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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Comparison of NAIP orthophotography and RapidEye satellite imagery for mapping of mining and mine reclamation


National Agriculture Imagery Program (NAIP) orthophotography is a potentially useful data source for land cover classification in the United States due to its nationwide and generally cloud-free coverage, low cost to the public, frequent update interval, and high spatial resolution. Nevertheless, there are challenges when working with NAIP imagery, especially regarding varying viewing geometry, radiometric normalization, and calibration. In this article, we compare NAIP orthophotography and RapidEye satellite imagery for high-resolution mapping of mining and mine reclamation within a mountaintop coal surface mine in the southern coalfields of West Virginia, USA. Two classification algorithms, support vector machines and random forests, were used to classify both data sets. Compared to the RapidEye classification, the NAIP classification resulted in lower overall accuracy and kappa and higher allocation disagreement and quantity disagreement. However, the accuracy of the NAIP classification was improved by reducing the number of classes mapped, using the near-infrared band, using textural measures and feature selection, and reducing the spatial resolution slightly by pixel aggregation or by applying a Gaussian low-pass filter. With such strategies, NAIP data can be a potential alternative to RapidEye satellite data for classification of surface mine land cover.

  • 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.