Analysis of variograms with various sample sizes from a multispectral image

Variogram plays a crucial role in remote sensing application and geostatistics. It is very important to estimate variogram reliably from sufficient data. In this study, the analysis of variograms computed on various sample sizes of remotely sensed data was conducted. A 100 ×100 - pixel subset was chosen randomly from an aerial multispectral image which contains three wavebands, Green, Red and near-infrared (NIR). Green, Red, NIR and Normalized Difference Vegetation Index (NDVI) datasets were imported into R software for spatial analysis. Variograms of these four full image datasets and sub-samples with simple random sampling method were investigated. In this case, half size of the subset image data was enough to reliably estimate the variograms for NIR and Red wavebands. To map the variation on NDVI within the weed field, ground sampling interval should be smaller than 12 m. The information will be particularly important for Kriging and also give a good guide of field sampling on the weed field in the future study.

[1]  Peter M. Atkinson,et al.  Geostatistics and remote sensing , 1998 .

[2]  Chenghai Yang,et al.  Relationships Between Yield Monitor Data and Airborne Multidate Multispectral Digital Imagery for Grain Sorghum , 2002, Precision Agriculture.

[3]  C. Woodcock,et al.  Autocorrelation and regularization in digital images. I. Basic theory , 1988 .

[4]  M. A. Oliver,et al.  Using the variogram to explore imagery of two different spatial resolutions , 2005 .

[5]  M. Meirvenne,et al.  Sampling strategy for quantitative soil mapping , 1991 .

[6]  W. Cohen,et al.  Semivariograms of digital imagery for analysis of conifer canopy structure. , 1990 .

[7]  A. Stein,et al.  Universal kriging and cokriging as a regression procedure. , 1991 .

[8]  C. Woodcock,et al.  Autocorrelation and regularization in digital images. II. Simple image models , 1989 .

[9]  Donald G. Bullock,et al.  Spatial variability of soybean quality data as a function of field topography: I. Spatial data analysis , 2002 .

[10]  Y. Ge,et al.  Spatial variation of fiber quality and associated loan rate in a dryland cotton field , 2008, Precision Agriculture.

[11]  C. Gascuel-Odoux,et al.  Variability of variograms and spatial estimates due to soil sampling: a case study , 1994 .

[12]  P. Curran.  The semivariogram in remote sensing: An introduction , 1988 .

[13]  R. Webster,et al.  Filtering SPOT imagery by kriging analysis , 2000 .

[14]  J. J. de Gruijter,et al.  Estimation of non-ergodic variograms and their sampling variance by design-based sampling strategies , 1994 .

[15]  R. Reese.  Geostatistics for Environmental Scientists , 2001 .

[16]  G. Matheron.  Principles of geostatistics , 1963 .

[17]  R. Webster,et al.  How geostatistics can help you , 1991 .

[18]  D. L. Karlen,et al.  Spatial Analysis of Soil Fertility Parameters , 2004, Precision Agriculture.

[19]  Peter M. Atkinson,et al.  Defining an optimal size of support for remote sensing investigations , 1995, IEEE Trans. Geosci. Remote. Sens..

[20]  Yufeng Ge,et al.  VNIR DIFFUSE REFLECTANCE SPECTROSCOPY FOR AGRICULTURAL SOIL PROPERTY DETERMINATION BASED ON REGRESSION-KRIGING , 2007 .

[21]  Y. Miao,et al.  Spatial Variability of Soil Properties, Corn Quality and Yield in Two Illinois, USA Fields: Implications for Precision Corn Management , 2006, Precision Agriculture.

[22]  Noel A Cressie,et al.  Statistics for Spatial Data. , 1992 .

[23]  Yubin Lan,et al.  Analysis of vegetation indices derived from aerial multispectral and ground hyperspectral data , 2009 .

[24]  Achim Dobermann,et al.  Geostatistical Integration of Yield Monitor Data and Remote Sensing Improves Yield Maps , 2004 .

[25]  Thomas Panagopoulos,et al.  Analysis of spatial interpolation for optimising management of a salinized field cultivated with lettuce , 2006 .

[26]  Alex B. McBratney,et al.  Site-Specific Durum Wheat Quality and Its Relationship to Soil Properties in a Single Field in Northern New South Wales , 2002, Precision Agriculture.

[27]  Peter M. Atkinson,et al.  Exploring the relation between spatial structure and wavelength: implications for sampling reflectance in the field , 1999 .

[28]  G. Heuvelink,et al.  A generic framework for spatial prediction of soil variables based on regression-kriging , 2004 .

[29]  Edward P. Richard,et al.  Sugarcane Yield, Sugarcane Quality, and Soil Variability in Louisiana , 2005 .

[30]  C. Woodcock,et al.  The use of variograms in remote sensing. I - Scene models and simulated images. II - Real digital images , 1988 .

[31]  C. Woodcock,et al.  The use of variograms in remote sensing: I , 1988 .

[32]  Alfred Stein,et al.  Methods for comparing spatial variability patterns of millet yield and soil data. , 1997 .

[33]  F. D. Whisler,et al.  Spatial Variability Analysis of Soil Physical Properties of Alluvial Soils , 2005 .