CORDIS · NATURE-FIRST

Forensic Intelligence and Remote Sensing Technologies for nature conservation

Код

NATURE-FIRST

Програма

HORIZON

Процедура

HORIZON-CL6-2021-BIODIV-01-02

Статус

CLOSED

Период

01.09.2022 – 31.08.2025

Официални данни · CORDIS · синхронизирано 18.10.2026 03:29 · официален запис

Участници

Описание

Biodiversity is under severe pressure due to a myriad of problems, including but not limited to habitat fragmentation, overexploitation, climate change, pollution, invasive species and hunting. Changes in land and sea use can lead to conflict situations with production animals and/or human communities (human-wildlife conflict). The exploitation of natural resources brings with it illegal activities: poaching of species of flora and fauna that have a high value on the (black) market, trading of rare and exotic animals and plants and setting fire to forestry and nature areas to force land-use designation changes to agriculture or commercial uses. To ensure that ecosystems are healthy, resilient to climate change and rich in biodiversity to keep delivering the essential range of services, we need better understanding of why and where biodiversity is declining and what the key triggers are. We propose a model-driven and continuous form of ecosystem monitoring. By assessing not only numbers of species and state, but also the modelled ecological and anthropogenic processes within an ecosystem, we are able to find cause-effect relations and improve our monitoring models based on retrofits and simulations to understand changes even better. The models (Digital Twins), are thus a means for learning and the creation of context to translate environmental observations into facts and actionable information (intelligence) for site managers and policy makers. As almost all pressures on biodiversity are man-induced, we combine the domains of ecology and forensic science. This novel approach gives us access to robust scientific methods to detect and recognise (traces of) human (illegal) activities that negatively affect the environment. We will make use of remote sensing & data science (e.g AI, semantics). To ensure that theory, models and practice reinforce each other, we use an iterative approach, including many demonstrations and field-tests to gain feedback and maximize impact.

Официални данни · CORDIS · синхронизирано 18.10.2026 03:29 · официален запис · Лиценз: © European Union, CC BY 4.0 (Commission reuse policy).