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
Forest site classification using Landsat 7 ETM data: A case study of Ma double dagger ka-Ormanustu forest, Turkey
Aforestation activities, silvicultural prescription, forest management decisions and land use planning are based on site information to develop appropriate actions for implementation. Forest site classification has been one of the major problems of Turkish forestry for long time. Both direct and indirect methods can be used to determine forest site productivity. Indirect methods are usually reserved for practical applications as they are relatively simple, yet provide less accurate site estimation. However, direct method is highly time-demanding, expensive and hard to conduct, necessitating the use of information technologies such as Geographic Information Systems (GIS) and Remote Sensing (RS). This study, first of all, generated a forest site map using both direct and indirect methods based on ground measurements in 567.2 ha sample area. Then, supervised classification was conducted on Landsat 7 ETM image using forest site map generated from direct method as ground measurements to generate site map. The classification resulted in moist site of 262.5 ha, very moist site of 122.5 ha and highly moist site of 191.2 ha in direct method; sites I-II cover 38.9 ha, III 289.6 ha, IV-V 143.5 ha and treeless-degraded areas of 104.2 ha in indirect method; moist site of 203.5 ha, very moist site of 232.1 ha and highly moist site of 140.6 ha in remote sensing method. However, 104.2 ha treeless and degraded areas were not determined by indirect method, yet by the other methods. Secondly, forest site map for the whole area (5,980.8 ha) was generated based on the site map generated by the direct method for sampled area. The Landsat 7 ETM image was classified based on the forest site map of sample area. The site index (SI) map for the whole area was generated using conventional inventory measurements. The classification resulted in sites I-II cover 134.1 ha, III 1,643.6 ha, IV-V 1,396.5 ha, treeless-degraded areas of 1,097.3 ha and settlement-agriculture areas of 1,709.3 ha in indirect method; moist site of 1,674.3 ha, very moist site of 853.6 ha, highly moist site of 1,729.6 ha and settlement-agriculture areas 1,723.3 ha in remote sensing method. Again the treeless- degraded areas of 1,097.3 ha were not determined by indirect method but by remote sensing method.
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