Leecaster Molly
Idaho National Environmental and Engineering Laboratory, Idaho Falls, ID 83415, USA.
Mar Environ Res. 2003 Jul-Aug;56(1-2):67-78. doi: 10.1016/S0141-1136(02)00325-2.
Maps are useful scientific tools for presenting environmental information, but the statistical techniques necessary to prepare scientifically rigorous maps have primarily focused on terrestrial habitats. This study compares three popular techniques (triangulation, kriging, and co-kriging) to map sediment grain size in Santa Monica Bay, California. Two grain size data sets, one collected in 1994 (79 sites) and one collected in 1997 and 1998 (149 sites) were used for model development. A bathymetric data set collected in 1997 was used as a model covariate. A third grain size data set (40 sites) collected in 1996 from independent sites was used for model evaluation. Predictions were compared to validation data by average difference, prediction mean square error (PMSE), and a goodness-of-prediction measure, G. The average difference between prediction and truth was similar for all methods, but the PMSE for triangulation was more than twice that for kriging or co-kriging, which were similar. The G measure also shows triangulation to be a far worse predictor than kriging and co-kriging. Small-scale differences were observed between kriging and co-kriging at steep depth contours, where co-kriging predicted values commensurate with the expected depth-defined grain size.
地图是呈现环境信息的有用科学工具，但制作科学严谨地图所需的统计技术主要集中在陆地栖息地。本研究比较了三种常用技术（三角测量法、克里金法和协同克里金法），以绘制加利福尼亚州圣莫尼卡湾的沉积物粒度图。两个粒度数据集，一个于1994年收集（79个站点），另一个于1997年和1998年收集（149个站点），用于模型开发。1997年收集的水深数据集用作模型协变量。1996年从独立站点收集的第三个粒度数据集（40个站点）用于模型评估。通过平均差异、预测均方误差（PMSE）和预测优度度量G将预测结果与验证数据进行比较。所有方法的预测值与真值之间的平均差异相似，但三角测量法的PMSE是克里金法或协同克里金法的两倍多，而后两者相似。G度量也表明，三角测量法的预测效果远不如克里金法和协同克里金法。在陡峭的深度等值线处，克里金法和协同克里金法之间观察到小规模差异，协同克里金法预测的值与预期的深度定义粒度相符。
