基于遥感数据的智利竹筴鱼渔场预报系统

根据中国2001-2009年东南太平洋作业船只提供的捕捞数据和卫星遥感反演的海表温度数据,建立了基于贝叶斯概率理论的智利竹筴鱼渔场预报系统。系统开发采用客户/服务器(C/S)体系模式,数据库则采用SQL Server 2000数据库管理系统,结合控件式GIS技术,构建了渔场分析和预报系统,并利用历史数据进行了模型精度验证。结果表明:预报渔场的准确度为72.6%,预报非渔场的准确率为57.5%;综合预报准确率达65%以上。渔汛盛期的渔场预报准确率要高于渔汛末期3%～22%,而非渔场的预报准确率低于渔汛末期4%～11%。因此,该预报系统对于南太平洋智利竹筴鱼渔场预测和捕捞活动具有一定的指导意义。但在实际预报过程中,也需结合其他相关环境因子(如叶绿素a、海面高度、海流等)对预报渔场进行修正。

Predicting system of Chilean jack mackerel fishing grounds based on remote sensing data

Based on the yearly catch data (2001-2009) of Chinese fishery and inversion SST by satellite remote sensing in the Southeast Pacific Ocean, the predicting system on fishing grounds founded was developed based on the Bayesian probability theory. The system adopts the client/service mode; while the database runs under the SQL Server 2000 data management system combined the interactive controls GIS technology. The system was also verified on its precision by using historical catch data. The results showed that the prediction accuracy of fishing ground and non-fishing ground were 72.6% and 57.5%, respectively, while the average predict rate of accuracy was above 65%. The prediction accuracy of fishing ground in peak period of fishing was 3%-22% higher than which in fishing later period, but the prediction accuracy of non-fishing ground was 4%-11% lower than which in later period of fishing. Hence, the system has important advising significance in predicting fishing ground and fishing activity on the Chilean jack mackerel in the South Pacific Ocean. But in the actual predicting process, it needs to revise the forecasting location of fishing ground integrated other environmental factors, such as chlorophyll a, sea level height, ocean current.