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Article

AIS and VBD Data Fusion for Marine Fishing Intensity Mapping and Analysis in the Northern Part of the South China Sea

by 1,2,3, 4,5, 2,4,5,*, 2,4,5 and 1,2,6,*
1
Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China
2
State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
3
National-Local Joint Engineering Research Center of Technologies and Applications for National Geographic State Monitoring, Lanzhou 730070, China
4
School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China
5
Collaborative Innovation Center of South China Sea Studies, Nanjing University, Nanjing 210093, China
6
Gansu Provincial Engineering Laboratory for National Geographic State Monitoring, Lanzhou 730070, China
*
Authors to whom correspondence should be addressed.
Academic Editors: Wolfgang Kainz and Yeran Sun
ISPRS Int. J. Geo-Inf. 2021, 10(5), 277; https://doi.org/10.3390/ijgi10050277
Received: 3 March 2021 / Revised: 7 April 2021 / Accepted: 26 April 2021 / Published: 28 April 2021
(This article belongs to the Special Issue Geovisualization and Social Media)
For the sustainable development of marine fishery resources, it is essential to comprehensively, accurately, and objectively obtain the spatial characteristics and evolution law of fishing intensity. However, previous studies have focused more on the use of single data sources, such as AIS (Automatic Information System) and VBD (VIIRS boat detection), to obtain fishing intensity information and, as such, have encountered some problems, such as insufficient comprehensive data coverage for ships, non-uniform spatial distribution of data signal acquisition, and insufficient accuracy in obtaining fishing intensity information. The development of big data and remote sensing Earth observation technology has provided abundant data sources and technical support for the acquisition of fishing intensity data for marine fisheries. Based on this situation, this paper proposes a framework that integrates the data of fishing vessels from two sources (AIS, with high space-time granularity, and VBD, with short revisit cycle and high sensitivity), in order to obtain such information based on closely matching and fusing the vector point data of ship positions. With the help of this framework and the strategy of indirectly representing fishing intensity by data point density after fusion, the spatial characteristics and rules of fishing intensity in typical seasons (February, April, September, and November) in the northern South China Sea in 2018 were systematically analyzed and investigated. The results revealed the following: (1) Matching and fusing AIS and VBD data can provide a better perspective to produce robust and accurate marine fishery intensity data. The two types of data have a low proximity match rate (approximately 1.89% and 6.73% of their respective inputs) and the matching success for fishing vessels in the data was 49.42%. (2) Single AIS data can be used for nearshore (50 to 70 km) marine fishery analysis research, while VBD data reflect the objective marine fishing in space, showing obvious complementarity with AIS. (3) The fishing intensity grid data obtained from the integrated data show that high-intensity fishing in the study area was concentrated in the coastal area of Maoming City, Guangdong (0–50 km); the coastal area of Guangxi Beihai (10–70 km); around Hainan Island in Zhangzhou (10–30 km); and the Sanya nearshore area (0–50 km). However, it did not decay with increasing offshore distance, such as at the Trans-Vietnamese boundary in the Beibu Gulf, near the China–Vietnam Common Fisheries Area (50 km) and high-intensity fishing areas. (4) The obtained fishing intensity data (AIS, VBD, and AIS + VBD) were quantitatively analyzed, showing that the CV (Coefficient of Variation) of the average for each month (after fusing the two types of data) was 0.995, indicating that the distribution of the combined data was better than that before fusion (before fusion: AIS = 0.879, VBD = 1.642). Therefore, the integration of AIS and VBD can meet the need for a more effective, comprehensive, and accurate fishing intensity analysis in marine fishery resources. View Full-Text
Keywords: geographic data; AIS; VBD; data fusion; fishing intensity; marine fisheries sustainable; northern South China Sea geographic data; AIS; VBD; data fusion; fishing intensity; marine fisheries sustainable; northern South China Sea
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MDPI and ACS Style

Li, X.; Xiao, Y.; Su, F.; Wu, W.; Zhou, L. AIS and VBD Data Fusion for Marine Fishing Intensity Mapping and Analysis in the Northern Part of the South China Sea. ISPRS Int. J. Geo-Inf. 2021, 10, 277. https://doi.org/10.3390/ijgi10050277

AMA Style

Li X, Xiao Y, Su F, Wu W, Zhou L. AIS and VBD Data Fusion for Marine Fishing Intensity Mapping and Analysis in the Northern Part of the South China Sea. ISPRS International Journal of Geo-Information. 2021; 10(5):277. https://doi.org/10.3390/ijgi10050277

Chicago/Turabian Style

Li, Xiaoen, Yang Xiao, Fenzhen Su, Wenzhou Wu, and Liang Zhou. 2021. "AIS and VBD Data Fusion for Marine Fishing Intensity Mapping and Analysis in the Northern Part of the South China Sea" ISPRS International Journal of Geo-Information 10, no. 5: 277. https://doi.org/10.3390/ijgi10050277

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