An Earth Observations based Environmental Performance Tool

The DIONE Environmental Performance Tool is an interactive visualisation dashboard that identifies and quantifies nine selected environmental indicators to produce an overall environmental scoreboard customised by country. Data from multiple monitoring sources are used, and the tool can extract tangible environmental impacts in Key Performance Indicators (KPIs) on a regional or national level. The aim is to provide valuable insights and actionable information about land conditions and land use to be used by CAP policymakers, Paying Agencies, environmental organisations and researchers.

Machine Learning (ML) and other scientific methods allow insightful decision making at the regional or national levels. The tool acts in conjunction with a beneficiaries’ compliance tool in an overall Green Accountability toolbox. Each environmental indicator comprises a single map or layer in spatial and temporal scales. A ‘layered approach’ will simultaneously present the environmental indicators as a series of overlaid layers. This approach will allow the end-users to access thematically combined information to support holistic and cross-cutting research, solutions and decisions. Each end-user will thus be able to visualise the different layers of information needed for each use case.

Several possible approaches will exist for combining metrics and indicators to assess an area's environmental status and potential degradation level. A second methodology will try to ‘fuse’ and combine the environmental indicators to produce an ‘environmental scoreboard’. The DIONE Environmental Performance Tool will be based on an environmental performance scoreboard and aspires to provide the opportunity to make environmental decision-making more data-driven and thus more thoughtful and durable.

DIONE’s environmental metrics methodology
Relevance for monitoring and evaluation of the CAP

The tool measures the environmental performance of the agricultural sector of countries or regions in a ‘multilayer’ environmental scoreboard. It is not a tool designed to evaluate the effects of policy on specific individual environmental indicators, and the value of the tool lies in the unique underlying database. The DIONE environmental metrics document makes a preliminary presentation of all possible data sources, and the evaluator can examine their availability. All information will be adaptable in its spatial and temporal dimensions, stretching from the local/regional to the national and from contemporary to the latest available historical data.

Thus, the evaluator can make use of the database underlying the tool in three ways. First, by extracting data that integrate multiple monitoring sources, the evaluator may approximate the value of indicators. For example, the soil organic matter indicator will be compiled by Copernicus Sentinel-1 and Sentinel-2 data, LUCAS and data from spectral handheld sensors that will provide space-borne and in situ spectral and laboratory measurements. Second, if the layers are combined with the compliance monitoring tool at the farm or parcel levels, the evaluator may pursue a quantitative assessment of the policy’s effects. Third, by utilising the ‘environmental scoreboard’, the melting pot for nine environmental indicators. The environmental scoreboard and its nine layers at the national level can support various geospatial analyses or data triangulation. Finally, the tool provides an excellent visualisation device that assists those carrying out monitoring and evaluation operations to communicate their findings to decision-makers and other stakeholders.

The tool is not automatically transferable to other regions and Member States for two reasons. First, data availability varies from country to country, depending on ancillary data. For example, certain Member States have already detailed soil maps from their extensive soil surveys, and it is easy to connect EO and in situ data. Second, all the machine learning and AI algorithms require training with the new data to recognise certain environmental variables. So, depending on data availability, the tool may require adaptation of algorithms and re-training.

Last modification date: 
10/12/2021