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基于北斗卫星船位数据分析象山拖网捕捞时空特征
引用本文:张胜茂,崔雪森,伍玉梅,郑巧玲,王晓璇,樊 伟. 基于北斗卫星船位数据分析象山拖网捕捞时空特征[J]. 农业工程学报, 2015, 31(7): 151-156
作者姓名:张胜茂  崔雪森  伍玉梅  郑巧玲  王晓璇  樊 伟
作者单位:1. 中国水产科学研究院东海水产研究所农业部东海与远洋渔业资源开发利用重点实验室,上海 200090;,1. 中国水产科学研究院东海水产研究所农业部东海与远洋渔业资源开发利用重点实验室,上海 200090;,1. 中国水产科学研究院东海水产研究所农业部东海与远洋渔业资源开发利用重点实验室,上海 200090;,1. 中国水产科学研究院东海水产研究所农业部东海与远洋渔业资源开发利用重点实验室,上海 200090; 2. 上海海洋大学海洋科学学院,上海 201306;,1. 中国水产科学研究院东海水产研究所农业部东海与远洋渔业资源开发利用重点实验室,上海 200090;,1. 中国水产科学研究院东海水产研究所农业部东海与远洋渔业资源开发利用重点实验室,上海 200090;
基金项目:中央级公益性科研院所基本科研业务费专项资金项目(东海水产研究所2014T13);公益性行业(农业)科研专项(201203018)
摘    要:为了快速大范围掌握捕捞努力量时空分布特点,借助北斗船位数据采用统计方法获取拖网捕捞状态的速度阈值,根据阈值判断捕捞状态点,捕捞状态点之间时间组成累计捕捞时间,累计捕捞时间与功率的乘积作为捕捞努力量,根据捕捞努力量分析拖网捕捞时空特征。2013年象山在近海拖网捕捞努力量从时间上可以分为3个时间段,即2~5月、6~9月、10~12月与1月。从空间来看捕捞努力量以象山附近的渔场为中心由高到低向外扩展,形成近似的同心圆。从拖网捕捞时间来看分为全年近海渔场、春秋季近海渔场、春秋冬外海渔场、春季或秋季周边外缘渔场。利用北斗数据提取方法计算6个网次时长,并与手工出海调查记录的时长比较,两者相对误差在5%以内。

关 键 词:渔船;监控;系统;北斗卫星;捕捞努力量
收稿时间:2014-12-26
修稿时间:2015-03-23

Analyzing space-time characteristics of Xiangshan trawling based on Beidou Vessel Monitoring System Data
Zhang Shengmao,Cui Xunsen,Wu Yumei,Zheng Qiaoling,Wang Xiaoxuan and Fan Wei. Analyzing space-time characteristics of Xiangshan trawling based on Beidou Vessel Monitoring System Data[J]. Transactions of the Chinese Society of Agricultural Engineering, 2015, 31(7): 151-156
Authors:Zhang Shengmao  Cui Xunsen  Wu Yumei  Zheng Qiaoling  Wang Xiaoxuan  Fan Wei
Affiliation:1. Key Laboratory of East China Sea & Oceanic Fishery Resources Exploitation and Utilization, Ministry of Agriculture, P.R. China, East China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Shanghai 200090, China;,1. Key Laboratory of East China Sea & Oceanic Fishery Resources Exploitation and Utilization, Ministry of Agriculture, P.R. China, East China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Shanghai 200090, China;,1. Key Laboratory of East China Sea & Oceanic Fishery Resources Exploitation and Utilization, Ministry of Agriculture, P.R. China, East China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Shanghai 200090, China;,1. Key Laboratory of East China Sea & Oceanic Fishery Resources Exploitation and Utilization, Ministry of Agriculture, P.R. China, East China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Shanghai 200090, China; 2. College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China;,1. Key Laboratory of East China Sea & Oceanic Fishery Resources Exploitation and Utilization, Ministry of Agriculture, P.R. China, East China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Shanghai 200090, China; and 1. Key Laboratory of East China Sea & Oceanic Fishery Resources Exploitation and Utilization, Ministry of Agriculture, P.R. China, East China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Shanghai 200090, China;
Abstract:Abstract: More than fifty thousand fishing vessels have installed the terminal unit of the Beidou satellite positing system since the Ministry of Agriculture of China started to construct fishing vessels monitoring system in Nansha islands in 2006. This system mainly aims to manage vessel fishing, fishery safety and emergency rescue. The data sent by the terminal unit have a temporal resolution of 3 minutes, and a spatial resolution of 10 meters. Thus, after carrying out these fishing services almost for 9 years, the system has recorded billions of historical cruising data for each vessel, such as time, position, speed, direction and rate of turn. These data can be analyzed deeply by big data mining technology. To deeply mine the fishery information from the historical data, the cooperation has been carried out between East China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences (ECSFRI) and several Beidou operators. Performing statistical computations for traditional fishing effort takes much time and effort, and the macro fishing intensity cannot be accessed immediately. Through the Beidou satellite vessel position monitoring system, the position, course, speed and other information of vessels can be got and used to data mining. The information of every point position of trawlers includes speed, course, time and other information. With the information, the state of trawlers can be determined. In this paper, the speed threshold of each vessel's fishing condition is obtained by the statistics of navigational speed. And fishing state points can be judged by combining the speed thresholds. When the speed and course are in the range of threshold value, the trawler is in the state of fishing. The fishing effort in the grid is calculated based on the cumulative fishing time in fishing state points of each vessel. There may be many trawlers in one fishing grid. The work of one trawler in fishing is divided into many hauls. In general, a haul lasts a few hours. When a haul is over, the next haul will get under way after some interval of time. Every haul has several discrete points of vessel position. The first summation is the cumulative fishing for one haul; the second summation is the cumulative fishing for several hauls of one trawler in a period of time; the third summation is the cumulative fishing of all trawlers in one fishing grid. The cumulative fishing is the product of the cumulative fishing time and the vessel power (kW·h). This method has the characteristics of real-time, large-scale, fast and high resolution, which can provide good service in fishery resource protection. Ship position, heading, speed and other information obtained from the Beidou Vessel Monitoring System can work for mining the trawler status, calculating cumulative fishing time and effect, as well as analyzing temporal and spatial characteristics on the basis of fishing effort. In 2013, trawling fishing effort of Xiangshan Harbor in offshore area could be divided into 3 periods which were from February to May, from June to September and from October to December and January. From a spatial perspective, fishing effort formed approximate concentric circles, centering on the fisheries near Xiangshan Harbor and expanding from high to low. From the perspective of trawling time, there were annual inshore fisheries, spring-autumn inshore fisheries, spring-autumn off-sea fisheries and spring-autumn outer edge fisheries. The duration of 6 net times were manually recorded at sea investigation. And the Beidou data have also been used to calculate the time. The relative error between them is less than 5%.
Keywords:fishing vessel   monitoring   systems   Beidou satellite   fishing effort
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