Image Processing Techniques Applied to Grain-Size Distribution and Fish Quantity Monitoring in Rivers

• Main Authors: Chang-Han Chung, 鍾昌翰
• Other Authors: Fi-John Chang
• Format: Others
• Language: zh-TW
• Published: 2012
• Online Access: http://ndltd.ncl.edu.tw/handle/26366432010355150526

博士 === 國立臺灣大學 === 生物環境系統工程學研究所 === 101 === The major purpose of this dissertation is to investigate river of ecological environment by using digital image processing techniques. To achieve this goal, two different cases which are (1) river material and (2) fish monitor have shown their accuracy and applicability. Traditional methods for measuring grain size distribution and investigating fish number are time-consuming and lab-intensive. Our experiences indicated volume-by-number would take approximeately an hour in field to collect a representative sample and an hour to conduct a grain-sieving process in laboratory. A lot of fishes would be damaged when shocking or fishing them. Those methods would influen the results and harm the river ecosystem. Recent advances in image processing techniques facilitate automated grain indenitication and fish monitor through digitial images. This study introduces a refined automated grain sizing method (R-AGS) incorporating a neural fuzzy network for automatically estimating the grain size distribution, specifically designed for digital images. A total of 130 images captured from Lanyang River bed, Taiwan, are used to assess R-AGS performance. Two prevalent image processing methods are implemented for comparative purpose. The results indicate that the proposed R-AGS significantly outperforms the other two comparative methods (i.e. reduced 20% of the root-mean square error). The second case is to count automatically the number of fish in pristine rivers. In this case, author presents a background termed as subtraction-based method in order to count the fish automatically. The experiment is performed in Jin-Gua-Liao River situated in the North of Taiwan and the underwater camera records the fish on the river during daytime and also during the night. To date there are little references on monitoring river fish. Author details the monitoring method of river fish and provide recommendations to implement this method. Our results show that: (1) our approach is approximate 80% accurate, (2) the studied river fish habitat is mainly in the glide environment, (3) the monitoring of fish at night is available, (4) the developed methodology provides not only long-term statistics but also delivers useful information on the fish behavior which is to assist hydrologists and ecological researcher in preserving the ecosystem. Summary, the results clearly indicate that our approach can be reliably used for automated grain-size measurement and fish number count with high accuracy and much less labor-intensive.