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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Differentiating mine-reclaimed grasslands from spectrally similar land cover using terrain variables and object-based machine learning classification


Incorporating ancillary, non-spectral data may improve the separability of land use/land cover classes. This study investigates the use of multi-temporal digital terrain data combined with aerial National Agriculture Imagery Program imagery for differentiating mine-reclaimed grasslands from non-mining grasslands across a broad region (6085 km(2)). The terrain data were derived from historical digital hypsography and a recent light detection and ranging data set. A geographic object-based image analysis (GEOBIA) approach, combined with two machine learning algorithms, Random Forests and Support Vector Machines, was used because these methods facilitate the use of ancillary data in classification. The results suggest that mine-reclaimed grasslands can be mapped accurately, with user's and producer's accuracies above 80%, due to a distinctive topographic signature in comparison with other spectrally similar grasslands within this landscape. The use of multi-temporal digital elevation model data and pre-mining terrain data only generally provided statistically significant increased classification accuracy in comparison with post-mining terrain data. Elevation change data were of value, and terrain shape variables generally improved the classification. GEOBIA and machine learning algorithms were useful in exploiting these non-spectral data, as data gridded at variable cell sizes can be summarized at the scale of image objects, allowing complex interactions between predictor variables to be characterized.

  • US
  • W_Virginia_Univ (US)
  • Alderson_Broaddus_Univ (US)
Data keywords
  • machine learning
Agriculture keywords
  • agriculture
Data topic
  • big data
  • modeling
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.