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Title:   Author  item
                                            Walthall, Charlesitem
                                            Timlin, Dennisitem
                                            Pachepsky, Yakovitem
                                            Dulaney, Wayneitem
                                            Daughtry, Craig
  Submitted to: American Society for Photogrammetry and Remote Sensing Proceedings
                                    Publication Type: Proceedings
                                    Publication Acceptance Date: 5/20/2002
                                    Publication  Date: 11/12/2002
                                    Citation: Walthall, C.L., Timlin, D.J., Pachepsky, Y.A., Dulaney, W.P., Daughtry, C.S., 2002. Exploiting image spatial variance for vegetatation patterns. In: Proceedings of American Society for Photogrammetry and Remote Sensing, November 12-14, 2002, Denver, Colorado, [CDROM].Interpretive Summary:
                                Technical Abstract: A fundamental tenet of geostatistics is that variables which are near to each other in space are likely to have similar values and thus exhibit autocorrelation. The information content inherent in the semivariogram, which describes variance as a function of separation distance, can be used to increase the accuracy of mapping foliage density expressed as leaf area index (LAI).  The semivariogram of a spectral vegetation index (SVI) image and coincident ground-based LAI measurements are shown to be correlated.  Stochastic imaging using a semivariogram derived from airborne imagery, and autoregressive techniques for mapping LAI are demonstrated. The use of the spatial structure of remotely sensed imagery as well as SVI spectral information may improve the prediction accuracies of vegetation parameter maps, especially at high spatial resolutions.  These approaches demonstrate supplemental ways of exploiting the information content of imagery beyond the traditional approach of relying solely on image spectral information to predict vegetation amount.


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