基于时序栅格的海洋异常事件提取方法

Time Series of Raster-oriented Method for Marine Abnormal Events Extraction

海洋异常事件(Marine Abnormal Event,MAE)可为区域海气相互作用和全球气候变化研究提供重要的时空特征参考,具有重要的科学意义。鉴此,本文基于长时间序列的栅格数据集,提出了一种海洋异常事件时空提取算法(Marine Abnormal Event Spatio-Temporal Extraction Method,MAESTEM)。MAESTEM的核心步骤包括事件的时间维度提取、事件的空间维度提取和事件追踪。在时间维度提取方面,将每一个栅格像元作为一维时间序列,计算其平均值和标准差作为判断每个时刻是否异常的标准,并根据异常持续发生的时间长短来提取时间维度的海洋异常事件(Temporal MAE,TMAE)。在空间维度提取方面,利用空间邻域统计方法,统计栅格像元的空间邻域中属于TMAE的个数,并通过空间维度异常判断规则获取空间维度的海洋异常事件(Spatial MAE,SMAE)。利用时刻状态的SMAE的空间拓扑关系,根据事件前后时刻覆盖的空间区域是否重叠以及事件持续的时间长短,实现异常事件的追踪。最后,通过提取太平洋海域1993年1月至2012年12月的月均海面高度异常(Sea Level Anomaly,SLA)事件,验证了该算法的有效性和实用性。

Marine Abnormal Event（MAE） is an abnormal decrease or increase of marine environmental parameters, which covers a specified spatial domain and lasts for a specified temporal duration. Marine abnormal event can provide the temporal and spatial characteristics of regional sea-air interactions and global climate change, which has an important scientific significance. Based on the above information, we propose a novel algorithm to extract the MAE from the long-term raster datasets, named as MAESTEM（Marine Abnormal Event Spatio- Temporal Extraction Method）. The MAESTEM has three key steps： the extraction of MAE at a temporal dimension, the extraction of MAE at a spatial dimension, and the tracking of MAE. At the temporal dimension, each grid pixel within an image is taken to be the one-dimensional time series, and its mean and standard deviation are taken as the criteria to define its abnormal snapshot status. If and only if the abnormal snapshot and its subsequent ones are not smaller than the specified threshold, i.e. T, the abnormal snapshots are defined as a temporal MAE, denoted by TMAE. We utilize the spatial neighborhood statistics method to count the number of spatial neighborhoods of a raster pixel which belongs to TMAE and to obtain the marine abnormal events at the spatial dimension, denoted as SAME, by using the spatial dimension abnormal extracting method. In the final step, we use the spatial topological relationship of SMAE to identify whether the SAMEs at the previous and post snapshots belongs to the same event. If they overlap, they are considered being in the same event. If the temporal duration of the event exceeds the temporal threshold, save the event, otherwise, delete it. Finally, the Pacific Ocean is taken as a research area, and its monthly averaged sea level anomaly（SLA）, which is obtained from the remote sensing imagery during a period from January 1993 to December 2012, is used to test the feasibility and efficiency of MAESTEM.