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1 November 2009 Evaluation of Airborne Lidar Data to Predict Vegetation Presence/Absence
Monica Palaseanu-Lovejoy, Amar Nayegandhi, John Brock, Robert Woodman, C. Wayne Wright
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This study evaluates the capabilities of the Experimental Advanced Airborne Research Lidar (EAARL) in delineating vegetation assemblages in Jean Lafitte National Park, Louisiana. Five-meter-resolution grids of bare earth, canopy height, canopy-reflection ratio, and height of median energy were derived from EAARL data acquired in September 2006. Ground-truth data were collected along transects to assess species composition, canopy cover, and ground cover. To decide which model is more accurate, comparisons of general linear models and generalized additive models were conducted using conventional evaluation methods (i.e., sensitivity, specificity, Kappa statistics, and area under the curve) and two new indexes, net reclassification improvement and integrated discrimination improvement. Generalized additive models were superior to general linear models in modeling presence/absence in training vegetation categories, but no statistically significant differences between the two models were achieved in determining the classification accuracy at validation locations using conventional evaluation methods, although statistically significant improvements in net reclassifications were observed.

Monica Palaseanu-Lovejoy, Amar Nayegandhi, John Brock, Robert Woodman, and C. Wayne Wright "Evaluation of Airborne Lidar Data to Predict Vegetation Presence/Absence," Journal of Coastal Research 2009(10053), 83-97, (1 November 2009).
Published: 1 November 2009
bare earth
general linear models
generalized additive models
vegetation classification
vegetation metrics
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