An exploratory spatial data analysis method (ESDA) was designed Apr.28,2002 for kriging monthly rainfall. Samples were monthly rainfall observed at 61 weather stations in eastern China over the period 1961-1998. Comp...An exploratory spatial data analysis method (ESDA) was designed Apr.28,2002 for kriging monthly rainfall. Samples were monthly rainfall observed at 61 weather stations in eastern China over the period 1961-1998. Comparison of five semivariogram models (Spherical, Exponential, Linear, Gaussian and Rational Quadratic) indicated that kriging fulfills the objective of finding better ways to estimate interpolation weights and can provide error information for monthly rainfall interpolation. ESDA yielded the three most common forms of experimental semivariogram for monthly rainfall in the area. All five models were appropriate for monthly rainfall interpolation but under different circumstances. Spherical, Exponential and Linear models perform as smoothing interpolator of the data, whereas Gaussian and Rational Quadratic models serve as an exact interpolator. Spherical, Exponential and Linear models tend to underestimate the values. On the contrary, Gaussian and Rational Quadratic models tend to overestimate the values. Since the suitable model for a specific month usually is not unique and each model does not show any bias toward one or more specific months, an ESDA is recommended for a better interpolation result.展开更多
Spatial heterogeneity is widely used in diverse applications, such as recognizing ecological process, guiding ecological restoration, managing land use, etc. Many researches have focused on the inherent scale multipli...Spatial heterogeneity is widely used in diverse applications, such as recognizing ecological process, guiding ecological restoration, managing land use, etc. Many researches have focused on the inherent scale multiplicity of spatial heterogeneity by using various environmental variables. How these variables affect their corresponding spatial heterogeneities, however, have received little attention. In this paper, we examined the effects of characteristics of normalized difference vegetation index （NDVI） and its related bands variable images, namely red and near infrared （NIR）, on their corresponding spatial heterogeneity detection based on variogram models. In a coastal wetland region, two groups of study sites with distinct fractal vegetation cover were tested and analyzed. The results show that： l） in high fractal vegetation cover （H-FVC） area, NDV! and NIR variables display a similar ability in detecting the spatial he- terogeneity caused by vegetation growing status structure; 2） in low fractal vegetation cover （L-FVC） area, the NIR and red variables outperform NDVI in the survey of soil spatial heterogeneity; and 3） generally, NIR variable is ubiquitously applicable for vegetation spatial heterogeneity investigation in different fractal vegetation covers. Moreover, as variable selection for remote sensing applications should fully take the characteristics of variables and the study object into account, the proposed variogram analysis method can make the variable selection objectively and scientifically, especially in studies related to spatial heterogeneity using remotely sensed data.展开更多
为了对区域电离层延迟进行实时模型化,利用中国区域GPS实测资料,基于球冠谐函数模型、低阶球谐函数模型、多项式模型和Kriging内插方法,构建了电离层延迟模型。重点讨论了电离层垂直总电子含量(vertical total electron content,VTEC)...为了对区域电离层延迟进行实时模型化,利用中国区域GPS实测资料,基于球冠谐函数模型、低阶球谐函数模型、多项式模型和Kriging内插方法,构建了电离层延迟模型。重点讨论了电离层垂直总电子含量(vertical total electron content,VTEC)的空间变异性、相关性的统计计算和Kriging内插估计方法,实现了中国区域VTEC格网实时建模。验证结果表明,高纬度地区VTEC拟合精度优于低纬度地区,Kriging内插估计和多项式模型结果的拟合内符合精度明显优于球冠谐函数模型和低阶球谐函数模型。但多项式拟合的格网,其方差则存在明显的边际效应,拟合区域中央精度较高,区域边缘地带精度明显下降;Kriging算法估计的格网点VTEC方差更符合实际情况,穿刺点多的地方,格网点精度较高。展开更多
文摘An exploratory spatial data analysis method (ESDA) was designed Apr.28,2002 for kriging monthly rainfall. Samples were monthly rainfall observed at 61 weather stations in eastern China over the period 1961-1998. Comparison of five semivariogram models (Spherical, Exponential, Linear, Gaussian and Rational Quadratic) indicated that kriging fulfills the objective of finding better ways to estimate interpolation weights and can provide error information for monthly rainfall interpolation. ESDA yielded the three most common forms of experimental semivariogram for monthly rainfall in the area. All five models were appropriate for monthly rainfall interpolation but under different circumstances. Spherical, Exponential and Linear models perform as smoothing interpolator of the data, whereas Gaussian and Rational Quadratic models serve as an exact interpolator. Spherical, Exponential and Linear models tend to underestimate the values. On the contrary, Gaussian and Rational Quadratic models tend to overestimate the values. Since the suitable model for a specific month usually is not unique and each model does not show any bias toward one or more specific months, an ESDA is recommended for a better interpolation result.
基金Under the auspices of National Key Technology Research and Development Program of China (No.2009BADB3B01-05)Knowledge Innovation Programs of Chinese Academy of Sciences (No. KSCX1-YW-09-13)
文摘Spatial heterogeneity is widely used in diverse applications, such as recognizing ecological process, guiding ecological restoration, managing land use, etc. Many researches have focused on the inherent scale multiplicity of spatial heterogeneity by using various environmental variables. How these variables affect their corresponding spatial heterogeneities, however, have received little attention. In this paper, we examined the effects of characteristics of normalized difference vegetation index （NDVI） and its related bands variable images, namely red and near infrared （NIR）, on their corresponding spatial heterogeneity detection based on variogram models. In a coastal wetland region, two groups of study sites with distinct fractal vegetation cover were tested and analyzed. The results show that： l） in high fractal vegetation cover （H-FVC） area, NDV! and NIR variables display a similar ability in detecting the spatial he- terogeneity caused by vegetation growing status structure; 2） in low fractal vegetation cover （L-FVC） area, the NIR and red variables outperform NDVI in the survey of soil spatial heterogeneity; and 3） generally, NIR variable is ubiquitously applicable for vegetation spatial heterogeneity investigation in different fractal vegetation covers. Moreover, as variable selection for remote sensing applications should fully take the characteristics of variables and the study object into account, the proposed variogram analysis method can make the variable selection objectively and scientifically, especially in studies related to spatial heterogeneity using remotely sensed data.
文摘为了对区域电离层延迟进行实时模型化,利用中国区域GPS实测资料,基于球冠谐函数模型、低阶球谐函数模型、多项式模型和Kriging内插方法,构建了电离层延迟模型。重点讨论了电离层垂直总电子含量(vertical total electron content,VTEC)的空间变异性、相关性的统计计算和Kriging内插估计方法,实现了中国区域VTEC格网实时建模。验证结果表明,高纬度地区VTEC拟合精度优于低纬度地区,Kriging内插估计和多项式模型结果的拟合内符合精度明显优于球冠谐函数模型和低阶球谐函数模型。但多项式拟合的格网,其方差则存在明显的边际效应,拟合区域中央精度较高,区域边缘地带精度明显下降;Kriging算法估计的格网点VTEC方差更符合实际情况,穿刺点多的地方,格网点精度较高。
