ENVISION – Monitoring of environmental practices for sustainable agriculture supported by earth observation

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ENVISION – Monitoring of environmental practices for sustainable agriculture supported by earth observation

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ENVISION aims to fulfil the need for continuous and systematic monitoring of agricultural land, shifting the focus from fragmented monitoring limited to specific fields and dates to territory-wide and all-year-round monitoring.

It is a market-led project coupling Earth Observation (EO) technology with state-of-the-art methodologies (automatic pixel/ texture/ object-oriented change detection and classification methods, machine learning, data fusion, multi-source and multi-temporal data management) along with heterogeneous types of data (in situ, open data, and historical on-field check data) to deliver a platform for the Paying Agencies and Certification Bodies. It is intended to provide timely cost-efficient and actionable insights for the compliance monitoring procedure of the Common Agricultural Policy (CAP) agri-environmental standards.

ENVISION makes use of the data made available through GEOSS and Copernicus to develop data products such as: Cultivated crop type maps; Soil Organic Carbon; Vegetation status; Crop growth (distinction between organic and conventional farming); Grassland mowing/ploughing; Soil erosion.
ENVISION is intended to increase the effectiveness of risk-based analysis for the selection of farms for inspection; increase the efficiency and transparency of Paying Agencies and Certification Bodies’ procedures when implementing the CAP; reduce the number of on-site visits by performing more targeted controls and reduce operational and administrative costs. Three key categories of business cases have been highlighted within the project, namely the monitoring of 1) multiple environmental and climate requirements of CAP, 2) soil condition, 3) organic farming requirements.

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Types of carbon farming measures

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Description of innovation

The project develops innovative monitoring tools for agriculture, involving EO technology, state-of-the-art methodologies and several heterogeneous types of data.

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