A comparative study of spatial interpolation methods for determining fishery resources density in the Yellow Sea

文献类型： 外文期刊

第一作者： Chen Yunlong

作者： Chen Yunlong;Shan Xiujuan;Jin Xianshi;Chen Yunlong;Shan Xiujuan;Jin Xianshi;Yang Tao;Dai Fangqun;Chen Yunlong;Shan Xiujuan;Jin Xianshi;Yang Tao;Dai Fangqun;Chen Yunlong;Yang Dingtian

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关键词： spatial interpolation methods; fishery resources density; Yellow Sea

期刊名称：ACTA OCEANOLOGICA SINICA （ 影响因子：1.431； 五年影响因子：1.445 ）

ISSN： 0253-505X

年卷期： 2016 年 35 卷 12 期

页码：

收录情况： SCI

摘要： Spatial interpolation is a common tool used in the study of fishery ecology, especially for the construction of ecosystem models. To develop an appropriate interpolation method of determining fishery resources density in the Yellow Sea, we tested four frequently used methods, including inverse distance weighted interpolation (IDW), global polynomial interpolation (GPI), local polynomial interpolation (LPI) and ordinary kriging (OK). A cross-validation diagnostic was used to analyze the efficacy of interpolation, and a visual examination was conducted to evaluate the spatial performance of the different methods. The results showed that the original data were not normally distributed. A log transformation was then used to make the data fit a normal distribution. During four survey periods, an exponential model was shown to be the best semivariogram model in August and October 2014, while data from January and May 2015 exhibited the pure nugget effect. Using a paired-samples t test, no significant differences (P > 0.05) between predicted and observed data were found in all four of the interpolation methods during the four survey periods. Results of the cross-validation diagnostic demonstrated that OK performed the best in August 2014, while IDW performed better during the other three survey periods. The GPI and LPI methods had relatively poor interpolation results compared to IDW and OK. With respect to the spatial distribution, OK was balanced and was not as disconnected as IDW nor as overly smooth as GPI and LPI, although OK still produced a few "bull's-eye" patterns in some areas. However, the degree of autocorrelation sometimes limits the application of OK. Thus, OK is highly recommended if data are spatially autocorrelated. With respect to feasibility and accuracy, we recommend IDW to be used as a routine interpolation method. IDW is more accurate than GPI and LPI and has a combination of desirable properties, such as easy accessibility and rapid processing.

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