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

Abstract

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.

Injuries and skin condition of Indo‐Pacific humpback dolphins (Sousa chinensis) in the northern South China Sea

“…The highest prevalence of human‐induced and unspecified injuries in JM, SWH, and SNB could be linked to the different types and intensities of human activities at each location. For example, fishing activities differ among locations (Guan et al, 2021) and experience both seasonal (e.g., Chen et al, 2022; Guan et al, 2021; Li et al, 2021) and annual variations (Lin et al, 2021). These variations, and the fact that it is impossible to date injuries, make it hard to link injury prevalence with a specific time and activity level at a specific location.…”

Assessing Potential Climatic and Human Pressures in Indonesian Coastal Ecosystems Using a Spatial Data-Driven Approach

“…During the analysis of pressure due to fishery activities, PFZ can be modeled using more parameters, such as TSS, sea surface height [122], sea surface salinity [123,124], and merged sea level of anomaly [125]; additionally, the PFZ can be quantitatively converted to the potential fish catch weight per area. Another important research consideration is the classification of the VBD data using specific algorithms to separate fishing vessels from other vessel types [71,126]. In addition, future research can formulate and recommend ideal fishing zones and blue carbon conservation zones to mutually encourage fishing productivity while preserving ecosystems to support the implementation of a blue economy [127].…”

Analysis of spatio-temporal characteristics of ships in Yangtze River estuary deepwater channel based on AIS data

“…The Automatic Identification System (AIS) is a brand-new navigational tool that is used between ships as well as between ships and the coast. It is widely employed in a variety of industries, including ship identification [1] , collision avoidance [2] , marine research and rescue [3] , and marine investigation [4] . Ship AIS data is spatio-temporal data, including ship dynamic information, static information and voyage information, which includes the distribution of ship behavior pattern [5] , ship maneuvering behavior characteristics [6] , ship traffic flow characteristics [7] and ship customary route [8] .…”