The ecosystems of China seas and coasts are undergoing rapid changes under the strong influences of both global climate change and anthropogenic activities.To understand the scope of these changes and the mechanisms b...The ecosystems of China seas and coasts are undergoing rapid changes under the strong influences of both global climate change and anthropogenic activities.To understand the scope of these changes and the mechanisms behind them is of paramount importance for the sustainable development of China,and for the establishment of national policies on environment protection and climate change mitigation.Here we provide a brief review of the impacts of global climate change and human activities on the oceans in general,and on the ecosystems of China seas and coasts in particular.More importantly,we discuss the challenges we are facing and propose several research foci for China seas/coasts ecosystem studies,including long-term time series observations on multiple scales,facilities for simulation study,blue carbon,coastal ecological security,prediction of ecosystem evolution and ecosystem-based management.We also establish a link to the Future Earth program from the perspectives of two newly formed national alliances,the China Future Ocean Alliance and the Pan-China Ocean Carbon Alliance.展开更多
This paper presents a real-time alga classifier designed for flow-cytometry-based marine alga monitoring systems.The difficulties of such classification include:1)the shape of the same algae category is deformable,and...This paper presents a real-time alga classifier designed for flow-cytometry-based marine alga monitoring systems.The difficulties of such classification include:1)the shape of the same algae category is deformable,and largely variant due to the individual differences and mature stage;2)the image of algae may vary due to different 3D positions to the imaging plane and partial occlusion;3)the images also contain unknown algae and contaminations.In the proposed method,several shape features were developed,a naive Bayes classifier(NBC)was trained to reject the contaminative objects and unknown algae,and a support vector machine(SVM)was used to classify the algae to taxonomic categories.Our approach achieved greater 90%accuracy on a collection of algal images.The test on contaminated algal image set(containing unknown algae and non-algae objects such as sands)also demonstrated promising results.展开更多
基金supported by the National Basic Research Program of China (2013CB955700,2015CB452900)the Service-Oriented Architecture (SOA) Project (201105021)
文摘The ecosystems of China seas and coasts are undergoing rapid changes under the strong influences of both global climate change and anthropogenic activities.To understand the scope of these changes and the mechanisms behind them is of paramount importance for the sustainable development of China,and for the establishment of national policies on environment protection and climate change mitigation.Here we provide a brief review of the impacts of global climate change and human activities on the oceans in general,and on the ecosystems of China seas and coasts in particular.More importantly,we discuss the challenges we are facing and propose several research foci for China seas/coasts ecosystem studies,including long-term time series observations on multiple scales,facilities for simulation study,blue carbon,coastal ecological security,prediction of ecosystem evolution and ecosystem-based management.We also establish a link to the Future Earth program from the perspectives of two newly formed national alliances,the China Future Ocean Alliance and the Pan-China Ocean Carbon Alliance.
基金High-Tech Research and Development Program of Chinagrant number:2003AA635160
文摘This paper presents a real-time alga classifier designed for flow-cytometry-based marine alga monitoring systems.The difficulties of such classification include:1)the shape of the same algae category is deformable,and largely variant due to the individual differences and mature stage;2)the image of algae may vary due to different 3D positions to the imaging plane and partial occlusion;3)the images also contain unknown algae and contaminations.In the proposed method,several shape features were developed,a naive Bayes classifier(NBC)was trained to reject the contaminative objects and unknown algae,and a support vector machine(SVM)was used to classify the algae to taxonomic categories.Our approach achieved greater 90%accuracy on a collection of algal images.The test on contaminated algal image set(containing unknown algae and non-algae objects such as sands)also demonstrated promising results.
