Submitted to: Intnl Conference On Geospatial Information In Agriculture And Forestry
Publication Type: Abstract Only
Publication Acceptance Date: November 7, 2001
Publication Date: November 20, 2001
Citation: Timlin, D.J., Walthall, C.L., Pachepsky, Y.A. 2001. Using spatial autoregression and conditional simulations to quantify relationships for geospatial data [abstract]. Intnl Conference On Geospatial Information In Agriculture And Forestry.
The spatial information in remotely sensed images is often not used to the advantage to improve calibrations of image color with plant factors. A number of statistical tools that include ordinary least squares regression (OLS) are used to develop equations that relate image color to plant leaf area or biomass. Methods such as OLS ignore the spatial relationships in the data which may result in erroneous conclusions. Furthermore, these methods generally provide a global fit to the data. Methods such as autoregression (AR) that corrects for spatial autocorrelation can be more effective in accounting for local information when developing calibration equations. We used data from an airborne scanner (AISA) image with a 1m X 1m resolution on a 4.5 ha field taken in July 1998. Leaf area indexes (LAI) was measured in a corn crop at 74 locations on an irregular grid at the same time as the image was acquired. We also measured soil moisture release curves on soil cores taken at the same locations. We used conditional simulations to interpolate the measured LAI to a 1m X 1m grid. The spatial variance structure from the image was used in the conditional simulations. We compared the AR model to ordinary least squares (OLS) regression that does not account for spatial information. The AR model provided a better fit of reflectance to the LAI data and soil water holding capacity to percent slope than the OLS model.