| 基于AIS的海上渔船捕捞活动聚集区提取方法研究 |
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| 引用本文： | 陈仁丽,吴晓青,刘柏静,王跃启,高猛.基于AIS的海上渔船捕捞活动聚集区提取方法研究[J].地球信息科学,2021,23(12):2163-2173. |
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| 作者姓名： | 陈仁丽 吴晓青 刘柏静 王跃启 高猛 |
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| 作者单位： | 中国科学院烟台海岸带研究所,烟台264003;中国科学院大学,北京100049;中国科学院烟台海岸带研究所,烟台264003;中国科学院海岸带环境过程与生态修复重点实验室,烟台264003 |
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| 基金项目： | 国家重点研发计划项目(2019YFD0900705);山东省基金项目(ZR2020MD014) |
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| 摘 要： | 船舶自动识别系统(Automatic Identification System, AIS)不仅是海上交通监管的有效工具,也为研究海上交通运输及其相关产业活动特征提供了一种良好的数据源。基于海上渔船AIS数据,本研究利用高斯混合模型(Gaussian Mixed Model,GMM)识别渔船捕捞活动状态,提出一种将核密度估计(Kernel Density Estimation,KDE)与热点分析(Hot Spot Analysis, HSA)相融合用于渔船捕捞活动聚集区提取的方法。结果显示：与单一KDE或HSA方法相比,二者相融合的方法将KDE的距离衰减效应与HSA统计指数相结合,在渔船捕捞活动聚集区提取中的应用效果较好、效率较高;采用该融合方法,基于2018年9—12月AIS数据,实现对渤海海峡周边海域渔船捕捞活动聚集区的提取,发现不同月份,渔船捕捞活动聚集区的分布范围和空间形态特征具有一定差异性,烟威近岸海域和渤海海峡是主要的捕捞活动聚集区,其结果可为该海域捕捞活动管理和海洋生态保护提供技术方法和决策支持。
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| 关 键 词： | AIS 捕捞活动 渔船行为 捕捞活动聚集区 高斯混合模型 核密度估计 热点分析 渤海海峡 |
| 收稿时间： | 2021-03-04 |
Mapping Method of Fishing Grounds based on Marine AIS Data |
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| CHEN Renli,WU Xiaoqing,LIU Baijing,WANG Yueqi,GAO Meng.Mapping Method of Fishing Grounds based on Marine AIS Data[J].Geo-information Science,2021,23(12):2163-2173. |
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| Authors: | CHEN Renli WU Xiaoqing LIU Baijing WANG Yueqi GAO Meng |
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| Institution: | 1. Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai 264003, China2. University of Chinese Academy of Sciences, Beijing 100049, China3. Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Chinese Academy of Sciences, Yantai 264003, China |
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| Abstract: | Automatic Identification System (AIS) is a ship-to-ship and ship-to-shore system used for ship monitoring. It can automatically and continuously broadcast static and dynamic information about the vessel's position and movement, as well as voyage-related information. More and more research suggests that AIS is not only an effective tool for maritime traffic supervision, but also a good data source for the study of maritime traffic and its related industrial activities. In China, the application of AIS equipment is relatively late. The research based on AIS data mainly focuses on the maritime traffic safety management, such as vessel collision avoidance and traffic flow statistics, while there are few reports in the fishery field. In order to provide technical support for the protection and recovery of inshore fishery resources in China, it is urgent to excavate the information value of AIS data and carry out the research on the spatiotemporal pattern of inshore fishing activities based on AIS data. Therefore, based on AIS data of fishing vessels at sea, this study used the Gaussian Mixed Model (GMM) to identify fishing behavior of fishing vessels and determine the speed threshold of fishing vessels in fishing activities. This study proposed a method combining Kernel Density Estimation (KDE) and Hot Spot Analysis (HSA) to map fishing grounds. The results show that, firstly, compared with single KDE or HSA method, the combination method combined the distance attenuation effect of KDE with quantitative statistical index of HSA, which can not only avoid the problem of range definition of fishing grounds in the KDE process, but also improve the efficiency of data analysis in the HSA process and the scattered distribution effect of fishing grounds in the extraction results of the HSA. The combination method had better application effects and higher efficiency in the mapping of fishing grounds. It provided a quick and simple evaluation method for the rapid acquisition of marine fishing information and the effectiveness evaluation of fishery resources protection and management measures. Secondly, based on the AIS data from September to December 2018, the combination method was used to map the spatiotemporal distribution of fishing grounds around the Bohai Strait. The study found that the fishing grounds around the Bohai Strait were mainly distributed in the inshore areas of Yanwei and the Bohai Strait. The distribution range of concentrated fishing activities in traditional fishing grounds of the Bohai Sea is relatively small, and the distribution range and spatial morphological characteristics of fishing grounds had some variabilities in different months. The results can provide technical methods and decision support for fishing management and marine ecological protection around the Bohai Strait. |
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| Keywords: | Automatic Identification System (AIS) fishing activity vessel behavior fishing grounds Gaussian Mixed Model (GMM) Kernel Density Estimation (KDE) Hot Spot Analysis (HSA) Bohai Strait |
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