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We present a research tool that supports marine ecologists' research by allowing analysis of long-term and continuous fish monitoring video content. The analysis can be used for instance to discover ecological phenomena such as changes in fish abundance and species composition over time and area. Two characteristics set our system apart from traditional ecological data collecting and processing methods. First, the continuous video recording results in enormous data volumes of monitoring data. Currently around a year of video recordings (containing over the 4 million fish observations) have been processed. Second, different from traditional manual recording and analysing the ecological data, the whole recording, analysing and presentation of results is automated in this system. On one hand, it saves the effort of manually examining every video, which is infeasible. On the other hand, no automatic video analysis method is perfect, so the user interface provides marine ecologists with multiple options to verify the data. Marine ecologists can examine the underlying videos, check results of automatic video analysis at different certainty levels computed by our system, and compare results generated by multiple versions of automatic video analysis software to verify the data in our system. This research tool enables marine ecologists for the first time to analyse long-term and continuous underwater video records.

Summary 1. We used high‐frequency in situ dissolved oxygen measurements to investigate the seasonal variability and factors regulating metabolism in a subtropical alpine lake in Taiwan between May 2004 and October 2005, specifically exploring how the typhoon season (from June or July to October) affects lake metabolism. 2. Gross primary production (GPP) and ecosystem respiration (R) both peaked in early summer and mid‐autumn but dropped during the typhoon season and winter. Yuan‐Yang Lake is a net heterotrophic ecosystem (annual mean net ecosystem production −39.6 μmole O2 m−3). 3. Compared to the summer peaks, seasonal averages of GPP and R decreased by approximately 50% and 25%, respectively, during the typhoon season. Ecosystem respiration was more resistant to external disturbances than GPP and showed strong daily variation during typhoon seasons. 4. Changes in the quality and quantity of dissolved organic carbon controlled the temporal dynamics and metabolic regulation. External disturbances (typhoons) caused increased allochthony, increasing DOC and water colour and influencing lake metabolism. 5. Seasonal winter mixing and typhoon‐induced water mixing in summer and autumn play a key role in determining the extent to which the lake is a seasonal carbon sink or source to the atmosphere.

With the increasing climatic extremes, the frequency and severity of urban flood events have intensified worldwide. In this study, image-based automated monitoring of flood formation and analyses of water level fluctuation were proposed as value-added intelligent sensing applications to turn a passive monitoring camera into a visual sensor. Combined with the proposed visual sensing method, traditional hydrological monitoring cameras have the ability to sense and analyze the local situation of flood events. This can solve the current problem that image-based flood monitoring heavily relies on continuous manned monitoring. Conventional sensing networks can only offer one-dimensional physical parameters measured by gauge sensors, whereas visual sensors can acquire dynamic image information of monitored sites and provide disaster prevention agencies with actual field information for decision-making to relieve flood hazards. The visual sensing method established in this study provides spatiotemporal information that can be used for automated remote analysis for monitoring urban floods. This paper focuses on the determination of flood formation based on image-processing techniques. The experimental results suggest that the visual sensing approach may be a reliable way for determining the water fluctuation and measuring its elevation and flood intrusion with respect to real-world coordinates. The performance of the proposed method has been confirmed; it has the capability to monitor and analyze the flood status, and therefore, it can serve as an active flood warning system.