Submitted to: Intnl Conference On Geospatial Information In Agriculture And Forestry
Publication Type: Abstract Only
Publication Acceptance Date: November 4, 2001
Publication Date: November 4, 2001
Accurate maps of within-field crop foliage density would greatly assist crop condition monitoring, yield estimation, and assessment of crop response patterns for effective decision making. Vegetation indices (VIs), which are linear combinations of remotely sensed crop spectral reflectances, are positively correlated with foliage density expressed as leaf area index (LAI). Numerous ground measurements of LAI are required to calibrate an ordinary least squares (OLS) relationship between VIs and LAI. The OLS equation is then applied to the rest of the imagery to map LAI. However, this approach does not fully exploit the inherent spatial information of imagery. Semivariograms describe spatial variance structure and can be derived from surface measurements or imagery. We calculate semivariograms for this analysis from a subset of ground-based LAI measurements, and from a remotely sensed image. These semivariograms are used to condition stochastic imaging geostatistical simulation to interpolate the LAI for a map. The interpolated LAI maps produced from the stochastic imaging are compared to an LAI map generated using the traditional OLS technique. The remaining LAI measurements are used to assess the accuracy of the approaches for mapping LAI.