Fast and flexible spatial sampling methods based on the Quadtree algorithm for ocean monitoring - Zhou, Yanzhi; Lin, Pengfei; Liu, Hailong; Zheng, Weipeng; Li, Xiaoxia; Zhang, Wenzhou (Frontiers in Marine Science, 2024)
Although existing in situ oceanographic data are sparse, such data still play an important role in submarine monitoring and forecasting. Considering budget limitations, an efficient spatial sampling scheme is critical to obtain data with much information from as few sampling stations as possible. This study improved existing sampling methods based on the Quadtree (QT) algorithm. In the first-phase sampling, the Gradient QT algorithm is recommended since it offers enhanced efficiency and flexibility compared to the Variance QT (VQT) algorithm and shares similar qualities with the VQT in terms of performance. In second-phase sampling, QT decomposition and the greedy algorithm are combined. QT decomposition is used to divide the region into small blocks first, and then within the small blocks, the greedy algorithm is applied to sampling simultaneously. This reduces the time cost (i.e., fast sampling) compared to the dynamic greedy algorithm and reduces the sampling error (or maintains the sampling quality) in space. Combining the two algorithms, we designed a long-term first-phase sampling and second-phase sampling scheme for multiple variables. Sea surface temperature, salinity, and velocities in the northern Bay of Bengal were chosen to illustrate the sampling effect. Finally, a plausible way to capture variation by considering changes in variation is discussed, and its usability is preliminarily verified.