| 多年平均气温数据空间化误差的尺度效应 |
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| 引用本文： | 廖顺宝,张赛.多年平均气温数据空间化误差的尺度效应[J].地球信息科学,2014(1):8-14. |
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| 作者姓名： | 廖顺宝 张赛 |
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| 作者单位： | [1]河南大学环境与规划学院,开封475004 [2]中国科学院地理科学与资源研究所,北京100101 |
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| 基金项目： | 中国科学院战略性先导科技专项“应对气候变化的碳收支认证及相关问题”（XDA05050000）. |
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| 摘 要： | 属性数据空间化是利用矢量数据生成栅格数据产品的有效方法,它有助于不同来源、不同格式之间的数据的综合分析。空间化是一种必然有误差伴随的过程,为探讨空间化误差与数据源密度、空间化模型方法,以及空间化分辨率之间的关系,本文利用7种水平的气象站点密度、5种空间化模型方法和19种栅格分辨率分析多年平均气温数据空间化误差与这3类影响因子之间的关系。分析发现:(1)气象站点密度的降低导致多年平均气温数据的空间化误差增加;(2)在IDW、Kriging、Adjusted IDW、Regression和Anusplin 5种空间化模型方法中,Adjusted IDW、Regression、Anusplin比IDW、Kriging的精度高;(3)随着栅格分辨率的变粗,多年平均气温数据空间化误差增大;(4)在影响空间化精度的3类因子中,空间化模型方法对空间化精度的影响最大,栅格分辨率次之,气象站点密度的影响最小。通过多元回归分析,建立了多年平均气温数据空间化误差与这3类影响因子之间的定量模型,可为空间化技术方案的制定提供参考和依据。
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| 关 键 词： | 气温 数据 空间化 误差 尺度效应 |
Scale Effect of Errors on Spatialization of Annual Mean Air Temperature Data |
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| LIAO Shunbao,ZHANG Sai.Scale Effect of Errors on Spatialization of Annual Mean Air Temperature Data[J].Geo-information Science,2014(1):8-14. |
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| Authors: | LIAO Shunbao ZHANG Sai |
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| Institution: | 1. College of Environment and Planning, Henan University, Kaifeng 475004, China; 2. Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China) |
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| Abstract: | Spatialization of attribute data is a way to output grid data products from vector data. It is beneficial to integrated analysis of geosciences data from various sources and in different formats. However, it is also a pro-cess companied with errors, and the errors are closely related to density of data sources, spatializing models and resolution of grid cells. In this paper, 7 levels of density of meteorological stations, 5 spatializing models and 19 levels of resolutions of grid cells were used to analyze the relationships between the errors of annual mean air temperature data spatialization and these affecting factors. The following conclusions were drawn： （a） Reduction of density of meteorological stations led to increasing of the spatialization errors. （b） Of the five models, Adjust-ed IDW, Regression and Anusplin had higher accuracy than IDW and Kriging. The reason is that both IDW and Kriging are spatial autocorrelation based interpolation methods. They neglect influence of underlying surface on air temperature. But, elevation factor is taken into account for Adjusted IDW, Regression and Anusplin. There- fore higher accuracy can be gained with the three interpolation methods. （c） The accuracy generally decreased with increasing of size of grid cells. The trend was significant especially for Adjusted IDW, Regression and Anusplin. （d） Of the three kinds of factors affecting accuracy of spatialization, the models had the greatest im-pact on the accuracy, the resolution of grid cells second and the density of meteorological stations the lowest. （e） For spatialization products of annual mean air temperature data at national scale, some spatial hereto-correlation interpolation methods, such as Adjusted IDW, Regress and Anusplin should be applied, and the size of grid cells should be smaller than ten kilometers by ten kilometers. In such a case, the mean absolute error for spatialization can be less than one degree centigrade. At last, a quantitative multiple regression model between spatialization er-rors and the three kinds of affecting factors was established. The model can be used to predict spatialization er-rors when some of the affecting factors change, so it can provide the basis for drawing up a plan for spatializa-tion of air temperature data. |
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| Keywords: | air temperature data spatialization errors scale effect |
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