Image fusion of satellite sensors can generate a high-resolution multi-spectral image from inputs of a high spatial resolution panchromatic image and a low spatial resolution multi-spectral image for feature extraction and target recognition, such as enclosure seines and floating rafts. However, there is currently no clear and definite method of image fusion for different aquaculture areas distribution extraction from high-resolution satellite images. This study uses three types of high-resolution remote sensing images, GF-1 (Gaofen-1), GF-2 (Gaofen-2), and WV-2 (WorldView-2), covering the raft and enclosure seines aquacultures in the Xiangshan Bay, China, to evaluate panchromatic and multispectral image fusion techniques to determine which is the best. This study applied PCA (principal component analysis), GS (Gram-Schmidt), and NNDiffuse (nearest neighbor diffusion) algorithms to panchromatic and multispectral images fusion of GF-1, GF-2, and WV-2. Two quantitative methods are used to evaluate the fusion effect. The first used seven statistical parameters, including gray mean value, standard deviation, information entropy, average gradient, correlation coefficient, deviation index, and spectral distortion. The second is the CQ_max index. Comparing the evaluation results by these seven common statistical indicators with the results of the image fusion evaluation by index CQ_max, the results prove that the CQ_max index can be applied to the evaluation of image fusion effects in different aquaculture areas. For the floating raft cultured area, the conclusion is consentaneous; NNDiffuse was also optimal for GF-1 and GF-2 data, and PCA was optimal for WV-2 data. For the enclosure seines culture area, the conclusion of quantitative evaluations is not consistent and it shows that there is no definite good method that can be applied to all areas; therefore, careful evaluation and selection of the best applicable image fusion method are required according to the study area and sensor images.

Understanding the spatial patterns of neon flying squid is important for the monitoring and management of fishery resources. Mesoscale eddies and the Kuroshio Extension play important roles in the variation of the fishing ground of Ommastrephes bartramii. However, the way in which eddies and the Kuroshio influence the distribution of Ommastrephes bartramii requires further understanding. In this study, the spatial variation in the distribution of fishing activity and the change of fishing ground gravity of squid were analyzed using automatic identification system (AIS) data. There is a positive correlation between the fishing ground gravity in latitudinal direction and Kuroshio Extension indicators based on the high-frequency eddy kinetic energy (EKE), which describes the Kuroshio variations. Furthermore, the Kuroshio Extension indicators show a positive relationship with the number of the eddies generated in the fishing ground. The results suggest that the changes in the dynamics of SST anomalies could be influenced by eddy-shedding processes in the upstream KE and then alter the distribution of the fishing ground for Ommastrephes bartramii. The Kuroshio index (mean high-frequency eddy kinetic energy between 32° and 37° N, 142° and 149° E) can be used as a good indicator of Kuroshio extension variations to investigate the squid fishing ground in the Kuroshio–Oyashio transition area.

Understanding the spatial distribution of fishing activity and suitable fishing areas is important for improving sustainable fisheries management and protecting vulnerable fish stocks. To identify climate-related habitat changes and variations in the distribution of fishing activity for squid-jigging vessels in the Northwest Pacific Ocean, two types (weighted arithmetic mean method, weighted-AMM; weighted geometric mean method, weighted-GMM) of habitat suitability index (HSI) models were developed in this study with marine environmental data at different depths. The boosted regression tree (BRT) model was adopted to access the monthly important environmental variables and the relative influence of the corresponding variables. The results showed that the weighted-AMM has better prediction performance than the weighted-GMM. The suitable fishing areas showed significant seasonal changes in both spatial location and coverage area. The hotspot map showed that the suitable fishing area for squid-jigging vessels was located in the scope of 42${}^{\circ }$ N∼44${}^{\circ }$ N, 155${}^{\circ }$ E∼170${}^{\circ }$ E throughout the year during 2012∼2019, which suggests that high squid-jigging fishing pressure should be given more attention in fishery management. The HSI model also had good prediction performance for the fishery data of Chinese companies, except for June and July. Additionally, fishing efforts could be used as alternative data for fishery research. The study has also suggested that fishery data are restricted by spatial and temporal distribution and fishing experience, which probably biases the results of the research.

Chub mackerel (Scomber japonicus) is a commercially important fish species which are widely distributed in the North Pacific. Based on the fishery data from China’s high-sea light-purse seine fishing from 2014 to 2020 and the marine environment factors, a mixed linear model considering the actual spatiotemporal stratification of the catch per unit effort (CPUE) was established to analyze the fixed and random effects of marine environmental factors on the CPUE of chub mackerel and to investigate the relationship between the abundance of chub mackerel resources in the Northwest Pacific and two marine environmental factors: sea surface temperature (SST) and chlorophyll-a concentration (CHL). The results showed that SST had a significant fixed effect on the CPUE. In contrast, the natural logarithm of chlorophyll (logCHL) had no fixed effect on the CPUE. Based on the monthly analysis, random fluctuations were observed in the impact of logCHL on the CPUE. LogCHL and CPUE show a positive correlation during spawning and wintering periods and a negative correlation during the feeding period. The study showed that when fishery sampling data exhibit spatiotemporal stratification, linear mixed models can effectively incorporate both the fixed and random effects of environmental factors on the CPUE of chub mackerel. Linear mixed models can play an important role in analyzing the fluctuations in resource abundance and the mechanisms governing the formation of fishing grounds for chub mackerel in the Northwest Pacific.

Chub mackerel (Scomber japonicus) is a commercially important pelagic species in the northwest Pacific Ocean. Accurate identification of its fishing grounds can provide a more robust and targeted scientific basis for fishery management and ecological research. Based on fishing effort and five environmental factors (i.e., sea surface temperature [SST], chlorophyll-a concentration [CHL], SST gradient [GSST], sea surface height [SSH], and current speed), this study developed a Classification and Regression Tree (CART) rule-guided MaxEnt model. Specifically, rules generated by the CART model were first extracted and then incorporated as constrained feature functions into MaxEnt for model training. To select the optimal model scheme, four combinations of rule compositions and feature function outputs were designed, and model performance on the validation dataset was evaluated using ROC curves. Finally, the model was further verified with in situ environmental and fisheries data from April to November 2024. Results showed that the predicted fishing grounds were highly aligned with the actual monthly fishing grounds in 2024, and the predicted migration routes matched the movement trajectory of fishing vessels. The model also exhibited satisfactory performance, achieving an average AUC of 0.722 ± 0.033, a sensitivity of 0.604, a specificity of 0.834, and a negative predictive value (NPV) of 0.978. In conclusion, the CART rule-guided MaxEnt model, integrating the interpretability of CART and the predictive power of MaxEnt, effectively predicts the spatial distribution of chub mackerel fishing grounds in the northwest Pacific Ocean, providing technical support for fishery management and ecological research.