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United States Department of Agriculture

Agricultural Research Service

Title: Exploiting Image Spatial Variance for Vegetation Patterns

Authors
item Walthall, Charles
item Timlin, Dennis
item Pachepsky, Yakov
item Dulaney, Wayne
item Daughtry, Craig

Submitted to: American Society for Photogrammetry and Remote Sensing Proceedings
Publication Type: Proceedings
Publication Acceptance Date: May 20, 2002
Publication Date: November 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].

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.

Last Modified: 9/22/2014