西北印度洋大眼金枪鱼渔场预报模型建立与模块开发

根据1990—2003年印度洋大眼金枪鱼延绳钓渔业数据和美国国家海洋和大气管理局提供的海表温度、叶绿素-a历史环境数据,应用环境因子叠加方法,构建了西印度洋大眼金枪鱼渔场预报模型,用于金枪鱼渔场预报。分析得出各月适宜海表温度、叶绿素-a浓度范围和历史高产区空间位置;导入实时海表温度、叶绿素-a等遥感栅格数据,分别提取适宜海表温度、适宜叶绿素-a浓度和历史高产区的空间栅格数据集,最后在空间上对3种栅格数据进行空间叠加并取交集。交集所指空间区域即为大眼金枪鱼潜在渔场位置。通过精度检验,表明该模型渔场预报精度为60.5%。并以VC++6.0工具为开发平台,对此模型进行了设计开发,实现了模块预报西北印度洋大眼金枪鱼渔场。

Development of Predictive Model and Module of Fishing Ground for Bigeye Tuna,Thunnus obesusand in the Northwest Indian Ocean

A predictive model of fishing ground for bigeye tuna,Thunnus obesusand,was established for the Northwest Indian Ocean NIO by bigeye tuna longline data including sea surface temperature(SST),chlorophyl-a(Chll) and environmental data from the Indian Ocean Tuna Commission(IOTC) and National Oceanic and Atmospheric Administration(NOAA) from 1990 to 2003 by spatial overlay methods.In the establishment of the predictive model,the optimum range of SST,and Chll and high catch spatial locations were calculated for each month by the method first,then real time remote sensing data of SST,and Chll were put and the spatial raster data set of the optimum SST,and Chl were obtained.At last three different raster data sets were overlaid and an intersection set was achieved.The spatial region of the intersection set represented potential fishing ground positions of bigeye tuna.The forecast accuracy rate of the precision test was 60.5%,and a software module on VC++6.0 platform was designed for reality of the fishing ground prediction module of bigeye tuna in NIO.