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
Estimating Fractional Shrub Cover Using Simulated EnMAP Data: A Comparison of Three Machine Learning Regression Techniques
Anthropogenic interventions in natural and semi-natural ecosystems often lead to substantial changes in their functioning and may ultimately threaten ecosystem service provision. It is, therefore, necessary to monitor these changes in order to understand their impacts and to support management decisions that help ensuring sustainability. Remote sensing has proven to be a valuable tool for these purposes, and especially hyperspectral sensors are expected to provide valuable data for quantitative characterization of land change processes. In this study, simulated EnMAP data were used for mapping shrub cover fractions along a gradient of shrub encroachment, in a study region in southern Portugal. We compared three machine learning regression techniques: Support Vector Regression (SVR); Random Forest Regression (RF); and Partial Least Squares Regression (PLSR). Additionally, we compared the influence of training sample size on the prediction performance. All techniques showed reasonably good results when trained with large samples, while SVR always outperformed the other algorithms. The best model was applied to produce a fractional shrub cover map for the whole study area. The predicted patterns revealed a gradient of shrub cover between regions affected by special agricultural management schemes for nature protection and areas without land use incentives. Our results highlight the value of EnMAP data in combination with machine learning regression techniques for monitoring gradual land change processes.
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