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There is currently great interest in lidar surveys of salt marshes to support coastal management and decision making. However, vertical uncertainty of lidar elevations is generally higher in salt marshes than in upland areas, and it can be difficult to empirically quantify due to the challenges of obtaining ground control in marshes. Assuming that most of the component uncertainties in the lidar geolocation equation will remain essentially constant over a relatively small location, it is posited that vertical uncertainty in a marsh will vary mostly as a function of surface and cover characteristics. These, in turn, should affect lidar waveforms recorded during the survey, and therefore, analysis of the waveform shapes may allow for prediction of vertical uncertainty variation. Waveforms at three test sites were used to compute 16 computationally efficient features that describe the shapes; and simple, multilinear, and principal component regressions were used to evaluate their ability to predict elevation differences between lidar and Global Positioning System ground control. The results show that a simple estimate of waveform width can explain over 50% of the total variability in elevation differences but that multilinear regression does not significantly improve the performance. Somewhat surprisingly, skewness of the waveform does not appear to be a good predictor of elevation differences in these cases.