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
Comparative Sensor Fusion Between Hyperspectral and Multispectral Satellite Sensors for Monitoring Microcystin Distribution in Lake Erie
Urban growth and agricultural production have caused an influx of nutrients into Lake Erie, leading to eutrophication in the water body. These conditions result in the formation of algal blooms, some of which are toxic due to the presence of Microcystis (a cyanobacteria), which produces the hepatotoxin microcystin. The hepatotoxin microcystin threatens human health and the ecosystem, and it is a concern for water treatment plants using the lake water as a tap water source. This study demonstrates the prototype of a near real-time early warning system using integrated data fusion and mining (IDFM) techniques with the aid of both hyperspectral (MERIS) and multispectral (MODIS and Landsat) satellite sensors to determine spatiotemporal microcystin concentrations in Lake Erie. In the proposed IDFM, the MODIS images with high temporal resolution are fused with the MERIS and Landsat images with higher spatial resolution to create synthetic images on a daily basis. The spatiotemporal distributions of microcystin within western Lake Erie were then reconstructed using the band data from the fused products with machine learning or data mining techniques such as genetic programming (GP) models. The performance of the data mining models derived using fused hyperspectral and fused multispectral sensor data are quantified using four statistical indices. These data mining models were further compared with traditional two-band models in terms of microcystin prediction accuracy. This study confirmed that GP models outperformed traditional two-band models, and additional spectral reflectance data offered by hyperspectral sensors produces a noticeable increase in the prediction accuracy especially in the range of low microcystin concentrations.
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