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
Remote sensing and spatial analysis based study for detecting deforestation and the associated drivers
Nowadays, remote-sensing technologies are becoming increasingly interlinked to the issue of deforestation. They offer a systematized and objective strategy to document, understand and simulate the deforestation process and its associated causes. In this context, the main goal of this study, conducted in the Blue Nile region of Sudan, in which most of the natural habitats were dramatically destroyed, was to develop spatial methodologies to assess the deforestation dynamics and its associated factors. To achieve that, optical multispectral satellite scenes (i.e., ASTER and LANDSAT) integrated with field survey in addition to multiple data sources were used for the analyses. Spatiotemporal Object Based Image Analysis (STOBIA) was applied to assess the change dynamics within the period of study. Broadly, the above mentioned analyses include; Object Based (OB) classifications, post-classification change detection, data fusion, information extraction and spatial analysis. Hierarchical multi-scale segmentation thresholds were applied and each class was delimited with semantic meanings by a set of rules associated with membership functions. Consequently, the fused multi-temporal data were introduced to create detailed objects of change classes from the input LU/LC classes. The dynamic changes were quantified and spatially located as well as the spatial and contextual relations from adjacent areas were analyzed. The main finding of the present study is that, the forest areas were drastically decreased, while the agrarian structure in conversion of forest into agricultural fields and grassland was the main force of deforestation. In contrast, the capability of the area to recover was clearly observed. The study concludes with a brief assessment of an 'oriented' framework, focused on the alarming areas where serious dynamics are located and where urgent plans and interventions are most critical, guided with potential solutions based on the identified driving forces.
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