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.
You can access and play with the graphs:
- Evolution of the number of publications between 2005 and 2015
- Map of most publishing countries between 2005 and 2015
- Network of country collaborations
- Network of institutional collaborations (+10 publications)
- Network of keywords relating to data - Link
Crop classification greatly benefits from the analysis of multi-temporal Earth Observation (EO) data within a growing season utilizing the distinct phenological behavior of each crop. RapidEye's high repetition rate increases the chances of providing sufficient high resolution image time series offering new ways of classifying crops. This study introduces a supervised decision tree (DT) classification approach using image objects in combination with seasonal statistics of various vegetation indices (VI) for crop identification. The aim of this study is, first, to show the potential of VI seasonal statistics for crop identification, and secondly, to evaluate the relative contribution of each variable to the overall classification accuracy. The results presented in this paper correspond to an area of 625 km(2) in Saxony-Anhalt, Germany. The cultivated landscape is characterized by large agricultural fields, with winter wheat, canola, corn and winter barley as the main crops. Crop identification accuracies were assessed on the basis of reference fields and the importance of each employed variable is assessed by rule set analysis. The classification accuracy for the test area demonstrates that the proposed approach of multi-temporal image analysis provides spatially detailed and thematically accurate information on the crop type distribution.
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