e-infrastructure Roadmap for Open Science in Agriculture

A bibliometric study

The e-ROSA project seeks to build a shared vision of a future sustainable e-infrastructure for research and education in agriculture in order to promote Open Science in this field and as such contribute to addressing related societal challenges. In order to achieve this goal, e-ROSA’s first objective is to bring together the relevant scientific communities and stakeholders and engage them in the process of coelaboration of an ambitious, practical roadmap that provides the basis for the design and implementation of such an e-infrastructure in the years to come.

This website highlights the results of a bibliometric analysis conducted at a global scale in order to identify key scientists and associated research performing organisations (e.g. public research institutes, universities, Research & Development departments of private companies) that work in the field of agricultural data sources and services. If you have any comment or feedback on the bibliometric study, please use the online form.

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Semi-automated, multi-source cartography of land cover. 1. Geneva transboundary area (medium resolution).


Semi-automated, multi-source cartography of land cover. 1. Geneva transboundary area (medium resolution). A land cover map is an essential layer of information for the management and planning of the territory Within the context of a trans-boundary endeavour between Switzerland and France (Projet d'Agglomeration Franco-Valdo-Genevoise, hereafter AFVG), a medium-resolution land cover map (CCSA04) was developed for the Canton of Geneva, part of the Canton of Vaud, and parts of the French departments of Ain and Haute-Savoie. Within the constraints of the available data, a 27-class legend was chosen to suit the needs of the AFVG project. This legend can be converted to the Swiss OTEMO and European CORINE standards. The applied mapping methodology can be described as multi-source, multi-scale and object-oriented. Multi-source refers to using the SPOT satellite imagery at two different resolutions (5m for the visible bands, 10m for the infrared), a Digital Elevation/Digital Height Model, and several vector layers. Multi-scale means that the analysis, within the Definiens Developer (TM) software, proceeded on different levels or scales. The object-oriented approach is based on the concept that the semantic information necessary to interpret an image is not contained in isolated pixels, but rather in objects and their mutual relationships. These objects, obtained through a segmentation process, were classified using multiple criteria, such as their spectral values, morphology and context. Some legend classes, such as buildings and roads, are defined by their spatial coincidence with a shape file. Others, such as forests and water, are derived from their spectral values. The altitude criterion was used to differentiate mountain grasslands from low-altitude meadows. The automated classification process was completed by some manual editing in the following cases: objects adjacent to roads, under-estimation of coniferous forest, agricultural surfaces falsely classified as deciduous forest, confusion between hard surfaces and bare soil, and objects hidden by shadows. In order to control the quality of the land cover map, a statistical procedure was applied. Two control points were randomly selected within each square kilometre of the AFVG area, and their land cover class was visually estimated from colour aerial photographs, based on a simplified legend. A contingency matrix was computed, yielding a Kappa coefficient of 89%, which exceeds the mandatory threshold of 75% requested by the project. It is considered that this quality level is quite acceptable, owing to the difficulties stemming from the heterogeneity of data between the Swiss and French entities: vector layers having different meaning and showing mismatch across the border, and the SPOT mosaic being a composite of scenes from two years, resulting in differences in radiometry The rugged topography in the Jura and pre-Alps also leads to extensive zones of shadows, which complicate the analysis. As an example of application of the CCSA04 map, land cover statistics were computed for the four entities. Finally recommendations are presented towards an improvement of the map-making process (revised legend, compatible data across borders and better imagery), in the perspective of future applications such as change mapping for the computation of land cover trends.

  • CH
  • UNEP_United_Nations_Env_Progr (CH)
Data keywords
  • semantic
Agriculture keywords
  • agriculture
Data topic
  • sensors
Document type

Inappropriate format for Document type, expected simple value but got array, please use list format

Institutions 10 co-publis
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    e-ROSA - e-infrastructure Roadmap for Open Science in Agriculture has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 730988.
    Disclaimer: The sole responsibility of the material published in this website lies with the authors. The European Union is not responsible for any use that may be made of the information contained therein.