四川少妇BBBB槡BBBB槡,婷婷五月色综合,男人天堂a

基于AIS數據的北印度洋漁船時空活動特征分析

單位：1. 國家海洋環(huán)境預報中心, 北京 100081;
2. 國家海洋環(huán)境預報中心自然資源部海洋災害預報技術重點實驗室, 北京 100081

分類號：P715;P724

出版年·卷·期（頁碼）：2025·42·第一期（48-55）

摘要：

針對海量的分鐘級數據，在區(qū)域劃分上參考中國近海漁區(qū)網格劃分方法，添加時間要素，在北印度洋區(qū)域（48.0°～116.5°E，28.5°N～26.5°S）以統一的時空要素建立網格并作為基礎統計單元。以此為基礎，統計漁船分布面積和作業(yè)時間，并通過幾何化處理繪制了時空分布熱力圖進行分析。結果表明：漁船的分布面積、活動密集程度與時間變量間存在相關性；存在一種劃分方式，使得頻次分布呈規(guī)律性且具備一定的結構特征，該結構特征較穩(wěn)定，不隨時間推移而變化。此外，推測了4個大洋漁場和4條航道的大致地理位置。

For massive minute level data, referring to the grid of China's offshore fishing areas in regional division, time element is added to establish a grid in the northern Indian Ocean region (48.0°~116.5°E, 28.5°N~26.5°S) as basic statistical unit. Based on the statistical unit, this study analyzes the distribution area, calculates operation time, and geometrically draws spatiotemporal distribution heat maps. Results show that there is a correlation between the distribution area of fishing vessels, the frequency of activity density, and time variables. There is a division method that makes the frequency distribution regular with a relative stable time-invariant structural characteristic. In addition, the approximate geographical locations of four oceanic fishing fields and four waterways are also speculated.

參考文獻：

[1] 應急管理部-教育部減災與應急管理研究院, 北京師范大學國家安全與應急管理學院, 應急管理部國家減災中心, 紅十字會與紅新月會國際聯合會. 全球自然災害評估報告[R]. 北京: 應急管理部-教育部減災與應急管理研究院, 北京師范大學國家安全與應急管理學院, 應急管理部國家減災中心, 紅十字會與紅新月會國際聯合會, 2019-2022. Academy of Disaster Reduction and Emergency Management Ministry of Emergency Management & Ministry of Education (ADREM), Beijing Normal University School of National Safety and Emergency Management, National Disaster Reduction Center of China, International Federation of Red Cross and Red Crescent Societies, IFRC. Global natural disaster assessment report[R]. Beijing: Academy of Disaster Reduction and Emergency Management Ministry of Emergency Management & Ministry of Education （ADREM）, Beijing Normal University School of National Safety and Emergency Management, National Disaster Reduction Center of China, International Federation of Red Cross and Red Crescent Societies, IFRC, 2019-2022.
[2] 中華人民共和國國務院新聞辦公室. 中國的遠洋漁業(yè)發(fā)展白皮書[R]. 北京: 中華人民共和國國務院新聞辦公室, 2023. The State Council Information Office of the People's Republic of China. Development of China's distant-water fisheries[R]. Beijing: The State Council Information Office of the People's Republic of China, 2023.
[3] 許振琦, 汪金濤, 雷林, 等. AIS數據在遠洋漁業(yè)中的應用研究進展[J]. 海洋漁業(yè), 2023, 45(2): 237-247. XU Z Q, WANG J T, LEI L, et al. Research progress on application of AIS data in distant-water fishery[J]. Marine Fisheries, 2023, 45(2): 237-247.
[4] 湯先峰, 張勝茂, 樊偉, 等. 基于北斗船位數據的江蘇漁船航次分析[J]. 漁業(yè)現代化, 2020, 47(1): 63-71. TANG X F, ZHANG S M, FAN W, et al. Voyage analysis of Jiangsu fishing vessels based on Beidou position data[J]. Fishery Modernization, 2020, 47(1): 63-71.
[5] 高明遠, 張勝茂, 湯先峰, 等. 中國近海捕撈機動漁船航次特征數據挖掘[J]. 大連海洋大學學報, 2021, 36(1): 147-154. GAO M Y, ZHANG S M, TANG X F, et al. Data mining of trip characteristics for offshore fishing vessels in China[J]. Journal of Dalian Ocean University, 2021, 36(1): 147-154.
[6] 王德興, 羅靜靜, 袁紅春. 漁船定位捕撈與環(huán)境因子的關聯分析[J]. 導航定位學報, 2019, 7(4): 42-49. WANG D X, LUO J J, YUAN H C. Association analysis between fishing vessel positioning and environmental factors[J]. Journal of Navigation and Positioning, 2019, 7(4): 42-49.
[7] 吳佳文, 官文江. 基于SNPP/VIIRS夜光遙感數據的東、黃海漁船時空分布及其變化特點[J]. 中國水產科學, 2019, 26(2): 221-231. WU J W, GUAN W J. Study on the distribution and variation of fishing vessels in East China Sea and Yellow Sea based on the nighttime light remote sensing data from SNPP/VIIRS[J]. Journal of Fishery Sciences of China, 2019, 26(2): 221-231.
[8] TANG G L, CAO Q K, LI X. Analysis of vessel behaviors in costal waterways using big AIS data[C]//2019 IEEE 4th International Conference on Cloud Computing and Big Data Analysis (ICCCBDA). Chengdu: IEEE, 2019: 290-294.
[9] ZHONG H Y, SONG X, YANG L. Vessel classification from spacebased AIS data using random forest[C]//20195th International Conference on Big Data and Information Analytics (BigDIA). Kunming: IEEE, 2019: 9-12.
[10] 趙東彬. 基于漁船作業(yè)數據的東海漁場特征分析[D]. 舟山: 浙江海洋大學, 2019. ZHAO D B. Analysis of fishery characteristics of East China Sea based on fishing vessel data[D]. Zhoushan: Zhejiang Ocean University, 2019.
[11] 吳寶福. 基于軌跡數據的漁船行為判別關鍵技術研究[D]. 杭州: 杭州電子科技大學, 2019. WU B F. Research on key technology of vessel behavior identification based on trajectory data[D]. Hangzhou: Hangzhou Dianzi University, 2019.
[12] ZHANG J L, GENG J L, WAN J, et al. An automatically learning and discovering human fishing behaviors scheme for CPSCN[J]. IEEE Access, 2018, 6: 19844-19858.
[13] 郭剛剛, 樊偉, 薛嘉倫, 等. 基于NPP/VIIRS夜光遙感影像的作業(yè)燈光圍網漁船識別[J]. 農業(yè)工程學報, 2017, 33(10): 245-251. GUO G G, FAN W, XUE J L, et al. Identification for operating pelagic light-fishing vessels based on NPP/VIIRS low light imaging data[J]. Transactions of the Chinese Society of Agricultural Engineering, 2017, 33(10): 245-251.
[14] WANG C, REN H X, LI H J. Vessel trajectory prediction based on AIS data and bidirectional GRU[C]//2020 International Conference on Computer Vision, Image and Deep Learning (CVIDL). Chongqing: IEEE, 2020: 260-264.
[15] FORTI N, MILLEFIORI L M, BRACA P, et al. Prediction oof vessel trajectories from AIS data via sequence-to-sequence recurrent neural networks[C]//ICASSP 2020—2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Barcelona: IEEE, 2020: 8936-8940.
[16] HUAN Y C, KANG X Y, WANG Y F, et al. IAVT: anomalous vessel trajectory detection using AIS data[C]//20225th International Conference on Artificial Intelligence and Big Data (ICAIBD). Chengdu: IEEE, 2022: 17-22.
[17] 陳俊霖, 趙國慶, 張勝茂, 等. 西北印度洋公海漁場鳶烏賊的時空分布變化規(guī)律[J/OL]. 水產學報, (2025-01-08). http://kns.cnki.net/kcms/detail/31.1283.S.20230530.1003.002.html.. CHEN J L, ZHAO G Q, ZHANG S M, et al. Study on temporal and spatial distribution characteristics of Symplectoteuthis oualaniensis in high seas fishing ground of northwest Indian Ocean[J/OL]. Journal of Fisheries of China, (2025-01-08). http://kns.cnki.net/kcms/detail/31.1283.S.20230530.1003.002.html.
[18] 張亞男, 官文江, 李陽東. 印度洋長鰭金槍魚棲息地指數模型的構建與驗證[J]. 上海海洋大學學報, 2020, 29(2): 268-279. ZHANG Y N, GUAN W J, LI Y D. Construction and verification of a habitat suitability index model for the Indian Ocean albacore tuna[J]. Journal of Shanghai Ocean University, 2020, 29(2): 268-279.
[19] 溫利紅, 張衡, 方舟, 等. 不同捕撈方式下印度洋北部鳶烏賊漁場時空分布差異[J]. 上海海洋大學學報, 2021, 30(6): 1079-1089. WEN L H, ZHANG H, FANG Z, et al. Spatial and temporal distribution of fishing ground of Sthenoteuthis oualaniensis in northern Indian Ocean with different fishing methods[J]. Journal of Shanghai Ocean University, 2021, 30(6): 1079-1089.
[20] 陳新軍, 錢衛(wèi)國, 劉必林, 等. 主要經濟大洋性魷魚資源漁場生產性調查與漁業(yè)概況[J]. 上海海洋大學學報, 2019, 28(3): 344-356. CHEN X J, QIAN W G, LIU B L, et al. Productive survey and fishery for major pelagic economic squid in the world[J]. Journal of Shanghai Ocean University, 2019, 28(3): 344-356.

服務與反饋：

【文章下載】【發(fā)表評論】【查看評論】【加入收藏】