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
A Per-pixel Stratified Classification Methodology for Land cover Mapping Based on Medium-Resolution Satellite Imagery
Classification is one of the most important procedures in the chain of extracting Land Use/Land Cover (LU/LC) information from remote sensing imagery. How to improve the classification accuracy is the key problem that has long bedeviled the researchers. Therefore, many new classification methods and technologies have been developed, such as the Artificial Neural Network classification, the Fuzzy classification, the Knowledge-Based classification, the Support Vector Machine classification and so on. This paper proposed a simple and flexible methodology for land cover mapping based on the knowledge rules for per-pixel judge who referred to many indexes, such as NDVI, NDBI, and some typical spectral characteristics of the land-objects. The Principal Components Analysis (PCA) was also employed to distinguish the city resident and the greenhouse of the agricultural land whose spectral signature was very approximate. In this study we selected the Xiangfan City in Hubei province as the study area and the multi-spectral bands of ETM+ data on Sep.2nd, 2002 as the study data. We also collected a higher resolution IKONOS image (2002) as the reference data for the visual interpretation and accuracy validate. During the study, we also set the supervised classification which based on maximum like hood classifier as the contrast test. Results from the study shows that the stratified Classification whose rules based on knowledge could give a accuracy as high as 78.2%, compared with the 70.5% of supervised classification. Because of the open framework in this classification methodology, other classification principle could be easily integrated with in order to improve the precision, such as the Fuzz Logic, GIS knowledge, etc.
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