Maps of crop-types, non-productive EFAs, permanent pastures and of farming activities (i.e. mowing, grazing)

DIONE develops the maps of the cultivated crop types, permanent pastures, farming practices and non-productive EFAs to assess compliance and support monitoring. Evaluators can re-use the data provided by these maps to serve many purposes.  

First, these maps and the data associated with them can be used, together with other data sources and other EO tools, in estimating environmental indicators. For example, an evaluator can estimate irrigation water needs using crop type maps, soil maps, meteorological data, and agronomic information. The estimated irrigation water needs is a good proxy for the “water use in agriculture” impact indicator.  

Second, crop type maps can e aluate the effects of agricultural policy measures on environmental indicators. For example, an evaluator can use IACS to get information on beneficiaries and non-beneficiaries of measures to reduce water consumption and compare their potential irrigation water needs.  

Third, crop type maps are data sources that can cross-validate and triangulate information received from other sources. For example, a crop type map can cross-validate information related to policy effects on crop allocation and its consequent impacts on environmental indicators.  

The examples above concern water but can be used for other indicators where prior knowledge of the grown crop is essential. For example, crop type maps can contribute, together with other data, to estimate indicators such as the potential nutrient use, the GHG emissions from managed soils, the soil erosion and soil organic matter, crop diversity, and others that depend on the type of soil cover. 

DIONE’s tool goes one step further to provide maps of non-productive EFAs. These include fallow land, hedges, trees, buffer strips, ponds, ditches, and other landscape features. These maps could support the evaluation of the impacts of policy interventions at the landscape level. Preserving non-productive features may result from policy measures, especially if these areas fall within the boundaries of Natura 2000 areas. Data on non-productive EFAs may help evaluate the policy supporting the restoration and enhancement of agricultural landscape features.    

The tool does not suffer from the usual caveats of EO tools and especially the inconclusiveness of the crop type identification due to cloudiness or the presence of small parcels. Its primary source of EO data is the public database of Copernicus. On top of that, the tool develops algorithms and processes to show how to use targeted drone flights and other Very High Resolution (VHR) data (e.g. DEIMOS, Pleiades) to achieve high-resolution mapping of crop types non-productive EFAs, permanent pastures and farming activities.  

The tool's adoption requires adaptation and application of the algorithms and training to recognize the crop types of the region and distinguish farm practices and non-productive EFAs. Adopting the tool assumes that the IT infrastructure is adequate and that the evaluator can use the data.