Classifying Nighttime Fishing Activity Patterns Using Global Image Standardization: A Comparison with Local Standardization in the Korean EEZ

Satellite-based nighttime light imagery offers a powerful means of monitoring fishing activity across data-sparse ocean regions, yet the influence of image standardization on machine-learning-based pattern classification has received little attention. This study analyzed Visible Infrared Imaging Radiometer Suite Day/Night Band (VIIRS DNB) monthly composite imagery of the Korean Exclusive Economic Zone (EEZ) from 2014 to 2024, applying an identical Convolutional Autoencoder and Gaussian Mixture Model (CAE-GMM) framework under both local and global standardization. Global standardization produced three or four major spatiotemporal clusters that broadly corresponded to seasonal fishing cycles: a high-intensity autumn–winter pattern, an expanded summer–autumn pattern, and a low-intensity winter–spring pattern. In the four-cluster solution, the fourth cluster represented the transition periods among the three seasonal patterns. Particularly, the strong autumn-winter pattern weakened after 2019 and was increasingly replaced by winter-spring clusters. Comparison of the two standardization methods showed that local standardization categorizes images primarily by their internal spatial light distribution, whereas global standardization additionally reflects brightness differences across the time series. As a result, images with similar spatial patterns but lower intensity, such as January through March in recent years, were assigned to a different cluster. These findings demonstrate that standardization is not just a preprocessing step but a decisive methodological choice that depends on the research objective in fisheries monitoring with nighttime lights.