The vertical accuracy of light detection and ranging (lidar) elevation measurements refers to how close those measurements are to the “true” elevations of an independent data source of better accuracy (e.g., Real Time Kinematic-Global Navigation Satellite Systems (RTK-GNSS) measurements). lidar elevation measurements are often mistakenly considered better when they have “high” vertical accuracy. However, high vertical accuracy (e.g., >0.15 m) means that the lidar obtains larger differences between the measured and “true” elevations. Lidar elevation measurements should be considered better with “low” vertical accuracy (e.g., <0.15 m) with smaller differences between the measured and “true” elevations. Accordingly, high accuracy lidar elevation measurements have larger errors compared to low accuracy lidar elevation measurements that have smaller errors. Since error is associated with all discreet measurements in science, it should be noted that uncertainty or error in lidar elevation measurement is not a careless mistake. Error or uncertainty in lidar elevation measurements may be expressed numerically by the standard deviation (σ), yet there are many standards that use a modified σ approach for assessing elevation measurement error, and the most commonly used is the National Standard for Spatial Data Accuracy (NSSDA) (FGDC, 1998). NSSDA uses statistical procedures to calculate the vertical Root Mean Square Error (RMSEz), or the square root of the average squared differences between the measured and “true” elevations of the same locations. It is important to conduct an error analysis on lidar elevation measurements in order to keep uncertainty to a minimum so that a reliable estimate is made of how large or small those measurement errors may be. There are many standards for assessing elevation measurement error, and a review of the most commonly used NSSDA and its applicability to lidar elevation can be found in Cooper et al. (2013). It should also be noted that the American Society for Photogrammetry and Remote Sensing (ASPRS) accuracy standards (ASPRS, 2014) provide new recommendations that accommodate state-of-the-art technologies such as lidar. While marshes are excluded in the ASPRS (2014) guidelines, the acknowledgment of impenetrable vegetation (i.e., mangroves) is a good start for critical habitats such as those in the coastal Everglades.