Cooperative scalar field mapping is an important task for multi-robot systems. However, the limited communication and computation resources of robots have hindered the application of cooperative field mapping in large-scale scenarios. This letter proposes an adaptive-resolution Gaussian process mapping with event-triggered distributed map fusion to overcome these resource limitations. A novel event-triggered communication mechanism is proposed for distributed map fusion under range-limited communication. A resolution adaptation method is developed to balance the mapping computation, communication resources, and accuracy. A closed-form approximated information metric is derived for faster map resolution optimization. Finally, the performances of the proposed algorithms are validated by real online light field mapping experiments.