A simple new algorithm to filter marine mammal Argos locations
A simple new algorithm to filter marine mammal Argos locations
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DOI：
10.1111/j.1748-7692.2007.00180.x
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发表时间：
2008-04-01
影响因子：
2.3
通讯作者：
Kovacs, Kit M.
中科院分区：
文献类型：
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作者：
Freitas, Carla;Lydersen, Christian;Kovacs, Kit M.
近几十年来，利用Argos系统的卫星遥测技术被广泛用于跟踪许多种类的海洋哺乳动物。然而，大多数这些物种的水生行为导致大量的位置具有低或未知的准确性。Argos数据通常会被过滤，以减少这些位置产生的噪音，通常是通过删除需要不切实际的游泳速度的数据点。不幸的是，这种方法排除了相当数量的具有高行进速度的高质量位置，这是两个位置在时间上非常接近的结果。我们提出了一种替代算法，基于游泳速度，连续位置之间的距离，和转弯角度。这种新的过滤器测试了来自9种不同海洋哺乳动物的67条轨迹：环纹，胡须，灰色，港口，南象，南极软毛海豹，海象，白鲸和独角鲸。该算法去除了相似百分比的低质量位置（Argos位置分类[LC] B和A）与仅基于游泳速度的过滤器相比，但保留了显著更高百分比的优质位置（对于LC 3，移除位置的平均SE%为4.1 +/- 0.8% vs. 12.6 +/- 1.2%;对于LC 2，为6.8 +/- 0.6% vs. 15.7 +/- 0.9%;和11.4 +/- 0.7%对LC 1的21.0 +/-0.97 °）。新的过滤器也更有效地消除了不太可能的，明显的偏离轨道的路径，导致更少的位置被登记在陆地上，并显着减少家庭范围的大小，当使用最小凸多边形方法，这是敏感的离群值。
During recent decades satellite telemetry using the Argos system has been used extensively to track many species of marine mammals. However, the aquatic behavior of most of these species results in a high number of locations with low or unknown accuracy. Argos data are often filtered to reduce the noise produced by these locations, typically by removing data points requiring unrealistic swimming speeds. Unfortunately, this method excludes a considerable number of good-quality locations that have high traveling speeds that are the result of two locations being taken very close in time. We present an alternative algorithm, based on swimming speed, distance between successive locations, and turning angles. This new filter was tested on 67 tracks from nine different marine mammal species: ringed, bearded, gray, harbor, southern elephant, and Antarctic fur seals, walruses, belugas, and nar-whals. The algorithm removed similar percentages of low-quality locations (Argos location classes [LC] B and A) compared to a filter based solely on swimming speed, but preserved significantly higher percentages of good-quality positions (mean SE% of locations removed was 4.1 +/- 0.8% vs. 12.6 +/- 1.2% for LC 3; 6.8 +/- 0.6% vs. 15.7 +/- 0.9% for LC 2; and 11.4 +/- 0.7% vs. 21.0 +/- 0.97o for LC 1). The new filter was also more effective at removing unlikely, conspicuous deviations from the track's path, resulting in fewer locations being registered on land and a significant reduction in home range size, when using the Minimum Convex Polygon method, which is sensitive to outliers.