## 4 Discussion  
Vessel monitoring systems and fishing logs effectively monitor and quantify fishing activities. The two data sets are complementary, and combining them has important implications for marine spatial planning and biodiversity conservation [28]. VMStools is a common open-source software for processing, analyzing, and visualizing fishing logs and VMS data [29]. It has some functional modules, such as fishing activity identification and vessel trajectory interpolation and reconstruction, which are widely used for estimating fishing efforts by vessels and assessing the impact of fishing activities on ecosystems [30; 31]. VMStools uses two standardized data formats, EFLALO (EU logbook data) and TACSAT (the VMS positions). Based on the vessel ID and voyage number, it matches the fishing log data to the corresponding VMS data for reconstructing the fishing vessel trajectory and analyzing fishing activities. Besides the limited input data format, the tool depends heavily on the completeness of the VMS data and the fishing logs and the ability to merge these data successfully and to estimate real fishing activity [29]. This study adopted another method based on matching the BeiDou VMS data of a small number of sample vessels with their fishing logs. First, we marked the real vessel state of each trajectory point of the sample vessels and then trained and verified the neural network model with a high identification accuracy. Finally, we used the model to identify the states of the non-sample vessels. The method of this study only needed to ensure the quality of the fishing logs of the sample vessels. There was no requirement for the quantity and integrity of the fishing logs of the non-sample vessels, which was perfectly applicable to the situation where the authenticity and timeliness of China's fishing logs could not be fully ensured [15]. However, it is best used for fishing activities with similar operating habits by fishing vessels because, in this case, the characteristics of fishing behavior extracted from sample vessels are more suitable for identifying fishing activities by non-sample vessels. In addition, if the trajectory reconstruction does not accurately capture the true path of a fishing vessel, it will lead to greater under- or overestimation of the fishing effort of the vessel [30]. The BeiDou VMS data have a temporal resolution of 3 min and a spatial resolution of approximately 10 m. Compared with that of other VMS data [32; 33], the extremely high spatial and  
Figure 8: Distribution of fishing intensity during the total allowable catch system (TACs) project in Haizhou Bay, Yellow Sea. temporal resolution of the BeiDou VMS data enabled this study to accurately evaluate the spatiotemporal distribution of fishing activities without the trajectory interpolation or reconstruction. Moreover, based on the BeiDou VMS data of the vessels of different fishing operation types, its temporal and spatial resolution could be reduced artificially to the same as other VMS data. We could use the original Beidou VMS data with high spatial and temporal resolution as a validation set, and we could train and test to obtain the trajectory interpolation or reconstruction models, which were suitable for other VMS data of the vessels with the corresponding fishing operation type, thereby effectively improving the spatiotemporal resolution and applicability of other VMS data. Different types of operating nets refer to different elements for identifying vessel states. At present, there are two major methods to identify the states based on VMS data: the first one is to judge the state by analyzing the changes in vessel speed. However, [PERSON] et al. [4] performed statistics on the speed identification thresholds of the fishing state in different studies and found that no speed threshold was suitable for all vessel state identifications. Meanwhile, [PERSON] et al. [32] noted that simply identifying the vessel state by the speed threshold would overestimate the number of vessel positions in the fishing state.