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
Identification of bamboo patches in the lower Gangetic plains using very high resolution WorldView 2 imagery
Realizing the potential usage, the country has identified National Bamboo mission for addressing the issues relating to the development of bamboo in the country. Therefore knowledge of spatial distribution of bamboo patches becomes necessary for the evaluation and monitoring of this resource. Medium resolution satellite imagery is ineffective for accurate classification when bamboo occurs in mosaic of small and mixed patches. The present study attempts to address this problem using Very High Resolution (VHR) multispectral (MS) WorldView 2 (WV 2) imagery in South 24 Parganas, West Bengal. West Bengal, a part of Bengal Bay ecological region is one of the areas where bamboo grows naturally. The classification was carried out using additional features namely second order texture components of the first 3 principal components (PCs) of pan-sharpened 8 MS bands. Supervised kernel based (Support Vector Machine, SVM) and ensemble based (Random Forest, RF) machine learning algorithms were applied on the dataset. Moreover, RF based variable importance was analyzed to find the most informative input predictors for classification of bamboo. Altogether seven land use land cover classes were mapped which include agricultural land, built-up, bamboo patches, two canopy classes, fallow land and water body. Variable importance analysis indicated that mean texture measure of PC1 and PC3, and spectral information of MS band 8 (NIR 2) were the most important predictor layers for bamboo mapping. Unlike, RF (accuracy 72%) higher overall accuracy of 80% was achieved using SVM classifier for bamboo mapping. Intermixing of bamboo was seen with other canopies.
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