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As the globe undergoes extreme climate changes, disaster events and their disaster scale continue to increase; therefore, it has become imperative to devise disaster prevention measures. Traditional flood monitoring devices, while operating in harsh environments, are often influenced by changes in weather conditions such light, rain, and fog. Consequently, recorded images are often blurred or damaged, which increases the possibility of errors in judgment or delays in the hazard mitigation process. In this study, an automated identification method for flood monitoring based on real-time video images is proposed. The method can be used by the Water Conservancy Agency of the Ministry of Economic Affairs automated for automated identification of flooded areas and for automated determination of the water levels of the main rivers from image data. This paper presents results to show that the capability to detect temporal changes in image sequences is crucial for an automated image-based flood alarm system used in disaster-monitoring applications. Keywords-flood alarm system; flood monitoring; hazard mitigation; image identification.

This book gives a start-to-finish overview of the whole Fish4Knowledge project, in 18 short chapters, each describing one aspect of the project. The Fish4Knowledge project explored the possibilities of big video data, in this case from undersea video. Recording and analyzing 90 thousand hours of video from ten camera locations, the project gives a 3 year view of fish abundance in several tropical coral reefs off the coast of Taiwan. The research system built a remote recording network, over 100 Tb of storage, supercomputer processing, video target detection and tracking, fish species recognition and analysis, a large SQL database to record the results and an efficient retrieval mechanism. Novel user interface mechanisms were developed to provide easy access for marine ecologists, who wanted to explore the dataset. The book is a useful resource for system builders, as it gives an overview of the many new methods that were created to build the Fish4Knowledge system in a manner that also allows readers to see how all the components fit together.

A grassroots, global network to examine lake function using sensor data? Why not? That was the perspective of Drs. Tim Kratz (limnologist), David Hamilton (limnologist), Peter Arzberger (mathematician), and Fang-Pang Lin (computer scientist) in 2004 when they hatched the idea of the Global Lake Ecological Observatory Network (Fig. 1). Nine years later, with over 380 members from 40 countries, and 50 publications to its credit, GLEON is growing at a rapid pace and pushing the boundaries of the practice of network science. GLEON is really three networks: a network of lakes, data, and people. Fil: Weathers, Kathleen C.. Cary Institute of Ecosystem Studies; Estados Unidos Fil: Hanson, Paul C. . University of Wisconsin. Center for Limnology; Estados Unidos Fil: Arzberger, Peter. University of California; Estados Unidos Fil: Brentrup, Jennifer. Miami University; Estados Unidos Fil: Brookes, Justin. University Of Adelaide; Australia Fil: Carey, Cayelan C.. Virginia Polytechnic Institute and State University. Department of Biological Sciences; Estados Unidos Fil: Gaiser, Evelyn. Florida International University; Estados Unidos Fil: Hamilton, David P. . University of Waikato; Nueva Zelanda Fil: Hong, Grace S.. University of Wisconsin. Center for Limnology; Estados Unidos Fil: Ibelings, Bas. Université de Genève; Suiza Fil: Istvánovics, Vera. Water Research Group of the Hungarian Academy of Sciences; Hungría Fil: Jennings, Eleanor. Dundalk Institute of Technology; Irlanda Fil: Kim, Bomchul. Kangwon National University; Corea del Sur Fil: Kratz, Tim . University of Wisconsin. Center for Limnology; Estados Unidos Fil: Lin, Fang-Pang . National Center for High-Performance Computing; República de China Fil: Muraoka, Kohji. University of Waikato; Nueva Zelanda Fil: O’Reilly, Catherine. Illinois State University; Estados Unidos Fil: Piccolo, Maria Cintia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Instituto Argentino de Oceanografía (i); Argentina Fil: Rose, Kevin C.. Smithsonian Environmental Research Center; Estados Unidos Fil: Ryder, Elizabeth. Dundalk Institute of Technology; Irlanda Fil: Zhu, Guangwei. Chinese Academy of Sciences. Nanjing Institute of Limnology and Geography and Limnology; China

GLEON (www.gleon.org) conducts innovative science by sharing and interpreting high-resolution sensor data to understand, predict, and communicate the role and response of lakes in a changing global environment. More than 380 members from 40 countries form networks of people, lakes, and data. GLEON encourages participation across multiple disciplines of environmental science and information technology, by openly and informally sharing ideas, expertise, and data, and by moving ideas to products via working groups. Education, especially the training of early career scientists, and outreach are central to GLEON's activities.

Long-term monitoring of the underwater environment is still labour intensive work. Using underwater surveillance cameras to monitor this environment has the potential advantage to make the task become less labour intensive. Also, the obtained data can be stored making the research reproducible. In this work, a system to analyse long-term underwater camera footage (more than 3 years of 12 hours a day underwater camera footage from 10 cameras) is described. This system uses video processing software to detect and recognise fish species. This footage is processed on supercomputers, which allow marine biologists to request automatic processing on these videos and afterwards analyse the results using a web-interface that allows them to display counts of fish species in the camera footage.