Kaohsiung Harbor is one of the major commercial ports in Taiwan, located at the hub of northeastern and southeastern Asia shipping lanes. Therefore there are a considerable number of commercial shipping channels distributed around Kaohsiung Harbor. The security of Kaohsiung Harbor becomes more difficult to defense than others due to the complexity of channels. In this study, Automatic Identification System (AIS) system is used to collect the ships information from June 1, 2010 to June 30, 2011. The collected AIS data were decoded, converted, corrected, integrated and analyzed systematically, which will become the base of future database. The information of the AIS includes Maritime Mobile Service Identity (MMSI), latitude and longitude, heading, course, speed, and others. The activities of ships can be monitored by AIS, so the density and distribution of ships on each major channel can be obtained by grid computing. By the results of one-year AIS data, three major shipping channels of Kaohsiung Harbor can be identified, which are north-western, north-southern, and east-western. Based on this kind of long term shipping statistics, possible novel harbor security defense may be founded. Although the AIS was designed to monitor the ship activities, but it can be viciously shut down, or signal is out of range sometimes, then it will become the possible security breach. Nevertheless, ships at sea will generate certain kind of noises, such as from engine and propeller. With efficient propagation of sound waves in water, acoustic technology may compensate the limitations of AIS, to be a feasible method of detecting unknown ships. In this study, acoustic modeling code ¡§Acoustic Module for Sea-surface Noise¡¨ (AMSN) is applied by using the ship position information from AIS, to calculate the related underwater noise sound field of Kaohsiung Harbor. Discussions were made on the dependence of noise level variation with ship density. As a conclusion, with sufficient understanding of sound field statistics of harbor, any anomaly of noise level can be an indication of hostile intrusion, thus harbor security can be further assured.

Observation of marine animals in their environment – whale-watching – has grown greatly in recent years, bringing risk to the animals. Of particular concern are harmful impacts on marine mammals, some of which are endangered. As a result, regulations have been developed for their protection, but these conservation measures require enforcement across a broad geographic region, which is difficult due to limited monitoring resources. A ship-borne information transmission system called AIS – Automatic Identification System – can provide information-rich marine vessel movement data that can be used to passively monitor vessels engaged in viewing wildlife, aiding regulatory bodies with compliance enforcement. Few studies explore the use of AIS data to determine when vessels are engaged in wildlife-viewing, and as such little guidance exists on how to implement classification models appropriately. The objective of this thesis is to use AIS data to evaluate the accuracy and utility of existing classification models to detect vessels engaged in observing wildlife, and determine whether information about species being observed can be extracted. Using a control set of observed cetacean encounter data, three classification models were statistically assessed. From this, a hidden Markov model was chosen for detailed analysis in the vicinity surrounding Vancouver Island, B.C., Canada. The resulting analysis concluded that a hidden Markov unsupervised classification approach was feasible for detecting vessel behaviours and differentiating species type. These findings suggest AIS can aid managers and the commercial whale-watching industry in making informed decisions regarding conservation regulations and their compliance. / Graduate / 2022-08-12

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