
Regions: Europe, Bulgaria, Denmark
Keywords: Science, Environment - science, People in science, Applied science, Computing, Technology

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