The NIVA Use Case (UC1b) “agro-environmental monitoring” is computing a biodiversity indicator based on the principle that landscape characteristics (mean field size, crop diversity and richness, quantity of semi-natural elements) influence the achievement of biodiversity potential. Examples of semi-natural elements are hedges, woods, rivers, ponds, rocks, etc. This indicator is computed on the cells on a kilometric grid.
Whereas the first characteristics may be easily derived from IACS, the computation of quantity of semi-natural elements requires external data. Several methods and sources have been envisaged for this computation. The approach that is proposed in the UC1b tool user guide is a negative method considering that the quantity of semi-natural elements is the whole cell area minus the agricultural area (derived from IACS) and minus the artificial elements, such as roads and buildings derived from topographic database.
The purpose of the training session was to compare the chosen method with alternative sources (large scale Land Cover data, Copernicus data on imperviousness, OSM) and methods (e.g. the positive method consisting in direct selection of semi-natural elements).
Keenan Ouaksel, student in ENSG (French National School of Geographic Sciences), carried some comparison work, both at global level (on a whole administrative unit) and at detailed level on a few cells of the grid. The analysis has shown that the methods based on detailed and authoritative data (IACS, national topographic data, national large scale Land Cover) are the more reliable ones.
Semi-natural elements manually captured from orthoimage in order to provide reference data