| 基于渔船AIS数据的南海北部海洋渔业捕捞强度空间特征挖掘 |
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| 引用本文： | 李晓恩,周亮,肖杨,吴文周,苏奋振,石伟.基于渔船AIS数据的南海北部海洋渔业捕捞强度空间特征挖掘[J].地球信息科学,2021,23(5):850-859. |
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| 作者姓名： | 李晓恩 周亮 肖杨 吴文周 苏奋振 石伟 |
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| 作者单位： | 1.兰州交通大学测绘与地理信息学院,兰州 7300702.南京大学地理与海洋科学学院,南京 2100233.中国科学院地理科学与资源研究所 资源与环境信息系统国家重点实验室,北京 1001014.地理国情监测技术应用国家地方联合工程研究中心,兰州 7300705.甘肃省地理国情监测工程实验室,兰州 7300706.中国南海研究协同创新中心,南京 2100937.中国科学院南海生态环境工程创新研究院,广州 510301 |
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| 基金项目： | 国家科技基础资源调查专项(2017FY201401);国家自然科学基金重大项目(41890854);中国科学院南海生态环境工程创新研究院自主部署项目(ISEE2018YB06);国家自然科学基金项目(41961027);兰州交通大学优秀平台支持(201806) |
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| 摘 要： | 高精度渔业捕捞强度数据是开展捕捞限额管理的前提与关键,也是海洋渔业资源可持续发展的重要保障。因此,本文以挖掘海洋渔业捕捞强度空间特征为出发点,选用2018年2、4、9和11月典型季节的中国籍6364艘渔船1.8亿条高时空粒度AIS数据。运用专家知识经验、空间统计及数据挖掘分析方法,以广西南岸北部湾渔场、广东沿岸和环海南岛周边海域为研究区域,对渔业捕捞强度空间特征展开了细致的挖掘与分析。结果表明：① 广东、广西两省（以下简称“两广”）沿岸海域渔业高强度捕捞主要呈现“团块”向外扩张汇聚成“条带”或“更大团块”的特征,而环海南岛周边主要呈现“团块状”特征;② 受渔业从业人员、渔业作业船舶数量、海洋渔场及海域环境影响,“两广”沿岸近海海域捕捞强度明显高于环海南岛周边海域; ③ 高强度捕捞区域主要集中于近岸30~50 km范围内,且近海捕捞强度高于远海区域,归因于研究区内中小型作业渔船占比较高,达50.9%;④ 渔业捕捞活动受农历传统春节及休渔期等政策因素的影响,春节期间的渔业捕捞强度是所选数据覆盖时间范围中最低的,并且休渔期后（9月）渔业捕捞强度明显高于休渔期前（4月）;⑤ 研究区海岸附近的大型渔港对近岸海域的高强度捕捞具有一定的辐射带动效应。本研究通过对高时空粒度的AIS数据进行处理、分析及深度挖掘,可为近岸海洋渔业捕捞强度探析提供重要数据支撑,服务于海洋渔业可持续发展。
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| 关 键 词： | 渔船AIS 大数据 南海北部海洋渔业 数据分析与挖掘 混合高斯模型 捕捞强度 空间特征分析 渔业资源可持续 |
| 收稿时间： | 2020-06-24 |
Spatial Characteristics Mining of Fishing Intensity in the Northern South China Sea based on Fishing Vessels AIS Data |
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| LI Xiaoen,ZHOU Liang,XIAO Yang,WU Wenzhou,SU Fenzhen,SHI Wei.Spatial Characteristics Mining of Fishing Intensity in the Northern South China Sea based on Fishing Vessels AIS Data[J].Geo-information Science,2021,23(5):850-859. |
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| Authors: | LI Xiaoen ZHOU Liang XIAO Yang WU Wenzhou SU Fenzhen SHI Wei |
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| Abstract: | High-precision fishing intensity data in the fishery is the prerequisite and key to carrying out fishing quota management, as well as the significant guarantee for the sustainable development of marine fishery resources. Therefore, the paper selects 180 million records of high spatiotemporal multi-granularity data of 6364 fishing vessels with Chinese nationality in typical seasons including February, April, September, and November of 2018, aiming to mine the spatial characteristics of the fishing intensity in marine fishery. It leverages expert knowledge and experience and employs spatial statistics and data mining analysis methods to conduct a thorough mining and analysis of the spatial characteristics of the fishing intensity. We take Beibu Gulf fisheries on the coast of Guangxi, the coast of Guangdong, and the surrounding sea areas of the Hainan Island as the study areas. The results show that: (1) The high-intensity fishing in coastal waters of Guangdong and Guangxi (referred to as "Liang Guang") mainly presents the characteristics of "clumps" expanding outward and converging into "bands" or "larger clumps", while the surrounding area of Hainan island mainly presents the characteristics of "clumps"; (2) Influenced by fishery workers, the number of fishing vessels, marine fisheries, and marine environment, the fishing intensity in the coastal waters of "Liang Guang" is apparently higher than that of the surrounding waters of Hainan Island; (3) The high-intensity fishing area is mainly concentrated within 30~50 km near the shore, and the intensity of offshore fishing is higher than that of the far-sea area, which is attributed to the high proportion (up to 50.9%) of small and medium-sized fishing vessels in the study area; (4) Fishing activities are affected by the traditional Lunar New Year, the fishing moratorium, and other policy factors, thus the fishing intensity during the Spring Festival being the lowest in the selected data coverage time range. In addition, the fishing intensity after the fishing moratorium (September) is significantly higher than that before the fishing moratorium (April); (5) The large fishing ports near the coast of the study area have a certain radiation-driven effect on the high-intensity fishing in the coastal waters. This study can provide important data support for the analysis of the fishing intensity of offshore marine fisheries and contribute to the sustainable development of the marine fishery, by processing, analyzing, and deeply mining AIS data with high spatiotemporal multi-granularity. |
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| Keywords: | fishing vessels AIS data big data Northern South China Sea fishery data analysis and mining Gaussian mixture model fishing intensity spatial characteristics analysis sustainable marine fisheries |
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