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
Supervised classification of land cover across space and time is a long-standing goal of the Earth Science community. Although most past and current analyses focus on detecting changes between two or more times, the opening of the USGS Landsat archive in 2009 has enabled exploration of methods for higher-frequency, time-serial monitoring of land cover dynamics. Modifying the protocols used to develop the 2001 National Land Cover Database (NLCD 2001), we fit a single classifier to a spatio-temporally distributed reference sample and applied the model to 55 Landsat-5 images covering a section of the North Carolina Piedmont Plateau from 1984 to 2007. A generalized classification scheme, multi-temporal sampling design, supervised classification based on intra-annual spectral indices, and design-based accuracy assessment yielded a time-series of 16 land cover maps from 1985 to 2006 with a spatial extent of 1.7x 10(6) ha, minimum mapping unit of 1 ha, and mean temporal interval of <2 years. Comparable to accuracy of the NLCD 2001 Land Cover Layer for the region, overall accuracy for a spatio-temporally independent test sample was 75%, with K=0.7. When weighted by class proportions, percent correctly classified and kappa rose to 88% and 0.84, respectively. The resulting map series shows spatially and temporally complex changes in water, urban, forest, and herbaceous cover resulting from natural and anthropogenic processes that would not be observable in either unior bi-temporal maps. Agricultural crop area dropped from similar to 45% in the 1980s to similar to 36% in the 1990s and then rose slightly to similar to 38% at the end of the period. Forest area increased to a maximum of similar to 55% in the 1990s and then dropped to similar to 53% in 2005. Urban growth appeared to be most rapid in the 1980s and 1990s and slowed thereafter. With continued focus on the semantics, causation, sampling, and uncertainty underlying spectral land cover classification, long-term series of Landsat images will provide increasingly robust, reliable records for a growing scientific user community. These multi-temporal datasets will be indispensable for understanding past land cover dynamics and predicting the implications of future change on the provision and management of ecosystem services. (C) 2012 Elsevier Inc. All rights reserved.
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